The development of an early warning system for climate-sensitive disease risk with a focus on dengue epidemics in Southeast Brazil

Previous studies demonstrate statistically significant associations between disease and climate variations, highlighting the potential for developing climate‐based epidemic early warning systems. However, limitations include failure to allow for non‐climatic confounding factors, limited geographical/temporal resolution, or lack of evaluation of predictive validity. Here, we consider such issues for dengue in Southeast Brazil using a spatio‐temporal generalised linear mixed model with parameters estimated in a Bayesian framework, allowing posterior predictive distributions to be derived in time and space. This paper builds upon a preliminary study by Lowe et al. but uses extended, more recent data and a refined model formulation, which, amongst other adjustments, incorporates past dengue risk to improve model predictions. For the first time, a thorough evaluation and validation of model performance is conducted using out‐of‐sample predictions and demonstrates considerable improvement over a model that mirrors current surveillance practice. Using the model, we can issue probabilistic dengue early warnings for pre‐defined ‘alert’ thresholds. With the use of the criterion ‘greater than a 50% chance of exceeding 300 cases per 100,000 inhabitants’, there would have been successful epidemic alerts issued for 81% of the 54 regions that experienced epidemic dengue incidence rates in February–April 2008, with a corresponding false alarm rate of 25%. We propose a novel visualisation technique to map ternary probabilistic forecasts of dengue risk. This technique allows decision makers to identify areas where the model predicts with certainty a particular dengue risk category, to effectively target limited resources to those districts most at risk for a given season. Copyright © 2012 John Wiley & Sons, Ltd.