AeDES2.0: An enhanced climate-and-health service for monitoring and forecasting environmental suitability of Aedes-borne disease transmission in hotspots

Aedes-borne diseases, such as dengue, Zika and Chikungunya, pose a grave threat to millions of people worldwide each year. Aware of potential compound effects regarding other important diseases, such as COVID-19, it has become imperative for health authorities to maintain a detailed surveillance of key environmental variables that can trigger epidemic episodes. While disease transmission is generally conditioned by multiple socio-economic factors, the environmental suitability for vectors and viruses to proliferate is a necessary –although not sufficient– condition that needs to be closely monitored and forecasted. As such, a comprehensive service that allows stakeholders to analyse and visualise environmental suitability on affected hotspots is crucial for communities to better prepare in the case of present and future outbreaks.

 

The newest version of the Aedes-borne Diseases Environmental Suitability (AeDES2) climate-and-health service is a next-generation, fully-operational monitoring system that reproduces and improves the previous version (Muñoz et al., 2020), broadening both the temporal and spatial scope while simultaneously enhancing both observational and forecasting quality. With AeDES2, 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 the environmental suitability values, health authorities can additionally analyse the estimated incidence or percentage of population at risk threshold –a key indicator for governing bodies to trigger the implementation of control measures to reduce the spread of the disease in an affected population.

 

AeDES2 incorporates four state-of-the-art environmental suitability models, considering both epidemiological factors for transmission probability and climate variables such as temperature values. On the monitoring side, AeDES2 provides a continuously updated monthly historical sequence of environmental suitability values by generating an ensemble with multiple observational references, hence providing uncertainty estimates in the monitoring system, an improvement over the previous version. On the prediction side, still under development, AeDES2 builds on its predecessor’s pattern-based multi-model calibration approach, assimilating new Machine Learning calibration methods such as neural networks, aiming to reliably reproduce key non-linear patterns that are used as predictors in the cross-validated forecast system.