Climate Risk Management (Jan 2022)
Spatio-temporal detection for dengue outbreaks in the Central Region of Malaysia using climatic drivers at mesoscale and synoptic scale
Abstract
The disease dengue is associated with both mesoscale and synoptic scale meteorology. However, previous studies for south-east Asia have found a very limited association between synoptic variables and the reported number of dengue cases. Hence there is an urgent need to establish a more clear association with dengue incidence rates and the most relevant meteorological variables in order to institute an early warning system.This article develops a rigorous Bayesian modelling framework to identify the most important covariates and their lagged effects for constructing an early warning system for the Central Region of Malaysia where the case rates have increased substantially in the recent past. Our modelling includes multiple synoptic scale Niño indices, which are related to the phenomenon of El Niño Southern Oscillation (ENSO), along with other relevant mesoscale environmental measurements and an unobserved variable derived from reanalysis data. An empirically well validated hierarchical Bayesian spatio-temporal is used to build a probabilistic early warning system for detecting an upcoming dengue epidemic.Our study finds a 46.87% increase in dengue cases due to one degree increase in the central equatorial Pacific sea surface temperature with a lag time of six weeks. We discover the existence of a mild association with relative risk 0.9774 (CI: 0.9602, 0.9947) between the rate of cases and a distant lagged cooling effect in the region of coastal South America related to a phenomenon called El Niño Modoki. The Bayesian model also establishes that the synoptic meteorological drivers can enhance short-term early detection of dengue outbreaks and these can also potentially be used to provide longer-term forecasts.
