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Zaw W, Lin Z, Ko Ko J, Rotejanaprasert C, Pantanilla N, Ebener S, Maude RJ. Dengue in Myanmar: Spatiotemporal epidemiology, association with climate and short-term prediction. PLoS Negl Trop Dis 2023; 17:e0011331. [PMID: 37276226 PMCID: PMC10270578 DOI: 10.1371/journal.pntd.0011331] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 06/15/2023] [Accepted: 04/24/2023] [Indexed: 06/07/2023] Open
Abstract
Dengue is a major public health problem in Myanmar. The country aims to reduce morbidity by 50% and mortality by 90% by 2025 based on 2015 data. To support efforts to reach these goals it is important to have a detailed picture of the epidemiology of dengue, its relationship to meteorological factors and ideally to predict ahead of time numbers of cases to plan resource allocations and control efforts. Health facility-level data on numbers of dengue cases from 2012 to 2017 were obtained from the Vector Borne Disease Control Unit, Department of Public Health, Myanmar. A detailed analysis of routine dengue and dengue hemorrhagic fever (DHF) incidence was conducted to examine the spatial and temporal epidemiology. Incidence was compared to climate data over the same period. Dengue was found to be widespread across the country with an increase in spatial extent over time. The temporal pattern of dengue cases and fatalities was episodic with annual outbreaks and no clear longitudinal trend. There were 127,912 reported cases and 632 deaths from 2012 and 2017 with peaks in 2013, 2015 and 2017. The case fatality rate was around 0.5% throughout. The peak season of dengue cases was from May to August in the wet season but in 2014 peak dengue season continued until November. The strength of correlation of dengue incidence with different climate factors (total rainfall, maximum, mean and minimum temperature and absolute humidity) varied between different States and Regions. Monthly incidence was forecasted 1 month ahead using the Auto Regressive Integrated Moving Average (ARIMA) method at country and subnational levels. With further development and validation, this may be a simple way to quickly generate short-term predictions at subnational scales with sufficient certainty to use for intervention planning.
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Affiliation(s)
- Win Zaw
- Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Zaw Lin
- Vector Borne Disease Control, Department of Public Health, Ministry of Health, Nay Pyi Taw, Myanmar
| | - July Ko Ko
- Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Chawarat Rotejanaprasert
- Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Neriza Pantanilla
- Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Steeve Ebener
- Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Richard James Maude
- Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
- Harvard TH Chan School of Public Health, Harvard University, Boston, Massachusetts, United States of America
- The Open University, Milton Keynes, United Kingdom
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Aryaprema VS, Steck MR, Peper ST, Xue RD, Qualls WA. A systematic review of published literature on mosquito control action thresholds across the world. PLoS Negl Trop Dis 2023; 17:e0011173. [PMID: 36867651 PMCID: PMC10016652 DOI: 10.1371/journal.pntd.0011173] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 03/15/2023] [Accepted: 02/14/2023] [Indexed: 03/04/2023] Open
Abstract
BACKGROUND Despite the use of numerous methods of control measures, mosquito populations and mosquito-borne diseases are still increasing globally. Evidence-based action thresholds to initiate or intensify control activities have been identified as essential in reducing mosquito populations to required levels at the correct/optimal time. This systematic review was conducted to identify different mosquito control action thresholds existing across the world and associated surveillance and implementation characteristics. METHODOLOGY/PRINCIPAL FINDINGS Searches for literature published from 2010 up to 2021 were performed using two search engines, Google Scholar and PubMed Central, according to PRISMA guidelines. A set of inclusion/exclusion criteria were identified and of the 1,485 initial selections, only 87 were included in the final review. Thirty inclusions reported originally generated thresholds. Thirteen inclusions were with statistical models that seemed intended to be continuously utilized to test the exceedance of thresholds in a specific region. There was another set of 44 inclusions that solely mentioned previously generated thresholds. The inclusions with "epidemiological thresholds" outnumbered those with "entomological thresholds". Most of the inclusions came from Asia and those thresholds were targeted toward Aedes and dengue control. Overall, mosquito counts (adult and larval) and climatic variables (temperature and rainfall) were the most used parameters in thresholds. The associated surveillance and implementation characteristics of the identified thresholds are discussed here. CONCLUSIONS/SIGNIFICANCE The review identified 87 publications with different mosquito control thresholds developed across the world and published during the last decade. Associated surveillance and implementation characteristics will help organize surveillance systems targeting the development and implementation of action thresholds, as well as direct awareness towards already existing thresholds for those with programs lacking available resources for comprehensive surveillance systems. The findings of the review highlight data gaps and areas of focus to fill in the action threshold compartment of the IVM toolbox.
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Affiliation(s)
- Vindhya S. Aryaprema
- Anastasia Mosquito Control District, St. Augustine, Florida, United States of America
| | - Madeline R. Steck
- Anastasia Mosquito Control District, St. Augustine, Florida, United States of America
| | - Steven T. Peper
- Anastasia Mosquito Control District, St. Augustine, Florida, United States of America
| | - Rui-de Xue
- Anastasia Mosquito Control District, St. Augustine, Florida, United States of America
| | - Whitney A. Qualls
- Anastasia Mosquito Control District, St. Augustine, Florida, United States of America
- * E-mail:
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Leung XY, Islam RM, Adhami M, Ilic D, McDonald L, Palawaththa S, Diug B, Munshi SU, Karim MN. A systematic review of dengue outbreak prediction models: Current scenario and future directions. PLoS Negl Trop Dis 2023; 17:e0010631. [PMID: 36780568 PMCID: PMC9956653 DOI: 10.1371/journal.pntd.0010631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 02/24/2023] [Accepted: 01/29/2023] [Indexed: 02/15/2023] Open
Abstract
Dengue is among the fastest-spreading vector-borne infectious disease, with outbreaks often overwhelm the health system and result in huge morbidity and mortality in its endemic populations in the absence of an efficient warning system. A large number of prediction models are currently in use globally. As such, this study aimed to systematically review the published literature that used quantitative models to predict dengue outbreaks and provide insights about the current practices. A systematic search was undertaken, using the Ovid MEDLINE, EMBASE, Scopus and Web of Science databases for published citations, without time or geographical restrictions. Study selection, data extraction and management process were devised in accordance with the 'Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies' ('CHARMS') framework. A total of 99 models were included in the review from 64 studies. Most models sourced climate (94.7%) and climate change (77.8%) data from agency reports and only 59.6% of the models adjusted for reporting time lag. All included models used climate predictors; 70.7% of them were built with only climate factors. Climate factors were used in combination with climate change factors (13.4%), both climate change and demographic factors (3.1%), vector factors (6.3%), and demographic factors (5.2%). Machine learning techniques were used for 39.4% of the models. Of these, random forest (15.4%), neural networks (23.1%) and ensemble models (10.3%) were notable. Among the statistical (60.6%) models, linear regression (18.3%), Poisson regression (18.3%), generalized additive models (16.7%) and time series/autoregressive models (26.7%) were notable. Around 20.2% of the models reported no validation at all and only 5.2% reported external validation. The reporting of methodology and model performance measures were inadequate in many of the existing prediction models. This review collates plausible predictors and methodological approaches, which will contribute to robust modelling in diverse settings and populations.
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Affiliation(s)
- Xing Yu Leung
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Rakibul M. Islam
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Mohammadmehdi Adhami
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Dragan Ilic
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Lara McDonald
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Shanika Palawaththa
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Basia Diug
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Saif U. Munshi
- Department of Virology, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh
| | - Md Nazmul Karim
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
- * E-mail:
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Li Z. Forecasting Weekly Dengue Cases by Integrating Google Earth Engine-Based Risk Predictor Generation and Google Colab-Based Deep Learning Modeling in Fortaleza and the Federal District, Brazil. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:13555. [PMID: 36294134 PMCID: PMC9603269 DOI: 10.3390/ijerph192013555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 10/15/2022] [Accepted: 10/18/2022] [Indexed: 06/16/2023]
Abstract
Efficient and accurate dengue risk prediction is an important basis for dengue prevention and control, which faces challenges, such as downloading and processing multi-source data to generate risk predictors and consuming significant time and computational resources to train and validate models locally. In this context, this study proposed a framework for dengue risk prediction by integrating big geospatial data cloud computing based on Google Earth Engine (GEE) platform and artificial intelligence modeling on the Google Colab platform. It enables defining the epidemiological calendar, delineating the predominant area of dengue transmission in cities, generating the data of risk predictors, and defining multi-date ahead prediction scenarios. We implemented the experiments based on weekly dengue cases during 2013-2020 in the Federal District and Fortaleza, Brazil to evaluate the performance of the proposed framework. Four predictors were considered, including total rainfall (Rsum), mean temperature (Tmean), mean relative humidity (RHmean), and mean normalized difference vegetation index (NDVImean). Three models (i.e., random forest (RF), long-short term memory (LSTM), and LSTM with attention mechanism (LSTM-ATT)), and two modeling scenarios (i.e., modeling with or without dengue cases) were set to implement 1- to 4-week ahead predictions. A total of 24 models were built, and the results showed in general that LSTM and LSTM-ATT models outperformed RF models; modeling could benefit from using historical dengue cases as one of the predictors, and it makes the predicted curve fluctuation more stable compared with that only using climate and environmental factors; attention mechanism could further improve the performance of LSTM models. This study provides implications for future dengue risk prediction in terms of the effectiveness of GEE-based big geospatial data processing for risk predictor generation and Google Colab-based risk modeling and presents the benefits of using historical dengue data as one of the input features and the attention mechanism for LSTM modeling.
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Affiliation(s)
- Zhichao Li
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
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Spatiotemporally comparative analysis of three common infectious diseases in China during 2013-2015. BMC Infect Dis 2022; 22:791. [PMID: 36258165 PMCID: PMC9580198 DOI: 10.1186/s12879-022-07779-4] [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: 07/17/2022] [Accepted: 10/05/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Dengue fever (DF), influenza, and hand, foot, and mouth disease (HFMD) have had several various degrees of outbreaks in China since the 1900s, posing a serious threat to public health. Previous studies have found that these infectious diseases were often prevalent in the same areas and during the same periods in China. METHODS This study combined traditional descriptive statistics and spatial scan statistic methods to analyze the spatiotemporal features of the epidemics of DF, influenza, and HFMD during 2013-2015 in mainland China at the provincial level. RESULTS DF got an intensive outbreak in 2014, while influenza and HFMD were stable from 2013 to 2015. DF mostly occurred during August-November, influenza appeared during November-next March, and HFMD happened during April-November. The peaks of these diseases form a year-round sequence; Spatially, HFMD generally has a much higher incidence than influenza and DF and covers larger high-risk areas. The hotspots of influenza tend to move from North China to the southeast coast. The southeastern coastal regions are the high-incidence areas and the most significant hotspots of all three diseases. CONCLUSIONS This study suggested that the three diseases can form a year-round sequence in southern China, and the southeast coast of China is a particularly high-risk area for these diseases. These findings may have important implications for the local public health agency to allocate the prevention and control resources.
