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

Abstract

Dengue is the world's most important vector-borne viral disease. The dengue mosquito and virus are sensitive to climate variability and change. Temperature, humidity and precipitation influence mosquito biology, abundance and habitat, and the virus replication speed. In this study, we develop a modelling procedure to quantify the added value of including climate information in a dengue model for the 76 provinces of Thailand, from 1982-2013. We first developed a seasonal-spatial model, to account for dependency structures from one month to the next and between provinces. We then tested precipitation and temperature variables at varying time lags, using linear and nonlinear functional forms, to determine an optimum combination of time lags to describe dengue relative risk. Model parameters were estimated using Integrated Nested Laplace Approximation (INLA).This approach provides a novel opportunity to perform model selection in a Bayesian framework, while accounting for underlying spatial and temporal dependency structures and linear or nonlinear functional forms. We quantified the additional variation explained by interannual climate variations, above that provided by the seasonal-spatial model. Overall, an additional 8% of the variance in dengue relative risk can be explained by accounting for interannual variations in precipitation and temperature in the previous month. The inclusion of nonlinear functions of climate in the model framework improved the model for 79% of the provinces. Therefore, climate forecast information could significantly contribute to a national dengue early warning system in Thailand.

Urban climate versus global climate change—what makes the difference for dengue?

“…The importance of incriminating large‐scale climate variables is that it potentially provides an accessible early warning system. Several studies have addressed the lag‐time associations of temperature and precipitation with dengue incidence and have reported highly variable results; it seems that there are different lag times depending also on the latitudinal position of the country . Dengue incidence characteristically follows seasonal patterns on an annual time scale, but increases in intensity on a multiannual scale.…”

“…Several studies have addressed the lag-time associations of temperature and 32 precipitation with dengue incidence and have reported highly variable results; it seems that there are different lag times depending also on the latitudinal position of the country. 48 Dengue incidence characteristically follows seasonal patterns on an annual time scale, but increases in intensity on a multiannual scale. The underlying causes of these periodic epidemics are not understood, but are thought to arise through a combination of intrinsic and extrinsic drivers.…”

Spatio-temporal data mining in ecological and veterinary epidemiology

“…A special issue in the journal Stochastic Environmental Research and Risk Assessment aimed to address this topic. Potential thematics included: spatiotemporal statistics (Biggeri et al 2016;Picado et al 2007), stochastic analysis (Heesterbeek 2000;Marx et al 2015), Bayesian maximum entropy modeling (Biggeri et al 2006;Juan et al 2016), big data analytics (Andreu-Perez et al 2015;Guernier et al 2016), GIS and Remote Sensing (Ferrè et al 2016;Norman et al 2012), Trajectories and GPS tracking (Demšar et al 2015;Zhang et al 2011), Agent Based Modelling calibrated with data (Dion et al 2011;Moustakas and Evans 2015;Smith et al 2016), decision making and risk assessment (Fei et al 2016;Lowe et al 2015), network and connectivity analysis (Nobert et al 2016;Ortiz-Pelaez et al 2006) and co-occurrence and moving objects (Miller 2012;Webb 2005). Nine contributions were finally accepted after peer reviewing.…”

Human health in the face of climate change

“…Locals in Southeast Asia know well that this is bad news, for the seasonal rains accompany the yearly rise of similar diseases, despite specific biological mechanisms that remain not totally understood. 14,15 Fortunately, the strain isolated in Asia is different from the one in the Americas, with the former having origins in Micronesia in 2007; it is apparently milder in severity than the Polynesian counterpart that threatens the Americas. But many uncertainties still remain to be understood: namely why this Asian outbreak has occurred now and not before, or in a few years, and what the role of climate and environmental changes are in these outbreaks.…”