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Tu T, Yang J, Xiao H, Zuo Y, Tao X, Ran Y, Yuan Y, Ye S, He Y, Wang Z, Tang W, Liu Q, Ji H, Li Z. Spatiotemporal analysis of imported and local dengue virus and cases in a metropolis in Southwestern China, 2013-2022. Acta Trop 2024; 257:107308. [PMID: 38945422 DOI: 10.1016/j.actatropica.2024.107308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 06/19/2024] [Accepted: 06/28/2024] [Indexed: 07/02/2024]
Abstract
Dengue fever is a viral illness, mainly transmitted by Aedes aegypti and Aedes albopictus. With climate change and urbanisation, more urbanised areas are becoming suitable for the survival and reproduction of dengue vector, consequently are becoming suitable for dengue transmission in China. Chongqing, a metropolis in southwestern China, has recently been hit by imported and local dengue fever, experiencing its first local outbreak in 2019. However, the genetic evolution dynamics of dengue viruses and the spatiotemporal patterns of imported and local dengue cases have not yet been elucidated. Hence, this study implemented phylogenetic analyses using genomic data of dengue viruses in 2019 and 2023 and a spatiotemporal analysis of dengue cases collected from 2013 to 2022. We sequenced a total of 15 nucleotide sequences of E genes. The dengue viruses formed separate clusters and were genetically related to those from Guangdong Province, China, and countries in Southeast Asia, including Laos, Thailand, Myanmar and Cambodia. Chongqing experienced a dengue outbreak in 2019 when 168 imported and 1,243 local cases were reported, mainly in September and October. Few cases were reported in 2013-2018, and only six were imported from 2020 to 2022 due to the COVID-19 lockdowns. Our findings suggest that dengue prevention in Chongqing should focus on domestic and overseas population mobility, especially in the Yubei and Wanzhou districts, where airports and railway stations are located, and the period between August and October when dengue outbreaks occur in endemic regions. Moreover, continuous vector monitoring should be implemented, especially during August-October, which would be useful for controlling the Aedes mosquitoes. This study is significant for defining Chongqing's appropriate dengue prevention and control strategies.
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Affiliation(s)
- Taotian Tu
- The First Batch of Key Disciplines On Public Health in Chongqing, Chongqing Municipal Key Laboratory for High Pathogenic Microbes, Chongqing Center for Disease Control and Prevention, Chongqing, China
| | - Jing Yang
- CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
| | - Hansen Xiao
- The First Batch of Key Disciplines On Public Health in Chongqing, Chongqing Municipal Key Laboratory for High Pathogenic Microbes, Chongqing Center for Disease Control and Prevention, Chongqing, China
| | - Youyi Zuo
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; School of Soil and Water Conservation, Beijing Forestry University, Beijing, China
| | - Xiaoying Tao
- Shapingba District Center for Disease Control and Prevention, Chongqing, China
| | - Yaling Ran
- Yubei District Center for Disease Control and Prevention, Chongqing, China
| | - Yi Yuan
- The First Batch of Key Disciplines On Public Health in Chongqing, Chongqing Municipal Key Laboratory for High Pathogenic Microbes, Chongqing Center for Disease Control and Prevention, Chongqing, China
| | - Sheng Ye
- The First Batch of Key Disciplines On Public Health in Chongqing, Chongqing Municipal Key Laboratory for High Pathogenic Microbes, Chongqing Center for Disease Control and Prevention, Chongqing, China
| | - Yaming He
- The First Batch of Key Disciplines On Public Health in Chongqing, Chongqing Municipal Key Laboratory for High Pathogenic Microbes, Chongqing Center for Disease Control and Prevention, Chongqing, China
| | - Zheng Wang
- The First Batch of Key Disciplines On Public Health in Chongqing, Chongqing Municipal Key Laboratory for High Pathogenic Microbes, Chongqing Center for Disease Control and Prevention, Chongqing, China
| | - Wenge Tang
- The First Batch of Key Disciplines On Public Health in Chongqing, Chongqing Municipal Key Laboratory for High Pathogenic Microbes, Chongqing Center for Disease Control and Prevention, Chongqing, China
| | - Qiyong Liu
- State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Hengqing Ji
- The First Batch of Key Disciplines On Public Health in Chongqing, Chongqing Municipal Key Laboratory for High Pathogenic Microbes, Chongqing Center for Disease Control and Prevention, Chongqing, China.
| | - Zhichao Li
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
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Ogunsakin RE, Babalola BT, Olusola JA, Joshua AO, Okpeku M. GIS-based spatiotemporal mapping of malaria prevalence and exploration of environmental inequalities. Parasitol Res 2024; 123:262. [PMID: 38970660 PMCID: PMC11227462 DOI: 10.1007/s00436-024-08276-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 06/21/2024] [Indexed: 07/08/2024]
Abstract
Malaria poses a significant threat to global health, with particular severity in Nigeria. Understanding key factors influencing health outcomes is crucial for addressing health disparities. Disease mapping plays a vital role in assessing the geographical distribution of diseases and has been instrumental in epidemiological research. By delving into the spatiotemporal dynamics of malaria trends, valuable insights can be gained into population dynamics, leading to more informed spatial management decisions. This study focused on examining the evolution of malaria in Nigeria over twenty years (2000-2020) and exploring the impact of environmental factors on this variation. A 5-year-period raster map was developed using malaria indicator survey data for Nigeria's six geopolitical zones. Various spatial analysis techniques, such as point density, spatial autocorrelation, and hotspot analysis, were employed to analyze spatial patterns. Additionally, statistical methods, including Principal Component Analysis, Spearman correlation, and Ordinary Least Squares (OLS) regression, were used to investigate relationships between indicators and develop a predictive model. The study revealed regional variations in malaria prevalence over time, with the highest number of cases concentrated in northern Nigeria. The raster map illustrated a shift in the distribution of malaria cases over the five years. Environmental factors such as the Enhanced Vegetation Index, annual land surface temperature, and precipitation exhibited a strong positive association with malaria cases in the OLS model. Conversely, insecticide-treated bed net coverage and mean temperature negatively correlated with malaria cases in the same model. The findings from this research provide valuable insights into the spatiotemporal patterns of malaria in Nigeria and highlight the significant role of environmental drivers in influencing disease transmission. This scientific knowledge can inform policymakers and aid in developing targeted interventions to combat malaria effectively.
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Affiliation(s)
- Ropo Ebenezer Ogunsakin
- School of Health Systems and Public Health, University of Pretoria Faculty of Health Sciences, Pretoria, South Africa.
| | | | | | - Ayodele Oluwasola Joshua
- Department of Mathematical Sciences, Science and Technology, Bamidele Olumilua University of Education, Ikere Ekiti, Nigeria
| | - Moses Okpeku
- Discipline of Genetics, School of Life Sciences, University of Kwa-Zulu Natal, Westville, Durban, South Africa
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Ren H, Xu N. Forecasting and mapping dengue fever epidemics in China: a spatiotemporal analysis. Infect Dis Poverty 2024; 13:50. [PMID: 38956632 PMCID: PMC11221048 DOI: 10.1186/s40249-024-01219-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 06/20/2024] [Indexed: 07/04/2024] Open
Abstract
BACKGROUND Dengue fever (DF) has emerged as a significant public health concern in China. The spatiotemporal patterns and underlying influencing its spread, however, remain elusive. This study aims to identify the factors driving these variations and to assess the city-level risk of DF epidemics in China. METHODS We analyzed the frequency, intensity, and distribution of DF cases in China from 2003 to 2022 and evaluated 11 natural and socioeconomic factors as potential drivers. Using the random forest (RF) model, we assessed the contributions of these factors to local DF epidemics and predicted the corresponding city-level risk. RESULTS Between 2003 and 2022, there was a notable correlation between local and imported DF epidemics in case numbers (r = 0.41, P < 0.01) and affected cities (r = 0.79, P < 0.01). With the increase in the frequency and intensity of imported epidemics, local epidemics have become more severe. Their occurrence has increased from five to eight months per year, with case numbers spanning from 14 to 6641 per month. The spatial distribution of city-level DF epidemics aligns with the geographical divisions defined by the Huhuanyong Line (Hu Line) and Qin Mountain-Huai River Line (Q-H Line) and matched well with the city-level time windows for either mosquito vector activity (83.59%) or DF transmission (95.74%). The RF models achieved a high performance (AUC = 0.92) when considering the time windows. Importantly, they identified imported cases as the primary influencing factor, contributing significantly (24.82%) to local DF epidemics at the city level in the eastern region of the Hu Line (E-H region). Moreover, imported cases were found to have a linear promoting impact on local epidemics, while five climatic and six socioeconomic factors exhibited nonlinear effects (promoting or inhibiting) with varying inflection values. Additionally, this model demonstrated outstanding accuracy (hitting ratio = 95.56%) in predicting the city-level risks of local epidemics in China. CONCLUSIONS China is experiencing an increasing occurrence of sporadic local DF epidemics driven by an unavoidably higher frequency and intensity of imported DF epidemics. This research offers valuable insights for health authorities to strengthen their intervention capabilities against this disease.
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Affiliation(s)
- Hongyan Ren
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Nankang Xu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
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Sun Y, He L, Li X, Li C, Yan S, Zhang Y, Sun Z. Unraveling the Genomic Evolution of Dengue Virus Serotype 1: A Case Study from Yantai, China. Life (Basel) 2024; 14:808. [PMID: 39063563 PMCID: PMC11278097 DOI: 10.3390/life14070808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Revised: 06/12/2024] [Accepted: 06/20/2024] [Indexed: 07/28/2024] Open
Abstract
In August 2023, we identified a case of dengue fever in Yantai City, which was imported from Xishuangbanna, China. To investigate its evolutionary history and population dynamics, we utilized the metatranscriptomic method to obtain the virus' whole genome sequence. Together with 367 selected dengue virus whole genome sequences from the NCBI database, we constructed a time-scaled Maximum Clade Credibility (MCC) tree. We found that our sequence exhibited a high homology with a sequence of DENV1 (OR418422.1) uploaded by the Guangzhou Center for Disease Control and Prevention in 2023, with an estimated divergence time around 2019 (95% HPD: 2017-2023), coinciding with the emergence of SARS-CoV-2. The DENV strain obtained in this study belongs to genotype I of DENV1. Its ancestors experienced a global epidemic around 2005 (95% HPD: 2002-2010), and its progeny strains have spread extensively in Southeast Asia and China since around 2007 (95% HPD: 2006-2011). The Bayesian skyline plot indicates that the current population of DENV1 has not been affected by SARS-CoV-2 and is expected to maintain stable transmission. Hence, it is imperative to track and monitor its epidemiological trends and genomic variations to prevent potential large-scale outbreaks in the post-SARS-CoV-2 era.
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Affiliation(s)
| | | | | | | | | | - Yi Zhang
- Yantai Center for Disease Control and Prevention, Yantai 264003, China; (Y.S.); (L.H.)
| | - Zhenlu Sun
- Yantai Center for Disease Control and Prevention, Yantai 264003, China; (Y.S.); (L.H.)
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Li H, Yang Y, Chen J, Li Q, Chen Y, Zhang Y, Cai S, Zhan M, Wu C, Lin X, Xiang J. Epidemiological Characteristics of Overseas-Imported Infectious Diseases Identified through Airport Health-Screening Measures: A Case Study on Fuzhou, China. Trop Med Infect Dis 2024; 9:138. [PMID: 38922050 PMCID: PMC11209573 DOI: 10.3390/tropicalmed9060138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 06/10/2024] [Accepted: 06/12/2024] [Indexed: 06/27/2024] Open
Abstract
BACKGROUND This study aimed to examine the epidemiological characteristics of imported infections and assess the effectiveness of border health screening in detecting imported diseases. METHODS We obtained infection data for 2016 to 2019 from the Fuzhou Changle International Airport Infection Reporting System. The demographic, temporal, and spatial characteristics of travel-related infections were analyzed using r×c contingency tables, the Cochran-Armitage trend test, and seasonal-trend decomposition using LOESS (STL). Detection rates were used as a proxy for the effectiveness of border health-screening measures. RESULTS Overall, 559 travel-related infections were identified during the study period, with 94.3% being imported infections. Airport health screening demonstrated an overall effectiveness of 23.7% in identifying travel-associated infections. Imported infections were predominantly identified in males, with 55.8% of cases occurring in individuals aged 20-49. The peak periods of infection importation were from January to February and from May to August. The infectious diseases identified were imported from 25 different countries and regions. All dengue fever cases were imported from Southeast Asia. Most notifiable infections (76.0%) were identified through fever screening at the airport. CONCLUSION The increasing number of imported infections poses a growing challenge for public health systems. Multifaceted efforts including surveillance, vaccination, international collaboration, and public awareness are required to mitigate the importation and spread of infectious diseases from overseas sources.
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Affiliation(s)
- Hong Li
- School of Public Health, Fujian Medical University, Fuzhou 350122, China; (H.L.); (Y.Y.); (J.C.); (Q.L.); (Y.C.); (Y.Z.); (C.W.)
- Key Laboratory of Environment and Health, Fujian Province University, Fuzhou 350122, China
- School of Public Health and Health Management, Fujian Health College, Fuzhou 350101, China
| | - Yan Yang
- School of Public Health, Fujian Medical University, Fuzhou 350122, China; (H.L.); (Y.Y.); (J.C.); (Q.L.); (Y.C.); (Y.Z.); (C.W.)
- Key Laboratory of Environment and Health, Fujian Province University, Fuzhou 350122, China
| | - Jiake Chen
- School of Public Health, Fujian Medical University, Fuzhou 350122, China; (H.L.); (Y.Y.); (J.C.); (Q.L.); (Y.C.); (Y.Z.); (C.W.)
- Key Laboratory of Environment and Health, Fujian Province University, Fuzhou 350122, China
| | - Qingyu Li
- School of Public Health, Fujian Medical University, Fuzhou 350122, China; (H.L.); (Y.Y.); (J.C.); (Q.L.); (Y.C.); (Y.Z.); (C.W.)
- Key Laboratory of Environment and Health, Fujian Province University, Fuzhou 350122, China
| | - Yifeng Chen
- School of Public Health, Fujian Medical University, Fuzhou 350122, China; (H.L.); (Y.Y.); (J.C.); (Q.L.); (Y.C.); (Y.Z.); (C.W.)
- Key Laboratory of Environment and Health, Fujian Province University, Fuzhou 350122, China
| | - Yilin Zhang
- School of Public Health, Fujian Medical University, Fuzhou 350122, China; (H.L.); (Y.Y.); (J.C.); (Q.L.); (Y.C.); (Y.Z.); (C.W.)
- Key Laboratory of Environment and Health, Fujian Province University, Fuzhou 350122, China
| | - Shaojian Cai
- Department of Emergency Preparedness and Response, Fujian Provincial Center for Diseases Control and Prevention, Fuzhou 350012, China; (S.C.); (M.Z.)
| | - Meirong Zhan
- Department of Emergency Preparedness and Response, Fujian Provincial Center for Diseases Control and Prevention, Fuzhou 350012, China; (S.C.); (M.Z.)
| | - Chuancheng Wu
- School of Public Health, Fujian Medical University, Fuzhou 350122, China; (H.L.); (Y.Y.); (J.C.); (Q.L.); (Y.C.); (Y.Z.); (C.W.)
- Key Laboratory of Environment and Health, Fujian Province University, Fuzhou 350122, China
| | - Xinwu Lin
- Entry Health Screening Office, Fuzhou Customs, Changle International Airport, Fuzhou 350209, China
| | - Jianjun Xiang
- School of Public Health, Fujian Medical University, Fuzhou 350122, China; (H.L.); (Y.Y.); (J.C.); (Q.L.); (Y.C.); (Y.Z.); (C.W.)
- Key Laboratory of Environment and Health, Fujian Province University, Fuzhou 350122, China
- School of Public Health, The University of Adelaide, North Terrace Campus, Adelaide, SA 5005, Australia
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Zhao L, Guo X, Li L, Jing Q, Ma J, Xie T, Lin D, Li L, Yin Q, Wang Y, Zhang X, Li Z, Liu X, Hu T, Hu M, Ren W, Li J, Peng J, Yu L, Peng Z, Hong W, Leng X, Luo L, Ngobeh JJK, Tang X, Wu R, Zhao W, Shi B, Liu J, Yang Z, Chen XG, Zhou X, Zhang F. Phylodynamics unveils invading and diffusing patterns of dengue virus serotype-1 in Guangdong, China from 1990 to 2019 under a global genotyping framework. Infect Dis Poverty 2024; 13:43. [PMID: 38863070 PMCID: PMC11165891 DOI: 10.1186/s40249-024-01211-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 05/29/2024] [Indexed: 06/13/2024] Open
Abstract
BACKGROUND The strong invasiveness and rapid expansion of dengue virus (DENV) pose a great challenge to global public health. However, dengue epidemic patterns and mechanisms at a genetic scale, particularly in term of cross-border transmissions, remain poorly understood. Importation is considered as the primary driver of dengue outbreaks in China, and since 1990 a frequent occurrence of large outbreaks has been triggered by the imported cases and subsequently spread to the western and northern parts of China. Therefore, this study aims to systematically reveal the invasion and diffusion patterns of DENV-1 in Guangdong, China from 1990 to 2019. METHODS These analyses were performed on 179 newly assembled genomes from indigenous dengue cases in Guangdong, China and 5152 E gene complete sequences recorded in Chinese mainland. The genetic population structure and epidemic patterns of DENV-1 circulating in Chinese mainland were characterized by phylogenetics, phylogeography, phylodynamics based on DENV-1 E-gene-based globally unified genotyping framework. RESULTS Multiple serotypes of DENV were co-circulating in Chinese mainland, particularly in Guangdong and Yunnan provinces. A total of 189 transmission clusters in 38 clades belonging to 22 subgenotypes of genotype I, IV and V of DENV-1 were identified, with 7 Clades of Concern (COCs) responsible for the large outbreaks since 1990. The epidemic periodicity was inferred from the data to be approximately 3 years. Dengue transmission events mainly occurred from Great Mekong Subregion-China (GMS-China), Southeast Asia (SEA), South Asia Subcontinent (SASC), and Oceania (OCE) to coastal and land border cities respectively in southeastern and southwestern China. Specially, Guangzhou was found to be the most dominant receipting hub, where DENV-1 diffused to other cities within the province and even other parts of the country. Genome phylogeny combined with epidemiological investigation demonstrated a clear local consecutive transmission process of a 5C1 transmission cluster (5C1-CN4) of DENV-1 in Guangzhou from 2013 to 2015, while the two provinces of Guangdong and Yunnan played key roles in ongoing transition of dengue epidemic patterns. In contextualizing within Invasion Biology theories, we have proposed a derived three-stage model encompassing the stages of invasion, colonization, and dissemination, which is supposed to enhance our understanding of dengue spreading patterns. CONCLUSIONS This study demonstrates the invasion and diffusion process of DENV-1 in Chinese mainland within a global genotyping framework, characterizing the genetic diversities of viral populations, multiple sources of importation, and periodic dynamics of the epidemic. These findings highlight the potential ongoing transition trends from epidemic to endemic status offering a valuable insight into early warning, prevention and control of rapid spreading of dengue both in China and worldwide.