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de Lima CL, da Silva ACG, Moreno GMM, Cordeiro da Silva C, Musah A, Aldosery A, Dutra L, Ambrizzi T, Borges IVG, Tunali M, Basibuyuk S, Yenigün O, Massoni TL, Browning E, Jones K, Campos L, Kostkova P, da Silva Filho AG, dos Santos WP. Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review. Front Public Health 2022; 10:900077. [PMID: 35719644 PMCID: PMC9204152 DOI: 10.3389/fpubh.2022.900077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 05/03/2022] [Indexed: 11/13/2022] Open
Abstract
Arboviruses are a group of diseases that are transmitted by an arthropod vector. Since they are part of the Neglected Tropical Diseases that pose several public health challenges for countries around the world. The arboviruses' dynamics are governed by a combination of climatic, environmental, and human mobility factors. Arboviruses prediction models can be a support tool for decision-making by public health agents. In this study, we propose a systematic literature review to identify arboviruses prediction models, as well as models for their transmitter vector dynamics. To carry out this review, we searched reputable scientific bases such as IEE Xplore, PubMed, Science Direct, Springer Link, and Scopus. We search for studies published between the years 2015 and 2020, using a search string. A total of 429 articles were returned, however, after filtering by exclusion and inclusion criteria, 139 were included. Through this systematic review, it was possible to identify the challenges present in the construction of arboviruses prediction models, as well as the existing gap in the construction of spatiotemporal models.
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Affiliation(s)
- Clarisse Lins de Lima
- Nucleus for Computer Engineering, Polytechnique School of the University of Pernambuco, Poli-UPE, Recife, Brazil
| | - Ana Clara Gomes da Silva
- Nucleus for Computer Engineering, Polytechnique School of the University of Pernambuco, Poli-UPE, Recife, Brazil
| | | | | | - Anwar Musah
- Centre for Digital Public Health and Emergencies, Institute for Risk and Disaster Reduction, University College London, London, United Kingdom
| | - Aisha Aldosery
- Centre for Digital Public Health and Emergencies, Institute for Risk and Disaster Reduction, University College London, London, United Kingdom
| | - Livia Dutra
- Department of Atmospheric Sciences, IAG-USP, University of São Paulo, São Paulo, Brazil
| | - Tercio Ambrizzi
- Department of Atmospheric Sciences, IAG-USP, University of São Paulo, São Paulo, Brazil
| | - Iuri V. G. Borges
- Department of Atmospheric Sciences, IAG-USP, University of São Paulo, São Paulo, Brazil
| | - Merve Tunali
- Boǧaziçi University, Institute of Environmental Sciences, Istanbul, Turkey
| | - Selma Basibuyuk
- Boǧaziçi University, Institute of Environmental Sciences, Istanbul, Turkey
| | - Orhan Yenigün
- Boǧaziçi University, Institute of Environmental Sciences, Istanbul, Turkey
| | - Tiago Lima Massoni
- Department of Systems and Computing, Federal University of Campina Grande, Campina Grande, Brazil
| | - Ella Browning
- Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, United Kingdom
| | - Kate Jones
- Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, United Kingdom
| | - Luiza Campos
- Department of Civil Environmental and Geomatic Engineering, University College London, London, United Kingdom
| | - Patty Kostkova
- Centre for Digital Public Health and Emergencies, Institute for Risk and Disaster Reduction, University College London, London, United Kingdom
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Faridah L, Fauziah N, Agustian D, Mindra Jaya IGN, Eka Putra R, Ekawardhani S, Hidayath N, Damar Djati I, Carvajal TM, Mayasari W, Ruluwedrata Rinawan F, Watanabe K. Temporal Correlation Between Urban Microclimate, Vector Mosquito Abundance, and Dengue Cases. JOURNAL OF MEDICAL ENTOMOLOGY 2022; 59:1008-1018. [PMID: 35305089 PMCID: PMC9113159 DOI: 10.1093/jme/tjac005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Indexed: 05/04/2023]
Abstract
Dengue Hemorrhagic Fever (DHF) is a major mosquito-borne viral disease. Studies have reported a strong correlation between weather, the abundance of Aedes aegypti, the vector of DHF virus, and dengue incidence. However, this conclusion has been based on the general climate pattern of wide regions. In general, however, the human population, level of infrastructure, and land-use change in rural and urban areas often produce localized climate patterns that may influence the interaction between climate, vector abundance, and dengue incidence. Thoroughly understanding this correlation will allow the development of a customized and precise local early warning system. To achieve this purpose, we conducted a cohort study, during January-December 2017, in 16 districts in Bandung, West Java, Indonesia. In the selected areas, local weather stations and modified light mosquito traps were set up to obtain data regarding daily weather and the abundance of adult female Ae. aegypti. A generalized linear model was applied to analyze the effect of local weather and female adult Ae. aegypti on the number of dengue cases. The result showed a significant non-linear correlation among mosquito abundance, maximum temperature, and dengue cases. Using our model, the data showed that the addition of a single adult Ae. aegypti mosquito increased the risk of dengue infection by 1.8%, while increasing the maximum temperature by one degree decreased the risk by 17%. This finding suggests specific actionable insights needed to supplement existing mosquito eradication programs.
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Affiliation(s)
- Lia Faridah
- Parasitology Division, Department of Biomedical Sciences, Faculty of Medicine Universitas Padjadjaran, Jl. Raya Bandung-Sumedang Km 21, Sumedang, 45363, West Java, Indonesia
- Graduate School of Science and Engineering, Ehime University, Bunkyo-cho 3, Matsuyama, Ehime, 790-8577, Japan
- Corresponding author, e-mail: ;
| | - Nisa Fauziah
- Parasitology Division, Department of Biomedical Sciences, Faculty of Medicine Universitas Padjadjaran, Jl. Raya Bandung-Sumedang Km 21, Sumedang, 45363, West Java, Indonesia
| | - Dwi Agustian
- Department of Public Health Faculty of Medicine Universitas Padjadjaran, Jl. Raya Bandung-Sumedang Km 21, Sumedang, 45363, West Java, Indonesia
| | - I Gede Nyoman Mindra Jaya
- Department of Statistics Universitas Padjadjaran, Jl. Raya Bandung-Sumedang Km 21, Sumedang, 45363, West Java, Indonesia
| | - Ramadhani Eka Putra
- School of Life Sciences and Technology, Insitut Teknologi Bandung, Jl. Ganeca 10, Bandung, 40132, West Java, Indonesia
- Biology Department, Insitut Teknologi Sumatera, Jl. Terusan Ryacudu, Desa Way Hui, Bandar Lampung, 35365, Lampung, Indonesia
| | - Savira Ekawardhani
- Parasitology Division, Department of Biomedical Sciences, Faculty of Medicine Universitas Padjadjaran, Jl. Raya Bandung-Sumedang Km 21, Sumedang, 45363, West Java, Indonesia
| | - Nurrachman Hidayath
- Dengue Study Group, Faculty of Medicine, Universitas Padjadjaran, Jl. Prof. Eyckman 38, Bandung, 40131, West Java, Indonesia
| | - Imam Damar Djati
- Faculty of Visual Art and Design, Industrial Design Section, Bandung Institute of Technology, Jl. Ganeca 10, Bandung, 40132, West Java, Indonesia
| | - Thaddeus M Carvajal
- Biological Control Research Unit, Center for Natural Science and Environmental Research-De La Salle University, Taft Ave Manila, Philippines
- Center for Marine Environmental Studies (CMES), Ehime University, Bunkyo-cho 3, Matsuyama, Ehime, Japan
| | - Wulan Mayasari
- Anatomy Division, Department of Biomedical Science, Faculty of Medicine Universitas Padjadjaran, Jl. Raya Bandung-Sumedang Km 21, Sumedang 45363, West Java, Indonesia
| | - Fedri Ruluwedrata Rinawan
- Department of Public Health Faculty of Medicine Universitas Padjadjaran, Jl. Raya Bandung-Sumedang Km 21, Sumedang, 45363, West Java, Indonesia
| | - Kozo Watanabe
- Center for Marine Environmental Studies (CMES), Ehime University, Bunkyo-cho 3, Matsuyama, Ehime, Japan
- Corresponding author, e-mail: ;
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Wu Y, Huang C. Climate Change and Vector-Borne Diseases in China: A Review of Evidence and Implications for Risk Management. BIOLOGY 2022; 11:biology11030370. [PMID: 35336744 PMCID: PMC8945209 DOI: 10.3390/biology11030370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 02/15/2022] [Accepted: 02/18/2022] [Indexed: 11/16/2022]
Abstract
Simple Summary Vector-borne diseases are among the most rapidly spreading infectious diseases and are widespread all around the world. In China, many types of vector-borne diseases have been prevalent in different regions, which is a serious public health problem with significant association with meteorological factors and weather events. Under the background of current severe climate change, the outbreaks and transmission of vector-borne diseases have been proven to be impacted greatly due to rapidly changing weather conditions. This study summarizes research progress on the association between climate conditions and all types of vector-borne diseases in China. A total of seven insect-borne diseases, two rodent-borne diseases, and a snail-borne disease were included, among which dengue fever is the most concerning mosquito-borne disease. Temperature, rainfall, and humidity have the most significant effect on vector-borne disease transmission, while the association between weather conditions and vector-borne diseases shows vast differences in China. We also make suggestions about future research based on a review of current studies. Abstract Vector-borne diseases have posed a heavy threat to public health, especially in the context of climate change. Currently, there is no comprehensive review of the impact of meteorological factors on all types of vector-borne diseases in China. Through a systematic review of literature between 2000 and 2021, this study summarizes the relationship between climate factors and vector-borne diseases and potential mechanisms of climate change affecting vector-borne diseases. It further examines the regional differences of climate impact. A total of 131 studies in both Chinese and English on 10 vector-borne diseases were included. The number of publications on mosquito-borne diseases is the largest and is increasing, while the number of studies on rodent-borne diseases has been decreasing in the past two decades. Temperature, precipitation, and humidity are the main parameters contributing to the transmission of vector-borne diseases. Both the association and mechanism show vast differences between northern and southern China resulting from nature and social factors. We recommend that more future research should focus on the effect of meteorological factors on mosquito-borne diseases in the era of climate change. Such information will be crucial in facilitating a multi-sectorial response to climate-sensitive diseases in China.
