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Xu C, Xu J, Wang L. Long-term effects of climate factors on dengue fever over a 40-year period. BMC Public Health 2024; 24:1451. [PMID: 38816722 PMCID: PMC11141019 DOI: 10.1186/s12889-024-18869-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 05/16/2024] [Indexed: 06/01/2024] Open
Abstract
BACKGROUND Dengue fever stands as one of the most extensively disseminated mosquito-borne infectious diseases worldwide. While numerous studies have investigated its influencing factors, a gap remains in long-term analysis, impeding the identification of temporal patterns, periodicity in transmission, and the development of effective prevention and control strategies. Thus, we aim to analyze the periodicity of dengue fever incidence and explore the association between various climate factors and the disease over an extended time series. METHODS By utilizing monthly dengue fever cases and climate data spanning four decades (1978-2018) in Guangdong province, China, we employed wavelet analysis to detect dengue fever periodicity and analyze the time-lag relationship with climate factors. Additionally, Geodetector q statistic was employed to quantify the explanatory power of each climate factor and assess interaction effects. RESULTS Our findings revealed a prolonged transmission period of dengue fever over the 40-year period, transitioning from August to November in the 1970s to nearly year-round in the 2010s. Moreover, we observed lags of 1.5, 3.5, and 3 months between dengue fever and temperature, relative humidity, and precipitation, respectively. The explanatory power of precipitation, temperature, relative humidity, and the Oceanic Niño Index (ONI) on dengue fever was determined to be 18.19%, 12.04%, 11.37%, and 5.17%, respectively. Dengue fever exhibited susceptibility to various climate factors, with notable nonlinear enhancement arising from the interaction of any two variables. Notably, the interaction between precipitation and humidity yielded the most significant effect, accounting for an explanatory power of 75.32%. CONCLUSIONS Consequently, future prevention and control strategies for dengue fever should take into account these climate changes and formulate corresponding measures accordingly. In regions experiencing the onset of high temperatures, humidity, and precipitation, it is imperative to initiate mosquito prevention and control measures within a specific window period of 1.5 months.
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Affiliation(s)
- Chengdong Xu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China.
| | - Jingyi Xu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Li Wang
- College of Geography and Environmental Science, Henan University, Kaifeng, China
- Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng, China
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Haque S, Mengersen K, Barr I, Wang L, Yang W, Vardoulakis S, Bambrick H, Hu W. Towards development of functional climate-driven early warning systems for climate-sensitive infectious diseases: Statistical models and recommendations. ENVIRONMENTAL RESEARCH 2024; 249:118568. [PMID: 38417659 DOI: 10.1016/j.envres.2024.118568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 02/22/2024] [Accepted: 02/25/2024] [Indexed: 03/01/2024]
Abstract
Climate, weather and environmental change have significantly influenced patterns of infectious disease transmission, necessitating the development of early warning systems to anticipate potential impacts and respond in a timely and effective way. Statistical modelling plays a pivotal role in understanding the intricate relationships between climatic factors and infectious disease transmission. For example, time series regression modelling and spatial cluster analysis have been employed to identify risk factors and predict spatial and temporal patterns of infectious diseases. Recently advanced spatio-temporal models and machine learning offer an increasingly robust framework for modelling uncertainty, which is essential in climate-driven disease surveillance due to the dynamic and multifaceted nature of the data. Moreover, Artificial Intelligence (AI) techniques, including deep learning and neural networks, excel in capturing intricate patterns and hidden relationships within climate and environmental data sets. Web-based data has emerged as a powerful complement to other datasets encompassing climate variables and disease occurrences. However, given the complexity and non-linearity of climate-disease interactions, advanced techniques are required to integrate and analyse these diverse data to obtain more accurate predictions of impending outbreaks, epidemics or pandemics. This article presents an overview of an approach to creating climate-driven early warning systems with a focus on statistical model suitability and selection, along with recommendations for utilizing spatio-temporal and machine learning techniques. By addressing the limitations and embracing the recommendations for future research, we could enhance preparedness and response strategies, ultimately contributing to the safeguarding of public health in the face of evolving climate challenges.
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Affiliation(s)
- Shovanur Haque
- Ecosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia
| | - Kerrie Mengersen
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia; Centre for Data Science (CDS), Queensland University of Technology (QUT), Brisbane, Australia
| | - Ian Barr
- World Health Organization Collaborating Centre for Reference and Research on Influenza, VIDRL, Doherty Institute, Melbourne, Australia; Department of Microbiology and Immunology, University of Melbourne, Victoria, Australia
| | - Liping Wang
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Division of Infectious disease, Chinese Centre for Disease Control and Prevention, China
| | - Weizhong Yang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Sotiris Vardoulakis
- HEAL Global Research Centre, Health Research Institute, University of Canberra, ACT Canberra, 2601, Australia
| | - Hilary Bambrick
- National Centre for Epidemiology and Population Health, The Australian National University, ACT 2601 Canberra, Australia
| | - Wenbiao Hu
- Ecosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia.
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Luo W, Liu Z, Ran Y, Li M, Zhou Y, Hou W, Lai S, Li SL, Yin L. Unraveling varying spatiotemporal patterns of dengue and associated exposure-response relationships with environmental variables in Southeast Asian countries before and during COVID-19. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.25.24304825. [PMID: 38585938 PMCID: PMC10996745 DOI: 10.1101/2024.03.25.24304825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
The enforcement of COVID-19 interventions by diverse governmental bodies, coupled with the indirect impact of COVID-19 on short-term environmental changes (e.g. plant shutdowns lead to lower greenhouse gas emissions), influences the dengue vector. This provides a unique opportunity to investigate the impact of COVID-19 on dengue transmission and generate insights to guide more targeted prevention measures. We aim to compare dengue transmission patterns and the exposure-response relationship of environmental variables and dengue incidence in the pre- and during-COVID-19 to identify variations and assess the impact of COVID-19 on dengue transmission. We initially visualized the overall trend of dengue transmission from 2012-2022, then conducted two quantitative analyses to compare dengue transmission pre-COVID-19 (2017-2019) and during-COVID-19 (2020-2022). These analyses included time series analysis to assess dengue seasonality, and a Distributed Lag Non-linear Model (DLNM) to quantify the exposure-response relationship between environmental variables and dengue incidence. We observed that all subregions in Thailand exhibited remarkable synchrony with a similar annual trend except 2021. Cyclic and seasonal patterns of dengue remained consistent pre- and during-COVID-19. Monthly dengue incidence in three countries varied significantly. Singapore witnessed a notable surge during-COVID-19, particularly from May to August, with cases multiplying several times compared to pre-COVID-19, while seasonality of Malaysia weakened. Exposure-response relationships of dengue and environmental variables show varying degrees of change, notably in Northern Thailand, where the peak relative risk for the maximum temperature-dengue relationship rose from about 3 to 17, and the max RR of overall cumulative association 0-3 months of relative humidity increased from around 5 to 55. Our study is the first to compare dengue transmission patterns and their relationship with environmental variables before and during COVID-19, showing that COVID-19 has affected dengue transmission at both the national and regional level, and has altered the exposure-response relationship between dengue and the environment.