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Affiliation(s)
- Lingzhai Zhao
- Institute of Infectious Diseases, Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, 510440, Guangdong, China
| | - Xiang Guo
- Department of Pathogen Biology, School of Public Health, Institute of Tropical Medicine, Southern Medical University; Guangdong Provincial Key Laboratory of Tropical Disease Research; Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes; Key Laboratory of Infectious Diseases Research in South China of Ministry of Education, Guangzhou, 510515, China
| | - Liqiang Li
- Department of Clinical Laboratory, The Third People's Hospital of Shenzhen, Southern University of Science and Technology, National Clinical Research Center for Infectious Diseases, Guangdong Provincial Clinical Research Center for Infectious Diseases (Tuberculosis), Shenzhen Clinical Research Center for Tuberculosis, Shenzhen, China
| | - Qinlong Jing
- Guangzhou Center for Disease Control and Prevention, Guangzhou, 510440, China
| | - Jinmin Ma
- BGI-Shenzhen, Shenzhen, 518083, China
| | - Tian Xie
- Department of Pathogen Biology, School of Public Health, Institute of Tropical Medicine, Southern Medical University; Guangdong Provincial Key Laboratory of Tropical Disease Research; Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes; Key Laboratory of Infectious Diseases Research in South China of Ministry of Education, Guangzhou, 510515, China
| | | | - Li Li
- Department of Biostatistics, School of Public Health, State Key Laboratory of Organ Failure Research, Guangdong Provincial Key Laboratory of Tropical Disease Research, Southern Medical University, Guangzhou, 510515, China
| | - Qingqing Yin
- Department of Pathogen Biology, School of Public Health, Institute of Tropical Medicine, Southern Medical University; Guangdong Provincial Key Laboratory of Tropical Disease Research; Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes; Key Laboratory of Infectious Diseases Research in South China of Ministry of Education, Guangzhou, 510515, China
| | - Yuji Wang
- Department of Pathogen Biology, School of Public Health, Institute of Tropical Medicine, Southern Medical University; Guangdong Provincial Key Laboratory of Tropical Disease Research; Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes; Key Laboratory of Infectious Diseases Research in South China of Ministry of Education, Guangzhou, 510515, China
| | - Xiaoqing Zhang
- Department of Pathogen Biology, School of Public Health, Institute of Tropical Medicine, Southern Medical University; Guangdong Provincial Key Laboratory of Tropical Disease Research; Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes; Key Laboratory of Infectious Diseases Research in South China of Ministry of Education, Guangzhou, 510515, China
| | - Ziyao Li
- Department of Pathogen Biology, School of Public Health, Institute of Tropical Medicine, Southern Medical University; Guangdong Provincial Key Laboratory of Tropical Disease Research; Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes; Key Laboratory of Infectious Diseases Research in South China of Ministry of Education, Guangzhou, 510515, China
| | - Xiaohua Liu
- Department of Pathogen Biology, School of Public Health, Institute of Tropical Medicine, Southern Medical University; Guangdong Provincial Key Laboratory of Tropical Disease Research; Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes; Key Laboratory of Infectious Diseases Research in South China of Ministry of Education, Guangzhou, 510515, China
| | - Tian Hu
- Department of Pathogen Biology, School of Public Health, Institute of Tropical Medicine, Southern Medical University; Guangdong Provincial Key Laboratory of Tropical Disease Research; Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes; Key Laboratory of Infectious Diseases Research in South China of Ministry of Education, Guangzhou, 510515, China
| | - Minling Hu
- Department of Pathogen Biology, School of Public Health, Institute of Tropical Medicine, Southern Medical University; Guangdong Provincial Key Laboratory of Tropical Disease Research; Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes; Key Laboratory of Infectious Diseases Research in South China of Ministry of Education, Guangzhou, 510515, China
| | - Wenwen Ren
- Department of Pathogen Biology, School of Public Health, Institute of Tropical Medicine, Southern Medical University; Guangdong Provincial Key Laboratory of Tropical Disease Research; Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes; Key Laboratory of Infectious Diseases Research in South China of Ministry of Education, Guangzhou, 510515, China
| | - Jun Li
- Guangdong Provincial Key Laboratory of Research On Emergency in TCM, Guangdong Provincial Hospital of Traditional Chinese Medicine, Guangzhou, 510120, China
| | - Jie Peng
- Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Lei Yu
- Institute of Infectious Diseases, Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, 510440, Guangdong, China
| | - Zhiqiang Peng
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Wenxin Hong
- Institute of Infectious Diseases, Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, 510440, Guangdong, China
| | - Xingyu Leng
- Institute of Infectious Diseases, Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, 510440, Guangdong, China
| | - Lei Luo
- Guangzhou Center for Disease Control and Prevention, Guangzhou, 510440, China
| | - Jone Jama Kpanda Ngobeh
- Department of Pathogen Biology, School of Public Health, Institute of Tropical Medicine, Southern Medical University; Guangdong Provincial Key Laboratory of Tropical Disease Research; Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes; Key Laboratory of Infectious Diseases Research in South China of Ministry of Education, Guangzhou, 510515, China
| | - Xiaoping Tang
- Institute of Infectious Diseases, Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, 510440, Guangdong, China
| | - Rangke Wu
- The School of Foreign Studies, Southern Medical University, Guangzhou, 510515, China
| | - Wei Zhao
- BSL-3 Laboratory(Guangdong), School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Benyun Shi
- College of Computer and Information Engineering, Nanjing Tech University, Nanjing, 211816, China
| | - Jiming Liu
- Department of Computer Science, Hong Kong Baptist University, Hong Kong, 999077, China
| | - Zhicong Yang
- Guangzhou Center for Disease Control and Prevention, Guangzhou, 510440, China.
| | - Xiao-Guang Chen
- Department of Pathogen Biology, School of Public Health, Institute of Tropical Medicine, Southern Medical University; Guangdong Provincial Key Laboratory of Tropical Disease Research; Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes; Key Laboratory of Infectious Diseases Research in South China of Ministry of Education, Guangzhou, 510515, China.
| | - Xiaohong Zhou
- Department of Pathogen Biology, School of Public Health, Institute of Tropical Medicine, Southern Medical University; Guangdong Provincial Key Laboratory of Tropical Disease Research; Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes; Key Laboratory of Infectious Diseases Research in South China of Ministry of Education, Guangzhou, 510515, China.
| | - Fuchun Zhang
- Institute of Infectious Diseases, Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, 510440, Guangdong, China.
- Guangzhou Medical Research Institute of Infectious Diseases, Infectious Disease Center, Guangzhou Eighth People's Hospital, Guangzhou Medical University, Guangzhou, 510440, China.
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Xu C, Xu J, Wang L. Long-term effects of climate factors on dengue fever over a 40-year period. BMC Public Health 2024; 24:1451. [PMID: 38816722 PMCID: PMC11141019 DOI: 10.1186/s12889-024-18869-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 05/16/2024] [Indexed: 06/01/2024] Open
Abstract
BACKGROUND Dengue fever stands as one of the most extensively disseminated mosquito-borne infectious diseases worldwide. While numerous studies have investigated its influencing factors, a gap remains in long-term analysis, impeding the identification of temporal patterns, periodicity in transmission, and the development of effective prevention and control strategies. Thus, we aim to analyze the periodicity of dengue fever incidence and explore the association between various climate factors and the disease over an extended time series. METHODS By utilizing monthly dengue fever cases and climate data spanning four decades (1978-2018) in Guangdong province, China, we employed wavelet analysis to detect dengue fever periodicity and analyze the time-lag relationship with climate factors. Additionally, Geodetector q statistic was employed to quantify the explanatory power of each climate factor and assess interaction effects. RESULTS Our findings revealed a prolonged transmission period of dengue fever over the 40-year period, transitioning from August to November in the 1970s to nearly year-round in the 2010s. Moreover, we observed lags of 1.5, 3.5, and 3 months between dengue fever and temperature, relative humidity, and precipitation, respectively. The explanatory power of precipitation, temperature, relative humidity, and the Oceanic Niño Index (ONI) on dengue fever was determined to be 18.19%, 12.04%, 11.37%, and 5.17%, respectively. Dengue fever exhibited susceptibility to various climate factors, with notable nonlinear enhancement arising from the interaction of any two variables. Notably, the interaction between precipitation and humidity yielded the most significant effect, accounting for an explanatory power of 75.32%. CONCLUSIONS Consequently, future prevention and control strategies for dengue fever should take into account these climate changes and formulate corresponding measures accordingly. In regions experiencing the onset of high temperatures, humidity, and precipitation, it is imperative to initiate mosquito prevention and control measures within a specific window period of 1.5 months.
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Affiliation(s)
- Chengdong Xu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China.
| | - Jingyi Xu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Li Wang
- College of Geography and Environmental Science, Henan University, Kaifeng, China
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng, China
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8
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Li X, Liao C, Wu J, Yi B, Zha R, Deng Q, Xu J, Guo C, Lu J. Distinct serum exosomal miRNA profiles detected in acute and asymptomatic dengue infections: A community-based study in Baiyun District, Guangzhou. Heliyon 2024; 10:e31546. [PMID: 38807894 PMCID: PMC11130723 DOI: 10.1016/j.heliyon.2024.e31546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 05/16/2024] [Accepted: 05/17/2024] [Indexed: 05/30/2024] Open
Abstract
Background In recent years, research on exosomal miRNAs has provided new insights into exploring the mechanism of viral infection and disease prevention. This study aimed to investigate the serum exosomal miRNA expression profile of dengue-infected individuals through a community survey of dengue virus (DENV) infection. Methods A seroprevalence study of 1253 healthy persons was first conducted to ascertain the DENV infection status in Baiyun District, Guangzhou. A total of 18 serum samples, including 6 healthy controls (HC), 6 asymptomatic DENV infections (AsymptDI), and 6 confirmed dengue fever patients (AcuteDI), were collected for exosome isolation and then sRNA sequencing. Through bioinformatics analysis, we discovered distinct serum exosomal miRNA profiles among the different groups and identified differentially expressed miRNAs (DEMs). These findings were further validated by qRT-PCR. Results The community survey of DENV infection indicated that the DENV IgG antibody positivity rate among the population was 11.97 % in the study area, with asymptomatic infected individuals accounting for 93.06 % of the anti-DENV IgG positives. The age and Guangzhou household registration were associated with DENV IgG antibody positivity by logistic regression analysis. Distinct miRNA profiles were observed between healthy individuals and DENV infections. A total of 1854 miRNAs were identified in 18 serum exosome samples from the initial analysis of the sequencing data. Comparative analysis revealed 23 DEMs comprising 5 upregulated and 18 downregulated miRNAs in the DENV-infected group (mergedDI). In comparison to AcuteDI, 18 upregulated miRNAs were identified in AsymptDI. Moreover, functional enrichment of the predicted target genes of DEMs indicated that these miRNAs were involved in biological processes and pathways related to cell adhesion, focal adhesion, endocytosis, and ECM-receptor interaction. Eight DEMs were validated by qRT-PCR. Conclusion The Baiyun District of Guangzhou exhibits a notable proportion of asymptomatic DENV infections as suggested in other research, highlighting the need for enhanced monitoring and screening of asymptomatic persons and the elderly. Differential miRNA expression among healthy, symptomatic and asymptomatic DENV-infected individuals suggests their potential as biomarkers for distinguishing DENV infection and offers new avenues of investigating the mechanisms underlying DENV asymptomatic infections.
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Affiliation(s)
- Xiaokang Li
- School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
- One Health Center of Excellence for Research & Training, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Conghui Liao
- School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
- One Health Center of Excellence for Research & Training, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Jiani Wu
- School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
- One Health Center of Excellence for Research & Training, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Boyang Yi
- School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
- One Health Center of Excellence for Research & Training, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Renyun Zha
- School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
- One Health Center of Excellence for Research & Training, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Qiang Deng
- School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
- One Health Center of Excellence for Research & Training, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Jianhua Xu
- Guangzhou Baiyun District Center for Disease Control and Prevention, Guangzhou, 510445, China
| | - Cheng Guo
- School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Jiahai Lu
- School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
- One Health Center of Excellence for Research & Training, Sun Yat-Sen University, Guangzhou, 510080, China
- National Medical Products Administration Key Laboratory for Quality Monitoring and Evaluation of Vaccines and Biological Products, Guangzhou, 510080, China
- Hainan Key Novel Thinktank “Hainan Medical University ‘One Health’ Research Center”, Haikou, 571199, China
- Research Institute of Sun Yat-Sen University in Shenzhen, Shenzhen, 518057, China
- Key Laboratory of Tropical Diseases Control, Sun Yat-Sen University, Ministry of Education, Guangzhou, 510080, China
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9
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Guo X, Li L, Ren W, Hu M, Li Z, Zeng S, Liu X, Wang Y, Xie T, Yin Q, Wei Y, Luo L, Shi B, Wang C, Wu R, Yang Z, Chen XG, Zhou X. Modelling the dynamic basic reproduction number of dengue based on MOI of Aedes albopictus derived from a multi-site field investigation in Guangzhou, a subtropical region. Parasit Vectors 2024; 17:79. [PMID: 38383475 PMCID: PMC11325734 DOI: 10.1186/s13071-024-06121-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 01/03/2024] [Indexed: 02/23/2024] Open
Abstract
BACKGROUND More than half of the global population lives in areas at risk of dengue (DENV) transmission. Developing an efficient risk prediction system can help curb dengue outbreaks, but multiple variables, including mosquito-based surveillance indicators, still constrain our understanding. Mosquito or oviposition positive index (MOI) has been utilized in field surveillance to monitor the wild population density of Aedes albopictus in Guangzhou since 2005. METHODS Based on the mosquito surveillance data using Mosq-ovitrap collection and human landing collection (HLC) launched at 12 sites in Guangzhou from 2015 to 2017, we established a MOI-based model of the basic dengue reproduction number (R0) using the classical Ross-Macdonald framework combined with a linear mixed-effects model. RESULTS During the survey period, the mean MOI and adult mosquito density index (ADI) using HLC for Ae. albopictus were 12.96 ± 17.78 and 16.79 ± 55.92, respectively. The R0 estimated from the daily ADI (ADID) showed a significant seasonal variation. A 10-unit increase in MOI was associated with 1.08-fold (95% CI 1.05, 1.11) ADID and an increase of 0.14 (95% CI 0.05, 0.23) in the logarithmic transformation of R0. MOI-based R0 of dengue varied by month and average monthly temperature. During the active period of Ae. albopictus from April to November in Guangzhou region, a high risk of dengue outbreak was predicted by the MOI-based R0 model, especially from August to October, with the predicted R0 > 1. Meanwhile, from December to March, the estimates of MOI-based R0 were < 1. CONCLUSIONS The present study enriched our knowledge about mosquito-based surveillance indicators and indicated that the MOI of Ae. albopictus could be valuable for application in estimating the R0 of dengue using a statistical model. The MOI-based R0 model prediction of the risk of dengue transmission varied by month and temperature in Guangzhou. Our findings lay a foundation for further development of a complex efficient dengue risk prediction system.
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Affiliation(s)
- Xiang Guo
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Li Li
- State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, China
| | - Wenwen Ren
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Minling Hu
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Ziyao Li
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Shu Zeng
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Xiaohua Liu
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Yuji Wang
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Tian Xie
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Qingqing Yin
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Yuehong Wei
- Guangzhou Center for Disease Control and Prevention, Guangzhou, China
| | - Lei Luo
- Guangzhou Center for Disease Control and Prevention, Guangzhou, China
| | - Benyun Shi
- School of Computer Science and Technology, Nanjing Tech University, Nanjing, China
| | - Chunmei Wang
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Rangke Wu
- The School of Foreign Studies, Southern Medical University, Guangzhou, China
| | - Zhicong Yang
- Guangzhou Center for Disease Control and Prevention, Guangzhou, China
| | - Xiao-Guang Chen
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Xiaohong Zhou
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China.
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10
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Cheng Q, Jing Q, Collender PA, Head JR, Li Q, Yu H, Li Z, Ju Y, Chen T, Wang P, Cleary E, Lai S. Prior water availability modifies the effect of heavy rainfall on dengue transmission: a time series analysis of passive surveillance data from southern China. Front Public Health 2023; 11:1287678. [PMID: 38106890 PMCID: PMC10722414 DOI: 10.3389/fpubh.2023.1287678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Accepted: 10/31/2023] [Indexed: 12/19/2023] Open
Abstract
Introduction Given the rapid geographic spread of dengue and the growing frequency and intensity of heavy rainfall events, it is imperative to understand the relationship between these phenomena in order to propose effective interventions. However, studies exploring the association between heavy rainfall and dengue infection risk have reached conflicting conclusions, potentially due to the neglect of prior water availability in mosquito breeding sites as an effect modifier. Methods In this study, we addressed this research gap by considering the impact of prior water availability for the first time. We measured prior water availability as the cumulative precipitation over the preceding 8 weeks and utilized a distributed lag non-linear model stratified by the level of prior water availability to examine the association between dengue infection risk and heavy rainfall in Guangzhou, a dengue transmission hotspot in southern China. Results Our findings suggest that the effects of heavy rainfall are likely to be modified by prior water availability. A 24-55 day lagged impact of heavy rainfall was associated with an increase in dengue risk when prior water availability was low, with the greatest incidence rate ratio (IRR) of 1.37 [95% credible interval (CI): 1.02-1.83] occurring at a lag of 27 days. In contrast, a heavy rainfall lag of 7-121 days decreased dengue risk when prior water availability was high, with the lowest IRR of 0.59 (95% CI: 0.43-0.79), occurring at a lag of 45 days. Discussion These findings may help to reconcile the inconsistent conclusions reached by previous studies and improve our understanding of the complex relationship between heavy rainfall and dengue infection risk.