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Affiliation(s)
- Yurong Wu
- Vanke School of Public Health, Tsinghua University, Beijing 100084, China;
- School of Public Health, Sun Yat-sen University, Guangzhou 510275, China
| | - Cunrui Huang
- Vanke School of Public Health, Tsinghua University, Beijing 100084, China;
- School of Public Health, Sun Yat-sen University, Guangzhou 510275, China
- Institute of Healthy China, Tsinghua University, Beijing 100084, China
- Correspondence:
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Singh G, Soman B. Spatiotemporal epidemiology and forecasting of dengue in the state of Punjab, India: Study protocol. Spat Spatiotemporal Epidemiol 2021; 39:100444. [PMID: 34774263 DOI: 10.1016/j.sste.2021.100444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 07/02/2021] [Accepted: 07/21/2021] [Indexed: 11/30/2022]
Abstract
Dengue burden in India is a major public health problem. The present study has been designed to understand mechanisms by which routine data generate evidence. Secondary data analysis of routine datasets to understand spatiotemporal epidemiology and forecast dengue will be conducted. Data science approach will be adopted to generate a reproducible framework in the R environment. The lab-confirmed dengue reported by the state health authorities from 01 January 2015 to 31 December 2019 will be included. Multiple climatic variables from satellite imagery, climatic models, vegetation and built-up indices, and sociodemographic variables will be explored as risk factors. Exploratory data analysis followed by statistical analysis and machine learning will be performed. Data analysis will include geospatial information analysis, time series analysis, and spatiotemporal analysis. The study will provide value addition to the existing disease surveillance mechanisms by developing a framework for incorporating multiple routine data sources available in the country.
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Affiliation(s)
- Gurpreet Singh
- Achutha Menon Centre for Health Science Studies, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, India
| | - Biju Soman
- Achutha Menon Centre for Health Science Studies, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, India..
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Wu W, Ren H, Lu L. Increasingly expanded future risk of dengue fever in the Pearl River Delta, China. PLoS Negl Trop Dis 2021; 15:e0009745. [PMID: 34559817 PMCID: PMC8462684 DOI: 10.1371/journal.pntd.0009745] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Accepted: 08/18/2021] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND In recent years, frequent outbreaks of dengue fever (DF) have become an increasingly serious public health issue in China, especially in the Pearl River Delta (PRD) with fast socioeconomic developments. Previous studies mainly focused on the historic DF epidemics, their influencing factors, and the prediction of DF risks. However, the future risks of this disease under both different socioeconomic development and representative concentration pathways (RCPs) scenarios remain little understood. METHODOLOGY AND PRINCIPAL FINDINGS In this study, a spatial dataset of gross domestic product (GDP), population density, and land use and land coverage (LULC) in 2050 and 2070 was obtained by simulation based on the different shared socioeconomic pathways (SSPs), and the future climatic data derived from the RCP scenarios were integrated into the Maxent models for predicting the future DF risk in the PRD region. Among all the variables included in this study, socioeconomics factors made the dominant contribution (83% or so) during simulating the current spatial distribution of the DF epidemics in the PRD region. Moreover, the spatial distribution of future DF risk identified by the climatic and socioeconomic (C&S) variables models was more detailed than that of the climatic variables models. Along with global warming and socioeconomic development, the zones with DF high and moderate risk will continue to increase, and the population at high and moderate risk will reach a maximum of 48.47 million (i.e., 63.78% of the whole PRD) under the RCP 4.5/SSP2 in 2070. CONCLUSIONS The increasing DF risk may be an inevitable public health threat in the PRD region with rapid socioeconomic developments and global warming in the future. Our results suggest that curbs in emissions and more sustainable socioeconomic growth targets offer hope for limiting the future impact of dengue, and effective prevention and control need to continue to be strengthened at the junction of Guangzhou-Foshan, north-central Zhongshan city, and central-western Dongguan city. Our study provides useful clues for relevant hygienic authorities making targeted adapting strategies for this disease.
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Affiliation(s)
- Wei Wu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- School of Geography and Ocean Science, Nanjing University, Nanjing, China
- Key Laboratory of Coastal zone Development and Protection, Ministry of Land and Resources of China, Nanjing, China
| | - Hongyan Ren
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- * E-mail:
| | - Liang Lu
- 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
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11
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Hussain-Alkhateeb L, Rivera Ramírez T, Kroeger A, Gozzer E, Runge-Ranzinger S. Early warning systems (EWSs) for chikungunya, dengue, malaria, yellow fever, and Zika outbreaks: What is the evidence? A scoping review. PLoS Negl Trop Dis 2021; 15:e0009686. [PMID: 34529649 PMCID: PMC8445439 DOI: 10.1371/journal.pntd.0009686] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Early warning systems (EWSs) are of increasing importance in the context of outbreak-prone diseases such as chikungunya, dengue, malaria, yellow fever, and Zika. A scoping review has been undertaken for all 5 diseases to summarize existing evidence of EWS tools in terms of their structural and statistical designs, feasibility of integration and implementation into national surveillance programs, and the users' perspective of their applications. METHODS Data were extracted from Cochrane Database of Systematic Reviews (CDSR), Google Scholar, Latin American and Caribbean Health Sciences Literature (LILACS), PubMed, Web of Science, and WHO Library Database (WHOLIS) databases until August 2019. Included were studies reporting on (a) experiences with existing EWS, including implemented tools; and (b) the development or implementation of EWS in a particular setting. No restrictions were applied regarding year of publication, language or geographical area. FINDINGS Through the first screening, 11,710 documents for dengue, 2,757 for Zika, 2,706 for chikungunya, 24,611 for malaria, and 4,963 for yellow fever were identified. After applying the selection criteria, a total of 37 studies were included in this review. Key findings were the following: (1) a large number of studies showed the quality performance of their prediction models but except for dengue outbreaks, only few presented statistical prediction validity of EWS; (2) while entomological, epidemiological, and social media alarm indicators are potentially useful for outbreak warning, almost all studies focus primarily or exclusively on meteorological indicators, which tends to limit the prediction capacity; (3) no assessment of the integration of the EWS into a routine surveillance system could be found, and only few studies addressed the users' perspective of the tool; (4) almost all EWS tools require highly skilled users with advanced statistics; and (5) spatial prediction remains a limitation with no tool currently able to map high transmission areas at small spatial level. CONCLUSIONS In view of the escalating infectious diseases as global threats, gaps and challenges are significantly present within the EWS applications. While some advanced EWS showed high prediction abilities, the scarcity of tool assessments in terms of integration into existing national surveillance systems as well as of the feasibility of transforming model outputs into local vector control or action plans tends to limit in most cases the support of countries in controlling disease outbreaks.
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Affiliation(s)
- Laith Hussain-Alkhateeb
- Global Health, School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | | | - Axel Kroeger
- Centre for Medicine and Society, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany
| | | | - Silvia Runge-Ranzinger
- Centre for Medicine and Society, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany
- Heidelberg Institute of Global Health, University of Heidelberg, Heidelberg, Germany
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12
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Application of time series methods for dengue cases in North India (Chandigarh). J Public Health (Oxf) 2021. [DOI: 10.1007/s10389-019-01136-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
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13
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Bal S, Sodoudi S. Modeling and prediction of dengue occurrences in Kolkata, India, based on climate factors. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2020; 64:1379-1391. [PMID: 32328786 DOI: 10.1007/s00484-020-01918-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Revised: 12/31/2019] [Accepted: 04/08/2020] [Indexed: 05/16/2023]
Abstract
Dengue is one of the most serious vector-borne infectious diseases in India, particularly in Kolkata and its neighbouring districts. Dengue viruses have infected several citizens of Kolkata since 2012 and it is amplifying every year. It has been derived from earlier studies that certain meteorological variables and climate change play a significant role in the spread and amplification of dengue infections in different parts of the globe. In this study, our primary objective is to identify the relative contribution of the putative drivers responsible for dengue occurrences in Kolkata and project dengue incidences with respect to the future climate change. The regression model was developed using maximum temperature, minimum temperature, relative humidity and rainfall as key meteorological factors on the basis of statistically significant cross-correlation coefficient values to predict dengue cases. Finally, climate variables from the Coordinated Regional Climate Downscaling Experiment (CORDEX) for South Asia region were input into the statistical model to project the occurrences of dengue infections under different climate scenarios such as Representative Concentration Pathways (RCP4.5 and RCP8.5). It has been estimated that from 2020 to 2100, dengue cases will be higher from September to November with more cases in RCP8.5 (872 cases per year) than RCP4.5 (531 cases per year). The present research further concludes that from December to February, RCP8.5 leads to suitable warmer weather conditions essential for the survival and multiplication of dengue pathogens resulting more than two times dengue cases in RCP8.5 than in RCP4.5. Furthermore, the results obtained will be useful in developing early warning systems and provide important evidence for dengue control policy-making and public health intervention.
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Affiliation(s)
- Sourabh Bal
- Institute for Meteorology, Free University of Berlin, Berlin, Germany.
- Department of Physics, Swami Vivekananda Institute of Science & Technology, Kolkata, India.
| | - Sahar Sodoudi
- Institute for Meteorology, Free University of Berlin, Berlin, Germany
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14
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Cheng YC, Lee FJ, Hsu YT, Slud EV, Hsiung CA, Chen CH, Liao CL, Wen TH, Chang CW, Chang JH, Wu HY, Chang TP, Lin PS, Ho HP, Hung WF, Chou JD, Tsou HH. Real-time dengue forecast for outbreak alerts in Southern Taiwan. PLoS Negl Trop Dis 2020; 14:e0008434. [PMID: 32716983 PMCID: PMC7384612 DOI: 10.1371/journal.pntd.0008434] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Accepted: 05/29/2020] [Indexed: 11/18/2022] Open
Abstract
Dengue fever is a viral disease transmitted by mosquitoes. In recent decades, dengue fever has spread throughout the world. In 2014 and 2015, southern Taiwan experienced its most serious dengue outbreak in recent years. Some statistical models have been established in the past, however, these models may not be suitable for predicting huge outbreaks in 2014 and 2015. The control of dengue fever has become the primary task of local health agencies. This study attempts to predict the occurrence of dengue fever in order to achieve the purpose of timely warning. We applied a newly developed autoregressive model (AR model) to assess the association between daily weather variability and daily dengue case number in 2014 and 2015 in Kaohsiung, the largest city in southern Taiwan. This model also contained additional lagged weather predictors, and developed 5-day-ahead and 15-day-ahead predictive models. Our results indicate that numbers of dengue cases in Kaohsiung are associated with humidity and the biting rate (BR). Our model is simple, intuitive and easy to use. The developed model can be embedded in a "real-time" schedule, and the data (at present) can be updated daily or weekly based on the needs of public health workers. In this study, a simple model using only meteorological factors performed well. The proposed real-time forecast model can help health agencies take public health actions to mitigate the influences of the epidemic. Meteorological conditions are the most frequently mentioned factors in the study of dengue fever. Some of the main factors other than the purely meteorological about which the public-health authorities might have data, such as numbers of cases or other current measurements of dengue outbreaks in neighboring cities, had been used in some of the past dengue studies. In this study, we developed models for predicting dengue case number based on past dengue case data and meteorological data. The goal of the models is to provide early warning of the occurrence of dengue fever to assist public health agencies in preparing an epidemic response plan.
