title: The development of an early warning system for climate-sensitive disease risk with a focus on dengue epidemics in Southeast Brazil.
abstract: 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.
authors.affiliation: Institut Català de Ciències del Clima, Barcelona, Spain. rachel.lowe@ic3.cat
authors.forename: Rachel
authors.initials: R
authors.lastname: Lowe
authors.forename: Trevor C
authors.initials: TC
authors.lastname: Bailey
authors.forename: David B
authors.initials: DB
authors.lastname: Stephenson
authors.forename: Tim E
authors.initials: TE
authors.lastname: Jupp
authors.forename: Richard J
authors.initials: RJ
authors.lastname: Graham
authors.forename: Christovam
authors.initials: C
authors.lastname: Barcellos
authors.forename: Marilia Sá
authors.initials: MS
authors.lastname: Carvalho
mesh_terms: D001499:Bayes Theorem; D056808:Biostatistics / Q000379:methods; D001938:Brazil / Q000453:epidemiology; D003715:Dengue / Q000453:epidemiology* / Q000517:prevention & control / Q000635:transmission; D058872:Epidemics / Q000517:prevention & control / Q000706:statistics & numerical data; D006801:Humans; D016014:Linear Models; D015233:Models, Statistical; D011634:Public Health; D012307:Risk Factors; D012621:Seasons; D014329:Tropical Climate