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Affiliation(s)
- Wei Luo
- GeoSpatialX Lab, Department of Geography, National University of Singapore, Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Zhihao Liu
- School of Geosciences, Yangtze University, Wuhan, China
| | - Yiding Ran
- GeoSpatialX Lab, Department of Geography, National University of Singapore, Singapore, Singapore
| | - Mengqi Li
- Department of Geography, University of Zurich, Zurich, Switzerland
| | - Yuxuan Zhou
- Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Weitao Hou
- School of Design and the Built Environment, Curtin University, Perth, Australia
| | - Shengjie Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, United Kingdom
| | - Sabrina L Li
- School of Geography, University of Nottingham, Nottingham, United Kingdom
| | - Ling Yin
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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4
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Cheng Q, Jing Q, Collender PA, Head JR, Li Q, Yu H, Li Z, Ju Y, Chen T, Wang P, Cleary E, Lai S. Prior water availability modifies the effect of heavy rainfall on dengue transmission: a time series analysis of passive surveillance data from southern China. Front Public Health 2023; 11:1287678. [PMID: 38106890 PMCID: PMC10722414 DOI: 10.3389/fpubh.2023.1287678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Accepted: 10/31/2023] [Indexed: 12/19/2023] Open
Abstract
Introduction Given the rapid geographic spread of dengue and the growing frequency and intensity of heavy rainfall events, it is imperative to understand the relationship between these phenomena in order to propose effective interventions. However, studies exploring the association between heavy rainfall and dengue infection risk have reached conflicting conclusions, potentially due to the neglect of prior water availability in mosquito breeding sites as an effect modifier. Methods In this study, we addressed this research gap by considering the impact of prior water availability for the first time. We measured prior water availability as the cumulative precipitation over the preceding 8 weeks and utilized a distributed lag non-linear model stratified by the level of prior water availability to examine the association between dengue infection risk and heavy rainfall in Guangzhou, a dengue transmission hotspot in southern China. Results Our findings suggest that the effects of heavy rainfall are likely to be modified by prior water availability. A 24-55 day lagged impact of heavy rainfall was associated with an increase in dengue risk when prior water availability was low, with the greatest incidence rate ratio (IRR) of 1.37 [95% credible interval (CI): 1.02-1.83] occurring at a lag of 27 days. In contrast, a heavy rainfall lag of 7-121 days decreased dengue risk when prior water availability was high, with the lowest IRR of 0.59 (95% CI: 0.43-0.79), occurring at a lag of 45 days. Discussion These findings may help to reconcile the inconsistent conclusions reached by previous studies and improve our understanding of the complex relationship between heavy rainfall and dengue infection risk.
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Affiliation(s)
- Qu Cheng
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qinlong Jing
- Department of Infectious Diseases, Guangzhou Center for Disease Control and Prevention, Guangzhou, China
| | - Philip A. Collender
- Division of Environmental Health Sciences, School of Public Health, , University of California, Berkeley, Berkeley, CA, United States
| | - Jennifer R. Head
- Division of Epidemiology, School of Public Health, University of California, Berkeley, Berkeley, CA, United States
| | - Qi Li
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hailan Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhichao Li
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | - Yang Ju
- School of Architecture and Urban Planning, Nanjing University, Nanjing, China
| | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, China
| | - Peng Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Eimear Cleary
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, United Kingdom
| | - Shengjie Lai
- WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton, United Kingdom
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Cheng Q, Jing Q, Collender PA, Head JR, Li Q, Yu H, Li Z, Ju Y, Chen T, Wang P, Cleary E, Lai S. Prior water availability modifies the effect of heavy rainfall on dengue transmission: a time series analysis of passive surveillance data from southern China. RESEARCH SQUARE 2023:rs.3.rs-3302421. [PMID: 37693392 PMCID: PMC10491345 DOI: 10.21203/rs.3.rs-3302421/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Background Given the rapid geographic spread of dengue and the growing frequency and intensity of heavy rainfall events, it is imperative to understand the relationship between these phenomena in order to propose effective interventions. However, studies exploring the association between heavy rainfall and dengue infection risk have reached conflicting conclusions. Methods In this study, we use a distributed lag non-linear model to examine the association between dengue infection risk and heavy rainfall in Guangzhou, a dengue transmission hotspot in southern China, stratified by prior water availability. Results Our findings suggest that the effects of heavy rainfall are likely to be modified by prior water availability. A 24-55 day lagged impact of heavy rainfall was associated with an increase in dengue risk when prior water availability was low, with the greatest incidence rate ratio (IRR) of 1.37 (95% credible interval (CI): 1.02-1.83) occurring at a lag of 27 days. In contrast, a heavy rainfall lag of 7-121 days decreased dengue risk when prior water availability was high, with the lowest IRR of 0.59 (95% CI: 0.43-0.79), occurring at a lag of 45 days. Conclusions These findings may help to reconcile the inconsistent conclusions reached by previous studies and improve our understanding of the complex relationship between heavy rainfall and dengue infection risk.