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Affiliation(s)
- Yu-Chieh Cheng
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Miaoli County, Taiwan
| | - Fang-Jing Lee
- National Mosquito-Borne Diseases Control Research Center, National Health Research Institutes, Miaoli County, Taiwan
| | - Ya-Ting Hsu
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Miaoli County, Taiwan
| | - Eric V. Slud
- Department of Mathematics, University of Maryland, College Park, Maryland, United States of America
| | - Chao A. Hsiung
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Miaoli County, Taiwan
| | - Chun-Hong Chen
- National Mosquito-Borne Diseases Control Research Center, National Health Research Institutes, Miaoli County, Taiwan
- National Institute of Infectious Diseases and Vaccinology, National Health Research Institutes, Miaoli County, Taiwan
| | - Ching-Len Liao
- National Mosquito-Borne Diseases Control Research Center, National Health Research Institutes, Miaoli County, Taiwan
- National Institute of Infectious Diseases and Vaccinology, National Health Research Institutes, Miaoli County, Taiwan
| | - Tzai-Hung Wen
- Department of Geography, National Taiwan University, Taipei, Taiwan
| | - Chiu-Wen Chang
- Department of Health, Kaohsiung City Government, Kaohsiung City, Taiwan
| | - Jui-Hun Chang
- Environmental Protection Bureau, Kaohsiung City Government, Kaohsiung City, Taiwan
| | - Hsiao-Yu Wu
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Miaoli County, Taiwan
| | - Te-Pin Chang
- National Mosquito-Borne Diseases Control Research Center, National Health Research Institutes, Miaoli County, Taiwan
| | - Pei-Sheng Lin
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Miaoli County, Taiwan
| | - Hui-Pin Ho
- Department of Health, Kaohsiung City Government, Kaohsiung City, Taiwan
| | - Wen-Feng Hung
- Soil and groundwater pollution remediation center, CPC Corporation, Taiwan
| | - Jing-Dong Chou
- Environmental Protection Bureau, Kaohsiung City Government, Kaohsiung City, Taiwan
| | - Hsiao-Hui Tsou
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Miaoli County, Taiwan
- Graduate Institute of Biostatistics, College of Public Health, China Medical University, Taichung, Taiwan
- * E-mail:
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15
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Wu S, Ren H, Chen W, Li T. Neglected Urban Villages in Current Vector Surveillance System: Evidences in Guangzhou, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 17:ijerph17010002. [PMID: 31861276 PMCID: PMC6981632 DOI: 10.3390/ijerph17010002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 12/14/2019] [Accepted: 12/15/2019] [Indexed: 12/28/2022]
Abstract
Numerous urban villages (UVs) with substandard living conditions that cause people to live there with vulnerability to health impacts, including vector-borne diseases such as dengue fever (DF), are major environmental and public health concerns in highly urbanized regions, especially in developing countries. It is necessary to explore the relationship between UVs and vector for effectively dealing with these problems. In this study, land-use types, including UVs, normal construction land (NCL), unused land (UL), vegetation, and water, were retrieved from the high-resolution remotely sensed imagery in the central area of Guangzhou in 2017. The vector density from May to October in 2017, including Aedes. albopictus (Ae. albopictus)’s Breteau index (BI), standard space index (SSI), and adult density index (ADI) were obtained from the vector surveillance system implemented by the Guangzhou Center for Disease Control and Prevention (CDC). Furthermore, the spatial and temporal patterns of vector monitoring sites and vector density were analyzed on a fine scale, and then the Geodetector tool was further employed to explore the relationships between vector density and land-use types. The monitoring sites were mainly located in NCL (55.70%–56.44%) and UV (13.14%–13.92%). Among the total monitoring sites of BI (79), SSI (312), and ADI (326), the random sites accounted for about 88.61%, 97.12%, and 98.47%, respectively. The density of Ae. albopictus was temporally related to rainfall and temperature and was obviously differentiated among different land-use types. Meanwhile, the grids with higher density, which were mostly concentrated in the Pearl River fork zone that collects a large number of UVs, showed that the density of Ae. albopictus was spatially associated with the UVs. Next, the results of the Geodetector illustrated that UVs posed great impact on the density of Ae. albopictus across the central region of Guangzhou. We suggest that the number of monitoring sites in the UVs should be appropriately increased to strengthen the current vector surveillance system in Guangzhou. This study will provide targeted guidance for local authorities, making more effective control and prevention measures on the DF epidemics.
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Affiliation(s)
- Sijia Wu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, Beijing 100101, China;
- College of Geographical Science, Fujian Normal University, No.8 Shangsan Road, Fuzhou 350007, China;
| | - Hongyan Ren
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, Beijing 100101, China;
- Correspondence: (H.R.); (T.L.)
| | - Wenhui Chen
- College of Geographical Science, Fujian Normal University, No.8 Shangsan Road, Fuzhou 350007, China;
| | - Tiegang Li
- Department of Infectious Diseases, Guangzhou Center for Disease Control and Prevention, Guangzhou 510440, China
- Correspondence: (H.R.); (T.L.)
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16
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Yuan B, Lee H, Nishiura H. Assessing dengue control in Tokyo, 2014. PLoS Negl Trop Dis 2019; 13:e0007468. [PMID: 31226116 PMCID: PMC6588210 DOI: 10.1371/journal.pntd.0007468] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Accepted: 05/14/2019] [Indexed: 01/11/2023] Open
Abstract
Background In summer 2014, an autochthonous outbreak of dengue occurred in Tokyo, Japan, in which Yoyogi Park acted as the focal area of transmission. Recognizing the outbreak, concerted efforts were made to control viral spread, which included mosquito control, public announcement of the outbreak, and a total ban on entering the park. We sought to assess the effectiveness of these control measures. Methodology/Principal findings We used a mathematical model to describe the transmission dynamics. Using dates of exposure and illness onset, we categorized cases into three groups according to the availability of these datasets. The infection process was parametrically modeled by generation, and convolution of the infection process and the incubation period was fitted to the data. By estimating the effective reproduction number, we determined that the effect of dengue risk communication together with mosquito control from 28 August 2014 was insufficiently large to lower the reproduction number to below 1. However, once Yoyogi Park was closed on 4 September, the value of the effective reproduction number began to fall below 1, and the associated relative reduction in the effective reproduction number was estimated to be 20%–60%. The mean incubation period was an estimated 5.8 days. Conclusions/Significance Regardless of the assumed number of generations of cases, the combined effect of mosquito control, risk communication, and park closure appeared to be successful in interrupting the chain of dengue transmission in Tokyo. Evaluating the interventions implemented during an outbreak of mosquito-borne disease is of utmost importance, offering lessons for future control strategies. By retrospectively analyzing data of the first autochthonous dengue epidemic of the 21st century in Tokyo, Japan, we assessed the effectiveness of the interventions. Once a dengue outbreak was confirmed in late August 2014, the government of Japan took drastic mosquito control measures, targeting both adults and larvae. News of the outbreak was also widely disseminated via mass media along with experts’ recommendations as to how people could avoid the risks of dengue infection. As the outbreak was not immediately controlled, the focal area of transmission, Yoyogi Park, was closed on 4 September. Using a mathematical model, we assessed how well dengue virus transmission was intervened in relation to the start times of interventions. As we incorporated precise timing into the model, we directly modeled the time of infection and accounted for the time delay from infection to illness onset. Thus, we revealed that mosquito control and risk communication measures alone could not interrupt the chain of transmission; however, adding park closure to these interventions was substantially effective in reducing the number of transmissions.
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Affiliation(s)
- Baoyin Yuan
- Graduate School of Medicine, Hokkaido University, Sapporo-shi, Hokkaido, Japan
- CREST, Japan Science and Technology Agency, Kawaguchi, Saitama, Japan
| | - Hyojung Lee
- Graduate School of Medicine, Hokkaido University, Sapporo-shi, Hokkaido, Japan
- CREST, Japan Science and Technology Agency, Kawaguchi, Saitama, Japan
| | - Hiroshi Nishiura
- Graduate School of Medicine, Hokkaido University, Sapporo-shi, Hokkaido, Japan
- CREST, Japan Science and Technology Agency, Kawaguchi, Saitama, Japan
- * E-mail:
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17
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Yi L, Xu X, Ge W, Xue H, Li J, Li D, Wang C, Wu H, Liu X, Zheng D, Chen Z, Liu Q, Bi P, Li J. The impact of climate variability on infectious disease transmission in China: Current knowledge and further directions. ENVIRONMENTAL RESEARCH 2019; 173:255-261. [PMID: 30928856 DOI: 10.1016/j.envres.2019.03.043] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 01/20/2019] [Accepted: 03/17/2019] [Indexed: 05/27/2023]
Abstract
BACKGROUND Climate change may lead to emerging and re-emerging infectious diseases and pose public health challenges to human health and the already overloaded healthcare system. It is therefore important to review current knowledge and identify further directions in China, the largest developing country in the world. METHODS A comprehensive literature review was conducted to examine the relationship between climate variability and infectious disease transmission in China in the new millennium. Literature was identified using the following MeSH terms and keywords: climatic variables [temperature, precipitation, rainfall, humidity, etc.] and infectious disease [viral, bacterial and parasitic diseases]. RESULTS Fifty-eight articles published from January 1, 2000 to May 30, 2018 were included in the final analysis, including bacterial diarrhea, dengue, malaria, Japanese encephalitis, HFRS, HFMD, Schistosomiasis. Each 1 °C rise may lead to 3.6%-14.8% increase in the incidence of bacillary dysentery disease in south China. A 1 °C rise was corresponded to an increase of 1.8%-5.9% in the weekly notified HFMD cases in west China. Each 1 °C rise of temperature, 1% rise in relative humidity and one hour rise in sunshine led to an increase of 0.90%, 3.99% and 0.68% in the monthly malaria cases, respectively. Climate change with the increased temperature and irregular patterns of rainfall may affect the pathogen reproduction rate, their spread and geographical distribution, change human behavior and influence the ecology of vectors, and increase the rate of disease transmission in different regions of China. CONCLUSION Exploring relevant adaptation strategies and the health burden of climate change will assist public health authorities to develop an early warning system and protect China's population health, especially in the new 1.5 °C scenario of the newly released IPCC special report.
