Forecasting the abundance of disease vectors with deep learning

Abstract

Arboviral diseases such as dengue, Zika, chikungunya or yellow fever are a worldwide concern. The abundance of vector species plays a key role in the emergence of outbreaks of these diseases, so forecasting these numbers is fundamental in preventive risk assessment. Here we describe and demonstrate a novel approach that uses state-of-the-art deep learning algorithms to forecast disease vector abundances. Unlike classical statistical and machine learning methods, deep learning models use time series data directly as predictors and identify the features that are most relevant from a predictive perspective. We demonstrate for the first time the application of this approach to predict short-term temporal trends in the number of Aedes aegypti mosquito eggs across Madeira Island for the period 2013 to 2019. Specifically, we apply the deep learning models to predict whether, in the following week, the number of Ae. aegypti eggs will remain unchanged, or whether it will increase or decrease, considering different percentages of change. We obtained high predictive performance for all years considered (mean AUC = 0.92 ± 0.05 SD). Our approach performed better than classical machine learning methods. We also found that the preceding numbers of eggs is a highly informative predictor of future trends. Linking our approach to disease transmission or importation models will contribute to operational, early warning systems of arboviral disease risk.