Authors: Sadie Jane Ryan*, University of Florida, Colin Jeremy Carlson, University of California at Berkeley, Erin Alicia Mordecai, Stanford University, Leah Renee Johnson, Virginia Tech
Topics: Medical and Health Geography, Quantitative Methods
Keywords: vectorborne disease, dengue, climate change, risk mapping
Session Type: Paper
Start / End Time: 12:40 PM / 2:20 PM
Room: Lafayette, Marriott, River Tower Elevators, 41st Floor
Forecasting the impacts of climate change on Aedes-borne viruses—especially dengue, chikungunya, and Zika—is a key component of public health preparedness We applied an empirically parameterized Bayesian model of Aedes-borne viruses as a function of temperature to predict current cumulative monthly global transmission risk, and projected risk in 2050 and 2070 based on general circulation models (GCMs). Our results show that shifting suitability will track optimal temperatures for transmission (26-29 °C), potentially leading to poleward shifts. Furthermore, especially for Ae. albopictus, extreme temperatures are likely to limit transmission risk in current zones of endemicity, especially the tropics. The patterns of impact of changing minimum and maximum predicted temperatures lead to idiosyncratic outcomes for people at risk in the future. Our model predicts that 6.1 billion people currently live in areas suitable for Ae. aegypti transmission, and 6.49 billion for Ae. albopictus, part of the year, under mean temperature predictions. However, this estimate varies under constraints of minimum and maximum temperatures. This also leads to a range of predictions under future scenarios: we find that some scenarios predict massive increases (1-2 billion people) of transmission suitability risk, while others predict decreased suitability. Importantly, the geographic shift in suitable temperatures as a result of climate change predictions allows for people currently at risk to entirely escape transmission, while new individuals in new areas are put at risk. We present synoptic descriptions of this range of predictions, to facilitate communication of the ranges created by climate model input variations.