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Affiliation(s)
- Liping Yi
- Division of Environmental Health, School of Public Health and Management, Weifang Medical University, Weifang, 261053, Shandong Province, PR China
| | - Xin Xu
- Department of Dentistry, Affiliated Hospital, Weifang Medical University, Weifang, 261053, Shandong Province, PR China
| | - Wenxin Ge
- Division of Environmental Health, School of Public Health and Management, Weifang Medical University, Weifang, 261053, Shandong Province, PR China
| | - Haibin Xue
- Clinical Laboratory, Weifang People's Hospital, Weifang, 261000. Shandong Province, PR China
| | - Jin Li
- Department of Dentistry, Weifang People's Hospital, Weifang, 261000, Shandong Province, PR China
| | - Daoyuan Li
- Department of Emergency, Weifang No.2 People's Hospital, Weifang, 261041, Shandong Province, PR China
| | - Chunping Wang
- Division of Environmental Health, School of Public Health and Management, Weifang Medical University, Weifang, 261053, Shandong Province, PR China
| | - Haixia Wu
- 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, 102206, PR China
| | - Xiaobo 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, 102206, PR China
| | - Dashan Zheng
- Division of Environmental Health, School of Public Health and Management, Weifang Medical University, Weifang, 261053, Shandong Province, PR China
| | - Zhe Chen
- Division of Environmental Health, School of Public Health and Management, Weifang Medical University, Weifang, 261053, Shandong Province, PR 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, 102206, PR China
| | - Peng Bi
- School of Public Health, The University of Adelaide, Adelaide, SA 5005, Australia; School of Public Health, Anhui Medical University, Hefei, 230032, Anhui Province, PR China.
| | - Jing Li
- Division of Environmental Health, School of Public Health and Management, Weifang Medical University, Weifang, 261053, Shandong Province, PR China; "Health Shandong" Major Social Risk Prediction and Governance Collaborative Innovation Center, Weifang, 261053, Shandong Province, PR China.
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18
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Bartlow AW, Manore C, Xu C, Kaufeld KA, Del Valle S, Ziemann A, Fairchild G, Fair JM. Forecasting Zoonotic Infectious Disease Response to Climate Change: Mosquito Vectors and a Changing Environment. Vet Sci 2019; 6:E40. [PMID: 31064099 PMCID: PMC6632117 DOI: 10.3390/vetsci6020040] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2019] [Revised: 04/12/2019] [Accepted: 04/29/2019] [Indexed: 12/20/2022] Open
Abstract
Infectious diseases are changing due to the environment and altered interactions among hosts, reservoirs, vectors, and pathogens. This is particularly true for zoonotic diseases that infect humans, agricultural animals, and wildlife. Within the subset of zoonoses, vector-borne pathogens are changing more rapidly with climate change, and have a complex epidemiology, which may allow them to take advantage of a changing environment. Most mosquito-borne infectious diseases are transmitted by mosquitoes in three genera: Aedes, Anopheles, and Culex, and the expansion of these genera is well documented. There is an urgent need to study vector-borne diseases in response to climate change and to produce a generalizable approach capable of generating risk maps and forecasting outbreaks. Here, we provide a strategy for coupling climate and epidemiological models for zoonotic infectious diseases. We discuss the complexity and challenges of data and model fusion, baseline requirements for data, and animal and human population movement. Disease forecasting needs significant investment to build the infrastructure necessary to collect data about the environment, vectors, and hosts at all spatial and temporal resolutions. These investments can contribute to building a modeling community around the globe to support public health officials so as to reduce disease burden through forecasts with quantified uncertainty.
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Affiliation(s)
- Andrew W Bartlow
- Los Alamos National Laboratory, Biosecurity and Public Health, Los Alamos, NM 87545, USA.
| | - Carrie Manore
- Los Alamos National Laboratory, Information Systems and Modeling, Los Alamos, NM 87545, USA.
| | - Chonggang Xu
- Los Alamos National Laboratory, Earth Systems Observations, Los Alamos, NM 87545, USA.
| | - Kimberly A Kaufeld
- Los Alamos National Laboratory, Statistical Sciences, Los Alamos, NM 87545, USA.
| | - Sara Del Valle
- Los Alamos National Laboratory, Information Systems and Modeling, Los Alamos, NM 87545, USA.
| | - Amanda Ziemann
- Los Alamos National Laboratory, Space Data Science and Systems, Los Alamos, NM 87545, USA.
| | - Geoffrey Fairchild
- Los Alamos National Laboratory, Information Systems and Modeling, Los Alamos, NM 87545, USA.
| | - Jeanne M Fair
- Los Alamos National Laboratory, Biosecurity and Public Health, Los Alamos, NM 87545, USA.
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Ren H, Wu W, Li T, Yang Z. Urban villages as transfer stations for dengue fever epidemic: A case study in the Guangzhou, China. PLoS Negl Trop Dis 2019; 13:e0007350. [PMID: 31022198 PMCID: PMC6504109 DOI: 10.1371/journal.pntd.0007350] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 05/07/2019] [Accepted: 03/30/2019] [Indexed: 12/21/2022] Open
Abstract
Background Numerous urban villages (UVs) and frequent infectious disease outbreaks are major environmental and public health concerns in highly urbanized regions, especially in developing countries. However, the spatial and quantitative associations between UVs and infections remain little understood on a fine scale. Methodology and principal findings In this study, the relationships between reported dengue fever (DF) epidemics during 2012–2017, gross domestic product (GDP), the traffic system (road density, bus and/or subway stations), and UVs derived from high-resolution remotely sensed imagery in the central area of Guangzhou, were explored using geographically weighted regression (GWR) models based on a 1 km × 1 km grid scale. Accounting for 16.53%–18.07% of residential area and 16.84%–18.02% of population, UVs possessed 28.55%–38.24% of total reported DF cases in the core area of Guangzhou. The density of DF cases and the DF incidence rates in UVs were 1.81–3.13 and 1.82–3.06 times of that of normal construction land. Approximately 90% of the total cases were concentrated in the UVs and their buffering zones of radius ranged from 0 to 500 m. Significantly positive associations were observed between gridded DF incidence rates and UV area (r = 0.33, P = 0.000), the number of bus stops (r = 0.49, P = 0.000) and subway stations (r = 0.27, P = 0.000), and road density (r = 0.39, P = 0.000). About 60% of spatial variations in the gridded DF incidence rates were interpreted by the different variables of GDP, UVs, and bus stops integrated in GWR models. Conclusions UVs likely acted as special transfer stations, receiving and/or exporting DF cases during epidemics. This work increases our understanding of the influences of UVs on vector-borne diseases in highly urbanized areas, supplying valuable clues to local authorities making targeted interventions for the prevention and control of DF epidemics. Due to the rapid urbanization of China, many villages in the urban fringe are enveloped by ever-expanding cities and become so-called urban villages (UVs). UVs are widely distributed in not only the Guangzhou core areas but also the other cities in the highly urbanized region of China (e.g., Shenzhen, Wuhan). UVs are commonly featured by poor sanitation, overcrowding population, absent infrastructure, and some environmental pollution due to the development is neither authorized nor planned, resulting in a high environmental suitability for some vectors (e.g., Aedes albopictus), as well as the vetor-borne diseases (i.e., dengue fever) in these regions. In this study, we demonstrated that UVs may serve as transfer stations for the transmission of DF epidemic in the regions with developed transportation, higher GDP and dense population. This is manifested as that the rates of DF incidences were significantly positively associated with UV area. Furthermore, the density of DF cases and the DF incidence rates in UVs were 1.81–3.13 and 1.82–3.06 times of that of normal construction land and about 90% of the total DF cases were concentrated in 500m radius of UVs’ buffers. And the aggregation effects of UVs on this epidemic in the central region were obviously affected by public traffic conditions at the grid level. This study is the first quantitative analysis of the spatial relationship between UVs, public transportation, road density, population density, GDP and DF epidemics, which will provide a useful reference for accurately preventing and controlling DF epidemic in urban regions with numerous UVs.
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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, China
- * E-mail: (HR); (ZY)
| | - Wei Wu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- College of Geographical Science, Fujian Normal University, Fuzhou, China
| | - Tiegang Li
- Department of Infectious Diseases, Guangzhou Center for Disease Control and Prevention, Guangzhou, People’s Republic of China
| | - Zhicong Yang
- Department of Infectious Diseases, Guangzhou Center for Disease Control and Prevention, Guangzhou, People’s Republic of China
- * E-mail: (HR); (ZY)
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20
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Wang L, Zhu B, Zha L, Jia L, Qiu S, Li P, Du X, Sun Y, Song H. The dengue outbreak of 2014 transformed the epidemic characteristics of dengue in Guangdong Province, China. J Infect 2019; 78:491-503. [PMID: 30849437 DOI: 10.1016/j.jinf.2019.03.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Accepted: 03/01/2019] [Indexed: 11/19/2022]
Affiliation(s)
- Ligui Wang
- Center for Disease Control and Prevention of Chinese People's Liberation Army, 20 Dongdajie Street, Fengtai District, Beijing 100071, China
| | - Binghua Zhu
- 305 Hospital of PLA, A13 Wenjin Street, Xicheng District, Beijing, 10017, China
| | - Lei Zha
- Center for Disease Control and Prevention of Chinese People's Liberation Army, 20 Dongdajie Street, Fengtai District, Beijing 100071, China
| | - Leili Jia
- Center for Disease Control and Prevention of Chinese People's Liberation Army, 20 Dongdajie Street, Fengtai District, Beijing 100071, China
| | - Shaofu Qiu
- Center for Disease Control and Prevention of Chinese People's Liberation Army, 20 Dongdajie Street, Fengtai District, Beijing 100071, China
| | - Peng Li
- Center for Disease Control and Prevention of Chinese People's Liberation Army, 20 Dongdajie Street, Fengtai District, Beijing 100071, China
| | - Xinying Du
- Center for Disease Control and Prevention of Chinese People's Liberation Army, 20 Dongdajie Street, Fengtai District, Beijing 100071, China
| | - Yansong Sun
- State Key Laboratory of Pathogen and Biosecurity, Institute of Microbiology and Epidemiology, Academy of Military Medical Science, 20 Dongdajie Street, Fengtai District, Beijing 100071, China.
| | - Hongbin Song
- Center for Disease Control and Prevention of Chinese People's Liberation Army, 20 Dongdajie Street, Fengtai District, Beijing 100071, China.
