Cluster-Based Bayesian SIRD Modeling of Chickenpox Epidemiology in India

This study presents a cluster-based Bayesian SIRD model to analyze the epidemiology of chickenpox (varicella) in India, utilizing data from 1990 to 2021. We employed an age-structured approach, dividing the population into juvenile, adult, and elderly groups, to capture the disease's transmission dynamics across diverse demographic groups. The model incorporates a Holling-type incidence function, which accounts for the saturation effect of transmission at high prevalence levels, and applies Bayesian inference to estimate key epidemiological parameters, including transmission rates, recovery rates, and mortality rates. The study further explores cluster analysis to identify regional clusters within India based on the similarities in chickenpox transmission dynamics, using criteria like incidence, prevalence, and mortality rates. We perform K-means clustering to uncover three distinct epidemiological regimes, which vary in terms of outbreak potential and age-specific dynamics. The findings highlight juveniles as the primary drivers of transmission, while the elderly face a disproportionately high mortality burden. Our results underscore the importance of age-targeted interventions and suggest that regional heterogeneity should be considered in public health strategies for disease control. The model offers a transparent, reproducible framework for understanding long-term transmission dynamics and supports evidence-based planning for chickenpox control in India. The practical utility of the model is further validated through a simulation study.