Spatio-temporal detection for dengue outbreaks in the Central Region of Malaysia using climatic drivers at mesoscale and synoptic scale

Abstract

Background: The disease dengue is associated with both mesoscale and synoptic scale meteorology. Previous studies of this disease, especially for south-east Asia, have found very limited association between synoptic meteorological variables and the number of dengue hospitalisations. However, to tackle future severe outbreaks and to institute an early warning system, it will immensely beneficial to find and establish more clear association with rate of dengue hospitalisations and the most relevant meteorological variables. Objectives: A rigorous Bayesian modelling framework is provided to identify the most important meteorological covariates and their lagged effects for developing an early warning system for dengue outbreaks in the Central Region of Malaysia. Method: We obtain the dengue hospitalisations count data and the demographic information in the Central Region of Malaysia. Along with other mesoscale environmental measurements such as local temperature, precipitation and ozone concentration level, we also examine multiple synoptic scale Ni&ntildeo indices which are related to the phenomenon of El Ni&ntildeo Southern Oscillation and an unobserved meteorological variable derived from reanalysis data. A probabilistic early warning system is built based on a Bayesian spatio-temporal hierarchical model with a physically interpretable complex structure. Results: Our study finds a 46.87\% of increase in dengue hospitalisations due to one degree increase in the sea surface temperature anomalies in the central equatorial Pacific region with a lag time of six weeks. We also discover the existence of a mild association between the rate of cases and a distant lagged cooling effect of 28 weeks related to a phenomenon called El Ni&ntildeo Modoki. The proposed model also observes significant amount of association with some other meteorological parameters with disease rates. These associations are assessed by using an optimal Bayesian spatio-temporal model that outperforms other candidate models in terms of estimated out-of-sample predictive accuracy and performance in correctly issuing the warnings early. Discussion: With the novel spatial dynamic early warning system presented, our results show that the synoptic meteorological drivers can enhance short-term detection of dengue outbreaks and these can also potentially be used to provide longer-term forecasts