From climate variables to health information - Predicting and monitoring mosquito-borne disease outbreaks with AeDES2

Mosquito-borne arboviruses pose a grave threat to millions of people worldwide each year, with climate change rapidly expanding hotspots of deadly Aedes-related diseases. Aware of potential compound effects regarding other important diseases, it has become imperative for health authorities to maintain a detailed surveillance of key variables that can trigger Aedes-borne epidemic episodes. Disease transmission is generally conditioned by multiple socio-economic factors, and among them, the environmental suitability for vectors and viruses to proliferate is a necessary –although not sufficient– condition that needs to be closely monitored. As such, a comprehensive service that allows stakeholders to detect and predict environmental suitability on affected hotspots is crucial for communities to better prepare in the case of present and future outbreaks.

To this end, AeDES2 is a next generation climate-and-health operational service that reproduces and improves computation of Aedes-borne Diseases Environmental Suitability over its previous version (Muñoz et al., 2020), expanding its temporal and spatial scope while simultaneously enhancing observational and forecasting quality of Aedes-related disease transmissibility. Users can consult the historical evolution of the environmental suitability values on any grid point of interest, as well as the expected future evolution up to three seasons in advance. Aside from environmental suitability values, health authorities can additionally utilize AeDES2 to analyse the estimated percentage of population at risk –crucial for governing bodies to implement control measures in order to reduce the spread of the disease.

AeDES2 incorporates four different environmental suitability models, translating temperature and precipitation values into environmental suitability outputs while considering epidemiological factors for transmission probability. Its monitoring system generates an up-to-date 12-member ensemble reference, providing a continuously updated historical sequence of environmental suitability values. On the forecasting side, AeDES2 builds on its predecessor’s pattern-based multi-model calibration, by assimilating state-of-the-art calibration methods such as causality-based calibration or multi-calibration techniques, aiming to reliably reproduce key non-linear patterns that are used as predictors in the cross-validated forecast system.