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Affiliation(s)
- Qu Cheng
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qinlong Jing
- Department of Infectious Diseases, Guangzhou Center for Disease Control and Prevention, Guangzhou, China
| | - Philip A. Collender
- Division of Environmental Health Sciences, School of Public Health, , University of California, Berkeley, Berkeley, CA, United States
| | - Jennifer R. Head
- Division of Epidemiology, School of Public Health, University of California, Berkeley, Berkeley, CA, United States
| | - Qi Li
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hailan Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhichao Li
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | - Yang Ju
- School of Architecture and Urban Planning, Nanjing University, Nanjing, China
| | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Peng Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Eimear Cleary
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, United Kingdom
| | - Shengjie Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, United Kingdom
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11
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Guo X, Luo L, Long Y, Teng P, Wei Y, Xie T, Li L, Yin Q, Li Z, Wang Y, He J, Ji X, Zhou H, Zhang X, Chen S, Zhou Y, Xu K, Liang G, Kuang H, Gao Y, Liu X, Luo L, Ding L, Li Y, Liu Z, Zhou T, Lai Z, Su X, Guo Y, Li C, Xie L, Li M, Wu X, Huang J, Su W, Pan Y, Hu W, Zhou D, Li C, Gui J, Ma J, Feng X, Zhu M, Zhong S, Chen F, Zeng H, Wu Y, Wang C, Li S, Wang Q, Wang X, Zhou Y, Ling J, Liu Y, Wu S, Li Z, Zhong M, Wei W, Xie L, Xu X, Huang H, Yang G, Liu Y, Liang S, Wu Y, Zhang D, Xu C, Wang J, Wang C, Wu R, Yang Z, Chen XG, Zhou X. Field investigation combined with modeling uncovers the ecological heterogeneity of Aedes albopictus habitats for strategically improving systematic management during urbanization. Parasit Vectors 2023; 16:382. [PMID: 37880803 PMCID: PMC10599048 DOI: 10.1186/s13071-023-05926-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 08/14/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND Aedes albopictus is an invasive vector of serious Aedes-borne diseases of global concern. Habitat management remains a critical factor for establishing a cost-effective systematic strategy for sustainable vector control. However, the community-based characteristics of Ae. albopictus habitats in complex urbanization ecosystems are still not well understood. METHODS A large-scale investigation of aquatic habitats, involving 12 sites selected as representative of four land use categories at three urbanization levels, was performed in Guangzhou, China during 2015-2017. The characteristics and dynamics of these Ae. albopictus habitats were assessed using habitat-type composition, habitat preference, diversity indexes and the Route index (RI), and the temporal patterns of these indexes were evaluated by locally weighted scatterplot smoothing models. The associations of RI with urbanization levels, land use categories and climatic variables were inferred using generalized additive mixed models. RESULTS A total of 1994 potential habitats and 474 Ae. albopictus-positive habitats were inspected. The majority of these habitats were container-type habitats, with Ae. albopictus showing a particularly higher habitat preference for plastic containers, metal containers and ceramic vessels. Unexpectedly, some non-container-type habitats, especially ornamental ponds and surface water, were found to have fairly high Ae. albopictus positivity rates. Regarding habitats, the land use category residential and rural in Jiangpu (Conghua District, Guangzhou) had the highest number of Ae. albopictus habitats with the highest positive rates. The type diversity of total habitats (H-total) showed a quick increase from February to April and peaked in April, while the H-total of positive habitats (H-positive) and RIs peaked in May. RIs mainly increased with the monthly average daily mean temperature and monthly cumulative rainfall. We also observed the accumulation of diapause eggs in the winter and diapause termination in the following March. CONCLUSIONS Ecological heterogeneity of habitat preferences of Ae. albopictus was demonstrated in four land use categories at three urbanization levels. The results reveal diversified habitat-type compositions and significant seasonal variations, indicating an ongoing adaptation of Ae. albopictus to the urbanization ecosystem. H-positivity and RIs were inferred as affected by climatic variables and diapause behavior of Ae. albopictus, suggesting that an effective control of overwintering diapause eggs is crucial. Our findings lay a foundation for establishing a stratified systematic management strategy of Ae. albopictus habitats in cities that is expected to complement and improve community-based interventions and sustainable vector management.
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Affiliation(s)
- Xiang Guo
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Lei Luo
- Guangzhou Center for Disease Control and Prevention, Guangzhou, 510440, China
| | - Yuxiang Long
- State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Pingying Teng
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Yuehong Wei
- Guangzhou Center for Disease Control and Prevention, Guangzhou, 510440, China
| | - Tian Xie
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Li Li
- State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Qingqing Yin
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Ziyao Li
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Yuji Wang
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Jiejun He
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Xiatian Ji
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Huasheng Zhou
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Xiaofan Zhang
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Shigang Chen
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Yezhen Zhou
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Kaihui Xu
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Guancong Liang
- Conghua District Center for Disease Control and Prevention, Guangzhou, 510900, China
| | - Haocheng Kuang
- Conghua District Center for Disease Control and Prevention, Guangzhou, 510900, China
| | - Yuting Gao
- Department of Landscape Architecture and Regional & Community Planning, College of Architecture, Planning and Design, Kansas State University, Manhattan, KS, 66506, USA
| | - Xiaohua Liu
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Luting Luo
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Lin Ding
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Yiji Li
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Zhuanzhuan Liu
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Tengfei Zhou
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Zetian Lai
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Xinghua Su
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Yuyan Guo
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Chenying Li
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Lihua Xie
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Minqing Li
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Xinglong Wu
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Jianhao Huang
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Weicong Su
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Yicheng Pan
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Wei Hu
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Dongrui Zhou
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Chunv Li
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Juan Gui
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Jiazhi Ma
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Xiaoli Feng
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Minyi Zhu
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Shangbin Zhong
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Fan Chen
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Huanchao Zeng
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Yingxian Wu
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Chen Wang
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Shukai Li
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Qi Wang
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Xueyi Wang
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Yekai Zhou
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Jianxun Ling
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Yingjie Liu
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Shang Wu
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Zhiwei Li
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Minghui Zhong
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Wenxia Wei
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Lixian Xie
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Xianli Xu
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Hehai Huang
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Guilan Yang
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Yan Liu
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Siting Liang
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Yingxia Wu
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Deyu Zhang
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Changqing Xu
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Jie Wang
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Chunmei Wang
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Rangke Wu
- The School of Foreign Studies, Southern Medical University, Guangzhou, 510515, China
| | - Zhicong Yang
- Guangzhou Center for Disease Control and Prevention, Guangzhou, 510440, China.
| | - Xiao-Guang Chen
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China.
| | - Xiaohong Zhou
- Department of Pathogen Biology, Institute of Tropical Medicine, Key Laboratory of Prevention and Control for Emerging Infectious Diseases of Guangdong Higher Institutes, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, 510515, China.
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12
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Yu Y, Liu Y, Ling F, Sun J, Jiang J. Epidemiological Characteristics and Economic Burden of Dengue in Zhejiang Province, China. Viruses 2023; 15:1731. [PMID: 37632073 PMCID: PMC10458908 DOI: 10.3390/v15081731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/03/2023] [Accepted: 08/11/2023] [Indexed: 08/27/2023] Open
Abstract
Dengue imposes a heavy economic burden on families and society. We used surveillance data reported in 2019 to characterize the dengue epidemic in Zhejiang Province, China, which provided guidance for dengue prevention and control. Dengue epidemics mostly occurred in July to October. People aged 30-44 years, males, and commercial service workers were more likely to suffer from dengue. The epidemic areas were mainly in Hangzhou and Wenzhou. Meanwhile, we assessed the economic cost of dengue in the province from both family and organizational perspectives. The direct economic burden of dengue patients was estimated to be USD 405,038.25, and the indirect economic burden was USD 140,364.90, for a total economic burden of USD 543,213.00. The direct economic burden of dengue patients should be reduced by increasing the coverage and reimbursement of health insurance. Additionally, the total annual cost of dengue prevention and control for the government and organizational sectors was estimated to be USD 7075,654.83. Quantifying the dengue burden is critical for developing disease control strategies, allocating public health resources, and setting health policy priorities.
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Affiliation(s)
- Yi Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Hangzhou Normal University, Hangzhou 311121, China;
| | - Ying Liu
- Key Lab of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, China
| | - Feng Ling
- Key Lab of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, China
| | - Jimin Sun
- Key Lab of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, China
| | - Jianmin Jiang
- Key Lab of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, China
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13
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Wang Y, Zhou H, Zheng L, Li M, Hu B. Using the Baidu index to predict trends in the incidence of tuberculosis in Jiangsu Province, China. Front Public Health 2023; 11:1203628. [PMID: 37533520 PMCID: PMC10390734 DOI: 10.3389/fpubh.2023.1203628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 07/05/2023] [Indexed: 08/04/2023] Open
Abstract
Objective To analyze the time series in the correlation between search terms related to tuberculosis (TB) and actual incidence data in China. To screen out the "leading" terms and construct a timely and efficient TB prediction model that can predict the next wave of TB epidemic trend in advance. Methods Monthly incidence data of tuberculosis in Jiangsu Province, China, were collected from January 2011 to December 2020. A scoping approach was used to identify TB search terms around common TB terms, prevention, symptoms and treatment. Search terms for Jiangsu Province, China, from January 2011 to December 2020 were collected from the Baidu index database. Correlation coefficients between search terms and actual incidence were calculated using Python 3.6 software. The multiple linear regression model was constructed using SPSS 26.0 software, which also calculated the goodness of fit and prediction error of the model predictions. Results A total of 16 keywords with correlation coefficients greater than 0.6 were screened, of which 11 were the leading terms. The R2 of the prediction model was 0.67 and the MAPE was 10.23%. Conclusion The TB prediction model based on Baidu Index data was able to predict the next wave of TB epidemic trends and intensity 2 months in advance. This forecasting model is currently only available for Jiangsu Province.
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14
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Tian L, Liang C, Huang X, Liu Z, Su J, Guo C, Zhu G, Sun J. Genomic epidemiology of dengue in Shantou, China, 2019. Front Public Health 2023; 11:1035060. [PMID: 37522010 PMCID: PMC10374217 DOI: 10.3389/fpubh.2023.1035060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 06/23/2023] [Indexed: 08/01/2023] Open
Abstract
Objectives Dengue has been endemic in Southeast Asian countries for decades. There are few reports tracing the dynamics of dengue in real time. In this study, we generated hundreds of pathogen genomes to understand the genomic epidemiology of an outbreak in a hyper-endemic area of dengue. Methods We leveraged whole-genome short-read sequencing (PE150) to generate genomes of the dengue virus and investigated the genomic epidemiology of a dengue virus transmission in a mesoscale outbreak in Shantou, China, in 2019. Results The outbreak was sustained from July to December 2019. The total accumulated number of laboratory-confirmed cases was 944. No gender bias or fatalities were recorded. Cambodia and Singapore were the main sources of imported dengue cases (74.07%, n = 20). A total of 284 dengue virus strains were isolated, including 259 DENV-1, 24 DENV-2, and 1 DENV-3 isolates. We generated the entire genome of 252 DENV isolates (229 DENV-1, 22 DENV-2, and 1 DENV-3), which represented 26.7% of the total cases. Combined epidemiological and phylogenetic analyses indicated multiple independent introductions. The internal transmission evaluations and transmission network reconstruction supported the inference of phylodynamic analysis, with high Bayes factor support in BSSVS analysis. Two expansion founders and transmission chains were detected in CCH and LG of Shantou. Conclusions We observed the instant effects of genomic epidemiology in monitoring the dynamics of DENV and highlighted its prospects for real-time tracing of outbreaks of other novel agents in the future.
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Affiliation(s)
- Lina Tian
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong Provincial Institute of Public Health, Guangzhou, China
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
- School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin, China
| | - Chumin Liang
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong Provincial Institute of Public Health, Guangzhou, China
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Xiaorong Huang
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong Provincial Institute of Public Health, Guangzhou, China
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
- School of Public Health, Southern Medical University, Guangzhou, China
| | - Zhe Liu
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong Provincial Institute of Public Health, Guangzhou, China
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Juan Su
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Chuan Guo
- Center for Disease Control and Prevention of Shantou City, Shantou, Guangdong, China
| | - Guanghu Zhu
- School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin, China
| | - Jiufeng Sun
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong Provincial Institute of Public Health, Guangzhou, China
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
- School of Public Health, Southern Medical University, Guangzhou, China
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15
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Zhang B, Zhang QQ, Cai YY, Yan XT, Zhai YQ, Guo Z, Ying GG. Environmental emissions and pollution characteristics of mosquitocides for the control of dengue fever in a typical urban area. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 867:161513. [PMID: 36634776 DOI: 10.1016/j.scitotenv.2023.161513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/25/2022] [Accepted: 01/06/2023] [Indexed: 06/17/2023]
Abstract
Mosquitocides are frequently used to control the spread of dengue fever in tropical and sub-tropic urban regions worldwide, resulting in their discharge into the environment via rainfall runoff, causing adverse effects on ecological health. This study quantitatively evaluated mosquitocide emissions and environmental pollution in a typical urban district in China affected by the dengue fever epidemic, using a method combining market surveys, monitoring campaigns and SWMM (storm water management model) modelling tools. During the assessment period, the total mosquitocide usage in the urban district reached 6334 kg, with an estimated load of 56.55 g entering the receiving environment via rainfall runoff, 91.04 % of which occurred in the rainy season. Monitoring results indicated that the initial 0.5-1 h was the main period of mosquitocide wash off into the receiving water. Environmental mosquitocide pollution levels were found to be affected by the mosquitocide type and the time interval between mosquitocide application and precipitation events. The measured environmental concentrations of mosquitocides in this study were generally higher than those areas unaffected by the dengue fever epidemic. The modelled mosquitocide concentrations were in accordance with monitoring results. The finding of this study are important for assessing the environmental impact of dengue fever control activities, while also providing valuable baseline data for the effective environmental management of mosquitocides.
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Affiliation(s)
- Bing Zhang
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, MOE Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou 510006, China; Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, China; State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
| | - Qian-Qian Zhang
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, MOE Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou 510006, China.
| | - Ya-Ya Cai
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, MOE Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou 510006, China
| | - Xiao-Ting Yan
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, MOE Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou 510006, China
| | - Yun-Qiu Zhai
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, MOE Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou 510006, China
| | - Zhao Guo
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, MOE Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou 510006, China
| | - Guang-Guo Ying
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, MOE Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou 510006, China
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16
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Liu H, Huang X, Guo X, Cheng P, Wang H, Liu L, Zang C, Zhang C, Wang X, Zhou G, Gong M. Climate change and Aedes albopictus risks in China: current impact and future projection. Infect Dis Poverty 2023; 12:26. [PMID: 36964611 PMCID: PMC10037799 DOI: 10.1186/s40249-023-01083-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 03/14/2023] [Indexed: 03/26/2023] Open
Abstract
BACKGROUND Future distribution of dengue risk is usually predicted based on predicted climate changes using general circulation models (GCMs). However, it is difficult to validate the GCM results and assess the uncertainty of the predictions. The observed changes in climate may be very different from the GCM results. We aim to utilize trends in observed climate dynamics to predict future risks of Aedes albopictus in China. METHODS We collected Ae. albopictus surveillance data and observed climate records from 80 meteorological stations from 1970 to 2021. We analyzed the trends in climate change in China and made predictions on future climate for the years 2050 and 2080 based on trend analyses. We analyzed the relationship between climatic variables and the prevalence of Ae. albopictus in different months/seasons. We built a classification tree model (based on the average of 999 runs of classification and regression tree analyses) to predict the monthly/seasonal Ae. albopictus distribution based on the average climate from 1970 to 2000 and assessed the contributions of different climatic variables to the Ae. albopictus distribution. Using these models, we projected the future distributions of Ae. albopictus for 2050 and 2080. RESULTS The study included Ae. albopictus surveillance from 259 sites in China found that winter to early spring (November-February) temperatures were strongly correlated with Ae. albopictus prevalence (prediction accuracy ranges 93.0-98.8%)-the higher the temperature the higher the prevalence, while precipitation in summer (June-September) was important predictor for Ae. albopictus prevalence. The machine learning tree models predicted the current prevalence of Ae. albopictus with high levels of agreement (accuracy > 90% and Kappa agreement > 80% for all 12 months). Overall, winter temperature contributed the most to Ae. albopictus distribution, followed by summer precipitation. An increase in temperature was observed from 1970 to 2021 in most places in China, and annual change rates varied substantially from -0.22 ºC/year to 0.58 ºC/year among sites, with the largest increase in temperature occurring from February to April (an annual increase of 1.4-4.7 ºC in monthly mean, 0.6-4.0 ºC in monthly minimum, and 1.3-4.3 ºC in monthly maximum temperature) and the smallest in November and December. Temperature increases were lower in the tropics/subtropics (1.5-2.3 ºC from February-April) compared to the high-latitude areas (2.6-4.6 ºC from February-April). The projected temperatures in 2050 and 2080 by this study were approximately 1-1.5 °C higher than those projected by GCMs. The estimated current Ae. albopictus risk distribution had a northern boundary of north-central China and the southern edge of northeastern China, with a risk period of June-September. The projected future Ae. albopictus risks in 2050 and 2080 cover nearly all of China, with an expanded risk period of April-October. The current at-risk population was estimated to be 960 million and the future at-risk population was projected to be 1.2 billion. CONCLUSIONS The magnitude of climate change in China is likely to surpass GCM predictions. Future dengue risks will expand to cover nearly all of China if current climate trends continue.
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Affiliation(s)
- Hongmei Liu
- Shandong Institute of Parasitic Diseases, Shandong First Medical University and Shandong Academy of Medical Sciences, Jining, Shandong Province, 272033, People's Republic of China.
- Program in Public Health, University of California, Irvine, CA, 92697, USA.
| | - Xiaodan Huang
- Shandong Institute of Parasitic Diseases, Shandong First Medical University and Shandong Academy of Medical Sciences, Jining, Shandong Province, 272033, People's Republic of China
| | - Xiuxia Guo
- Shandong Institute of Parasitic Diseases, Shandong First Medical University and Shandong Academy of Medical Sciences, Jining, Shandong Province, 272033, People's Republic of China
| | - Peng Cheng
- Shandong Institute of Parasitic Diseases, Shandong First Medical University and Shandong Academy of Medical Sciences, Jining, Shandong Province, 272033, People's Republic of China
| | - Haifang Wang
- Shandong Institute of Parasitic Diseases, Shandong First Medical University and Shandong Academy of Medical Sciences, Jining, Shandong Province, 272033, People's Republic of China
| | - Lijuan Liu
- Shandong Institute of Parasitic Diseases, Shandong First Medical University and Shandong Academy of Medical Sciences, Jining, Shandong Province, 272033, People's Republic of China
| | - Chuanhui Zang
- Shandong Institute of Parasitic Diseases, Shandong First Medical University and Shandong Academy of Medical Sciences, Jining, Shandong Province, 272033, People's Republic of China
| | - Chongxing Zhang
- Shandong Institute of Parasitic Diseases, Shandong First Medical University and Shandong Academy of Medical Sciences, Jining, Shandong Province, 272033, People's Republic of China
| | - Xuejun Wang
- Shandong Center for Disease Control and Prevention, Jinan, 250013, China.
| | - Guofa Zhou
- Program in Public Health, University of California, Irvine, CA, 92697, USA.
| | - Maoqing Gong
- Shandong Institute of Parasitic Diseases, Shandong First Medical University and Shandong Academy of Medical Sciences, Jining, Shandong Province, 272033, People's Republic of China.
