Data-Driven Modeling of Seasonal Dengue Dynamics in Bangladesh: A Bayesian-Stochastic Approach

TL;DR

This paper develops a Bayesian-stochastic nonlinear SEIR model with seasonality for dengue in Bangladesh, using real data to improve outbreak prediction and inform public health strategies.

Contribution

It introduces a novel transmission rate function and combines Bayesian inference with a CTMC framework for more accurate modeling of dengue dynamics.

Findings

Model accurately fits historical dengue data

Provides probabilistic outbreak predictions

Offers a robust framework for policy guidance

Abstract

Bangladesh's worsening dengue crisis, fueled by its tropical climate, poor waste management infrastructure, rapid urbanization, and dense population, has led to increasingly deadly outbreaks, posing a significant public health threat. To address this, we propose a nonlinear, time-nonhomogeneous SEIR model incorporating seasonality through a novel transmission rate function. The model parameters are estimated using Bayesian inference with the Metropolis-Hastings algorithm in a Markov Chain Monte Carlo (MCMC) framework, calibrated with real-life dengue data from Bangladesh. To account for stochasticity and better assess outbreak probabilities, we extend the model to a time-nonhomogeneous continuous-time Markov chain (CTMC) framework. Our model provides new insights that can guide policymakers and offer a robust mathematical framework to better combat this crisis.