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Affiliation(s)
- Qu Cheng
- Huazhong University of Science and Technology
| | - Qinlong Jing
- Guangzhou Center for Disease Control and Prevention
| | | | | | - Qi Li
- Huazhong University of Science and Technology
| | - Hailan Yu
- Huazhong University of Science and Technology
| | | | | | | | - Peng Wang
- Huazhong University of Science and Technology
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Motlogeloa O, Fitchett JM. Climate and human health: a review of publication trends in the International Journal of Biometeorology. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2023:10.1007/s00484-023-02466-8. [PMID: 37129619 PMCID: PMC10153057 DOI: 10.1007/s00484-023-02466-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 03/06/2023] [Accepted: 03/27/2023] [Indexed: 05/03/2023]
Abstract
The climate-health nexus is well documented in the field of biometeorology. Since its inception, Biometeorology has in many ways become the umbrella under which much of this collaborative research has been conducted. Whilst a range of review papers have considered the development of biometeorological research and its coverage in this journal, and a few have reviewed the literature on specific diseases, none have focused on the sub-field of climate and health as a whole. Since its first issue in 1957, the International Journal of Biometeorology has published a total of 2183 papers that broadly consider human health and its relationship with climate. In this review, we identify a total of 180 (8.3%, n = 2183) of these papers that specifically focus on the intersection between meteorological variables and specific, named diagnosable diseases, and explore the publication trends thereof. The number of publications on climate and health in the journal increases considerably since 2011. The largest number of publications on the topic was in 2017 (18) followed by 2021 (17). Of the 180 studies conducted, respiratory diseases accounted for 37.2% of the publications, cardiovascular disease 17%, and cerebrovascular disease 11.1%. The literature on climate and health in the journal is dominated by studies from the global North, with a particular focus on Asia and Europe. Only 2.2% and 8.3% of these studies explore empirical evidence from the African continent and South America respectively. These findings highlight the importance of continued research on climate and human health, especially in low- and lower-middle-income countries, the populations of which are more vulnerable to climate-sensitive illnesses.
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Affiliation(s)
- Ogone Motlogeloa
- School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg, South Africa
| | - Jennifer M Fitchett
- School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg, South Africa.
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Li C, Liu Z, Li W, Lin Y, Hou L, Niu S, Xing Y, Huang J, Chen Y, Zhang S, Gao X, Xu Y, Wang C, Zhao Q, Liu Q, Ma W, Cai W, Gong P, Luo Y. Projecting future risk of dengue related to hydrometeorological conditions in mainland China under climate change scenarios: a modelling study. Lancet Planet Health 2023; 7:e397-e406. [PMID: 37164516 DOI: 10.1016/s2542-5196(23)00051-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 02/01/2023] [Accepted: 02/28/2023] [Indexed: 05/12/2023]
Abstract
BACKGROUND We have limited knowledge on the impact of hydrometeorological conditions on dengue incidence in China and its associated disease burden in a future with a changed climate. This study projects the excess risk of dengue caused by climate change-induced hydrometeorological conditions across mainland China. METHODS In this modelling study, the historical association between the Palmer drought severity index (PDSI) and dengue was estimated with a spatiotemporal Bayesian hierarchical model from 70 cities. The association combined with the dengue-transmission biological model was used to project the annual excess risk of dengue related to PDSI by 2100 across mainland China, under three representative concentration pathways ([RCP] 2·6, RCP 4·5, and RCP 8·5). FINDINGS 93 101 dengue cases were reported between 2013 and 2019 in mainland China. Dry and wet conditions within 3 months lag were associated with increased risk of dengue. Locations with potential dengue risk in China will expand in the future. The hydrometeorological changes are projected to substantially affect the risk of dengue in regions with mid-to-low latitudes, especially the coastal areas under high emission scenarios. By 2100, the annual average increased excess risk is expected to range from 12·56% (95% empirical CI 9·54-22·24) in northwest China to 173·62% (153·15-254·82) in south China under the highest emission scenario. INTERPRETATION Hydrometeorological conditions are predicted to increase the risk of dengue in the future in the south, east, and central areas of mainland China in disproportionate patterns. Our findings have implications for the preparation of public health interventions to minimise the health hazards of non-optimal hydrometeorological conditions in a context of climate change. FUNDING National Natural Science Foundation of China.
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Affiliation(s)
- Chuanxi Li
- Department of Epidemiology, Shandong University, Jinan, China; School of Public Health, Cheeloo College of Medicine, and Shandong University Climate Change and Health Centre, Shandong University, Jinan, China
| | - Zhao Liu
- School of Linkong Economics and Management, Beijing Institute of Economics and Management, Beijing, China
| | - Wen Li
- Department of Epidemiology, Shandong University, Jinan, China; School of Public Health, Cheeloo College of Medicine, and Shandong University Climate Change and Health Centre, Shandong University, Jinan, China
| | - Yuxi Lin
- Department of Epidemiology, Shandong University, Jinan, China; School of Public Health, Cheeloo College of Medicine, and Shandong University Climate Change and Health Centre, Shandong University, Jinan, China
| | - Liangyu Hou
- Department of Epidemiology, Shandong University, Jinan, China; School of Public Health, Cheeloo College of Medicine, and Shandong University Climate Change and Health Centre, Shandong University, Jinan, China
| | - Shuyue Niu
- Department of Epidemiology, Shandong University, Jinan, China; School of Public Health, Cheeloo College of Medicine, and Shandong University Climate Change and Health Centre, Shandong University, Jinan, China
| | - Yue Xing
- Department of Epidemiology, Shandong University, Jinan, China; School of Public Health, Cheeloo College of Medicine, and Shandong University Climate Change and Health Centre, Shandong University, Jinan, China
| | - Jianbin Huang
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China; Beijing Yanshan Earth Critical Zone National Research Station, Chinese Academy of Sciences, Beijing, China
| | - Yidan Chen
- State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), School of Environment, Tsinghua University, Beijing, China
| | - Shangchen Zhang
- Department of Earth System Science, Institute for Global Change Studies, Ministry of Education Key Laboratory for Earth System Modelling, Tsinghua University, Beijing, China
| | - Xuejie Gao
- College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, China; Climate Change Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
| | - Ying Xu
- National Climate Centre, China Meteorological Administration, Beijing, China
| | - Can Wang
- State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), School of Environment, Tsinghua University, Beijing, China
| | - Qi Zhao
- Department of Epidemiology, Shandong University, Jinan, China; School of Public Health, Cheeloo College of Medicine, and Shandong University Climate Change and Health Centre, Shandong University, Jinan, China; Department of Epidemiology, IUF-Leibniz Research Institute for Environmental Medicine, Düsseldorf, Germany.
