Spatio-temporal modelling of climate-sensitive disease risk: Towards an early warning system for dengue in Brazil

Abstract

a b s t r a c tThis paper considers the potential for using seasonal climate forecasts in developing an early warning system for dengue fever epidemics in Brazil. In the first instance, a generalised linear model (GLM) is used to select climate and other covariates which are both readily available and prove significant in prediction of confirmed monthly dengue cases based on data collected across the whole of Brazil for the period January 2001 to December 2008 at the microregion level (typically consisting of one large city and several smaller municipalities). The covariates explored include temperature and precipitation data on a 2:51 Â 2:51 longitude-latitude grid with time lags relevant to dengue transmission, an El Niñ o Southern Oscillation index and other relevant socio-economic and environmental variables. A negative binomial model formulation is adopted in this model selection to allow for extra-Poisson variation (overdispersion) in the observed dengue counts caused by unknown/unobserved confounding factors and possible correlations in these effects in both time and space. Subsequently, the selected global model is refined in the context of the South East region of Brazil, where dengue predominates, by reverting to a Poisson framework and explicitly modelling the overdispersion through a combination of unstructured and spatio-temporal structured random effects. The resulting spatio-temporal hierarchical model (or GLMM-generalised linear mixed model) is implemented via a Bayesian framework using Markov Chain Monte Carlo (MCMC). Dengue predictions are found to be enhanced both spatially and temporally when using the GLMM and the Bayesian framework allows posterior predictive distributions for dengue cases to be derived, which can be useful for developing a dengue alert system. Using this model, we conclude that seasonal climate forecasts could have potential value in helping to predict dengue incidence months in advance of an epidemic in South East Brazil.

Modelización del efecto de la variabilidad climática local sobre la transmisión de dengue en Medellín (Colombia) mediante análisis de series temporales

“…A pesar de los diversos hallazgos sobre la asociación entre la transmisión del dengue y la precipitación, y de que en otros estudios no se ha observado tal relación (incluso se ha descrito como una relación no estacionaria y probablemente espuria) (37) Las demostraciones de la fuerte asociación entre la incidencia de dengue y los factores climáticos, han permitido la construcción de diversos modelos (15,(44)(45)(46). En particular, considerando las condiciones climáticas locales para un período de 11 años, en el presente estudio se desarrolló un modelo que captó los principales cambios en la tendencia en la incidencia de dengue en Medellín por efecto de la precipitación.…”

Climate and Non-Climate Drivers of Dengue Epidemics in Southern Coastal Ecuador

“…30,38 A negative binomial model was used to account for overdispersion found in the dengue count data (extra-Poisson variation), 39,40 where y t is monthly dengue cases, μ t is mean cases, e t is expected cases, and ρ t is the dengue relative risk (Eq. 1).…”

“…28 To date, there has been limited success in developing an operational EWS for dengue, although several studies have shown the potential to develop such a system. [29][30][31] Aims of the study. The objective of this study is to assess the importance of climate and non-climate drivers of interannual variability in dengue fever in southern coastal Ecuador.…”

Quantifying the added value of climate information in a spatio-temporal dengue model

“…Typically, the most significant time lags between temperature/precipitation and dengue are found to be around 1-2 months (Arcari et al, 2007;Cheong et al, 2013;Descloux et al, 2012;García et al, 2011;Gharbi et al, 2011;Gomes et al, 2012;Jeefoo et al, 2010;Lowe et al, 2011;Wu et al, 2007) , although some studies report lags of around 3-4 months (Bi et al, 2001;Chen et al, 2012;Depradine and Lovell, 2004;Yu et al, 2011) .…”