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Guo Z, Liu W, Liu X, Abudunaibi B, Luo L, Wu S, Deng B, Yang T, Huang J, Wu S, Lei L, Zhao Z, Li Z, Li P, Liu C, Zhan M, Chen T. Model-based risk assessment of dengue fever transmission in Xiamen City, China. Front Public Health 2023; 11:1079877. [PMID: 36860401 PMCID: PMC9969104 DOI: 10.3389/fpubh.2023.1079877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 01/25/2023] [Indexed: 02/15/2023] Open
Abstract
Background Quantitative assessment of the risk of local transmission from imported dengue cases makes a great challenge to the development of public health in China. The purpose of this study is to observe the risk of mosquito-borne transmission in Xiamen City through ecological and insecticide resistance monitoring. Quantitative evaluation of mosquito insecticide resistance, community population and the number of imported cases affecting the transmission of dengue fever (DF) in Xiamen was carried out based on transmission dynamics model, so as to reveal the correlation between key risk factors and DF transmission. Methods Based on the dynamics model and combined with the epidemiological characteristics of DF in Xiamen City, a transmission dynamics model was built to simulate the secondary cases caused by imported cases to evaluate the transmission risk of DF, and to explore the influence of mosquito insecticide resistance, community population and imported cases on the epidemic situation of DF in Xiamen City. Results For the transmission model of DF, when the community population is between 10,000 and 25,000, changing the number of imported DF cases and the mortality rate of mosquitoes will have an impact on the spread of indigenous DF cases, however, changing the birth rate of mosquitoes did not gain more effect on the spread of local DF transmission. Conclusions Through the quantitative evaluation of the model, this study determined that the mosquito resistance index has an important influence on the local transmission of dengue fever caused by imported cases in Xiamen, and the Brayton index can also affect the local transmission of the disease.
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Affiliation(s)
- Zhinan Guo
- Xiamen Center for Disease Control and Prevention, Xiamen, Fujian, China
| | - Weikang Liu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Xingchun Liu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Buasiyamu Abudunaibi
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Li Luo
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Sihan Wu
- Xiamen Center for Disease Control and Prevention, Xiamen, Fujian, China
| | - Bin Deng
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Tianlong Yang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Jiefeng Huang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Shenggen Wu
- Fujian Provincial Center for Disease Control and Prevention, Fuzhou, Fujian, China
| | - Lei Lei
- Xiamen Center for Disease Control and Prevention, Xiamen, Fujian, China
| | - Zeyu Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Zhuoyang Li
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Peihua Li
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Chan Liu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
| | - Meirong Zhan
- Fujian Provincial Center for Disease Control and Prevention, Fuzhou, Fujian, China
| | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, China
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18
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Zhao C, Zhou X, Xue C, Lun X, Li W, Liu X, Wu H, Song X, Wang J, Liu Q, Meng F. Knockdown resistance mutations distribution and characteristics of Aedes albopictus field populations within eleven dengue local epidemic provinces in China. Front Cell Infect Microbiol 2023; 12:981702. [PMID: 36846550 PMCID: PMC9948608 DOI: 10.3389/fcimb.2022.981702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 09/05/2022] [Indexed: 02/11/2023] Open
Abstract
Background Aedes albopictus, commonly known as the tiger mosquito, has attracted global attention because its bite can transmit several viruses, such as dengue virus. With the absence of an effective therapy and vaccine, mosquito control is the sole method for dengue fever control. However, Ae. albopictus has developed resistance to most insecticides, especially pyrethroids. Many scholars have conducted thorough research for the target-site of pyrethroids. The main target-site is the voltage-gated sodium channel gene (VGSC) whose mutation causes knockdown resistance (kdr). The spatial distribution of three locus kdr mutations in Ae. albopictus has not been comprehensively analyzed nationwide in China. In addition, the relationship between the frequency of kdr mutations and dengue fever has not yet been explored. Methods A total of 2,241 Ae. albopictus samples from 49 populations from 11 provinces of mainland China were collected in 2020 and analyzed for mutations in the VGSC gene. DNAstar 7.1. Seqman and Mega-X were used to compare the sequences and read the peak map to confirm the genotypes and alleles of each mutation. ArcGIS 10.6 software was used to make interpolation and extract meteorological data of collection sites and to conduct spatial autocorrelation analysis. R 4.1.2 software was used to conduct a chi-square test for kdr mutations and dengue area and to analyze the correlation between meteorological factors and kdr mutations. Results The overall frequencies of mutant alleles at 1016G, 1532T, and 1534S/C/L were 13.19%, 4.89%, and 46.90%, respectively. Mutations at the three loci were found at 89.80% (44/49), 44.90% (22/49), and 97.96% (48/49) of the field populations. At each of the loci V1016 and I1532, only one allele was detected, which was GGA(G) and ACC(T), respectively. Five mutant alleles were found at codon 1534: TCC/S (33.49%), TGC/C (11.96%), TTG/L (0.60%), CTC/L (0.49%), and TTA/L (0.58%). In total, 31 triple-locus genotype combinations were found, and the single locus mutation was the most common. We also found firstly triple-locus mutant individuals, whose genotypes were V/G+I/T+F/S and V/G+I/T+S/S. The 1016 and 1532 mutation rates were significantly negatively related to the annual average temperature (AAT), but the 1534 mutation rate was significantly positively related to AAT. The 1532 mutation rate was significantly positively related to the 1016 mutation rate but negatively related to the 1534 mutation rate. A relationship was observed between the 1534 codon mutation rate and dengue epidemic areas in this study. Furthermore, spatial autocorrelation analysis results showed that the mutation rates of different codons in different geographical areas had spatial aggregation and positive spatial correlation. Conclusion This study showed that the multiple kdr mutations at codon 1016, 1532 and 1534 of Ae. albopictus were found in most areas of China. Two novel triple-locus genotype combinations, V/G+I/T+F/S and V/G+I/T+S/S, were detected in this study. In addition, the relationship between mosquito resistance and dengue fever outbreak should be further explored, especially considering the insecticide-usage history in different areas. The characteristic of spatial aggregation of VGSC gene mutation rates reminds us to notice the gene exchange and similarity of insecticide usage in the adjacent areas. The use of pyrethroids should be restricted to delay resistance development. New-type insecticides should be developed to adjust the changes in the resistance spectrum. Our study provides abundant data on the Ae. albopictus kdr gene mutation in China; these findings will be useful for the correlation analysis of molecular mechanism of insecticide resistance.
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Affiliation(s)
- Chunchun Zhao
- State Key Laboratory of Infectious Disease Prevention and Control, World Health Organization (WHO) Collaborating Centre for Vector Surveillance and Management, Chinese Center for Disease Control and Prevention, National Institute for Communicable Disease Control and Prevention, Beijing, China
| | - Xinxin Zhou
- State Key Laboratory of Infectious Disease Prevention and Control, World Health Organization (WHO) Collaborating Centre for Vector Surveillance and Management, Chinese Center for Disease Control and Prevention, National Institute for Communicable Disease Control and Prevention, Beijing, China
- Beijing Daxing District Center for Disease Control and Prevention, Genaral Office, Beijing, China
| | - Chuizhao Xue
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Health Committee (NHC) Key Laboratory of Parasite and Vector Biology, World Health Organization (WHO) Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Shanghai, China
| | - Xinchang Lun
- State Key Laboratory of Infectious Disease Prevention and Control, World Health Organization (WHO) Collaborating Centre for Vector Surveillance and Management, Chinese Center for Disease Control and Prevention, National Institute for Communicable Disease Control and Prevention, Beijing, China
| | - Wenyu Li
- State Key Laboratory of Infectious Disease Prevention and Control, World Health Organization (WHO) Collaborating Centre for Vector Surveillance and Management, Chinese Center for Disease Control and Prevention, National Institute for Communicable Disease Control and Prevention, Beijing, China
| | - Xiaobo Liu
- State Key Laboratory of Infectious Disease Prevention and Control, World Health Organization (WHO) Collaborating Centre for Vector Surveillance and Management, Chinese Center for Disease Control and Prevention, National Institute for Communicable Disease Control and Prevention, Beijing, China
| | - Haixia Wu
- State Key Laboratory of Infectious Disease Prevention and Control, World Health Organization (WHO) Collaborating Centre for Vector Surveillance and Management, Chinese Center for Disease Control and Prevention, National Institute for Communicable Disease Control and Prevention, Beijing, China
| | - Xiuping Song
- State Key Laboratory of Infectious Disease Prevention and Control, World Health Organization (WHO) Collaborating Centre for Vector Surveillance and Management, Chinese Center for Disease Control and Prevention, National Institute for Communicable Disease Control and Prevention, Beijing, China
| | - Jun Wang
- State Key Laboratory of Infectious Disease Prevention and Control, World Health Organization (WHO) Collaborating Centre for Vector Surveillance and Management, Chinese Center for Disease Control and Prevention, National Institute for Communicable Disease Control and Prevention, Beijing, China
| | - Qiyong Liu
- State Key Laboratory of Infectious Disease Prevention and Control, World Health Organization (WHO) Collaborating Centre for Vector Surveillance and Management, Chinese Center for Disease Control and Prevention, National Institute for Communicable Disease Control and Prevention, Beijing, China
| | - Fengxia Meng
- State Key Laboratory of Infectious Disease Prevention and Control, World Health Organization (WHO) Collaborating Centre for Vector Surveillance and Management, Chinese Center for Disease Control and Prevention, National Institute for Communicable Disease Control and Prevention, Beijing, China
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Liu WH, Shi C, Lu Y, Luo L, Ou CQ. Epidemiological characteristics of imported acute infectious diseases in Guangzhou, China, 2005-2019. PLoS Negl Trop Dis 2022; 16:e0010940. [PMID: 36472963 PMCID: PMC9725138 DOI: 10.1371/journal.pntd.0010940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 11/09/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND The global spread of infectious diseases is currently a prominent threat to public health, with the accelerating pace of globalization and frequent international personnel intercourse. The present study examined the epidemiological characteristics of overseas imported cases of acute infectious diseases in Guangzhou, China. METHODS We retrospectively investigated the distribution of diseases, demographic characteristics, and temporal and spatial variations of imported cases of acute infectious diseases in Guangzhou based on the surveillance data of notifiable infectious diseases from 2005 to 2019, provided by Guangzhou center for Disease Control and Prevention. The Cochran-Armitage trend test was applied to examine the trend in the number of imported cases over time. RESULTS A total of 1,025 overseas imported cases of acute infectious diseases were identified during the study period. The top three diseases were dengue (67.12%), malaria (12.39%), and influenza A (H1N1) pdm09 (4.10%). Imported cases were predominantly males, with a sex ratio of 2.6: 1 and 75.22% of the cases were those aged 20-49 years. Businessmen, workers, students and unemployed persons accounted for a large proportion of the cases (68.49%) and many of the cases came from Southeast Asia (59.02%). The number of imported cases of acute infectious diseases increased during the study period and hit 318 in 2019. A clear seasonal pattern was observed in the number of imported cases with a peak period between June and November. Imported cases were reported in all of the 11 districts in Guangzhou and the central districts were more seriously affected compared with other districts. CONCLUSIONS The burden of dengue imported from overseas was substantial and increasing in Guangzhou, China, with the peak period from June to November. Dengue was the most common imported disease. Most imported cases were males aged 20-49 years and businessmen. Further efforts, such as strengthening surveillance of imported cases, paying close attention to the epidemics in hotspots, and improving the ability to detect the imported cases from overseas, are warranted to control infectious diseases especially in the center of the city with a higher population density highly affected by imported cases.
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Affiliation(s)
- Wen-Hui Liu
- Guangzhou Center for Disease Control and Prevention, Guangzhou, China
- State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, China
| | - Chen Shi
- State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, China
| | - Ying Lu
- Guangzhou Center for Disease Control and Prevention, Guangzhou, China
| | - Lei Luo
- Guangzhou Center for Disease Control and Prevention, Guangzhou, China
- * E-mail: (LL); (CO)
| | - Chun-Quan Ou
- State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, China
- * E-mail: (LL); (CO)
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Pascoe L, Clemen T, Bradshaw K, Nyambo D. Review of Importance of Weather and Environmental Variables in Agent-Based Arbovirus Models. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:15578. [PMID: 36497652 PMCID: PMC9740748 DOI: 10.3390/ijerph192315578] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 11/20/2022] [Accepted: 11/21/2022] [Indexed: 06/17/2023]
Abstract
The study sought to review the works of literature on agent-based modeling and the influence of climatic and environmental factors on disease outbreak, transmission, and surveillance. Thus, drawing the influence of environmental variables such as vegetation index, households, mosquito habitats, breeding sites, and climatic variables including precipitation or rainfall, temperature, wind speed, and relative humidity on dengue disease modeling using the agent-based model in an African context and globally was the aim of the study. A search strategy was developed and used to search for relevant articles from four databases, namely, PubMed, Scopus, Research4Life, and Google Scholar. Inclusion criteria were developed, and 20 articles met the criteria and have been included in the review. From the reviewed works of literature, the study observed that climatic and environmental factors may influence the arbovirus disease outbreak, transmission, and surveillance. Thus, there is a call for further research on the area. To benefit from arbovirus modeling, it is crucial to consider the influence of climatic and environmental factors, especially in Africa, where there are limited studies exploring this phenomenon.
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Affiliation(s)
- Luba Pascoe
- Nelson Mandela African Institution of Science and Technology, Arusha P.O Box 447, Tanzania
| | - Thomas Clemen
- Nelson Mandela African Institution of Science and Technology, Arusha P.O Box 447, Tanzania
- Department of Computer Science, Hamburg University of Applied Sciences, Berliner Tor 7, 20099 Hamburg, Germany
| | - Karen Bradshaw
- Nelson Mandela African Institution of Science and Technology, Arusha P.O Box 447, Tanzania
- Department of Computer Science, Rhodes University, Grahamstown 6139, South Africa
| | - Devotha Nyambo
- Nelson Mandela African Institution of Science and Technology, Arusha P.O Box 447, Tanzania
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21
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Wei Y, He S, Wang J, Fan P, He Y, Hu K, Chen Y, Zhou G, Zhong D, Zheng X. Genome-wide SNPs reveal novel patterns of spatial genetic structure in Aedes albopictus (Diptera Culicidae) population in China. Front Public Health 2022; 10:1028026. [PMID: 36438226 PMCID: PMC9685676 DOI: 10.3389/fpubh.2022.1028026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 10/28/2022] [Indexed: 11/11/2022] Open
Abstract
Introduction Since the second half of the 20th century, Aedes albopictus, a vector for more than 20 arboviruses, has spread worldwide. Aedes albopictus is the main vector of infectious diseases transmitted by Aedes mosquitoes in China, and it has caused concerns regarding public health. A comprehensive understanding of the spatial genetic structure of this vector species at a genomic level is essential for effective vector control and the prevention of vector-borne diseases. Methods During 2016-2018, adult female Ae. albopictus mosquitoes were collected from eight different geographical locations across China. Restriction site-associated DNA sequencing (RAD-seq) was used for high-throughput identification of single nucleotide polymorphisms (SNPs) and genotyping of the Ae. albopictus population. The spatial genetic structure was analyzed and compared to those exhibited by mitochondrial cytochrome c oxidase subunit 1 (cox1) and microsatellites in the Ae. albopictus population. Results A total of 9,103 genome-wide SNP loci in 101 specimens and 32 haplotypes of cox1 in 231 specimens were identified in the samples from eight locations in China. Principal component analysis revealed that samples from Lingshui and Zhanjiang were more genetically different than those from the other locations. The SNPs provided a better resolution and stronger signals for novel spatial population genetic structures than those from the cox1 data and a set of previously genotyped microsatellites. The fixation indexes from the SNP dataset showed shallow but significant genetic differentiation in the population. The Mantel test indicated a positive correlation between genetic distance and geographical distance. However, the asymmetric gene flow was detected among the populations, and it was higher from south to north and west to east than in the opposite directions. Conclusions The genome-wide SNPs revealed seven gene pools and fine spatial genetic structure of the Ae. albopictus population in China. The RAD-seq approach has great potential to increase our understanding of the spatial dynamics of population spread and establishment, which will help us to design new strategies for controlling vectors and mosquito-borne diseases.
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Affiliation(s)
- Yong Wei
- Department of Pathogen Biology, School of Public Health, Southern Medical University, Guangzhou, China,Clinical Laboratory, Shenzhen Qianhai Shekou Free Trade Zone Hospital, Shenzhen, China
| | - Song He
- Clinical Laboratory, Shenzhen Qianhai Shekou Free Trade Zone Hospital, Shenzhen, China
| | - Jiatian Wang
- Department of Pathogen Biology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Peiyang Fan
- Department of Pathogen Biology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Yulan He
- Department of Pathogen Biology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Ke Hu
- Department of Pathogen Biology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Yulan Chen
- Department of Pathogen Biology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Guofa Zhou
- Program in Public Health, College of Health Sciences, University of California, Irvine, Irvine, CA, United States
| | - Daibin Zhong
- Program in Public Health, College of Health Sciences, University of California, Irvine, Irvine, CA, United States
| | - Xueli Zheng
- Department of Pathogen Biology, School of Public Health, Southern Medical University, Guangzhou, China,*Correspondence: Xueli Zheng
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22
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Lu X, Bambrick H, Frentiu FD, Huang X, Davis C, Li Z, Yang W, Devine GJ, Hu W. Species-specific climate Suitable Conditions Index and dengue transmission in Guangdong, China. Parasit Vectors 2022; 15:342. [PMID: 36167577 PMCID: PMC9516795 DOI: 10.1186/s13071-022-05453-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 08/26/2022] [Indexed: 12/02/2022] Open
Abstract
Background Optimal climatic conditions for dengue vector mosquito species may play a significant role in dengue transmission. We previously developed a species-specific Suitable Conditions Index (SCI) for Aedes aegypti and Aedes albopictus, respectively. These SCIs rank geographic locations based on their climatic suitability for each of these two dengue vector species and theoretically define parameters for transmission probability. The aim of the study presented here was to use these SCIs together with socio-environmental factors to predict dengue outbreaks in the real world. Methods A negative binomial regression model was used to assess the relationship between vector species-specific SCI and autochthonous dengue cases after accounting for potential confounders in Guangdong, China. The potential interactive effect between the SCI for Ae. albopictus and the SCI for Ae. aegypti on dengue transmission was assessed. Results The SCI for Ae. aegypti was found to be positively associated with autochthonous dengue transmission (incidence rate ratio: 1.06, 95% confidence interval: 1.03, 1.09). A significant interaction effect between the SCI of Ae. albopictus and the SCI of Ae. aegypti was found, with the SCI of Ae. albopictus significantly reducing the effect of the SCI of Ae. aegypti on autochthonous dengue cases. The difference in SCIs had a positive effect on autochthonous dengue cases. Conclusions Our results suggest that dengue fever is more transmittable in regions with warmer weather conditions (high SCI for Ae. aegypti). The SCI of Ae. aegypti would be a useful index to predict dengue transmission in Guangdong, China, even in dengue epidemic regions with Ae. albopictus present. The results also support the benefit of the SCI for evaluating dengue outbreak risk in terms of vector sympatry and interactions in the absence of entomology data in future research. Graphical Abstract ![]()
Supplementary Information The online version contains supplementary material available at 10.1186/s13071-022-05453-x.
