The importance of human population characteristics in modeling Aedes aegypti distributions and assessing risk of mosquito-borne infectious diseases

• Published in: Tropical Medicine and Health
• Main Authors: Julie F. Obenauer, T. Andrew Joyner, Joseph B. Harris
• Format: Article in Journal/Newspaper
• Language: English
• Published: BMC 2017
• Subjects: Aedes aegypti; Zika virus; Climate change; Species distribution model; Maxent; Human population; Arctic medicine. Tropical medicine; RC955-962; Arctic
• Online Access: https://doi.org/10.1186/s41182-017-0078-1; https://doaj.org/article/aaa78627e6f44d0294bfb33a48cfbabd

Abstract Background The mosquito Aedes aegypti has long been a vector for human illness in the Southeastern United States. In the past, it has been responsible for outbreaks of dengue, chikungunya, and yellow fever and, very recently, the Zika virus that has been introduced to the region. Multiple studies have modeled the geographic distribution of Ae. aegypti as a function of climate factors; however, this ignores the importance of humans to the anthropophilic biter. Furthermore, Ae. aegypti thrives in areas where humans have created standing water sites, such as water storage containers and trash. As models are developed to examine the potential impact of climate change, it becomes increasingly important to include the most comprehensive set of predictors possible. Results This study uses Maxent, a species distribution model, to evaluate the effects of adding poverty and population density to climate-only models. Performance was evaluated through model fit statistics, such as AUC, omission, and commission, as well as individual variable contributions and response curves. Models which included both population density and poverty exhibited better predictive power and produced more precise distribution maps. Furthermore, the two human population characteristics accounted for much of the model contribution—more so than climate variables. Conclusions Modeling mosquito distributions without accounting for their dependence on local human populations may miss factors that are very important to niche realization and subsequent risk of infection for humans. Further research is needed to determine if additional human characteristics should be evaluated for model inclusion.