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Liu J, Tian X, Deng Y, Du Z, Liang T, Hao Y, Zhang D. Risk Factors Associated with Dengue Virus Infection in Guangdong Province: A Community-Based Case-Control Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16040617. [PMID: 30791547 PMCID: PMC6406885 DOI: 10.3390/ijerph16040617] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2018] [Revised: 02/03/2019] [Accepted: 02/14/2019] [Indexed: 01/06/2023]
Abstract
Dengue fever (DF) is a mosquito-borne infectious disease that is now an epidemic in China, Guangdong Province, in particular and presents high incidence rates of DF. Effective preventive measures are critical for controlling DF in China given the absence of a licensed vaccination program in the country. This study aimed to explore the individual risk factors for the dengue virus infection in Guangdong Province and to provide a scientific basis for the future prevention and control of DF. A case-control study including 237 cases and 237 controls was performed. Cases were defined for samples who were IgG-antibody positive or IgM-antibody positive, and willing to participate in the questionnaire survey. Additionally, the controls were selected through frequency matching by age, gender and community information from individuals who tested negative for IgG and IgM and volunteered to become part of the samples. Data were collected from epidemiological questionnaires. Univariate analysis was performed for the preliminary screening of 28 variables that were potentially related to dengue virus infection, and multivariate analysis was performed through unconditioned logistic regression analysis to analyze statistically significant variables. Multivariate analysis revealed two independent risk factors: Participation in outdoor sports (odds ratio (OR) = 1.80, 95% confidence interval (CI) 1.17 to 2.78), and poor indoor daylight quality (OR = 2.27, 95% CI 1.03 to 5.03). Two protective factors were identified through multivariate analysis: 2 occupants per room (OR = 0.43, 95% CI 0.28 to 0.65) or ≥3 occupants per room (OR = 0.45, 95% CI 0.23 to 0.89) and air-conditioner use (OR = 0.46, 95% CI 0.22 to 0.97). The results of this study were conducive for investigating the risk factors for dengue virus infection in Guangdong Province. Effective and efficient strategies for improving environmental protection and anti-mosquito measures must be provided. In addition, additional systematic studies are needed to explore other potential risk factors for DF.
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Affiliation(s)
- Jundi Liu
- School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
| | - Xiaolu Tian
- School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
| | - Yu Deng
- School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
| | - Zhicheng Du
- School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
| | - Tianzhu Liang
- School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
| | - Yuantao Hao
- School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
| | - Dingmei Zhang
- School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
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Hettiarachchige C, von Cavallar S, Lynar T, Hickson RI, Gambhir M. Risk prediction system for dengue transmission based on high resolution weather data. PLoS One 2018; 13:e0208203. [PMID: 30521550 PMCID: PMC6283552 DOI: 10.1371/journal.pone.0208203] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Accepted: 11/13/2018] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND Dengue is the fastest spreading vector-borne viral disease, resulting in an estimated 390 million infections annually. Precise prediction of many attributes related to dengue is still a challenge due to the complex dynamics of the disease. Important attributes to predict include: the risk of and risk factors for an infection; infection severity; and the timing and magnitude of outbreaks. In this work, we build a model for predicting the risk of dengue transmission using high-resolution weather data. The level of dengue transmission risk depends on the vector density, hence we predict risk via vector prediction. METHODS AND FINDINGS We make use of surveillance data on Aedes aegypti larvae collected by the Taiwan Centers for Disease Control as part of the national routine entomological surveillance of dengue, and weather data simulated using the IBM's Containerized Forecasting Workflow, a high spatial- and temporal-resolution forecasting system. We propose a two stage risk prediction system for assessing dengue transmission via Aedes aegypti mosquitoes. In stage one, we perform a logistic regression to determine whether larvae are present or absent at the locations of interest using weather attributes as the explanatory variables. The results are then aggregated to an administrative division, with presence in the division determined by a threshold percentage of larvae positive locations resulting from a bootstrap approach. In stage two, larvae counts are estimated for the predicted larvae positive divisions from stage one, using a zero-inflated negative binomial model. This model identifies the larvae positive locations with 71% accuracy and predicts the larvae numbers producing a coverage probability of 98% over 95% nominal prediction intervals. This two-stage model improves the overall accuracy of identifying larvae positive locations by 29%, and the mean squared error of predicted larvae numbers by 9.6%, against a single-stage approach which uses a zero-inflated binomial regression approach. CONCLUSIONS We demonstrate a risk prediction system using high resolution weather data can provide valuable insight to the distribution of risk over a geographical region. The work also shows that a two-stage approach is beneficial in predicting risk in non-homogeneous regions, where the risk is localised.
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Affiliation(s)
- Chathurika Hettiarachchige
- IBM Research Australia, Southgate, Victoria, Australia
- School of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria, Australia
| | | | - Timothy Lynar
- IBM Research Australia, Southgate, Victoria, Australia
| | - Roslyn I. Hickson
- IBM Research Australia, Southgate, Victoria, Australia
- School of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria, Australia
| | - Manoj Gambhir
- IBM Research Australia, Southgate, Victoria, Australia
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23
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Yu G, Yang R, Yu D, Cai J, Tang J, Zhai W, Wei Y, Chen S, Chen Q, Zhong G, Qin J. Impact of meteorological factors on mumps and potential effect modifiers: An analysis of 10 cities in Guangxi, Southern China. ENVIRONMENTAL RESEARCH 2018; 166:577-587. [PMID: 29966878 DOI: 10.1016/j.envres.2018.06.047] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Revised: 06/19/2018] [Accepted: 06/21/2018] [Indexed: 05/04/2023]
Abstract
BACKGROUND In the current context of global climate change, understanding the impact of climate on respiratory infectious diseases such as mumps and the potential modified factors is crucial, especially in developing countries. However, research on the climate-related incidence of mumps is rare, inconsistent and mainly limited to a single city or region. METHODS Daily mumps cases and meteorological variables of 10 cities in Guangxi, Southern China were collected for 2005-2017. Two-stage analyses were performed to assess the relationship between meteorological factors and mumps incidence during two time-periods: 2005-2012 and 2013-2017, separately. First, a Poisson regression model that allows over-dispersion was used to estimate the city-specific climate-related morbidity after controlling for temporal trends, day of week, and national statutory holidays. Then, we used a multivariate meta-analytical model to pool the city-specific effect estimates and conducted subgroup analyses. Multivariate meta-regression was applied to detect potential effect modifiers. RESULTS Non-linear relationships were observed among mean temperature, wind speed, and mumps incidence in 2005-2012. The impact of high temperature on mumps incidence was short and rapid, whereas the impact of low temperature was long and slow. The total cumulative relative risk (RR) associated with hot temperature was 1.18 [95% Confidence Interval (CI): 0.93, 1.48], which was calculated by comparing the incidence of mumps above the 90th percentile of temperature with its incidence at the median temperature at lag of 0-30 days. Meanwhile, the RR associated with cold temperature was calculated to be 1.50 (95% CI: 1.08, 2.10) by comparing the incidence of mumps below the 10th percentile of temperature with its incidence at the median temperature. Similarly, the RRs associated with windless and windy conditions for the total population were 1.23 (95% CI: 1.04, 1.46) and 0.83 (95% CI: 0.67, 1.02), respectively. Effects based on extreme temperature and wind speed conditions were more prominent in males than in females. Compared with children and adults, adolescents (5-14 years old) were more sensitive to extreme weather conditions. Geographical latitude, Population density, GDP per capita, Number of health institutions, Highly educated population and Inoculation rate were considered the most likely associated modifiers. In addition, the correlation between meteorological factors and the incidence of mumps and modification of socioeconomic factors after 2013 showed similar curves compared with results in 2005-2012, but the cumulative effect was not statistically significant. CONCLUSIONS Meteorological factors, such as temperature and wind speed, exert a significant impact on the incidence of mumps. The relationship varies depending on gender and age. Socioeconomic factors such as vaccination, GDP, geographical latitude, etc. may substantially affect the weather-related mumps incidence.
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Affiliation(s)
- Guoqi Yu
- Department of Environmental and Occupational Health, Guangxi Medical University, Shuangyong Road 22, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Rencong Yang
- Guangxi Center for Disease Control and Prevention, Institute of Vaccination, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Dongmei Yu
- Department of Environmental and Occupational Health, Guangxi Medical University, Shuangyong Road 22, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Jiansheng Cai
- Department of Environmental and Occupational Health, Guangxi Medical University, Shuangyong Road 22, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Jiexia Tang
- Department of Environmental and Occupational Health, Guangxi Medical University, Shuangyong Road 22, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Wenwen Zhai
- Department of Health Related Social and Behavioral Science, West China School of Public Health, Sichuan University, Chengdu, Sichuan, China
| | - Yi Wei
- Department of Environmental and Occupational Health, Guangxi Medical University, Shuangyong Road 22, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Shiyi Chen
- Department of Environmental and Occupational Health, Guangxi Medical University, Shuangyong Road 22, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Quanhui Chen
- Department of Environmental and Occupational Health, Guangxi Medical University, Shuangyong Road 22, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Ge Zhong
- Guangxi Center for Disease Control and Prevention, Institute of Vaccination, Nanning, Guangxi Zhuang Autonomous Region, China.
| | - Jian Qin
- Department of Environmental and Occupational Health, Guangxi Medical University, Shuangyong Road 22, Nanning, Guangxi Zhuang Autonomous Region, China; Guangxi Colleges and Universities Key Laboratory of Prevention and Control of Highly Prevalent Diseases, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.
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24
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Tohidinik HR, Mohebali M, Mansournia MA, Niakan Kalhori SR, Ali-Akbarpour M, Yazdani K. Forecasting zoonotic cutaneous leishmaniasis using meteorological factors in eastern Fars province, Iran: a SARIMA analysis. Trop Med Int Health 2018; 23:860-869. [PMID: 29790236 DOI: 10.1111/tmi.13079] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
OBJECTIVES To predict the occurrence of zoonotic cutaneous leishmaniasis (ZCL) and evaluate the effect of climatic variables on disease incidence in the east of Fars province, Iran using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. METHODS The Box-Jenkins approach was applied to fit the SARIMA model for ZCL incidence from 2004 to 2015. Then the model was used to predict the number of ZCL cases for the year 2016. Finally, we assessed the relation of meteorological variables (rainfall, rainy days, temperature, hours of sunshine and relative humidity) with ZCL incidence. RESULTS SARIMA(2,0,0) (2,1,0)12 was the preferred model for predicting ZCL incidence in the east of Fars province (validation Root Mean Square Error, RMSE = 0.27). It showed that ZCL incidence in a given month can be estimated by the number of cases occurring 1 and 2 months, as well as 12 and 24 months earlier. The predictive power of SARIMA models was improved by the inclusion of rainfall at a lag of 2 months (β = -0.02), rainy days at a lag of 2 months (β = -0.09) and relative humidity at a lag of 8 months (β = 0.13) as external regressors (P-values < 0.05). The latter was the best climatic variable for predicting ZCL cases (validation RMSE = 0.26). CONCLUSIONS Time series models can be useful tools to predict the trend of ZCL in Fars province, Iran; thus, they can be used in the planning of public health programmes. Introducing meteorological variables into the models may improve their precision.