| | - Qiyong Liu
- Department of Epidemiology, Shandong University, Jinan, China; Department of Vector Control, Shandong University, Jinan, China; School of Public Health, Cheeloo College of Medicine, and Shandong University Climate Change and Health Centre, Shandong University, Jinan, China; State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Centre for Disease Control and Prevention, Beijing, China.
| | - Wei Ma
- Department of Epidemiology, Shandong University, Jinan, China; School of Public Health, Cheeloo College of Medicine, and Shandong University Climate Change and Health Centre, Shandong University, Jinan, China.
| | - Wenjia Cai
- Ministry of Education Ecological Field Station for East Asian Migratory Birds, Tsinghua University, Beijing, China
| | - Peng Gong
- Institute for Climate and Carbon Neutrality, Department of Earth Sciences and Geography, University of Hong Kong, Hong Kong, China
| | - Yong Luo
- Department of Earth System Science, Institute for Global Change Studies, Ministry of Education Key Laboratory for Earth System Modelling, Tsinghua University, Beijing, China
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Damtew YT, Tong M, Varghese BM, Anikeeva O, Hansen A, Dear K, Zhang Y, Morgan G, Driscoll T, Capon T, Bi P. Effects of high temperatures and heatwaves on dengue fever: a systematic review and meta-analysis. EBioMedicine 2023; 91:104582. [PMID: 37088034 PMCID: PMC10149186 DOI: 10.1016/j.ebiom.2023.104582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 04/03/2023] [Accepted: 04/06/2023] [Indexed: 04/25/2023] Open
Abstract
BACKGROUND Studies have shown that dengue virus transmission increases in association with ambient temperature. We performed a systematic review and meta-analysis to assess the effect of both high temperatures and heatwave events on dengue transmission in different climate zones globally. METHODS A systematic literature search was conducted in PubMed, Scopus, Embase, and Web of Science from January 1990 to September 20, 2022. We included peer reviewed original observational studies using ecological time series, case crossover, or case series study designs reporting the association of high temperatures and heatwave with dengue and comparing risks over different exposures or time periods. Studies classified as case reports, clinical trials, non-human studies, conference abstracts, editorials, reviews, books, posters, commentaries; and studies that examined only seasonal effects were excluded. Effect estimates were extracted from published literature. A random effects meta-analysis was performed to pool the relative risks (RRs) of dengue infection per 1 °C increase in temperature, and further subgroup analyses were also conducted. The quality and strength of evidence were evaluated following the Navigation Guide systematic review methodology framework. The review protocol has been registered in the International Prospective Register of Systematic Reviews (PROSPERO). FINDINGS The study selection process yielded 6367 studies. A total of 106 studies covering more than four million dengue cases fulfilled the inclusion criteria; of these, 54 studies were eligible for meta-analysis. The overall pooled estimate showed a 13% increase in risk of dengue infection (RR = 1.13; 95% confidence interval (CI): 1.11-1.16, I2 = 98.0%) for each 1 °C increase in high temperatures. Subgroup analyses by climate zones suggested greater effects of temperature in tropical monsoon climate zone (RR = 1.29, 95% CI: 1.11-1.51) and humid subtropical climate zone (RR = 1.20, 95% CI: 1.15-1.25). Heatwave events showed association with an increased risk of dengue infection (RR = 1.08; 95% CI: 0.95-1.23, I2 = 88.9%), despite a wide confidence interval. The overall strength of evidence was found to be "sufficient" for high temperatures but "limited" for heatwaves. Our results showed that high temperatures increased the risk of dengue infection, albeit with varying risks across climate zones and different levels of national income. INTERPRETATION High temperatures increased the relative risk of dengue infection. Future studies on the association between temperature and dengue infection should consider local and regional climate, socio-demographic and environmental characteristics to explore vulnerability at local and regional levels for tailored prevention. FUNDING Australian Research Council Discovery Program.
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Affiliation(s)
- Yohannes Tefera Damtew
- School of Public Health, The University of Adelaide, Adelaide, South Australia, 5005, Australia; College of Health and Medical Sciences, Haramaya University, P.O.BOX 138, Dire Dawa, Ethiopia.
| | - Michael Tong
- National Centre for Epidemiology and Population Health, ANU College of Health and Medicine, The Australian National University, Canberra ACT, 2601, Australia.
| | - Blesson Mathew Varghese
- School of Public Health, The University of Adelaide, Adelaide, South Australia, 5005, Australia.
| | - Olga Anikeeva
- School of Public Health, The University of Adelaide, Adelaide, South Australia, 5005, Australia.
| | - Alana Hansen
- School of Public Health, The University of Adelaide, Adelaide, South Australia, 5005, Australia.
| | - Keith Dear
- School of Public Health, The University of Adelaide, Adelaide, South Australia, 5005, Australia.
| | - Ying Zhang
- School of Public Health, Faculty of Medicine and Health, The University of Sydney, New South Wales, 2006, Australia.
| | - Geoffrey Morgan
- School of Public Health, Faculty of Medicine and Health, The University of Sydney, New South Wales, 2006, Australia.
| | - Tim Driscoll
- School of Public Health, Faculty of Medicine and Health, The University of Sydney, New South Wales, 2006, Australia.
| | - Tony Capon
- Monash Sustainable Development Institute, Monash University, Melbourne, Victoria, Australia.
| | - Peng Bi
- School of Public Health, The University of Adelaide, Adelaide, South Australia, 5005, Australia.
