Geostatistical mapping of the seasonal spread of under-reported dengue cases in Bangladesh

Abstract

Geographical mapping of dengue in resource-limited settings is crucial for targeting control interventions but is challenging due to the problem of zero-inflation because many cases are not reported. We developed a negative binomial generalised linear mixed effect model accounting for zero-inflation, spatial, and temporal random effects to investigate the spatial variation in monthly dengue cases in Bangladesh. The model was fitted to the district-level (64 districts) monthly reported dengue cases aggregated over the period 2000 to 2009 and Bayesian inference was performed using the integrated nested Laplace approximation. We found that mean monthly temperature and its interaction with mean monthly diurnal temperature range, lagged by two months were significantly associated with dengue incidence. Mean monthly rainfall at two months lag was positively associated with dengue incidence. Densely populated districts and districts bordering India or Myanmar had higher incidence than others. The model estimated that 92% of the annual dengue cases occurred between August and September. Cases were identified across the country with 94% in the capital Dhaka (located almost in the middle of the country). Less than half of the affected districts reported cases as observed from the surveillance data. The proportion reported varied by month with a higher proportion reported in high-incidence districts, but dropped towards the end of high transmission season.

Author summary

A better understanding of spatial and temporal variation in dengue risk is invaluable since it guides intervention strategies and facilitates effective health resource allocation. Transmission of dengue depends on the distribution and abundance of the mosquito vectors which are sensitive to climatic and environmental factors including temperature, rainfall, and population density. By modelling dengue-climate relationships, the burden of dengue can be estimated in locations where data on transmission are not available permitting identification of high-risk areas. This reveals the extent of under-reporting in dengue surveillance data in resource-limited countries which is essential to estimating the changing burden of dengue.