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Affiliation(s)
- Hamid Reza Tohidinik
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Mehdi Mohebali
- Department of Medical Parasitology and Mycology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.,Center for Research of Endemic Parasites of Iran, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Ali Mansournia
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Sharareh R Niakan Kalhori
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Science, Tehran, Iran
| | | | - Kamran Yazdani
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
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25
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Liu J, Deng Y, Jing Q, Chen X, Du Z, Liang T, Yang Z, Zhang D, Hao Y. Dengue Infection Spectrum in Guangzhou: A Cross-Sectional Seroepidemiology Study among Community Residents between 2013 and 2015. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2018; 15:ijerph15061227. [PMID: 29891781 PMCID: PMC6025390 DOI: 10.3390/ijerph15061227] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Revised: 05/14/2018] [Accepted: 05/30/2018] [Indexed: 11/16/2022]
Abstract
The majority of dengue virus infections are asymptomatic, which could potentially facilitate the transmission of dengue fever and increase the percentage of sever dengue fever manifestations. This cross-sectional study explored the sero-prevalence of dengue virus infection in Guangzhou to clarify the infection spectrum. In total, 2085 serum samples were collected from residents of 34 communities. All samples were selected from a 200,000-sample database holding serum collected from community residents living in Liwan and Yuexiu districts of Guangzhou between September 2013 and August 2015, and 17 to 28 individuals of each age group were chosen per month. Dengue immunoglobulin G (IgG) and immunoglobulin M (IgM) antibodies were tested by enzyme-linked immunosorbent assay. Symptomatic infected individuals were identified via follow-up questionnaires. Among 2085 serum samples, anti-dengue IgG and IgM positive rates were 11.80% and 3.98%, respectively. The IgG antibody positive rate increased with age and was higher in poorly educated people than in highly educated people and in married individuals than in single individuals. Approximately 96.71% of dengue virus infections and an estimated 13.68% of the whole population were asymptomatic. Such high asymptomatic-infection rates have an impact on the local spread of dengue fever. Stricter surveillance, such as a network of rapid diagnostic laboratories, screening of residents in the epidemic season, and other integrated control measures are necessary.
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Affiliation(s)
- Jundi Liu
- School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
| | - Yu Deng
- School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
| | - Qinlong Jing
- Department of Infectious Disease, Guangzhou Centre for Disease Control and Prevention, Guangzhou 510440, China.
| | - Xiashi Chen
- School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
| | - Zhicheng Du
- School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
| | - Tianzhu Liang
- School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
| | - Zhicong Yang
- Department of Infectious Disease, Guangzhou Centre for Disease Control and Prevention, Guangzhou 510440, China.
| | - Dingmei Zhang
- School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
| | - Yuantao Hao
- School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
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26
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Imported cases and minimum temperature drive dengue transmission in Guangzhou, China: evidence from ARIMAX model. Epidemiol Infect 2018; 146:1226-1235. [PMID: 29781412 PMCID: PMC9134281 DOI: 10.1017/s0950268818001176] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Dengue is the fastest spreading mosquito-transmitted disease in the world. In China, Guangzhou City is believed to be the most important epicenter of dengue outbreaks although the transmission patterns are still poorly understood. We developed an autoregressive integrated moving average model incorporating external regressors to examine the association between the monthly number of locally acquired dengue infections and imported cases, mosquito densities, temperature and precipitation in Guangzhou. In multivariate analysis, imported cases and minimum temperature (both at lag 0) were both associated with the number of locally acquired infections (P < 0.05). This multivariate model performed best, featuring the lowest fitting root mean squared error (RMSE) (0.7520), AIC (393.7854) and test RMSE (0.6445), as well as the best effect in model validation for testing outbreak with a sensitivity of 1.0000, a specificity of 0.7368 and a consistency rate of 0.7917. Our findings suggest that imported cases and minimum temperature are two key determinants of dengue local transmission in Guangzhou. The modelling method can be used to predict dengue transmission in non-endemic countries and to inform dengue prevention and control strategies.
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27
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Li C, Lu Y, Liu J, Wu X. Climate change and dengue fever transmission in China: Evidences and challenges. THE SCIENCE OF THE TOTAL ENVIRONMENT 2018; 622-623:493-501. [PMID: 29220773 DOI: 10.1016/j.scitotenv.2017.11.326] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Revised: 11/28/2017] [Accepted: 11/28/2017] [Indexed: 06/07/2023]
Abstract
Dengue Fever (DF) has become one of the most serious infectious diseases in China. Dengue virus and its vector (Aedes mosquito) are known to be sensitive to climate condition. Climate impacts DF through affecting three essential bioecological aspects: DF virus, vector (mosquito) and DF transmission environment. Weather-based DF model, mosquito model and climate model are the three pillars to help the prediction of DF distribution. Through a systematic review of literature between 1980 and 2017, this paper summarizes empirical evidences in China on the impact of climate change on DF; it further reviews the related DF incidence models and their findings on how changes in weather factors may impact DF occurrences in China. Compared with some well-known research projects in the western countries, there is a lack of knowledge in China regarding how the spatiotemporal distribution of DF will respond to climate change. However, being able to predict DF distribution is key to China's efforts to prevent and control DF transmission. We conclude this paper by recommending four focused areas for China: promoting more advanced research on the relationship between extreme weather events and DF, developing regional-specific models for the high risk regions of DF in south China, encouraging interdisciplinary collaboration between climate studies and health services, and enhancing public health education and management at national, regional and local levels.
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Affiliation(s)
- Chenlu Li
- College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
| | - Yongmei Lu
- Department of Geography, Texas State University, San Marcos, TX 78666-4684, USA.
| | - Jianing Liu
- College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
| | - Xiaoxu Wu
- College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China.
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28
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Chen Y, Chu CW, Chen MIC, Cook AR. The utility of LASSO-based models for real time forecasts of endemic infectious diseases: A cross country comparison. J Biomed Inform 2018; 81:16-30. [PMID: 29496631 PMCID: PMC7185473 DOI: 10.1016/j.jbi.2018.02.014] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Revised: 01/19/2018] [Accepted: 02/24/2018] [Indexed: 01/09/2023]
Abstract
A LASSO based forecast model for endemic infectious diseases is proposed. Predictions at 4 weeks achieve desirable accuracy. Models predict outbreaks but may struggle to predict outbreak size.
Introduction Accurate and timely prediction for endemic infectious diseases is vital for public health agencies to plan and carry out any control methods at an early stage of disease outbreaks. Climatic variables has been identified as important predictors in models for infectious disease forecasts. Various approaches have been proposed in the literature to produce accurate and timely predictions and potentially improve public health response. Methods We assessed how the machine learning LASSO method may be useful in providing useful forecasts for different pathogens in countries with different climates. Separate LASSO models were constructed for different disease/country/forecast window with different model complexity by including different sets of predictors to assess the importance of different predictors under various conditions. Results There was a more apparent cyclicity for both climatic variables and incidence in regions further away from the equator. For most diseases, predictions made beyond 4 weeks ahead were increasingly discrepant from the actual scenario. Prediction models were more accurate in capturing the outbreak but less sensitive to predict the outbreak size. In different situations, climatic variables have different levels of importance in prediction accuracy. Conclusions For LASSO models used for prediction, including different sets of predictors has varying effect in different situations. Short term predictions generally perform better than longer term predictions, suggesting public health agencies may need the capacity to respond at short-notice to early warnings.
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Affiliation(s)
- Yirong Chen
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Tahir Foundation Building, 12 Science Drive 2, 117549, Singapore
| | - Collins Wenhan Chu
- Genome Institute of Singapore, 60 Biopolis Street, Genome, 138672, Singapore
| | - Mark I C Chen
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Tahir Foundation Building, 12 Science Drive 2, 117549, Singapore; Department of Clinical Epidemiology, Communicable Disease Centre, Tan Tock Seng Hospital, Singapore, Moulmein Road, 308433, Singapore
| | - Alex R Cook
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Tahir Foundation Building, 12 Science Drive 2, 117549, Singapore.
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Spatial and Temporal Characteristics of 2014 Dengue Outbreak in Guangdong, China. Sci Rep 2018; 8:2344. [PMID: 29402909 PMCID: PMC5799376 DOI: 10.1038/s41598-018-19168-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Accepted: 12/22/2017] [Indexed: 11/23/2022] Open
Abstract
The record-breaking number of dengue cases reported in Guangdong, China in 2014 has been topic for many studies. However, the spatial and temporal characteristics of this unexpectedly explosive outbreak are still poorly understood. We adopt an integrated approach to ascertain the spatial-temporal progression of the outbreak in each city in Guangdong as well as in each district in Guangzhou, where the majority of cases occurred. We utilize the Richards model, which determines the waves of reported cases at each location and identifies the turning point for each wave, in combination with a spatial association analysis conducted by computing the standardized G* statistic that measures the degree of spatial autocorrelation of a set of geo-referenced data from a local perspective. We found that Yuexiu district in Guangzhou was the initial hot spot for the outbreak, subsequently spreading to its neighboring districts in Guangzhou and other cities in Guangdong province. Hospital isolation of cases during early stage of outbreak in neighboring Zhongshan (in effort to prevent disease transmission to the vectors) might have played an important role in the timely mitigation of the disease. Integration of modeling approach and spatial association analysis allows us to pinpoint waves that spread the disease to communities beyond the borders of the initially affected regions.
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Ahmad S, Asif M, Talib R, Adeel M, Yasir M, Chaudary MH. Surveillance of intensity level and geographical spreading of dengue outbreak among males and females in Punjab, Pakistan: A case study of 2011. J Infect Public Health 2017; 11:472-485. [PMID: 29103928 DOI: 10.1016/j.jiph.2017.10.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Revised: 07/22/2017] [Accepted: 10/12/2017] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Dengue fever is viral disease which spreads due to the bite of the Aedes aegypti mosquito. In recent years, it has affected around 40% population of the world. Its endemic flow has led to a large disease burden, in terms of human and financial resources. METHODS Geographical Information Systems (GIS) are normally used to develop epidemiological thematic maps. This study explores the patterns and hotspots, associated with the catastrophic outbreak of dengue, in Punjab, in 2011. The ArcView software was used to analyze the data reported by the district hospitals of Punjab. Twenty-one-thousand cases were reported from March to December 2011, with 300 causalities. RESULTS AND CONCLUSION This research reveals that from among the total 37 epidemiological weeks, the maximum impact was observed between weeks 22 and 27. The geographical flow and hotspots associated with dengue have been shown through thematic maps. A positive correlation between the risk for dengue and age was observed. The findings of this research can help health officials and decision-makers alert the public about future outbreaks and take preventive measures to considerably reduce the mortality and morbidity associated with the disease.