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Jia Q, Cai Y, Yuan X, Li B, Li B. The Degradation Process of Typical Neonicotinoid Insecticides in Tidal Streams in Subtropical Cities: A Case Study of the Wuchong Stream, South China. TOXICS 2023; 11:203. [PMID: 36976968 PMCID: PMC10057386 DOI: 10.3390/toxics11030203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 01/31/2023] [Accepted: 02/02/2023] [Indexed: 06/18/2023]
Abstract
Neonicotinoid insecticides (NEOs) are commonly used to prevent unwanted insects in urban fields. Degradation processes have been one of the important environmental behaviors of NEOs in an aquatic environment. In this research, hydrolysis, biodegradation, and photolysis processes of four typical NEOs (i.e., thiacloprid (THA), clothianidin (CLO), acetamiprid (ACE), and imidacloprid (IMI)) were examined through the adoption of response surface methodology-central composite design (RSM-CCD) for an urban tidal stream in South China. The influences of multiple environmental parameters and concentration levels on the three degradation processes of these NEOs were then evaluated. The results indicated that the three degradation processes of the typical NEOs followed a pseudo-first-order reaction kinetics model. The primary degradation process of the NEOs were hydrolysis and photolysis processes in the urban stream. The hydrolysis degradation rate of THA was the highest (1.97 × 10-5 s-1), and that of CLO was the lowest (1.28 × 10-5 s-1). The temperature of water samples was the main environmental factor influencing the degradation processes of these NEOs in the urban tidal stream. Salinity and humic acids could inhibit the degradation processes of the NEOs. Under the influence of extreme climate events, the biodegradation processes of these typical NEOs could be suppressed, and other degradation processes could be further accelerated. In addition, extreme climate events could pose severe challenges to the migration and degradation process simulation of NEOs.
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Affiliation(s)
- Qunpo Jia
- Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, Institute of Environmental and Ecological Engineering, Guangdong University of Technology, Guangzhou 510006, China
- Key Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of Education, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou 510006, China
| | - Yanpeng Cai
- Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, Institute of Environmental and Ecological Engineering, Guangdong University of Technology, Guangzhou 510006, China
- Key Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of Education, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou 510006, China
| | - Xiao Yuan
- Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, Institute of Environmental and Ecological Engineering, Guangdong University of Technology, Guangzhou 510006, China
- Key Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of Education, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou 510006, China
| | - Bowen Li
- Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, Institute of Environmental and Ecological Engineering, Guangdong University of Technology, Guangzhou 510006, China
- Key Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of Education, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou 510006, China
| | - Bo Li
- Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, Institute of Environmental and Ecological Engineering, Guangdong University of Technology, Guangzhou 510006, China
- Key Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of Education, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou 510006, China
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10
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Barboza LA, Chou-Chen SW, Vásquez P, García YE, Calvo JG, Hidalgo HG, Sanchez F. Assessing dengue fever risk in Costa Rica by using climate variables and machine learning techniques. PLoS Negl Trop Dis 2023; 17:e0011047. [PMID: 36638136 PMCID: PMC9879398 DOI: 10.1371/journal.pntd.0011047] [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: 06/17/2022] [Revised: 01/26/2023] [Accepted: 12/20/2022] [Indexed: 01/14/2023] Open
Abstract
Dengue fever is a vector-borne disease affecting millions yearly, mostly in tropical and subtropical countries. Driven mainly by social and environmental factors, dengue incidence and geographical expansion have increased in recent decades. Therefore, understanding how climate variables drive dengue outbreaks is challenging and a problem of interest for decision-makers that could aid in improving surveillance and resource allocation. Here, we explore the effect of climate variables on relative dengue risk in 32 cantons of interest for public health authorities in Costa Rica. Relative dengue risk is forecast using a Generalized Additive Model for location, scale, and shape and a Random Forest approach. Models use a training period from 2000 to 2020 and predicted climatic variables obtained with a vector auto-regressive model. Results show reliable projections, and climate variables predictions allow for a prospective instead of a retrospective study.
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Affiliation(s)
- Luis A. Barboza
- Centro de Investigación en Matemática Pura y Aplicada - Escuela de Matemática, Universidad de Costa Rica, San José, Costa Rica
| | - Shu-Wei Chou-Chen
- Centro de Investigación en Matemática Pura y Aplicada - Escuela de Estadística, Universidad de Costa Rica, San José, Costa Rica
| | - Paola Vásquez
- Centro de Investigación en Matemática Pura y Aplicada, Universidad de Costa Rica, San José, Costa Rica
| | - Yury E. García
- Centro de Investigación en Matemática Pura y Aplicada, Universidad de Costa Rica, San José, Costa Rica
- Department of Public Health Sciences, University of California Davis, California, United States of America
- * E-mail:
| | - Juan G. Calvo
- Centro de Investigación en Matemática Pura y Aplicada - Escuela de Matemática, Universidad de Costa Rica, San José, Costa Rica
| | - Hugo G. Hidalgo
- Centro de Investigaciones Geofísicas and Escuela de Física, Universidad de Costa Rica, San José, Costa Rica
| | - Fabio Sanchez
- Centro de Investigación en Matemática Pura y Aplicada - Escuela de Matemática, Universidad de Costa Rica, San José, Costa Rica
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11
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Li C, Wang Z, Yan Y, Qu Y, Hou L, Li Y, Chu C, Woodward A, Schikowski T, Saldiva PHN, Liu Q, Zhao Q, Ma W. Association Between Hydrological Conditions and Dengue Fever Incidence in Coastal Southeastern China From 2013 to 2019. JAMA Netw Open 2023; 6:e2249440. [PMID: 36598784 PMCID: PMC9857674 DOI: 10.1001/jamanetworkopen.2022.49440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