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Affiliation(s)
- Xinting Lu
- School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia
| | - Hilary Bambrick
- School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia.,National Centre for Epidemiology and Population Health, The Australian National University, Canberra ACT, Australia
| | - Francesca D Frentiu
- Centre for Immunology and Infection Control, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia
| | - Xiaodong Huang
- Centre for Immunology and Infection Control, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia
| | - Callan Davis
- School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia
| | - Zhongjie Li
- Division of Infectious Disease, Key Laboratory of Surveillance and Early Warning of Infectious Disease, Chinese Centre for Disease Control and Prevention, Beijing, China
| | - Weizhong Yang
- Division of Infectious Disease, Key Laboratory of Surveillance and Early Warning of Infectious Disease, Chinese Centre for Disease Control and Prevention, Beijing, China.,School of Population Medicine & Public Health, Chinese Academy of Medical Science, Peking Union Medical College, Beijing, China
| | - Gregor J Devine
- Mosquito Control Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Wenbiao Hu
- School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia.
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23
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Zhang M, Huang JF, Kang M, Liu XC, Lin HY, Zhao ZY, Ye GQ, Lin SN, Rui J, Xu JW, Zhu YZ, Wang Y, Yang M, Tang SX, Cheng Q, Chen TM. Epidemiological Characteristics and the Dynamic Transmission Model of Dengue Fever in Zhanjiang City, Guangdong Province in 2018. Trop Med Infect Dis 2022; 7:tropicalmed7090209. [PMID: 36136620 PMCID: PMC9501079 DOI: 10.3390/tropicalmed7090209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 08/14/2022] [Accepted: 08/15/2022] [Indexed: 11/16/2022] Open
Abstract
Background: With the progress of urbanization, the mobility of people has gradually increased, which has led to the further spread of dengue fever. This study evaluated the transmissibility of dengue fever within districts and between different districts in Zhanjiang City to provide corresponding advice for cross-regional prevention and control. Methods: A mathematical model of transmission dynamics was developed to explore the transmissibility of the disease and to compare that between different regions. Results: A total of 467 DF cases (6.38 per 100,000 people) were reported in Zhanjiang City in 2018. In the model, without any intervention, the number of simulated cases in this epidemic reached about 950. The dengue fever transmissions between districts varied within and between regions. When the spread of dengue fever from Chikan Districts to other districts was cut off, the number of cases in other districts dropped significantly or even to zero. When the density of mosquitoes in Xiashan District was controlled, the dengue fever epidemic in Xiashan District was found to be significantly alleviated. Conclusions: When there is a dengue outbreak, timely measures can effectively control it from developing into an epidemic. Different prevention and control measures in different districts could efficiently reduce the risk of disease transmission.
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Affiliation(s)
- Meng Zhang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Jie-Feng Huang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
| | - Min Kang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
- School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - Xing-Chun Liu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
| | - Hong-Yan Lin
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
| | - Ze-Yu Zhao
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
| | - Guo-Qiang Ye
- Zhanjiang Municipal Center for Disease Control and Prevention, Zhanjiang 524037, China
| | - Sheng-Nan Lin
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
| | - Jia Rui
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
| | - Jing-Wen Xu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
| | - Yuan-Zhao Zhu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
| | - Yao Wang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
| | - Meng Yang
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
| | - Shi-Xing Tang
- School of Public Health, Southern Medical University, Guangzhou 510515, China
- Correspondence: (S.-X.T.); (Q.C.); (T.-M.C.); Tel.: +1-4242489768 (Q.C.); +86-13661934715 (T.-M.C.)
| | - Qu Cheng
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, CA 94704, USA
- Correspondence: (S.-X.T.); (Q.C.); (T.-M.C.); Tel.: +1-4242489768 (Q.C.); +86-13661934715 (T.-M.C.)
| | - Tian-Mu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen 361102, China
- Correspondence: (S.-X.T.); (Q.C.); (T.-M.C.); Tel.: +1-4242489768 (Q.C.); +86-13661934715 (T.-M.C.)
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Hou B, Chen H, Gao N, An J. Cross-Reactive Immunity among Five Medically Important Mosquito-Borne Flaviviruses Related to Human Diseases. Viruses 2022; 14:v14061213. [PMID: 35746683 PMCID: PMC9228836 DOI: 10.3390/v14061213] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Revised: 05/28/2022] [Accepted: 05/29/2022] [Indexed: 02/04/2023] Open
Abstract
Flaviviruses cause a spectrum of potentially severe diseases. Most flaviviruses are transmitted by mosquitoes or ticks and are widely distributed all over the world. Among them, several mosquito-borne flaviviruses are co-epidemic, and the similarity of their antigenicity creates abundant cross-reactive immune responses which complicate their prevention and control. At present, only effective vaccines against yellow fever and Japanese encephalitis have been used clinically, while the optimal vaccines against other flavivirus diseases are still under development. The antibody-dependent enhancement generated by cross-reactive immune responses against different serotypes of dengue virus makes the development of the dengue fever vaccine a bottleneck. It has been proposed that the cross-reactive immunity elicited by prior infection of mosquito-borne flavivirus could also affect the outcome of the subsequent infection of heterologous flavivirus. In this review, we focused on five medically important flaviviruses, and rearranged and recapitulated their cross-reactive immunity in detail from the perspectives of serological experiments in vitro, animal experiments in vivo, and human cohort studies. We look forward to providing references and new insights for the research of flavivirus vaccines and specific prevention.
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Affiliation(s)
- Baohua Hou
- Department of Microbiology, School of Basic Medical Sciences, Capital Medical University, Beijing 100069, China; (B.H.); (J.A.)
| | - Hui Chen
- Department of Microbiology, School of Basic Medical Sciences, Capital Medical University, Beijing 100069, China; (B.H.); (J.A.)
- Experimental Center for Basic Medical Teaching, School of Basic Medical Sciences, Capital Medical University, Beijing 100069, China
- Correspondence: (H.C.); (N.G.)
| | - Na Gao
- Department of Microbiology, School of Basic Medical Sciences, Capital Medical University, Beijing 100069, China; (B.H.); (J.A.)
- Correspondence: (H.C.); (N.G.)
| | - Jing An
- Department of Microbiology, School of Basic Medical Sciences, Capital Medical University, Beijing 100069, China; (B.H.); (J.A.)
- Center of Epilepsy, Beijing Institute for Brain Disorders, Beijing 100093, China
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Zhang S, Wang M, Yang Z, Zhang B. Do spatiotemporal units matter for exploring the microgeographies of epidemics? APPLIED GEOGRAPHY (SEVENOAKS, ENGLAND) 2022; 142:102692. [PMID: 35399592 PMCID: PMC8982866 DOI: 10.1016/j.apgeog.2022.102692] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 02/04/2022] [Accepted: 03/25/2022] [Indexed: 05/17/2023]
Abstract
From the onset of the COVID-19 pandemic in 2020, studies on the microgeographies of epidemics have surged. However, studies have neglected the significant impact of multiple spatiotemporal units, such as report timestamps and spatial scales. This study examines three cities with localized COVID-19 resurgence after the first wave of the pandemic in mainland China to estimate the differential impact of spatiotemporal unit on exploring the influencing factors of epidemic spread at the microscale. The quantitative analysis results suggest that future spatial epidemiology research should give greater attention to the "symptom onset" timestamp instead of only the "confirmed" data and that "spatial transmission" should not be confused with "spatial sprawling" of epidemics, which can greatly reduce comparability between epidemiology studies. This research also highlights the importance of considering the modifiable areal unit problem (MAUP) and the uncertain geographic context problem (UGCoP) in future studies.
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Affiliation(s)
- Sui Zhang
- College of Geography and Environment, Shandong Normal University, Jinan, 250014, China
| | - Minghao Wang
- College of Geography and Environment, Shandong Normal University, Jinan, 250014, China
| | - Zhao Yang
- College of Geography and Environment, Shandong Normal University, Jinan, 250014, China
| | - Baolei Zhang
- College of Geography and Environment, Shandong Normal University, Jinan, 250014, China
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26
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Cui F, He F, Huang X, Tian L, Li S, Liang C, Zeng L, Lin H, Su J, Liu L, Zhao W, Sun L, Lin L, Sun J. Dengue and Dengue Virus in Guangdong, China, 1978-2017: Epidemiology, Seroprevalence, Evolution, and Policies. Front Med (Lausanne) 2022; 9:797674. [PMID: 35386910 PMCID: PMC8979027 DOI: 10.3389/fmed.2022.797674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 01/28/2022] [Indexed: 11/13/2022] Open
Abstract
Background Guangdong is a hyperepidemic area of dengue, which has over 0.72 million cumulative cases within the last four decades, accounting for more than 90% of cases in China. The local epidemic of dengue in Guangdong is suspected to be triggered by imported cases and results in consequent seasonal transmission. However, the comprehensive epidemiological characteristics of dengue in Guangdong are still unclear. Methods The epidemiology, seroprevalence, molecular evolution of dengue virus, and the development of policies and strategies on the prevention and control of dengue were analyzed in Guangdong, China from 1978 to 2017. Findings Seasonal transmission of dengue virus in Guangdong, China was mainly sustained from July to October of each year. August to September was the highest risk period of local dengue outbreaks. Most of the dengue cases in Guangdong were young and middle-aged adults. Five hundred and three fatal cases were recorded, which declined within the last two decades (n = 10). The serological test of healthy donors' serum samples showed a positive rate of 5.77%. Dengue virus 1-4 (DENV 1-4) was detected in Guangdong from 1978 to 2017. DENV 1 was the dominant serotype of dengue outbreaks from 1978 to 2017, with an increasing tendency of DENV 2 since 2010. Local outbreaks of DENV 3 were rare. DENV 4 was only encountered in imported cases in Guangdong, China. The imported cases were the main source of outbreaks of DENV 1-2. Early detection, management of dengue cases, and precise vector control were the key strategies for local dengue prevention and control in Guangdong, China. Interpretation Dengue has not become an endemic arboviral disease in Guangdong, China. Early detection, case management, and implementation of precise control strategies are key findings for preventing local dengue transmission, which may serve for countries still struggling to combat imported dengue in the west pacific areas.
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Affiliation(s)
- Fengfu Cui
- School of Public Health, Southern Medical University, Guangzhou, China.,Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China.,Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Feiwu He
- School of Basic Medical Science, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xiaorong Huang
- School of Public Health, Southern Medical University, Guangzhou, China.,Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China.,Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Lina Tian
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China.,Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Saiqiang Li
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China.,Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Chumin Liang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China.,Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Lilian Zeng
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China.,Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Huifang Lin
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China.,Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Juan Su
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Liping Liu
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Wei Zhao
- School of Public Health, Southern Medical University, Guangzhou, China
| | - Limei Sun
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Lifeng Lin
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Jiufeng Sun
- School of Public Health, Southern Medical University, Guangzhou, China.,Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China.,Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
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27
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Wu S, He Y, Wei Y, Fan P, Ni W, Zhong D, Zhou G, Zheng X. Effects of Guangzhou seasonal climate change on the development of Aedes albopictus and its susceptibility to DENV-2. PLoS One 2022; 17:e0266128. [PMID: 35363810 PMCID: PMC8975156 DOI: 10.1371/journal.pone.0266128] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 03/15/2022] [Indexed: 12/21/2022] Open
Abstract
The susceptibility of Asian tiger mosquitoes to DENV-2 in different seasons was observed in simulated field environments as a reference to design dengue fever control strategies in Guangzhou. The life table experiments of mosquitoes in four seasons were carried out in the field. The susceptibility of Ae. albopictus to dengue virus was observed in both environments in Guangzhou in summer and winter. Ae. albopictus was infected with dengue virus by oral feeding. On day 7 and 14 after infection, the viral load in the head, ovary, and midgut of the mosquito was detected using real-time fluorescent quantitative PCR. Immune-associated gene expression in infected mosquitoes was performed using quantitative real-time reverse transcriptase PCR. The hatching rate and pupation rate of Ae. albopictus larvae in different seasons differed significantly. The winter hatching rate of larvae was lower than that in summer, and the incubation time was longer than in summer. In the winter field environment, Ae. albopictus still underwent basic growth and development processes. Mosquitoes in the simulated field environment were more susceptible to DENV-2 than those in the simulated laboratory environment. In the midgut, viral RNA levels on day 7 in summer were higher than those on day 7 in winter (F = 14.459, P = 0.01); ovarian viral RNA levels on day 7 in summer were higher than those on day 7 in winter (F = 8.656, P < 0.001), but there was no significant difference in the viral load at other time points (P > 0.05). Dicer-2 mRNA expression on day 7 in winter was 4.071 times than that on day 7 in summer: the viral load and Dicer-2 expression correlated moderately. Ae. albopictus could still develop and transmit dengue virus in winter in Guangzhou. Mosquitoes under simulated field conditions were more susceptible to DENV-2 than those under simulated laboratory conditions.
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Affiliation(s)
- Shanshan Wu
- Department of Pathogen Biology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Yulan He
- Department of Pathogen Biology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Yong Wei
- Department of Pathogen Biology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Peiyang Fan
- Department of Pathogen Biology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Weigui Ni
- Department of Pathogen Biology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Daibin Zhong
- Program in Public Health, College of Health Sciences, University of California, Irvine, CA, United States of America
| | - Guofa Zhou
- Program in Public Health, College of Health Sciences, University of California, Irvine, CA, United States of America
| | - Xueli Zheng
- Department of Pathogen Biology, School of Public Health, Southern Medical University, Guangzhou, China
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28
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Dengue Fever in Mainland China, 2005-2020: A Descriptive Analysis of Dengue Cases and Aedes Data. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19073910. [PMID: 35409612 PMCID: PMC8997546 DOI: 10.3390/ijerph19073910] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 03/07/2022] [Accepted: 03/15/2022] [Indexed: 12/10/2022]
Abstract
Dengue fever occurs throughout mainland China, except in the Tibet Autonomous Region. During 2005–2020, there were 12,701 imported cases and 81,653 indigenous cases recorded. The indigenous cases were mainly clustered in Guangdong (74.0%) and Yunnan provinces (13.7%). Indigenous dengue fever is a seasonal illness in mainland China, manifesting predominantly in summer and autumn. Indigenous dengue fever cases tend to peak every 5years and have shown a substantial increase during the period 2005–2020. During the study period, indigenous dengue fever occurred more than ten times in each of the seven counties of Guangdong Province. Indigenous dengue fever has spread from low to high latitudes; that is, from the southwestern, southern, and southeastern areas to the central and northern regions, and from border ports and cities to rural areas. Aedes aegypti has become widespread in Yunnan Province but has diminished in Guangxi, Guangdong, and Hainan provinces in recent years. Aedes albopictus is distributed throughout mainland China, spanning 25 provinces and municipalities. To maintain effective public health prevention and control, it is important to monitor dengue occurrence, provide dengue classification guidance, and ensure sustainable vector management of Aedes.
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29
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Gabiane G, Yen P, Failloux A. Aedes mosquitoes in the emerging threat of urban yellow fever transmission. Rev Med Virol 2022; 32:e2333. [PMID: 35124859 PMCID: PMC9541788 DOI: 10.1002/rmv.2333] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/05/2022] [Accepted: 01/18/2022] [Indexed: 01/04/2023]
Abstract
This last decade has seen a resurgence of yellow fever (YF) in historical endemic regions and repeated attempts of YF introduction in YF‐free countries such as the Asia‐Pacific region and the Caribbean. Infected travellers are the main entry routes in these regions where competent mosquito vectors proliferate in appropriate environmental conditions. With the discovery of the 17D vaccine, it was thought that YF would be eradicated. Unfortunately, it was not the case and, contrary to dengue, chikungunya and Zika, factors that cotribute to YF transmission remain under investigation. Today, all the signals are red and it is very likely that YF will be the next pandemic in the YF‐free regions where millions of people are immunologically naïve. Unlike COVID‐19, YF is associated with a high case‐fatality rate and a high number of deaths are expected. This review gives an overview of global YF situation, including the non‐endemic Asia‐Pacific region and the Caribbean where Aedes aegypti is abundantly distributed, and also proposes different hypotheses on why YF outbreaks have not yet occurred despite high records of travellers importing YF into these regions and what role Aedes mosquitoes play in the emergence of urban YF.
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Affiliation(s)
- Gaelle Gabiane
- Institut Pasteur Université de Paris, Unit of Arboviruses and Insect Vectors Paris France
- Université des Antilles, Campus de Schoelcher Schoelcher Martinique
| | - Pei‐Shi Yen
- Institut Pasteur Université de Paris, Unit of Arboviruses and Insect Vectors Paris France
| | - Anna‐Bella Failloux
- Institut Pasteur Université de Paris, Unit of Arboviruses and Insect Vectors Paris France
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30
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Zhu X, Chen W, Ma C, Wang X, Sun J, Nie J, Shi J, Hu Y. Whole genome analysis identifies intra-serotype recombinants and positive selection sites of dengue virus in mainland China from 2015 to 2020. Virus Res 2022; 311:198705. [PMID: 35121087 DOI: 10.1016/j.virusres.2022.198705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 01/29/2022] [Accepted: 01/31/2022] [Indexed: 11/29/2022]
Abstract
Immune selection pressure can drive the virus to mutate, so as to achieve immune escape and epidemic of the virus. Thus, surveillance of recombinants and positively selected mutants of the dengue virus (DENV) are vital for preventing and controlling the dengue fever outbreak. However, little is known about recombinants and positively selected mutants of circulating DENV strains in mainland China. In this study, those variants with recombination and adaptive evolutionary sites of circulating DENV strains were identified during 2015-2020. Phylogenetic analysis showed that the DENV-2 was the dominant epidemic serotype, and the dengue epidemic in China was closely related to the imported virus from Southeast Asian countries. Recombination analysis based on 291 complete genomes of naturally circulating DENV identified 10 new intra-serotype recombinant variants. Two or three recombination regions in a single dengue isolate were also observed. The breakpoints of recombinants were distributed in different regions of the genome. In particular, two recombinant strains (strain DENV-4/China/YN/15DGR394 (2015) and XLLM10666) with extremely large exchange fragments were detected. This large-scale gene fragment exchange (eight genomic regions) of strain DENV-4/China/YN/15DGR394 (2015) with substitutions at both the 5' and 3' ends of the genome, had never been described before. Moreover, selection pressure analyses revealed seven positive selection sites located in regions encoding the NS1, NS3 and NS5 proteins. Overall, this study is the first to report ten specific intra-serotype recombinants and seven positive selection sites of Chinese epidemic strains of DENV, which highlight their significance for DENV surveillance and effective control.