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Affiliation(s)
- Shahbaz Ahmad
- Department of Computer Science, National Textile University, Faisalabad, Pakistan.
| | - Muhammad Asif
- Department of Computer Science, National Textile University, Faisalabad, Pakistan.
| | - Ramzan Talib
- Department of Computer Science, Government College University, Faisalabad, Pakistan.
| | - Muhammad Adeel
- Department of Computer Science, National Textile University, Faisalabad, Pakistan.
| | - Muhammad Yasir
- Department of Computer Science, University of Engineering and Technology, Faisalabad Campus, Pakistan.
| | - Muhammad H Chaudary
- Department of Computer Science, Comsats Institute of Information Technology, Lahore, Pakistan.
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31
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Zhang JH, Yuan J, Wang T. Direct cost of dengue hospitalization in Zhongshan, China: Associations with demographics, virus types and hospital accreditation. PLoS Negl Trop Dis 2017; 11:e0005784. [PMID: 28771479 PMCID: PMC5557582 DOI: 10.1371/journal.pntd.0005784] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2017] [Revised: 08/15/2017] [Accepted: 07/06/2017] [Indexed: 11/24/2022] Open
Abstract
Background Zhongshan City of Guangdong Province (China) is a key provincial and national level area for dengue fever prevention and control. The aim of this study is to analyze how the direct hospitalization costs and the length of stay of dengue hospitalization cases vary according to associated factors such as the demographics, virus types and hospital accreditation. Method This study is based on retrospective census data from the Chinese National Disease Surveillance Reporting System. Totally, the hospital administrative data of 1432 confirmed dengue inpatients during 2013–2014 was obtained. A quantile regression model was applied to analyze how the direct cost of Dengue hospitalization varies with the patient demographics and hospital accreditation across the data distribution. The Length of Stay (LOS) was also examined. Main findings The average direct hospitalization cost of a dengue case in this study is US$ 499.64 during 2013, which corresponded to about 3.71% of the gross domestic product per capita in Zhongshan that year. The mean of the Length of Stay (LOS) is 7.2 days. The multivariate quantile regression results suggest that, after controlling potential compounding variables, the median hospitalization costs of male dengue patients were significantly higher than female ones by about US$ 18.23 (p<0.1). The hospitalization cost difference between the pediatric and the adult patients is estimated to be about US$ 75.25 at the median (p<0.01), but it increases sharply among the top 25 percentiles and reaches US$ 329 at the 90th percentile (p<0.01). The difference between the senior (older than 64 years old) and the adult patients increases steadily across percentiles, especially sharply among the top quartiles too. The LOS of the city-level hospitals is significantly shorter than that in the township-level hospitals by one day at the median (p<0.05), but no significant differences in their hospitalization costs. Conclusions The direct hospitalization costs of dengue cases vary widely according to the associated demographics factors, virus types and hospital accreditations. The findings in this study provide information for adopting hospitalization strategy, cost containment and patient allocation in dengue prevention and control. Also the results can be used as the cost-effective reference for future dengue vaccine adoption strategy in China. There is little literature estimating dengue disease burdens and treatment costs worldwide; however, still fewer studies focus on the hospitalization cost of Dengue. Using the quantile regression method to analyze the administrative data of 1,432 confirmed dengue inpatients in Zhongshan City (Guangdong, China) during 2013 and 2014, this study examines the relationship of the direct cost of Dengue hospitalization and the associated factors. The Length of Stay (LOS) was also examined. The findings in this study will help to explain how the hospitalization cost varies with associated factors, providing information for adopting hospitalization strategy, cost containment and patient allocation in dengue prevention and control, as well as reference for future dengue vaccine cost-effective analysis.
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Affiliation(s)
- Jing Hua Zhang
- School of Business, Macau University of Science and Technology, Taipa, Macau, China
- * E-mail:
| | - Juan Yuan
- Zhongshan Center for Disease Control and Prevention, Zhongshan, China
| | - Tao Wang
- Zhongshan Center for Disease Control and Prevention, Zhongshan, China
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Cheng Q, Jing Q, Spear RC, Marshall JM, Yang Z, Gong P. The interplay of climate, intervention and imported cases as determinants of the 2014 dengue outbreak in Guangzhou. PLoS Negl Trop Dis 2017. [PMID: 28640895 PMCID: PMC5507464 DOI: 10.1371/journal.pntd.0005701] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Dengue is a fast spreading mosquito-borne disease that affects more than half of the population worldwide. An unprecedented outbreak happened in Guangzhou, China in 2014, which contributed 52 percent of all dengue cases that occurred in mainland China between 1990 and 2015. Our previous analysis, based on a deterministic model, concluded that the early timing of the first imported case that triggered local transmission and the excessive rainfall thereafter were the most important determinants of the large final epidemic size in 2014. However, the deterministic model did not allow us to explore the driving force of the early local transmission. Here, we expand the model to include stochastic elements and calculate the successful invasion rate of cases that entered Guangzhou at different times under different climate and intervention scenarios. The conclusion is that the higher number of imported cases in May and June was responsible for the early outbreak instead of climate. Although the excessive rainfall in 2014 did increase the success rate, this effect was offset by the low initial water level caused by interventions in late 2013. The success rate is strongly dependent on mosquito abundance during the recovery period of the imported case, since the first step of a successful invasion is infecting at least one local mosquito. The average final epidemic size of successful invasion decreases exponentially with introduction time, which means if an imported case in early summer initiates the infection process, the final number infected can be extremely large. Therefore, dengue outbreaks occurring in Thailand, Singapore, Malaysia and Vietnam in early summer merit greater attention, since the travel volumes between Guangzhou and these countries are large. As the climate changes, destroying mosquito breeding sites in Guangzhou can mitigate the detrimental effects of the probable increase in rainfall in spring and summer. An unprecedented dengue outbreak occurred in Guangzhou, 2014, with 38,036 reported cases in contrast to 73,179 cases in all of mainland China from 1990 to 2015. In an earlier analysis using a deterministic model, we concluded the early timing of local transmission to be the most important determinant of this outbreak. Here we use a stochastic model to explore the reasons why the outbreak happened earlier in 2014. Our results identified the higher number of imported cases in May and June to be the most probable explanation. Based on the investigation of the determinants of success rate and final epidemic size, this work provides suggestions for reducing dengue outbreak potential and epidemic size in the future. More attention should be paid to imported case detection and vector control measures in early summer, because this is the time when successful invasion can result in high incidence of infection and the success rate of each imported case begins to rise. Destroying mosquito breeding sites can reduce the maximum water level of the system and attenuate the role played by climate. In addition, interventions within 10 days after the introduction of imported cases is still effective in preventing further transmission.
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Affiliation(s)
- Qu Cheng
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, People’s Republic of China
| | - Qinlong Jing
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou, Guangdong, People’s Republic of China
- Department of Infectious Diseases, Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, People’s Republic of China
| | - Robert C. Spear
- Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, California, United States of America
| | - John M. Marshall
- Division of Biostatistics and Epidemiology, School of Public Health, University of California, Berkeley, Berkeley, California, United States of America
| | - Zhicong Yang
- Guangzhou Center for Disease Control and Prevention, Guangzhou, Guangdong, People’s Republic of China
- Key Laboratory of Tropical Disease Control (Sun Yat-sen University), Ministry of Education, Guangzhou, Guangdong, People’s Republic of China
- * E-mail: (PG); (ZY)
| | - Peng Gong
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, People’s Republic of China
- Joint Center for Global Change Studies, Beijing, People’s Republic of China
- * E-mail: (PG); (ZY)
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Ecological Niche Modeling Identifies Fine-Scale Areas at High Risk of Dengue Fever in the Pearl River Delta, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2017; 14:ijerph14060619. [PMID: 28598355 PMCID: PMC5486305 DOI: 10.3390/ijerph14060619] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/14/2017] [Revised: 05/31/2017] [Accepted: 06/01/2017] [Indexed: 11/17/2022]
Abstract
Dengue fever (DF) is one of the most common and rapidly spreading mosquito-borne viral diseases in tropical and subtropical regions. In recent years, this imported disease has posed a serious threat to public health in China, especially in the Pearl River Delta (PRD). Although the severity of DF outbreaks in the PRD is generally associated with known risk factors, fine scale assessments of areas at high risk for DF outbreaks are limited. We built five ecological niche models to identify such areas including a variety of climatic, environmental, and socioeconomic variables, as well as, in some models, extracted principal components. All the models we tested accurately identified the risk of DF, the area under the receiver operating characteristic curve (AUC) were greater than 0.8, but the model using all original variables was the most accurate (AUC = 0.906). Socioeconomic variables had a greater impact on this model (total contribution 55.27%) than climatic and environmental variables (total contribution 44.93%). We found the highest risk of DF outbreaks on the border of Guangzhou and Foshan (in the central PRD), and in northern Zhongshan (in the southern PRD). Our fine-scale results may help health agencies to focus epidemic monitoring tightly on the areas at highest risk of DF outbreaks.
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Atique S, Abdul SS, Hsu CY, Chuang TW. Meteorological influences on dengue transmission in Pakistan. ASIAN PAC J TROP MED 2016; 9:954-961. [PMID: 27794388 DOI: 10.1016/j.apjtm.2016.07.033] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2016] [Revised: 06/19/2016] [Accepted: 07/18/2016] [Indexed: 11/27/2022] Open
Abstract
OBJECTIVE To identify the influences of local and regional climate phenomena on dengue transmission in Lahore District of Pakistan, from 2006 to 2014. METHODS Time-series models were applied to analyze associations between reported cases of dengue and climatic parameters. The coherence trend of regional climate phenomena (IOD and ENSO) was evaluated with wavelet analysis. RESULTS The minimum temperature 4 months before the dengue outbreak played the most important role in the Lahore District (P = 0.03). A NINO 3.4 index 9 months before the outbreaks exhibited a significant negative effect on dengue transmission (P = 0.02). The IOD exhibited a synchronized pattern with dengue outbreak from 2010 to 2012. The ENSO effect (NINO 3.4 index) might have played a more important role after 2012. CONCLUSIONS This study provides preliminary results of climate influences on dengue transmission in the Lahore District of Pakistan. An increasing dengue transmission risk accompanied by frequent climate changes should be noted. Integrating the influences of climate variability into disease prevention strategies should be considered by public health authorities.
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Affiliation(s)
- Suleman Atique
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Shabbir Syed Abdul
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Chien-Yeh Hsu
- Master Program in Global Health and Development, Taipei Medical University, Taipei, Taiwan; Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan
| | - Ting-Wu Chuang
- Department of Molecular Parasitology and Tropical Diseases, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
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