IMPORTANCE Dengue fever is a climate-sensitive infectious disease. However, its association with local hydrological conditions and the role of city development remain unclear. OBJECTIVE To quantify the association between hydrological conditions and dengue fever incidence in China and to explore the modification role of city development in this association. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study collected data between January 1, 2013, and December 31, 2019, from 54 cities in 4 coastal provinces in southeast China. The Standardized Precipitation Evapotranspiration Index (SPEI) was calculated from ambient temperature and precipitation, with SPEI thresholds of 2 for extreme wet conditions and -2 for extreme dry conditions. The SPEI-dengue fever incidence association was examined over a 6-month lag, and the modification roles of 5 city development dimensions were assessed. Data were analyzed in May 2022. EXPOSURES City-level monthly temperature, precipitation, SPEI, and annual city development indicators from 2013 to 2019. MAIN OUTCOMES AND MEASURES The primary outcome was city-level monthly dengue fever incidence. Spatiotemporal bayesian hierarchal models were used to examine the SPEI-dengue fever incidence association over a 6-month lag period. An interaction term between SPEI and each city development indicator was added into the model to assess the modification role of city development. RESULTS Included in the analysis were 70 006 dengue fever cases reported in 54 cities in 4 provinces in China from 2013 to 2019. Overall, a U-shaped cumulative curve was observed, with wet and dry conditions both associated with increased dengue fever risk. The relative risk [RR] peaked at a 1-month lag for extreme wet conditions (1.27; 95% credible interval [CrI], 1.05-1.53) and at a 6-month lag for extreme dry conditions (1.63; 95% CrI, 1.29-2.05). The RRs of extreme wet and dry conditions were greater in areas with limited economic development, health care resources, and income per capita. Extreme dry conditions were higher and prolonged in areas with more green space per capita (RR, 1.84; 95% CrI, 1.37-2.46). Highly urbanized areas had a higher risk of dengue fever after extreme wet conditions (RR, 1.80; 95% CrI, 1.26-2.56), while less urbanized areas had the highest risk of dengue fever in extreme dry conditions (RR, 1.70; 95% CrI, 1.11-2.60). CONCLUSIONS AND RELEVANCE Results of this study showed that extreme hydrological conditions were associated with increased dengue fever incidence within a 6-month lag period, with different dimensions of city development playing various modification roles in this association. These findings may help in developing climate change adaptation strategies and public health interventions against dengue fever.
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Affiliation(s)
- Chuanxi Li
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Shandong University Climate Change and Health Center, Shandong University, Jinan, China
| | - Zhendong Wang
- Dezhou Center for Disease Control and Prevention, Dezhou, China
| | - Yu Yan
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Shandong University Climate Change and Health Center, Shandong University, Jinan, China
| | - Yinan Qu
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Shandong University Climate Change and Health Center, Shandong University, Jinan, China
| | - Liangyu Hou
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Shandong University Climate Change and Health Center, Shandong University, Jinan, China
| | - Yijie Li
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Shandong University Climate Change and Health Center, Shandong University, Jinan, China
| | - Cordia Chu
- Centre for Environment and Population Health, School of Medicine, Griffith University, Nathan, Queensland, Australia
| | - Alistair Woodward
- Department of Epidemiology and Biostatistics, School of Population Health, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Tamara Schikowski
- Department of Epidemiology, IUF-Leibniz Research Institute for Environmental Medicine, Düsseldorf, Germany
| | | | - Qiyong Liu
- Shandong University Climate Change and Health Center, Shandong University, Jinan, China
- 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
- Department of Vector Control, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Qi Zhao
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Shandong University Climate Change and Health Center, Shandong University, Jinan, China
- Department of Epidemiology, IUF-Leibniz Research Institute for Environmental Medicine, Düsseldorf, Germany
| | - Wei Ma
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Shandong University Climate Change and Health Center, Shandong University, Jinan, China
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12
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Li C, Zhao Z, Yan Y, Liu Q, Zhao Q, Ma W. Short-term effects of tropical cyclones on the incidence of dengue: a time-series study in Guangzhou, China. Parasit Vectors 2022; 15:358. [PMID: 36203178 PMCID: PMC9535872 DOI: 10.1186/s13071-022-05486-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Accepted: 09/16/2022] [Indexed: 11/12/2022] Open
Abstract
Background Limited evidence is available about the association between tropical cyclones and dengue incidence. This study aimed to examine the effects of tropical cyclones on the incidence of dengue and to explore the vulnerable populations in Guangzhou, China. Methods Weekly dengue case data, tropical cyclone and meteorological data during the tropical cyclones season (June to October) from 2015 to 2019 were collected for the study. A quasi-Poisson generalized linear model combined with a distributed lag non-linear model was conducted to quantify the association between tropical cyclones and dengue, controlling for meteorological factors, seasonality, and long-term trend. Proportion of dengue cases attributable to tropical cyclone exposure was calculated. The effect difference by sex and age groups was calculated to identify vulnerable populations. The tropical cyclones were classified into two levels to compare the effects of different grades of tropical cyclones on the dengue incidence. Results Tropical cyclones were associated with an increased number of dengue cases with the maximum risk ratio of 1.41 (95% confidence interval 1.17–1.69) in lag 0 week and cumulative risk ratio of 2.13 (95% confidence interval 1.28–3.56) in lag 0–4 weeks. The attributable fraction was 6.31% (95% empirical confidence interval 1.96–10.16%). Men and the elderly were more vulnerable to the effects of tropical cyclones than the others. The effects of typhoons were stronger than those of tropical storms among various subpopulations. Conclusions Our findings indicate that tropical cyclones may increase the incidence of dengue within a 4-week lag in Guangzhou, China, and the effects were more pronounced in men and the elderly. Precautionary measures should be taken with a focus on the identified vulnerable populations to control the transmission of dengue associated with tropical cyclones. Graphical Abstract ![]()
Supplementary Information The online version contains supplementary material available at 10.1186/s13071-022-05486-2.
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Affiliation(s)
- Chuanxi Li
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.,Shandong University Climate Change and Health Center, Jinan, China
| | - Zhe Zhao
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.,Shandong University Climate Change and Health Center, Jinan, China
| | - Yu Yan
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.,Shandong University Climate Change and Health Center, Jinan, China
| | - Qiyong Liu
- Shandong University Climate Change and Health Center, Jinan, China.,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
| | - Qi Zhao
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China. .,Shandong University Climate Change and Health Center, Jinan, China. .,Department of Epidemiology, IUF-Leibniz Research Institute for Environmental Medicine, Düsseldorf, Germany.
| | - Wei Ma
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China. .,Shandong University Climate Change and Health Center, Jinan, China.
