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Predicting suitability of regions for Zika outbreaks with zero-inflated models trained using climate data

By Samantha Roth, Miranda I. Teboh-Ewungkem, Ming Li

Posted 16 Sep 2019
bioRxiv DOI: 10.1101/771279

In recent years, Zika spread through the Americas. This virus has been linked to Guillain-Barré syndrome, which can lead to paralysis, and microcephaly, a severe birth defect. Zika is transmitted by Aedes (Ae.) aegypti, a mosquito whose geographic range has expanded and is anticipated to continue shifting as the climate changes. We used statistical models to predict regional suitability for autochthonous Zika transmission using climatic variables. By suitability for Zika, we mean the potential for an outbreak to occur based on the climate’s habitability for Ae. aegypti. We trained zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) regression models to predict Zika outbreak suitability using 20 subsets of climate variables for 102 regions. Variable subsets were selected for the final models based on importance to Ae. aegypti survival and their performance in aiding prediction of Zika-suitable regions. We determined the two best models to both be ZINB models. The best model’s regressors were winter mean temperature, yearly minimum temperature, and population, and the second-best model’s regressors were winter mean temperature and population. These two models were then run on bias-corrected climate projections to predict future climate suitability for Zika, and they generated reasonable predictions. The predictions find that most of the sampled regions are expected to become more suitable for Zika outbreaks. The regions with the greatest risk have increasingly mild winters and high human populations. These predictions are based on the most extreme scenario for climate change, a scenario that we will continue to be on track for if no significant emissions reduction measures are taken.

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