Extreme weather conditions and dengue outbreak in Guangdong, China: Spatial heterogeneity based on climate variability

Cheng, Jian; Bambrick, Hilary; Yakob, Laith; Devine, Gregor J.; Frentiu, Francesca D.; Williams, Gail; Li, Zhongjie; Yang, Weizhong

doi:10.1016/j.envres.2021.110900

Cited by 22 publications

(21 citation statements)

References 59 publications

(111 reference statements)

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“…However, there is still limited research probing the predictive modeling of the complexities of dengue disease models with the backdrop of climatically critical variables. One tool that has recently surfaced as an emerging modeling strategy is machine learning modeling for predictive dengue disease dynamics (37). While there exists research into the effects of dengue dynamics in relation to climate, these are often backed by traditional statistical models.…”

Section: Historical Understanding Of Dengue and Climate In Indiamentioning

confidence: 99%

“…Although there is growing statistical evidence indicating that dengue outbreaks are associated with temperature, rainfall, and humidity, few studies have examined this relationship along coastlines and with the inclusion of extreme weather events, including both a critical spatial component (47) and a critical climate event worth examining (37). The authors have identified that other variables such as river levels are also included along with climate variables (48), however, these studies are out of the study scope.…”

Section: Dengue and Climate Changementioning

confidence: 99%

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Applications of artificial intelligence in predicting dengue outbreaks in the face of climate change: a case study along coastal India

Tallam¹,

Quang²

2023

Preprint

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The climate crisis will have an increasingly profound effect on the global distribution and burden of infectious diseases. Climate-sensitive diseases can serve as critical case studies for assessing public health priorities in the face of epidemics. Preliminary results denote that machine learning-based predictive modeling measures can be successfully applied to understanding environmental disease transmission dynamics. Ultimately, machine learning models can be trained to detect climate-sensitive diseases early, diseases which might represent serious threats to human health, food safety, and economies. We explore how machine learning can serve as a tool for better understanding climate-sensitive diseases, taking dengue dynamics along the Godavari River of coastal India as our case study. We hypothesize that a climate-driven predictive model with controlled calibration can help us understand several of the most critical relationships and climate characteristics of climate-sensitive disease dynamics.

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Section: Historical Understanding Of Dengue and Climate In Indiamentioning

confidence: 99%

Section: Dengue and Climate Changementioning

confidence: 99%

Applications of artificial intelligence in predicting dengue outbreaks in the face of climate change: a case study along coastal India

Tallam¹,

Quang²

2023

Preprint

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“… 6 , 7 Evidence has demonstrated that extreme weather events, such as floods, tropical cyclones, and droughts, play important roles in dengue fever incidence. 8 , 9 Theoretically, seasonal or interannual change of hydrological conditions may also affect dengue fever incidence by altering the development of mosquitoes and vector-host contact. For example, the anomaly of wet conditions and associated precipitation may enrich the static water source, which provides the ideal breeding habitat for Aedes aegypti (Linnaeus) mosquitoes, the primary vector of dengue virus transmission.…”

Section: Introductionmentioning

confidence: 99%

Association Between Hydrological Conditions and Dengue Fever Incidence in Coastal Southeastern China From 2013 to 2019

Wang²,

Yan

et al. 2023

JAMA Netw Open

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ImportanceDengue fever is a climate-sensitive infectious disease. However, its association with local hydrological conditions and the role of city development remain unclear.ObjectiveTo 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 ParticipantsThis 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.ExposuresCity-level monthly temperature, precipitation, SPEI, and annual city development indicators from 2013 to 2019.Main Outcomes and MeasuresThe 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.ResultsIncluded 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 RelevanceResults 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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“…China is one of the most vulnerable countries with respect to climate change [11]. Many studies have found the significant positive relationship between weather conditions and vector-borne diseases in China, including the effects of meteorological factors, such as temperature, precipitation, humidity, wind speed [12][13][14][15][16][17], and extreme weather events (e.g., extremely high temperatures, extremely high rainfall, and tropical cyclones) [18][19][20][21][22][23][24]. The 2020 China Report of the Lancet Countdown on Health and Climate Change showed that China's climate suitability for mosquito-borne dengue fever has increased by 37% in the past half century [25].…”

Section: Introductionmentioning

confidence: 99%

Climate Change and Vector-Borne Diseases in China: A Review of Evidence and Implications for Risk Management

Huang

2022

Biology

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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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Extreme weather conditions and dengue outbreak in Guangdong, China: Spatial heterogeneity based on climate variability

Cited by 22 publications

References 59 publications

Applications of artificial intelligence in predicting dengue outbreaks in the face of climate change: a case study along coastal India

Applications of artificial intelligence in predicting dengue outbreaks in the face of climate change: a case study along coastal India

Association Between Hydrological Conditions and Dengue Fever Incidence in Coastal Southeastern China From 2013 to 2019

Climate Change and Vector-Borne Diseases in China: A Review of Evidence and Implications for Risk Management

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