A Climate-Driven Mechanistic Transmission Model to Characterize Dengue Epidemiology in Dhaka, Bangladesh

Dengue is a major public health concern in tropical and sub-tropical areas of the world. Dengue virus is transmitted to humans primarily by female Aedes aegypti mosquitoes and its epidemiology is heavily influenced by the climate. We developed a mechanistic compartmental model to characterize how climate and the evolution of population-level immunity influence changes in the epidemiology of dengue. The model explicitly incorporates aquatic and adult stages of the mosquito vector and cross-immunity (to another serotype) to better represent the transmission of dengue compared to previous models. It incorporates parameters related to mosquito development, behaviour, and mortality that are dependent on temperature, rainfall, and humidity. We calibrated the model to available dengue seroprevalence estimates for Dhaka, Bangladesh as it is a well-observed densely populated endemic area for dengue and will be strongly impacted by climate change. We then simulated dengue epidemiology for the period of 1995-2014 and compared the simulation output with available epidemiologic data. The model produced repeating annual outbreaks of dengue with strong seasonality, as observed in the dengue case reports for Dhaka. The median number of annual dengue infections produced by the model was 1.6 million cases (IQR: 0.6-2.3 million) and the median of the annual daily peak size of dengue infections was 24,121 (IQR: 7,807-39,308) across the simulations. These figures while exceeding reported cases in Dhaka, which do not include asymptomatic infections or cases not seeking healthcare, align with previous estimates. The modelled seroprevalence of 62% (IQR: 58– 75%) aligned with survey results from Dhaka. In most model simulations, dengue infections occurred during August to December with the median peak occurring in October. The model outputs demonstrated a high degree of conformity to the reported data in terms of seasonality, and seroprevalence, indicating that the model provides a good representation of overall dengue epidemiology in Dhaka. By accounting for climate variables, host immunity, and their interplay, our model is well-suited for evaluating the long-term impact of climate change on dengue transmission in Dhaka and other settings where climate and epidemiological data are available.