Assessing the Impact of Vaccination on Rotavirus Transmission Dynamics Using Bayesian Inference

TL;DR

This study uses Bayesian inference with a stochastic epidemiological model to quantify how rotavirus vaccination in the UK affected disease transmissibility over time, revealing dynamic shifts post-vaccine introduction.

Contribution

It introduces a novel application of Sequential Monte Carlo methods to estimate time-varying transmissibility in rotavirus, enhancing understanding of vaccination impact on disease dynamics.

Findings

Transmissibility decreased following vaccine rollout.

Dynamic fluctuations in transmission reflect vaccination effects.

Quantitative framework for long-term epidemiological assessment.

Abstract

The introduction of the rotavirus vaccine in the United Kingdom (UK) in 2013 led to a noticeable decline in laboratory reports in subsequent years. To assess the impact of vaccination on rotavirus transmissibility we calibrated a stochastic compartmental epidemiological model using Sequential Monte Carlo (SMC) methods. Our analysis focuses on estimating the time-varying transmissibility parameter and documenting its evolution before and after vaccine rollout. We observe distinct periods of increasing and decreasing transmissibility, reflecting the dynamic response of rotavirus spread to immunization efforts. These findings improve our understanding of vaccination-driven shifts in disease transmission and provide a quantitative framework for evaluating long-term epidemiological trends.