Abstract

Vector-borne infectious diseases, particularly mosquito-borne, pose a substantial threat to populations throughout South and Southeast Asia. Outbreaks have affected this region several times during the early years of the 21st century, notably through outbreaks of Chikungunya and Dengue. These diseases are believed to be highly prevalent at endemic levels in the region as well. With a changing global climate, the impacts of changes in ambient temperatures and precipitation levels on mosquito populations are important for understanding the effects on risk of mosquito-borne disease outbreaks. This study aims to make use of a large data set to determine how risk of mosquito-borne infectious disease outbreaks relates to the highest monthly average temperature and precipitation for each year in South and Southeast Asia. Generalized additive models were used in a marked point process to fit nonlinear trends relating temperature and precipitation to outbreak risk, fitting splines for temperature and precipitation. Confounding factors for nation affluence, climate type, and ability to report outbreaks were also included. Parabolic trends for both temperature and precipitation were observed relating to outbreak risk. The trend for temperature, which was significant, showed that outbreak risk peaks near 33.5°C as the highest monthly average temperature. Though not significant, a trend for precipitation was observed showing risk peaking when the highest monthly average precipitation is 650mm. Peak levels of temperature and precipitation were identified for outbreak risk. These findings support the notion of a poleward shift in the distribution of mosquitoes within this region rather than a poleward expansion in geographic range.

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