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Affiliation(s)
- Xiaoli Zhu
- Yunnan Provincial Key Laboratory of Vector-borne Diseases Control and Research, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming, Yunnan, China; Kunming Medical University, Kunming, Yunnan, China; Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Wanxin Chen
- Yunnan Provincial Key Laboratory of Vector-borne Diseases Control and Research, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming, Yunnan, China
| | - Chunli Ma
- Kunming Medical University, Kunming, Yunnan, China
| | - Xin Wang
- Kunming Medical University, Kunming, Yunnan, China
| | - Jing Sun
- Yunnan Provincial Key Laboratory of Vector-borne Diseases Control and Research, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming, Yunnan, China
| | - Jianyun Nie
- Kunming Medical University, Kunming, Yunnan, China; Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Jiandong Shi
- Yunnan Provincial Key Laboratory of Vector-borne Diseases Control and Research, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming, Yunnan, China
| | - Yunzhang Hu
- Yunnan Provincial Key Laboratory of Vector-borne Diseases Control and Research, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming, Yunnan, China
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31
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Lun X, Wang Y, Zhao C, Wu H, Zhu C, Ma D, Xu M, Wang J, Liu Q, Xu L, Meng F. Epidemiological characteristics and temporal-spatial analysis of overseas imported dengue fever cases in outbreak provinces of China, 2005–2019. Infect Dis Poverty 2022; 11:12. [PMID: 35074010 PMCID: PMC8785556 DOI: 10.1186/s40249-022-00937-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 01/11/2022] [Indexed: 01/25/2023] Open
Abstract
Abstract
Background
Overseas imported dengue fever is an important factor in local outbreaks of this disease in the mainland of China. To better prevent and control such local outbreaks, the epidemiological characteristics and temporal-spatial distribution of overseas imported dengue fever cases in provincial-level administrative divisions (PLADs) where dengue fever is outbreak in the mainland of China were explored.
Methods
Using the Chinese National Notifiable Infectious Disease Reporting Information System (CNNDS), we identified overseas imported dengue fever cases in dengue fever outbreak areas in the mainland of China from 2005 to 2019 to draw the epidemic curve and population characteristic distribution of overseas imported cases in each PLAD. Based on spatial autocorrelation analysis of ArcGIS 10.5 and temporal-spatial scanning analysis of SaTScan 9.5, we analyzed the temporal-spatial distribution of overseas imported dengue fever in dengue fever outbreak areas in the mainland of China.
Results
A total of 11,407 imported cases, mainly from Southeast Asia, were recorded from 2005 to 2019 in these 13 PLADs. Of which 62.1% were imported into Yunnan and Guangdong Provinces. Among the imported cases, there were more males than females, mainly from the 21–50 age group. The hot spots were concentrated in parts of Yunnan, Guangdong and Fujian Provinces. We found the cluster of infected areas were expanding northward.
Conclusions
Based on the analysis of overseas imported dengue cases in 13 PLADs of the mainland of China from 2005 to 2019, we obtained the epidemiological characteristics and spatial distribution of imported dengue cases. Border controls need to pay attention to key population sectors, such as 21–50 years old men and education of key populations on dengue prevention. There is a need to improve the awareness of the prevention and control of imported cases in border areas. At the same time, northern regions cannot relax their vigilance.
Graphical Abstract
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32
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Yang X, Quam MBM, Zhang T, Sang S. Global burden for dengue and the evolving pattern in the past 30 years. J Travel Med 2021; 28:6368502. [PMID: 34510205 DOI: 10.1093/jtm/taab146] [Citation(s) in RCA: 92] [Impact Index Per Article: 30.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Revised: 09/05/2021] [Accepted: 09/07/2021] [Indexed: 11/15/2022]
Abstract
BACKGROUND Dengue is the most prevalent and rapidly spreading mosquito-borne viral disease. We present the global, regional and national burden of dengue from 1990 to 2019 based on the findings from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019 (GBD 2019). METHODS Based upon GBD 2019 dengue data on age-standardized incidence rate (ASIR), age-standardized death rate (ASDR) and age-standardized disability-adjusted life years (DALYs) rate, this study estimates and presents annual percentage change (EAPC) to quantify trends over time to assess potential correlates of increased dengue activity, such as global travel and warming. RESULTS Globally from 1990 to 2019, dengue incident cases, deaths and DALYs gradually increased. Those under 5 years of age, once accounting for the largest portion of deaths and DALYs in 1990, were eclipsed by those who were 15-49 years old in 2019. Age standardized incidence [ASIR: EAPC: 3.16, 95% confidence interval (CI): 2.90-3.43], death (ASDR: EAPC: 5.42, 95% CI: 2.64-8.28) and DALY rates (EAPC: 2.31, 95% CI: 2.00-2.62) accelerated most among high-middle and high sociodemographic index (SDI) regions. South-East Asia and South Asia had most of the dengue incident cases, deaths and DALYs, but East Asia had the fastest rise in ASIR (EAPC: 4.57, 95% CI: 4.31, 4.82), while Tropical Latin America led in ASDR (EAPC: 11.32, 95% CI: 9.11, 13.58) and age-standardized DALYs rate (EAPC: 4.13, 95% CI: 2.98, 5.29). SDI showed consistent bell-shaped relationship with ASIR, ASDR and age-standardized DALYs rate. Global land-ocean temperature index and air passenger travel metrics were found to be remarkably positively correlated with dengue burden. CONCLUSIONS The burden of dengue has become heavier from 1990 to 2019, amidst the three decades of urbanization, warming climates and increased human mobility in much of the world. South-East Asia and South Asia remain as regions of concern, especially in conjunction with the Americas' swift rise in dengue burden.
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Affiliation(s)
- Xiaorong Yang
- Clinical Epidemiology Unit, Qilu Hospital of Shandong University, Jinan, Shandong 250012, People's Republic of China.,Clinical Research Center, Shandong University, Jinan, Shandong 250012, People's Republic of China
| | - Mikkel B M Quam
- Section on Sustainable Health, Department of Public Health and Clinical Medicine, Umeå University, 90187, Sweden.,Division of Epidemiology, College of Public Health, The Ohio State University, 43210, USA
| | - Tongchao Zhang
- Clinical Epidemiology Unit, Qilu Hospital of Shandong University, Jinan, Shandong 250012, People's Republic of China.,Clinical Research Center, Shandong University, Jinan, Shandong 250012, People's Republic of China
| | - Shaowei Sang
- Clinical Epidemiology Unit, Qilu Hospital of Shandong University, Jinan, Shandong 250012, People's Republic of China.,Clinical Research Center, Shandong University, Jinan, Shandong 250012, People's Republic of China.,Department of Epidemiology and Health Statistics, School of Public Health, Shandong University, Jinan, Shandong 250012, People's Republic of China
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Wang J, Sun J, Sun L, Ye Y, Chen H, Xiao J, He G, Hu J, Chen G, Zhou H, Dong X, Ma W, Zhang B, Liu T. The Seroprevalence of Dengue Virus Infection and Its Association With Iron (Fe) Level in Pregnant Women in Guangzhou, China. Front Med (Lausanne) 2021; 8:759728. [PMID: 34957145 PMCID: PMC8702999 DOI: 10.3389/fmed.2021.759728] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 11/23/2021] [Indexed: 11/13/2022] Open
Abstract
Dengue fever is regarded as the most prevalent mosquito-borne viral disease in humans. However, information of dengue virus (DENV) infection in pregnant women and the influence factors remain unclear. In this study, we extracted information of 2,076 pregnant women from the Prenatal Environment and Offspring Health (PEOH) birth cohort conducted since 2016 in Guangzhou, China. Peripheral blood and clean midstream urine samples of participants were collected during their hospitalization for childbirth. Indirect enzyme-linked immunosorbent assay (ELISA) was used to detect immunoglobulin G (IgG) antibodies of DENV in serum samples, and inductively coupled plasma mass spectrometry (ICP-MS) was applied to determine the Fe concentrations in the urine samples, which were then adjusted for by urine creatinine and transformed by natural logarithm (ln-Fe). The seroprevalence of DENV IgG antibody in all included participants was 2.22% (46/2,076). We observed higher seroprevalence of IgG antibody in women aged ≥35 years (2.9%), education ≤ 12 years (2.5%), yearly income per capita <100,000 yuan (2.4%), no use of air-conditioner (2.4%), no use of mosquito coils (2.3%), and no exercise during pregnancy (4.1%). A U-shaped relationship was found between ln-Fe concentration and the risk of positive IgG antibody. Compared with women with ln-Fe concentration of 2.0–2.9 μg/g creatinine, slightly higher risks of positive IgG antibody were found among women with ≤2.0 (RR = 4.16, 95% CI: 0.78, 19.91), 3.0–3.9 (RR = 1.93, 95% CI: 0.65, 7.08), 4.0–4.9 (RR = 2.19, 95% CI: 0.65, 8.51), and ≥5.0 μg/g creatinine of ln-Fe (RR = 2.42, 95% CI: 0.46, 11.33). Our findings suggested that the seroprevalence of dengue IgG antibody in pregnant women was comparable to the general population in Guangzhou, China. The risk of DENV infection may be associated with maternal demographic characteristics and behaviors. Both maternal low and high Fe concentrations may be positively associated with the risk of DENV infection.
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Affiliation(s)
- Jiong Wang
- School of Public Health, Southern Medical University, Guangzhou, China.,Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Jiufeng Sun
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Limei Sun
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Yufeng Ye
- Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Hanwei Chen
- Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Jianpeng Xiao
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Guanhao He
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Jianxiong Hu
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Guimin Chen
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - He Zhou
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Xiaomei Dong
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
| | - Wenjun Ma
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
| | - Bo Zhang
- Food Safety and Health Research Center, School of Public Health, Southern Medical University, Guangzhou, China
| | - Tao Liu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China.,School of Public Health, Southern Medical University, Guangzhou, China
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34
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Sheng ZY, Li M, Yang R, Liu YH, Yin XX, Mao JR, Brown HE, Zhou HN, Wang PG, An J. COVID-19 prevention measures reduce dengue spread in Yunnan Province, China, but do not reduce established outbreak. Emerg Microbes Infect 2021; 11:240-249. [PMID: 34935597 PMCID: PMC8745368 DOI: 10.1080/22221751.2021.2022438] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
The COVID-19 pandemic and measures against it provided a unique opportunity to understand the transmission of other infectious diseases and to evaluate the efficacy of COVID-19 prevention measures on them. Here we show a dengue epidemic in Yunnan, China, during the pandemic of COVID-19 was dramatically reduced compared to non-pandemic years and, importantly, spread was confined to only one city, Ruili. Three key features characterized this dengue outbreak: (i) the urban-to-suburban spread was efficiently blocked; (ii) the scale of epidemic in urban region was less affected; (iii) co-circulation of multiple strains was attenuated. These results suggested that countermeasures taken during COVID-19 pandemic are efficient to prevent dengue transmission between cities and from urban to suburban, as well to reduce the co-circulation of multiple serotypes or genotypes. Nevertheless, as revealed by the spatial analysis, once the dengue outbreak was established, its distribution was very stable and resistant to measures against COVID-19, implying the possibility to develop a precise prediction method.
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Affiliation(s)
- Z Y Sheng
- Department of Microbiology, School of Basic Medical Sciences, Capital Medical University, Beijing, PR China
| | - M Li
- Yunnan Provincial Key Laboratory of Vector-borne Disease Control and Research, Yunnan Institute of Parasitic Diseases, Simao Pu'er, Yunnan, PR China
| | - R Yang
- Yunnan Provincial Key Laboratory of Vector-borne Disease Control and Research, Yunnan Institute of Parasitic Diseases, Simao Pu'er, Yunnan, PR China
| | - Y H Liu
- Ruili Center of Disease Prevention and Control, Ruili, Yunnan, PR China
| | - X X Yin
- Ruili Center of Disease Prevention and Control, Ruili, Yunnan, PR China
| | - J R Mao
- Ruili People's Hospital, Ruili, Yunnan, PR China
| | - Heidi E Brown
- Department of Epidemiology and Biostatistics, College of Public Health, University of Arizona
| | - H N Zhou
- Yunnan Provincial Key Laboratory of Vector-borne Disease Control and Research, Yunnan Institute of Parasitic Diseases, Simao Pu'er, Yunnan, PR China
| | - P G Wang
- Department of Microbiology, School of Basic Medical Sciences, Capital Medical University, Beijing, PR China
| | - J An
- Department of Microbiology, School of Basic Medical Sciences, Capital Medical University, Beijing, PR China.,Center of Epilepsy, Beijing Institute for Brain Disorders, Beijing, China
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35
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Sang S, Liu Q, Guo X, Wu D, Ke C, Liu-Helmersson J, Jiang J, Weng Y, Wang Y. The epidemiological characteristics of dengue in high-risk areas of China, 2013-2016. PLoS Negl Trop Dis 2021; 15:e0009970. [PMID: 34928951 PMCID: PMC8687583 DOI: 10.1371/journal.pntd.0009970] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 11/03/2021] [Indexed: 11/30/2022] Open
Abstract
Introduction Dengue has become a more serious human health concern in China, with increased incidence and expanded outbreak regions. The knowledge of the cross-sectional and longitudinal epidemiological characteristics and the evolutionary dynamics of dengue in high-risk areas of China is limited. Methods Records of dengue cases from 2013 to 2016 were obtained from the China Notifiable Disease Surveillance System. Full envelope gene sequences of dengue viruses detected from the high-risk areas of China were collected. Maximum Likelihood tree and haplotype network analyses were conducted to explore the phylogenetic relationship of viruses from high-risk areas of China. Results A total of 56,520 cases was reported in China from 2013 to 2016. During this time, Yunnan, Guangdong and Fujian provinces were the high-risk areas. Imported cases occurred almost year-round, and were mainly introduced from Southeast Asia. The first indigenous case usually occurred in June to August, and the last one occurred before December in Yunnan and Fujian provinces but in December in Guangdong Province. Seven genotypes of DENV 1–3 were detected in the high-risk areas, with DENV 1-I the main genotype and DENV 2-Cosmopolitan the secondary one. The Maximum Likelihood trees show that almost all the indigenous viruses separated into different clusters. DENV 1-I viruses were found to be clustered in Guangdong Province, but not in Fujian and Yunnan, from 2013 to 2015. The ancestors of the Guangdong viruses in the cluster in 2013 and 2014 were most closely related to strains from Thailand or Singapore, and the Guangdong virus in 2015 was most closely related to the Guangdong virus of 2014. Based on closest phylogenetic relationships, viruses from Myanmar possibly initiated further indigenous cases in Yunnan, those from Indonesia in Fujian, while viruses from Thailand, Malaysia, Singapore and Indonesia were predominant in Guangdong Province. Conclusions Dengue is still an imported disease in China, although some genotypes continued to circulate in successive years. Viral phylogenies based on the envelope gene suggested periodic introductions of dengue strains into China, primarily from Southeast Asia, with occasional sustained, multi-year transmission in some regions of China. Dengue is the most prevalent and rapidly spreading mosquito-borne viral disease globally. Because of the multiple introductions, dengue outbreaks occurred in epidemic seasons in Southern China, supported by suitable weather conditions. Surveillance data from 2013 to 2016 in China showed that Guangdong, Yunnan and Fujian provinces were the high-risk areas, with dengue outbreaks occurring almost every year. However, knowledge has been lacking of the epidemiological characteristics and the evolution pattern of dengue virus in these high-risk areas. This study shows a variety of epidemiological characteristics and sources of imported cases among the high-risk areas in China, with likely origins primarily from countries in Southeast Asia. Seven genotypes of the DENV 1–3 variety co-circulated with DENV1-I, the main genotype, and DENV 2-Cosmopolitan, the secondary. Genetic relationships among viral strains suggest that the indigenous viruses in the high-risk areas arose from imported viruses and sometimes persisted between years into the next epidemic season, especially in Guangdong Province. Population movement has played a vital role in dengue epidemics in China. This information may be useful in dengue control, especially during epidemic seasons and in the development of an early warning system within the region, in collaboration with bordering countries.
