Subseasonal Forecasting of Aedes-borne Disease Transmission

Aedes-borne diseases, such as dengue and chikungunya, are responsible for more than 50 million infections worldwide every year, with an overall increase of 30-fold in the last 50 years, mainly due to city population growth and more frequent travels. In the United States of America, the vast majority of Aedes-borne infections are imported from endemic regions by travelers, who can become new sources of mosquito infection once they are back in the country if the exposed population is susceptible to the disease, and if suitable environmental conditions for the mosquitoes and the virus are present. Since the susceptibility of the human population can be determined via periodic monitoring campaigns, environmental suitability for presence of mosquitoes and viruses becomes one of the most important pieces of information for decision makers in the health sector. Here, we develop a subseasonal to seasonal monitoring and forecasting system for environmental suitability of transmission of Aedes-borne diseases for the US, Central America, the Caribbean and northern South America, using multiple calibrated ento-epidemiological models, climate models, and quality-controlled temperature observations. We show that the predictive skill of this new system is higher than that of any of the individual models, and illustrate how a combination of deterministic and probabilistic forecasts can inform key prevention and control strategies.