Modelling Climate-Sensitive Disease Risk: A Decision Support Tool for Public Health Services
In order to control the spread of diseases and prepare for epidemics, decision support systems are required that take into account the multifaceted array of factors that contribute to increased disease risk. Climate forecasts, which predict the average climate conditions for forthcoming months/seasons, provide an opportunity to incorporate precursory climate information into decision support systems for climate-sensitive diseases. This aids epidemic planning months in advance, for diseases such as dengue, cholera, West Nile virus, chikungunya and malaria, among others. Here, we present a versatile model framework, which quantifies the extent to which climate indicators can explain variations in disease risk, while at the same time taking into account their interplay with the intrinsic/internal features of disease dynamics, which ultimately shape their epidemic structure. The framework can be adapted to model any climate-sensitive disease at different spatial/temporal scales and geographical settings. We provide case studies, quantifying the impact of climate on dengue and malaria in South America, Southeast Asia and Africa.
KeywordsDengue Malaria Public health Disease prediction model Early warning system Climate services
The research leading to these results has received funding from the DENFREE project (grant agreement no. 282378) and EUPORIAS project (grant agreement no. 308291) funded by the European Commission’s Seventh Framework Research Programme.
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