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13
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Chen Y, Chang Z, Zhao Y, Liu Y, Fu J, Liu Y, Liu X, Kong D, Han Y, Tang S, Fan Z. Association of extreme precipitation with hospitalizations for acute myocardial infarction in Beijing, China: A time-series study. Front Public Health 2022; 10:1024816. [PMID: 36238253 PMCID: PMC9551252 DOI: 10.3389/fpubh.2022.1024816] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 09/13/2022] [Indexed: 01/28/2023] Open
Abstract
Background In the context of global climate changes, increasing extreme weather events have aroused great public concern. Limited evidence has focused on the association between extreme precipitation and hospitalizations for acute myocardial infarction (AMI). Our study aimed to examine the effect of extreme precipitation on AMI hospitalizations. Methods Daily AMI hospitalizations, weather variables and air pollution data in Beijing from 2013 to 2018 were obtained. We used a time-series analysis with a distributed lag model to evaluate the association of extreme precipitation (≥95th percentile of daily precipitation) with AMI hospitalizations. Subgroup analysis was conducted to identify the vulnerable subpopulations and further assessed the attributable burden. Results Extreme precipitation increased the risk of AMI hospitalizations with significant single-day effects from Lag 4 to Lag 11, and the maximum cumulative effects at Lag 0-14 (CRR = 1.177, 95% CI: 1.045, 1.326). Older people (≥65 years) and females were more vulnerable to extreme precipitation. The attributable fraction and numbers of extreme precipitation on AMI hospitalizations were 0.68% (95% CI: 0.20%, 1.12%) and 854 (95% CI: 244, 1,395), respectively. Conclusion Extreme precipitation is correlated with a higher risk of AMI hospitalizations. The elderly (≥65 years) and females are more susceptible to AMI triggered by extreme precipitation.
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14
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Bhatia S, Bansal D, Patil S, Pandya S, Ilyas QM, Imran S. A Retrospective Study of Climate Change Affecting Dengue: Evidences, Challenges and Future Directions. Front Public Health 2022; 10:884645. [PMID: 35712272 PMCID: PMC9197220 DOI: 10.3389/fpubh.2022.884645] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Accepted: 04/26/2022] [Indexed: 11/30/2022] Open
Abstract
Climate change is unexpected weather patterns that can create an alarming situation. Due to climate change, various sectors are affected, and one of the sectors is healthcare. As a result of climate change, the geographic range of several vector-borne human infectious diseases will expand. Currently, dengue is taking its toll, and climate change is one of the key reasons contributing to the intensification of dengue disease transmission. The most important climatic factors linked to dengue transmission are temperature, rainfall, and relative humidity. The present study carries out a systematic literature review on the surveillance system to predict dengue outbreaks based on Machine Learning modeling techniques. The systematic literature review discusses the methodology and objectives, the number of studies carried out in different regions and periods, the association between climatic factors and the increase in positive dengue cases. This study also includes a detailed investigation of meteorological data, the dengue positive patient data, and the pre-processing techniques used for data cleaning. Furthermore, correlation techniques in several studies to determine the relationship between dengue incidence and meteorological parameters and machine learning models for predictive analysis are discussed. In the future direction for creating a dengue surveillance system, several research challenges and limitations of current work are discussed.
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Affiliation(s)
- Surbhi Bhatia
- Department of Information Systems, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia
| | - Dhruvisha Bansal
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune, India
| | - Seema Patil
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune, India
| | - Sharnil Pandya
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune, India
| | - Qazi Mudassar Ilyas
- Department of Information Systems, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia
| | - Sajida Imran
- Department of Computer Engineering, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia
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15
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Wang T, Wang J, Rao J, Han Y, Luo Z, Jia L, Chen L, Wang C, Zhang Y, Zhang J. Meta-analysis of the effects of ambient temperature and relative humidity on the risk of mumps. Sci Rep 2022; 12:6440. [PMID: 35440700 PMCID: PMC9017417 DOI: 10.1038/s41598-022-10138-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 04/01/2022] [Indexed: 11/28/2022] Open
Abstract
Many studies have shown that the relationship between ambient temperature, relative humidity and mumps has been highlighted. However, these studies showed inconsistent results. Therefore, the goal of our study is to conduct a meta-analysis to clarify this relationship and to quantify the size of these effects as well as the potential factors. Systematic literature researches on PubMed, Embase.com, Web of Science Core Collection, Cochrane library, Chinese BioMedical Literature Database (CBM) and China National Knowledge Infrastructure (CNKI) were performed up to February 7, 2022 for articles analyzing the relationships between ambient temperature, relative humidity and incidence of mumps. Eligibility assessment and data extraction were conducted independently by two researchers, and meta-analysis was performed to synthesize these data. We also assessed sources of heterogeneity by study region, regional climate, study population. Finally, a total of 14 studies were screened out from 1154 records and identified to estimate the relationship between ambient temperature, relative humidity and incidence of mumps. It was found that per 1 °C increase and decrease in the ambient temperature were significantly associated with increased incidence of mumps with RR of 1.0191 (95% CI: 1.0129–1.0252, I2 = 92.0%, Egger’s test P = 0.001, N = 13) for per 1 °C increase and 1.0244 (95% CI: 1.0130–1.0359, I2 = 86.6%, Egger’s test P = 0.077, N = 9) for per 1 °C decrease. As to relative humidity, only high effect of relative humidity was slightly significant (for per 1 unit increase with RR of 1.0088 (95% CI: 1.0027–1.0150), I2 = 72.6%, Egger’s test P = 0.159, N = 9). Subgroup analysis showed that regional climate with temperate areas may have a higher risk of incidence of mumps than areas with subtropical climate in cold effect of ambient temperature and low effect of relative humidity. In addition, meta-regression analysis showed that regional climate may affect the association between incidence of mumps and cold effect of ambient temperature. Our results suggest ambient temperature could affect the incidence of mumps significantly, of which both hot and cold effect of ambient temperature may increase the incidence of mumps. Further studies are still needed to clarify the relationship between the incidence of mumps and ambient temperature outside of east Asia, and many other meteorological factors. These results of ambient temperature are important for establishing preventive measures on mumps, especially in temperate areas. The policy-makers should pay more attention to ambient temperature changes and take protective measures in advance.