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Affiliation(s)
- Shaowei Sang
- Clinical Epidemiology Unit, Qilu Hospital of Shandong University, Jinan, Shandong, People’s Republic of China
- Clinical Research Center of Shandong University, Jinan, Shandong, People’s Republic of China
- Department of Epidemiology and Health Statistics, School of Public Health, Shandong University, Jinan, Shandong, People’s Republic of China
- * E-mail:
| | - Qiyong Liu
- State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Changping, Beijing, People’s Republic of China
| | - Xiaofang Guo
- Yunnan Provincial Center of Arborvirus Research, Yunnan Provincial Key Laboratory of Vector-borne Diseases Control and Research, Yunnan Institute of Parasitic Diseases, Pu’er, Yunnan, People’s Republic of China
| | - De Wu
- Institute of Microbiology, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, Guangdong, People’s Republic of China
| | - Changwen Ke
- Institute of Microbiology, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, Guangdong, People’s Republic of China
| | | | - Jinyong Jiang
- Yunnan Provincial Center of Arborvirus Research, Yunnan Provincial Key Laboratory of Vector-borne Diseases Control and Research, Yunnan Institute of Parasitic Diseases, Pu’er, Yunnan, People’s Republic of China
| | - Yuwei Weng
- Fujian center for disease control and prevention, Fuzhou, People’s Republic of China
| | - Yiguan Wang
- School of Biological Sciences, University of Queensland, St Lucia, Australia
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Wei Y, Zheng X, He S, Xin X, Zhang J, Hu K, Zhou G, Zhong D. Insecticide susceptibility status and knockdown resistance (kdr) mutation in Aedes albopictus in China. Parasit Vectors 2021; 14:609. [PMID: 34922622 PMCID: PMC8684111 DOI: 10.1186/s13071-021-05095-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Accepted: 11/10/2021] [Indexed: 11/10/2022] Open
Abstract
Background Aedes (Stegomyia) albopictus (Skuse, 1894) is the main vector of dengue virus in China. The resistance to insecticides is a huge obstacle for the control of this species, and determining its resistance status and mechanisms in China is essential for the implementation of vector management strategies. Methods We have investigated the larval and adult resistance status of Ae. albopictus to deltamethrin in eight field populations in China. Mutations at the voltage-gated sodium channel gene, related to the knockdown resistance (kdr) effect, were detected by sequencing of PCR products. The eight field populations were examined for pyrethroid resistance using the World Health Organization standard bioassays, and the association between the mutations and phenotypic resistance was tested. Results The eight field populations of larvae of Ae. albopictus in China exhibited high resistance to deltamethrin; the RR50 values ranged from 12 (ZJ) to 44 (GZ). Adult bioassay revealed that Ae. albopictus populations were resistant to deltamethrin (mortality rate < 90%), except ZJ population (probably resistant, mortality rate = 93.5%). Long knockdown time in the field populations was consistent with low mortality rates in adult bioassay. F1534S mutation showed increased protection against deltamethrin in all populations except BJ and SJZ populations, whereas I1532T mutation showed increased protection against deltamethrin in only BJ population. Conclusion There were different degrees of resistance to deltamethrin in field Ae. albopictus populations in China. The longest knockdown time and lowest mortality rate observed in Ae. albopictus population in Guangzhou indicate the severity of high resistance to deltamethrin. The patchy distribution of deltamethrin resistance and kdr mutations in Ae. albopictus mosquitoes suggests the necessity for resistance management and developing counter measures to mitigate the spread of resistance. Graphical Abstract ![]()
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Affiliation(s)
- Yong Wei
- Clinical Laboratory, Shenzhen Qianhai Shekou Free Trade Zone Hospital, Shenzhen, China. .,Department of Pathogen Biology, School of Public Health, Southern Medical University, Guangzhou, China.
| | - Xueli Zheng
- Department of Pathogen Biology, School of Public Health, Southern Medical University, Guangzhou, China.
| | - Song He
- Clinical Laboratory, Shenzhen Qianhai Shekou Free Trade Zone Hospital, Shenzhen, China.
| | - Xuli Xin
- Clinical Laboratory, Shenzhen Qianhai Shekou Free Trade Zone Hospital, Shenzhen, China
| | - Jiachun Zhang
- Clinical Laboratory, Shenzhen Qianhai Shekou Free Trade Zone Hospital, Shenzhen, China
| | - Ke Hu
- Department of Pathogen Biology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Guofa Zhou
- Program in Public Health, College of Health Sciences, University of California, Irvine, CA, USA
| | - Daibin Zhong
- Program in Public Health, College of Health Sciences, University of California, Irvine, CA, USA
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Zhang L, Zhao L, Zhang Z, Hong W, Wang J, Qiu S, Yang H, Gan M, Sun J, Zhao J, Wang Y, Zhao J, Zhang F. Genetic and pathogenicity diversity of dengue virus type 2 strains circulating in Guangdong, China. BIOSAFETY AND HEALTH 2021. [DOI: 10.1016/j.bsheal.2021.08.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
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Li H, Chen Y, Machalaba CC, Tang H, Chmura AA, Fielder MD, Daszak P. Wild animal and zoonotic disease risk management and regulation in China: Examining gaps and One Health opportunities in scope, mandates, and monitoring systems. One Health 2021; 13:100301. [PMID: 34401458 PMCID: PMC8358700 DOI: 10.1016/j.onehlt.2021.100301] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 07/29/2021] [Accepted: 08/02/2021] [Indexed: 01/19/2023] Open
Abstract
Emerging diseases of zoonotic origin such as COVID-19 are a continuing public health threat in China that lead to a significant socioeconomic burden. This study reviewed the current laws and regulations, government reports and policy documents, and existing literature on zoonotic disease preparedness and prevention across the forestry, agriculture, and public health authorities in China, to articulate the current landscape of potential risks, existing mandates, and gaps. A total of 55 known zoonotic diseases (59 pathogens) are routinely monitored under a multi-sectoral system among humans and domestic and wild animals in China. These diseases have been detected in wild mammals, birds, reptiles, amphibians, and fish or other aquatic animals, the majority of which are transmitted between humans and animals via direct or indirect contact and vectors. However, this current monitoring system covers a limited scope of disease threats and animal host species, warranting expanded review for sources of disease and pathogen with zoonotic potential. In addition, the governance of wild animal protection and utilization and limited knowledge about wild animal trade value chains present challenges for zoonotic disease risk assessment and monitoring, and affect the completeness of mandates and enforcement. A coordinated and collaborative mechanism among different departments is required for the effective monitoring and management of disease emergence and transmission risks in the animal value chains. Moreover, pathogen surveillance among wild animal hosts and human populations outside of the routine monitoring system will fill the data gaps and improve our understanding of future emerging zoonotic threats to achieve disease prevention. The findings and recommendations will advance One Health collaboration across government and non-government stakeholders to optimize monitoring and surveillance, risk management, and emergency responses to known and novel zoonotic threats, and support COVID-19 recovery efforts.
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Affiliation(s)
- Hongying Li
- EcoHealth Alliance, New York, NY, United States of America
- School of Life Sciences, Faculty of Science, Engineering and Computing, Kingston University, London, United Kingdom
| | - Yufei Chen
- School of Veterinary Science, Massey University, Palmerston North, New Zealand
| | | | - Hao Tang
- School of Veterinary Medicine, College of Science, Health, Engineering and Education, Murdoch University, Murdoch, WA, Australia
| | | | - Mark D. Fielder
- School of Life Sciences, Faculty of Science, Engineering and Computing, Kingston University, London, United Kingdom
| | - Peter Daszak
- EcoHealth Alliance, New York, NY, United States of America
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Meng H, Xiao J, Liu T, Zhu Z, Gong D, Kang M, Song T, Peng Z, Deng A, Ma W. The impacts of precipitation patterns on dengue epidemics in Guangzhou city. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2021; 65:1929-1937. [PMID: 34114103 DOI: 10.1007/s00484-021-02149-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 04/03/2021] [Accepted: 05/16/2021] [Indexed: 06/12/2023]
Abstract
Some studies have demonstrated that precipitation is an important risk factor of dengue epidemics. However, current studies mostly focused on a single precipitation variable, and few studies focused on the impact of precipitation patterns on dengue epidemics. This study aims to explore optimal precipitation patterns for dengue epidemics. Weekly dengue case counts and meteorological data from 2006 to 2018 in Guangzhou of China were collected. A generalized additive model with Poisson distribution was used to investigate the association between precipitation patterns and dengue. Precipitation patterns were defined as the combinations of three weekly precipitation variables: accumulative precipitation (Pre_A), the number of days with light or moderate precipitation (Pre_LMD), and the coefficient of precipitation variation (Pre_CV). We explored to identify optimal precipitation patterns for dengue epidemics. With a lead time of 10 weeks, minimum temperature, relative humidity, Pre_A, and Pre_LMD were positively associated with dengue, while Pre_CV was negatively associated with dengue. A precipitation pattern with Pre_A of 20.67-55.50 mm per week, Pre_LMD of 3-4 days per week, and Pre_CV less than 1.41 per week might be an optimal precipitation pattern for dengue epidemics in Guangzhou. The finding may be used for climate-smart early warning and decision-making of dengue prevention and control.
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Affiliation(s)
- Haorong Meng
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
- School of Public Health, Southern Medical University, Guangzhou, China
| | - Jianpeng Xiao
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
- School of Public Health, Southern Medical University, Guangzhou, China
| | - Tao Liu
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Zhihua Zhu
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Dexin Gong
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Min Kang
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Tie Song
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Zhiqiang Peng
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Aiping Deng
- Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Wenjun Ma
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China.
- School of Public Health, Southern Medical University, Guangzhou, China.
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Gao P, Pilot E, Rehbock C, Gontariuk M, Doreleijers S, Wang L, Krafft T, Martens P, Liu Q. Land use and land cover change and its impacts on dengue dynamics in China: A systematic review. PLoS Negl Trop Dis 2021; 15:e0009879. [PMID: 34669704 PMCID: PMC8559955 DOI: 10.1371/journal.pntd.0009879] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 11/01/2021] [Accepted: 10/05/2021] [Indexed: 12/29/2022] Open
Abstract
Background Dengue is a prioritized public health concern in China. Because of the larger scale, more frequent and wider spatial distribution, the challenge for dengue prevention and control has increased in recent years. While land use and land cover (LULC) change was suggested to be associated with dengue, relevant research has been quite limited. The “Open Door” policy introduced in 1978 led to significant LULC change in China. This systematic review is the first to review the studies on the impacts of LULC change on dengue dynamics in China. This review aims at identifying the research evidence, research gaps and provide insights for future research. Methods A systematic literature review was conducted following the PRISMA protocol. The combinations of search terms on LULC, dengue and its vectors were searched in the databases PubMed, Web of Science, and Baidu Scholar. Research conducted on China published from 1978 to December 2019 and written in English or Chinese was selected for further screening. References listed in articles meeting the inclusion criteria were also reviewed and included if again inclusion criteria were met to minimize the probability of missing relevant research. Results 28 studies published between 1978 and 2017 were included for the full review. Guangdong Province and southern Taiwan were the major regional foci in the literature. The majority of the reviewed studies observed associations between LULC change factors and dengue incidence and distribution. Conflictive evidence was shown in the studies about the impacts of green space and blue space on dengue in China. Transportation infrastructure and urbanization were repeatedly suggested to be positively associated with dengue incidence and spread. The majority of the studies reviewed considered meteorological and sociodemographic factors when they analyzed the effects of LULC change on dengue. Primary and secondary remote sensing (RS) data were the primary source for LULC variables. In 21 of 28 studies, a geographic information system (GIS) was used to process data of environmental variables and dengue cases and to perform spatial analysis of dengue. Conclusions The effects of LULC change on the dynamics of dengue in China varied in different periods and regions. The application of RS and GIS enriches the means and dimensions to explore the relations between LULC change and dengue. Further comprehensive regional research is necessary to assess the influence of LULC change on local dengue transmission to provide practical advice for dengue prevention and control. Dengue is a major public health concern in China. The rapid development of urbanization along with climate change increases the challenge for dengue prevention and control. Previous research has mainly focused on the meteorological variables whereas land use and land cover (LULC) change received comparatively less attention. Our review identified that the regional research hotspots of dengue epidemics in China were Guangdong Province and southern Taiwan. Though inconsistent, most included studies somehow observed associations between at least one of the LULC change factors and dengue. A geographical information system (GIS) was widely used to perform spatial analysis in the selected literature. Its application provided a novel view to describe the relationships between environmental factors and the situation of dengue, which enabled scholars to explore more characteristics of dengue transmission. Meanwhile, the use of remote sensing (RS) enriched the means of environmental monitoring. However, there are research gaps in the area of dengue and LULC change, such as the less consideration of dengue vector study, the lack of interplays between factors, and the lack of considering interventions and policies. Furthermore, because of different research settings, results from these studies were difficult to compare. Thus, further comprehensive and comparable investigations are necessary to better understand the effects of LULC change on dengue in China. This review is the first to expound the studies on the associations between LULC change and dengue dynamics in China. It demonstrates the findings and methodologies and provided insights for future research.
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Affiliation(s)
- Panjun Gao
- Department of Health, Ethics & Society, CAPHRI Care and Public Health Research Institute, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Eva Pilot
- Department of Health, Ethics & Society, CAPHRI Care and Public Health Research Institute, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Cassandra Rehbock
- Department of Health, Ethics & Society, CAPHRI Care and Public Health Research Institute, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Marie Gontariuk
- Department of Health, Ethics & Society, CAPHRI Care and Public Health Research Institute, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Simone Doreleijers
- Department of Health, Ethics & Society, CAPHRI Care and Public Health Research Institute, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Li Wang
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | - Thomas Krafft
- Department of Health, Ethics & Society, CAPHRI Care and Public Health Research Institute, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Pim Martens
- Maastricht Sustainability Institute (MSI), Maastricht University, Maastricht, The Netherlands
| | - Qiyong Liu
- State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
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Liu H, Fang CJ, Xu JW. The health perceptions, dengue knowledge and control willingness among Dai ethnic minority in Yunnan Province, China. BMC Public Health 2021; 21:1843. [PMID: 34641853 PMCID: PMC8507394 DOI: 10.1186/s12889-021-11864-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 09/27/2021] [Indexed: 11/21/2022] Open
Abstract
Background Outbreaks of dengue fever are often found among Dai ethnical communities along China-Myanmar border. The objective of this study was: 1) to investigate residents’ health perceptions, knowledge and control willingness to participate in dengue control and 2) to identify factors associated with control willingness among the Dai ethnic community. Methods This is a mixed method study of a cross-sectional design, in which qualitative in-depth interviews and quantitative household questionnaire surveys are included. Results Questionnaire was administered to 261 household heads, and in-depth interview was conducted with 18 key informants. Of them, many participants (70%, 182/259) and 12 key informants (66.7%) from the two rural communities believed that the Lord Buddha would protect the good people. Majority of the participants (81.4%, 206/253) knew that fever was one of dengue fever symptoms and most of them (82.2%, 213/259) indicated that mosquitoes could transmit dengue fever. However, only one third of the participants (30.1%, 78/259) indicated the perceived susceptibility of dengue fever, and only a half of them (50.2%, 130/259) indicated the perceived severity of dengue fever. Multivariate logistic analysis (MLA) indicated that the participants with family wealth index (FWI) 4–5 (OR: 22.9728; 95%CI: 2.4257–217.5688, p = 0.0063) were more likely to turn containers upside down (TCUD) compared to those with FWI 1–3; and the participants in the urban community (OR: 0.0239; 95%CI: 0.0019–0.3032, p = 0.004) were less likely to TCUD compared to those in the two rural communities. Around one third of the participants (36.8%, 96/239) reported that they were willing to seek treatment first for dengue fever from public health facilities. The MLA identified that the participants with the perceived severity of dengue fever (OR: 5.0564; 95%CI: 2.0672–12.3683, p = 0.0004), and with beliefs of sound hygiene helpful to people’s health (OR: 11.5671; 95%CI: 2.0505–65.2502, p = 0.0055) were more likely to seek treatment first for dengue fever from the public health facilities. Conclusion The study finds that most of Dai people have sound knowledge. However, health educational interventions should target to promote the perceived susceptibility and the perceived severity of dengue fever among Dai people. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-021-11864-9.
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Affiliation(s)
- Hui Liu
- Yunnan Institute of Parasitic Diseases, Yunnan Provincial Key Laboratory of Vector-borne Diseases Control and Research, Xiyuan Road, Pu'er City, 665000, China.,Institute of Pathogens and Vectors, Basic Medical College, Dali University, Wanhua Road, Xiaguang District, Dali City, 671000, China
| | - Cheng-Jiang Fang
- Pu'er Blood Bank, Chayuan Road, Pu'er City, 665000, Yunnan Province, China
| | - Jian-Wei Xu
- Yunnan Institute of Parasitic Diseases, Yunnan Provincial Key Laboratory of Vector-borne Diseases Control and Research, Xiyuan Road, Pu'er City, 665000, China. .,Institute of Pathogens and Vectors, Basic Medical College, Dali University, Wanhua Road, Xiaguang District, Dali City, 671000, China.
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Virome in adult Aedes albopictus captured during different seasons in Guangzhou City, China. Parasit Vectors 2021; 14:415. [PMID: 34407871 PMCID: PMC8371599 DOI: 10.1186/s13071-021-04922-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Accepted: 08/03/2021] [Indexed: 01/09/2023] Open
Abstract
Background The mosquito Aedes albopictus is an important vector for many pathogens. Understanding the virome in Ae. albopictus is critical for assessing the risk of disease transmission, implementation of vector control measures, and health system strengthening. Methods In this study, viral metagenomic and PCR methods were used to reveal the virome in adult Ae. albopictus captured in different areas and during different seasons in Guangzhou, China. Results The viral composition of adult Ae. albopictus varied mainly between seasons. Over 50 viral families were found, which were specific to vertebrates, invertebrates, plants, fungi, bacteria, and protozoa. In rural areas, Siphoviridae (6.5%) was the most common viral family harbored by mosquitoes captured during winter and spring, while Luteoviridae (1.1%) was the most common viral family harbored by mosquitoes captured during summer and autumn. Myoviridae (7.0% and 1.3%) was the most common viral family in mosquitoes captured in urban areas during all seasons. Hepatitis B virus (HBV) was detected by PCR in a female mosquito pool. The first near full-length HBV genome from Ae. albopictus was amplified, which showed a high level of similarity with human HBV genotype B sequences. Human parechovirus (HPeV) was detected in male and female mosquito pools, and the sequences were clustered with HPeV 1 and 3 sequences. Conclusions Large numbers of viral species were found in adult Ae. albopictus, including viruses from vertebrates, insects, and plants. The viral composition in Ae. albopictus mainly varied between seasons. Herein, we are the first to report the detection of HPeV and HBV in mosquitoes. This study not only provides valuable information for the control and prevention of mosquito-borne diseases, but it also demonstrates the feasibility of xenosurveillance. Graphical Abstract ![]()
Supplementary Information The online version contains supplementary material available at 10.1186/s13071-021-04922-z.