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Affiliation(s)
- Taiwu Wang
- Department of Infectious Disease Prevention and Control, Center for Disease Control and Prevention of Eastern Theater Command, Nanjing, 210002, China
| | - Junjun Wang
- Nanjing Center for Disease Control and Prevention, Nanjing, 210002, China.,Chinese Field Epidemiology Training Program, Beijing, 100050, China
| | - Jixian Rao
- Department of Infectious Disease Prevention and Control, Center for Disease Control and Prevention of Eastern Theater Command, Nanjing, 210002, China
| | - Yifang Han
- Department of Infectious Disease Prevention and Control, Center for Disease Control and Prevention of Eastern Theater Command, Nanjing, 210002, China
| | - Zhenghan Luo
- Department of Infectious Disease Prevention and Control, Center for Disease Control and Prevention of Eastern Theater Command, Nanjing, 210002, China
| | - Lingru Jia
- Wuxi Center of Joint Logistic Support Force, Wuxi, 214000, China
| | - Leru Chen
- Department of Infectious Disease Prevention and Control, Center for Disease Control and Prevention of Eastern Theater Command, Nanjing, 210002, China
| | - Chunhui Wang
- Department of Infectious Disease Prevention and Control, Center for Disease Control and Prevention of Eastern Theater Command, Nanjing, 210002, China
| | - Yao Zhang
- Department of Epidemiology, College of Preventive Medicine, Army Medical University, Chongqing, 400038, China.
| | - Jinhai Zhang
- Department of Infectious Disease Prevention and Control, Center for Disease Control and Prevention of Eastern Theater Command, Nanjing, 210002, China.
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16
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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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Patil S, Pandya S. Forecasting Dengue Hotspots Associated With Variation in Meteorological Parameters Using Regression and Time Series Models. Front Public Health 2021; 9:798034. [PMID: 34900929 PMCID: PMC8661059 DOI: 10.3389/fpubh.2021.798034] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 11/04/2021] [Indexed: 11/13/2022] Open
Abstract
For forecasting the spread of dengue, monitoring climate change and its effects specific to the disease is necessary. Dengue is one of the most rapidly spreading vector-borne infectious diseases. This paper proposes a forecasting model for predicting dengue incidences considering climatic variability across nine cities of Maharashtra state of India over 10 years. The work involves the collection of five climatic factors such as mean minimum temperature, mean maximum temperature, relative humidity, rainfall, and mean wind speed for 10 years. Monthly incidences of dengue for the same locations are also collected. Different regression models such as random forest regression, decision trees regression, support vector regress, multiple linear regression, elastic net regression, and polynomial regression are used. Time-series forecasting models such as holt's forecasting, autoregressive, Moving average, ARIMA, SARIMA, and Facebook prophet are implemented and compared to forecast the dengue outbreak accurately. The research shows that humidity and mean maximum temperature are the major climate factors and exhibit strong positive and negative correlation, respectively, with dengue incidences for all locations of Maharashtra state. Mean minimum temperature and rainfall are moderately positively correlated with dengue incidences. Mean wind speed is a less significant factor and is weakly negatively correlated with dengue incidences. Root mean square error (RMSE), mean absolute error (MAE), and R square error (R 2) evaluation metrics are used to compare the performance of the prediction model. Random Forest Regression is the best-fit regression model for five out of nine cities, while Support Vector Regression is for two cities. Facebook Prophet Model is the best fit time series forecasting model for six out of nine cities. Based on the prediction, Mumbai, Thane, Nashik, and Pune are the high-risk regions, especially in August, September, and October. The findings exhibit an effective early warning system that would predict the outbreak of other infectious diseases. It will help the relevant authorities to take accurate preventive measures.
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Affiliation(s)
- Seema Patil
- Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India
| | - Sharnil Pandya
- Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India
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18
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刘 金, 李 晓, 王 海, 唐 时, 万 成. [Dengue virus E protein-based luciferase immunosorbent assay for detecting dengue virus IgG antibody]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2021; 41:1747-1751. [PMID: 34916204 PMCID: PMC8685698 DOI: 10.12122/j.issn.1673-4254.2021.11.22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Indexed: 06/14/2023]
Abstract
OBJECTIVE To establish a luciferase immunosorbent assay (DENV-LISA) based on dengue virus (DENV) E protein, a specific antigen of DENV, for detection of DENV IgG antibody. METHODS The fused expression plasmids of DENV1-E1 and DENV2-E2 with luciferase were constructed. The plasmids were transfected into 293T cells, and the fusion protein containing the specific antigen and luciferase was obtained for establishing DENV-LISA. The specificity and sensitivity of DENV-LISA were assessed and compared with those of commercial DENV IgG antibody detection kit (ELISA). RESULTS The established DENV-LISA had a positive detection rate of 32.4% and a specificity of 96.6%, showing a similar positive detection rate with the commercial ELISA kit (35.3%; P>0.05). DENV-LISA was capable of detecting positive samples with a 1: 6400 dilution with a high sensitivity. The test values of DENV-LISA did not differ significantly between plates or within plates in the same batch (P> 0.05), suggesting a good reproducibility of the test. CONCLUSION The luciferase immunosorbent assay based on DENV E protein has high specificity and sensitivity for detecting DENV IgG antibody, and can be used for early screening, surveillance and epidemiological investigation of DENV infection.
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Affiliation(s)
- 金月 刘
- />南方医科大学公共卫生学院,广东 广州 510515School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - 晓霞 李
- />南方医科大学公共卫生学院,广东 广州 510515School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - 海鹰 王
- />南方医科大学公共卫生学院,广东 广州 510515School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - 时幸 唐
- />南方医科大学公共卫生学院,广东 广州 510515School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - 成松 万
- />南方医科大学公共卫生学院,广东 广州 510515School of Public Health, Southern Medical University, Guangzhou 510515, China
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