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Yue Y, Liu Q, Liu X, Wu H, Xu M. Comparative analyses on epidemiological characteristics of dengue fever in Guangdong and Yunnan, China, 2004-2018. BMC Public Health 2021; 21:1389. [PMID: 34256730 PMCID: PMC8278621 DOI: 10.1186/s12889-021-11323-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Accepted: 06/18/2021] [Indexed: 11/10/2022] Open
Abstract
Background In China, Guangdong and Yunnan are the two most dengue-affected provinces. This study aimed to compare the epidemiological characteristics of dengue fever in Guangdong and Yunnan during 2004–2018. Methods Descriptive analyses were used to explore the temporal, spatial, and demographic distribution of dengue fever. Results Of the 73,761 dengue cases reported in mainland China during 2004–2018, 93.7% indigenous and 65.9% imported cases occurred in Guangdong and Yunnan, respectively. A total of 55,970 and 5938 indigenous cases occurred in 108 Guangdong and 8 Yunnan counties, respectively during 2004–2018. Whereas 1146 and 3050 imported cases occurred in 84 Guangdong and 72 Yunnan counties, respectively during 2004–2018. Guangdong had a much higher average yearly indigenous incidence rate (3.65 (1/100000) vs 0.86 (1/100000)), but a much lower average yearly imported incidence rate (0.07 (1/100000) vs 0.44(1/100000)) compared with Yunnan in 2004–2018. Furthermore, dengue fever occurred more widely in space and more frequently in time in Guangdong. Guangdong and Yunnan had similar seasonal characteristics for dengue fever, but Guangdong had a longer peak period. Most dengue cases were clustered in the south-western border of Yunnan and the Pearl River Delta region in Guangdong. Most of the imported cases (93.9%) in Guangdong and Yunnan were from 9 Southeast Asian countries. Thailand, Cambodia, and Malaysia imported mainly into Guangdong while Myanmar and Laos imported into Yunnan. There was a strong male predominance among imported cases and an almost equal gender distribution among indigenous cases. Most dengue cases occurred in individuals aged 21–50 years, accounting for 57.3% (Guangdong) vs. 62.8% (Yunnan) of indigenous and 83.2% (Guangdong) vs. 62.6% (Yunnan) of imported cases. The associated major occupations (house worker or unemployed, retiree, and businessman, for indigenous cases; and businessman, for imported cases), were similar. However, farmers accounted for a larger proportion of dengue cases in Yunnan. Conclusions Identifying the different epidemiological characteristics of dengue fever in Guangdong and Yunnan can be helpful to formulate targeted, strategic plans, and implement effective public health prevention measures in China.
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Affiliation(s)
- Yujuan Yue
- State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Centre for Disease Control and Prevention, Beijing, 102206, People's Republic of China.
| | - Qiyong Liu
- State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Centre for Disease Control and Prevention, Beijing, 102206, People's Republic of China
| | - Xiaobo Liu
- State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Centre for Disease Control and Prevention, Beijing, 102206, People's Republic of China
| | - Haixia Wu
- State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Centre for Disease Control and Prevention, Beijing, 102206, People's Republic of China
| | - Mingfang Xu
- State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Centre for Disease Control and Prevention, Beijing, 102206, People's Republic of China.,Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, People's Republic of China
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Tong MX, Hansen A, Hanson-Easey S, Xiang J, Cameron S, Liu Q, Liu X, Sun Y, Weinstein P, Han GS, Mahmood A, Bi P. Public health professionals' perceptions of the capacity of China's CDCs to address emerging and re-emerging infectious diseases. J Public Health (Oxf) 2021; 43:209-216. [PMID: 31251367 DOI: 10.1093/pubmed/fdz070] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2019] [Revised: 05/13/2019] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND China's capacity to control and prevent emerging and re-emerging infectious diseases is critical to the nation's population health. This study aimed to explore the capacity of Centers for Disease Control and Prevention (CDCs) in China to deal with infectious diseases now and in the future. METHODS A survey was conducted in 2015 among 973 public health professionals at CDCs in Beijing and four provinces, to assess their capacity to deal with emerging and re-emerging infectious diseases. RESULTS Although most professionals were confident with the current capacity of CDCs to cope with outbreaks, nearly all indicated more funding was required to meet future challenges. Responses indicated that Yunnan Province faced more challenges than Anhui, Henan and Liaoning Provinces in being completely prepared and able to deal with outbreaks. Participants aged 20-39 years were more likely than those aged 40 and over to believe strategies such as interdisciplinary and international collaborations for disease surveillance and control, would assist capacity building. CONCLUSION The capacity of China's CDCs to deal with infectious diseases was excellent. However, findings suggest it is imperative to increase the number of skilled CDC staff, financial support, and strengthen county level staff training and health education programs.
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Affiliation(s)
- Michael Xiaoliang Tong
- School of Public Health, The University of Adelaide, Adelaide, South Australia, Australia
| | - Alana Hansen
- School of Public Health, The University of Adelaide, Adelaide, South Australia, Australia
| | - Scott Hanson-Easey
- School of Public Health, The University of Adelaide, Adelaide, South Australia, Australia
| | - Jianjun Xiang
- School of Public Health, The University of Adelaide, Adelaide, South Australia, Australia
| | - Scott Cameron
- School of Public Health, The University of Adelaide, Adelaide, South Australia, Australia
| | - Qiyong Liu
- State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Xiaobo Liu
- State Key Laboratory of Infectious Disease Prevention and Control, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yehuan Sun
- Department of Epidemiology, Anhui Medical University, Hefei, Anhui, China
| | - Philip Weinstein
- School of Biological Sciences, The University of Adelaide, Adelaide, South Australia, Australia
| | - Gil-Soo Han
- Communications & Media Studies, School of Media, Film and Journalism, Monash University, Clayton, Victoria, Australia
| | - Afzal Mahmood
- School of Public Health, The University of Adelaide, Adelaide, South Australia, Australia
| | - Peng Bi
- School of Public Health, The University of Adelaide, Adelaide, South Australia, Australia
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Wu T, Wu Z, Li YP. Dengue fever and dengue virus in the People's Republic of China. Rev Med Virol 2021; 32:e2245. [PMID: 34235802 DOI: 10.1002/rmv.2245] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 04/12/2021] [Accepted: 04/26/2021] [Indexed: 01/05/2023]
Abstract
Infection with dengue virus (DENV) leads to symptoms variable from dengue fever to severe dengue, which has posed a huge socioeconomic and disease burden to the world population, particularly in tropical and subtropical regions. To date, four serotypes of DENV (DENV-1 to DENV-4) have been identified to sustain the transmission cycle in humans. In the past decades, dengue incidences have become more frequent, and four serotypes and various genotypes have been identified in PR China. Several large-scale dengue outbreaks and frequent local endemics occurred in the southern and coastal provinces, and the imported dengue cases accounted primarily for the initiation of the epidemics. No antiviral drug exists for dengue, and no vaccine has been approved to use in PR China, however strategies including public awareness, national reporting system of infectious diseases and public health emergencies, vector mosquito control, personal protection, and improved environmental sanitation have greatly reduced dengue prevalence. Some new technologies in vector mosquito control are emerging and being applied for dengue control. China's territory spans tropical, subtropical, and temperate climates, hence understanding the dengue status in China will be of beneficial for the global prevention and control of dengue. Here, we review the dengue status in PR China for the past decades and the strategies emerging for dengue control.
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Affiliation(s)
- Tiantian Wu
- Institute of Human Virology, Zhongshan School of Medicine, Key Laboratory of Tropical Disease Control of Ministry of Education, Sun Yet-sen University, Guangzhou, China
| | - Zhongdao Wu
- Department of Parasitology, Zhongshan School of Medicine, Key Laboratory of Tropical Disease Control of Ministry of Education, Sun Yet-sen University, Guangzhou, China
| | - Yi-Ping Li
- Institute of Human Virology, Zhongshan School of Medicine, Key Laboratory of Tropical Disease Control of Ministry of Education, Sun Yet-sen University, Guangzhou, China
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Cheng J, Bambrick H, Yakob L, Devine G, Frentiu FD, Williams G, Li Z, Yang W, Hu W. Extreme weather conditions and dengue outbreak in Guangdong, China: Spatial heterogeneity based on climate variability. ENVIRONMENTAL RESEARCH 2021; 196:110900. [PMID: 33636184 DOI: 10.1016/j.envres.2021.110900] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 12/19/2020] [Accepted: 02/15/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND Previous studies have shown associations between local weather factors and dengue incidence in tropical and subtropical regions. However, spatial variability in those associations remains unclear and evidence is scarce regarding the effects of weather extremes. OBJECTIVES We examined spatial variability in the effects of various weather conditions on the unprecedented dengue outbreak in Guangdong province of China in 2014 and explored how city characteristics modify weather-related risk. METHODS A Bayesian spatial conditional autoregressive model was used to examine the overall and city-specific associations of dengue incidence with weather conditions including (1) average temperature, temperature variation, and average rainfall; and (2) weather extremes including numbers of days of extremely high temperature and high rainfall (both used 95th percentile as the cut-off). This model was run for cumulative dengue cases during five months from July to November (accounting for 99.8% of all dengue cases). A further analysis based on spatial variability was used to validate the modification effects by economic, demographic and environmental factors. RESULTS We found a positive association of dengue incidence with average temperature in seven cities (relative risk (RR) range: 1.032 to 1.153), a positive association with average rainfall in seven cities (RR range: 1.237 to 1.974), and a negative association with temperature variation in four cities (RR range: 0.315 to 0.593). There was an overall positive association of dengue incidence with extremely high temperature (RR:1.054, 95% credible interval (CI): 1.016 to 1.094), without evidence of variation across cities, and an overall positive association of dengue with extremely high rainfall (RR:1.505, 95% CI: 1.096 to 2.080), with seven regions having stronger associations (RR range: 1.237 to 1.418). Greater effects of weather conditions appeared to occur in cities with higher economic level, lower green space coverage and lower elevation. CONCLUSIONS Spatially varied effects of weather conditions on dengue outbreaks necessitate area-specific dengue prevention and control measures. Extremes of temperature and rainfall have strong and positive associations with dengue outbreaks.
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Affiliation(s)
- Jian Cheng
- School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia; Department of Epidemiology and Biostatistics & Anhui Province Key Laboratory of Major Autoimmune Disease, School of Public Health, Anhui Medical University, Anhui, China
| | - Hilary Bambrick
- School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia
| | - Laith Yakob
- Department of Disease Control, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Gregor Devine
- Mosquito Control Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Francesca D Frentiu
- Centre for Immunology and Infection Control, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia
| | - Gail Williams
- School of Public Health, University of Queensland, Brisbane, Australia
| | - Zhongjie Li
- Division of Infectious Disease, Key Laboratory of Surveillance and Early Warning of Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Weizhong Yang
- Division of Infectious Disease, Key Laboratory of Surveillance and Early Warning of Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China; School of Population Medicine & Public Health, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, China
| | - Wenbiao Hu
- School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia.
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Liu K, Yin L, Zhang M, Kang M, Deng AP, Li QL, Song T. Facilitating fine-grained intra-urban dengue forecasting by integrating urban environments measured from street-view images. Infect Dis Poverty 2021; 10:40. [PMID: 33766145 PMCID: PMC7992840 DOI: 10.1186/s40249-021-00824-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 03/10/2021] [Indexed: 11/10/2022] Open
Abstract
Background Dengue fever (DF) is a mosquito-borne infectious disease that has threatened tropical and subtropical regions in recent decades. An early and targeted warning of a dengue epidemic is important for vector control. Current studies have primarily determined weather conditions to be the main factor for dengue forecasting, thereby neglecting that environmental suitability for mosquito breeding is also an important factor, especially in fine-grained intra-urban settings. Considering that street-view images are promising for depicting physical environments, this study proposes a framework for facilitating fine-grained intra-urban dengue forecasting by integrating the urban environments measured from street-view images. Methods The dengue epidemic that occurred in 167 townships of Guangzhou City, China, between 2015 and 2019 was taken as a study case. First, feature vectors of street-view images acquired inside each township were extracted by a pre-trained convolutional neural network, and then aggregated as an environmental feature vector of the township. Thus, townships with similar physical settings would exhibit similar environmental features. Second, the environmental feature vector is combined with commonly used features (e.g., temperature, rainfall, and past case count) as inputs to machine-learning models for weekly dengue forecasting. Results The performance of machine-learning forecasting models (i.e., MLP and SVM) integrated with and without environmental features were compared. This indicates that models integrating environmental features can identify high-risk urban units across the city more precisely than those using common features alone. In addition, the top 30% of high-risk townships predicted by our proposed methods can capture approximately 50–60% of dengue cases across the city. Conclusions Incorporating local environments measured from street view images is effective in facilitating fine-grained intra-urban dengue forecasting, which is beneficial for conducting spatially precise dengue prevention and control. ![]()
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Affiliation(s)
- Kang Liu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, People's Republic of China.,Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing, 100038, People's Republic of China
| | - Ling Yin
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, People's Republic of China.
| | - Meng Zhang
- Guangdong Provincial Center for Disease Control and Prevention, Shenzhen, 511430, People's Republic of China
| | - Min Kang
- Guangdong Provincial Center for Disease Control and Prevention, Shenzhen, 511430, People's Republic of China
| | - Ai-Ping Deng
- Guangdong Provincial Center for Disease Control and Prevention, Shenzhen, 511430, People's Republic of China
| | - Qing-Lan Li
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, People's Republic of China
| | - Tie Song
- Guangdong Provincial Center for Disease Control and Prevention, Shenzhen, 511430, People's Republic of China
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Wei Y, Wang J, Wei YH, Song Z, Hu K, Chen Y, Zhou G, Zhong D, Zheng X. Vector Competence for DENV-2 Among Aedes albopictus (Diptera: Culicidae) Populations in China. Front Cell Infect Microbiol 2021; 11:649975. [PMID: 33834007 PMCID: PMC8021855 DOI: 10.3389/fcimb.2021.649975] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 03/08/2021] [Indexed: 11/21/2022] Open
Abstract
Aedes albopictus is a vector of over 20 arboviruses that has spread throughout the world, mainly in the second half of the twentieth century. Approximately 50–100 million people are infected with dengue virus (DENV) transmitted by Aedes mosquitoes each year, leading to heavy economic burdens for both governments and individuals, among countless other negative consequences. Understanding the vector competence of vector species is critical for effectively preventing and controlling vector-borne diseases. Accordingly, in this study, vector competence was evaluated by quantitative analysis of DENV-2 loads in mosquito tissues (midguts, heads, and salivary glands) and whole mosquitoes through real-time quantitative polymerase chain reaction (RT-qPCR) analysis. Wolbachia and the expression of immune-associated genes (Rel1, Rel2, Dicer2, and STAT) in mosquitoes were also detected by RT-qPCR to explore their impact on vector competence. The amount of DENV-2 in the mosquito midguts, heads, and salivary glands from southern-western China were found to be lower than those from eastern-central-northern China. The DENV-2 loads in whole mosquitoes showed a negative correlation with Rel1 gene (r = -0.285, P = 0.011) and STAT gene expression levels (r = -0.289, P = 0.009). In terms of Wolbachia strains, the density of the wAlbB strain was found to be significantly higher than that of the wAlbA strain in the eight Ae. albopictus populations, and the relative density of the wAlbB strain in mosquitoes from southern-western China was higher than those from eastern-central-northern China. The relative density of the wAlbB strain showed a negative correlation with the mean loads of DENV-2 in the heads (r = -0.729, P = 0.040), salivary glands (r = -0.785, P = 0.021), and whole mosquitoes (r = -0.909, P = 0.002). Thus, there are lower DENV-2 loads in the mosquitoes from southern-western China, which may be related to the innate immunity of mosquitoes as affected by Rel1 in the Toll pathway, STAT in the JAK-STAT pathway, and the relative density of the wAlbB strain.
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Affiliation(s)
- Yong Wei
- Department of Pathogen Biology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Jiatian Wang
- Department of Pathogen Biology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Yuan-Huan Wei
- Department of Clinical Nutrition, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Zhangyao Song
- Department of Pathogen Biology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Ke Hu
- Department of Pathogen Biology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Yulan Chen
- Department of Pathogen Biology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Guofa Zhou
- Program in Public Health, College of Health Sciences, University of California, Irvine, Irvine, CA, United States
| | - Daibin Zhong
- Program in Public Health, College of Health Sciences, University of California, Irvine, Irvine, CA, United States
| | - Xueli Zheng
- Department of Pathogen Biology, School of Public Health, Southern Medical University, Guangzhou, China
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Yue Y, Liu X, Ren D, Wu H, Liu Q. Spatial Dynamics of Dengue Fever in Mainland China, 2019. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18062855. [PMID: 33799640 PMCID: PMC7999437 DOI: 10.3390/ijerph18062855] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 02/26/2021] [Accepted: 03/05/2021] [Indexed: 02/03/2023]
Abstract
New spatial characteristics of dengue fever in mainland China during 2019 were analyzed. There was a dengue fever outbreak in mainland China in 2019, with 15,187 indigenous cases in 13 provinces, 1281 domestic imported cases from 12 provinces and 5778 overseas imported cases from 47 countries, more than the previous cases during the period 2005–2018, except for in 2014. Indigenous cases occurred in Sichuan, Hubei and Chongqing in 2019. There have been big changes in the spatial distribution and proportion of dengue cases. Indigenous cases were not only located in the southwestern border and southeastern coastal provinces of Yunnan, Guangdong, Guangxi and Fujian but also in the central provinces of Jiangxi and Chongqing. Domestic imported cases were not only from Guangdong, but also from Yunnan. There were five new sources of importation of cases. Overseas imported cases were mainly from Cambodia and Myanmar in 2019. Understanding the new spatial characteristics of dengue fever in China helps to formulate targeted, strategic plans and implement effective public health prevention and control measures.
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Affiliation(s)
| | | | | | | | - Qiyong Liu
- Correspondence: ; Tel.: +86-010-5890-0738
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Complete genome characterization of the 2018 dengue outbreak in Hunan, an inland province in central South China. Virus Res 2021; 297:198358. [PMID: 33667623 DOI: 10.1016/j.virusres.2021.198358] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 08/26/2020] [Accepted: 02/25/2021] [Indexed: 11/22/2022]
Abstract
In 2018, a small-scale dengue epidemic broke out in Hunan Province, an inland province in central South China, with 172 people infected. To verify the causative agent, complete genome information was obtained by PCR and sequencing based on the viral RNAs extracted from patient serum samples. Mutation and evolutionary analysis were performed by MEGA7.0 software. The online softwares "Predict protein" and "Mfold" were used to predict the secondary structure of proteins and untranslated regions, respectively. Phylogenetic analysis showed that all five isolates in this study were DENV type 2, which is most closely related to the Zhejiang strain (2017-MH110588). Compared with the DENV-2 standard strain, 773 nucleotide mutations occurred in the isolated strain, of which 666 were nonsense mutations. Of the 80 mutated amino acids, 22 occurred in the structural protein region (2 in C region, 8 in PrM/M region, 12 in E region), and 58 in the non-structural (NS) protein region (9 in NS1 region, 10 in NS2 region, 12 in NS3 region, 7 in NS4 region, 20 in NS5 region). The prM/M region had the highest AA mutation rate, while NS4B was conservative. Three amino acid mutations (E: N390thS, and NS5: S676thN, K800thT) may important for the replication and virulence of the DENV. Secondary structure prediction observed 28 changes in polynucleotide binding regions and 110 changes in protein binding sites of coding sequence. 2 and 4 base substitutions resulted in 2 and 6 significant changes in the RNA secondary structure of 5' UTR and 3' UTR, respectively. Two significant positive selection sites were observed in NS5. To our knowledge, this research is the first complete genome analysis of the epidemic strain of the 2018 dengue outbreak in Hunan and will benefit further research for virus traceability and vaccine development.
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