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

2025-06-17

AI Summary

This study quantifies the impact of the rotavirus vaccine introduced in the UK in 2013 on viral transmission dynamics. Method: We developed a stochastic compartmental model and, for the first time in the UK context, integrated sequential Monte Carlo (SMC) Bayesian inference with time-varying effective reproduction number (Rₜ) estimation to reconstruct the temporal evolution of the transmission rate. Contribution/Results: The analysis reveals a non-monotonic decline in Rₜ post-vaccination, precisely identifying inflection points where transmission rates significantly decreased—demonstrating a strong temporal association between rising immunization coverage and transmission suppression. The model accurately reproduces the observed decline in laboratory-confirmed cases. This work introduces a dynamic attribution framework for immunization interventions, enabling rigorous real-world evaluation of vaccine effectiveness through mechanistic, data-driven inference of transmission dynamics.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Stochastic Optimization
• Intelligent Robots: State Estimation

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• Assess vaccination impact on rotavirus transmission dynamics
• Estimate time-varying transmissibility pre and post-vaccination
• Evaluate long-term epidemiological trends using quantitative framework

Innovation

• Bayesian inference for transmission dynamics
• Sequential Monte Carlo model calibration
• Time-varying transmissibility parameter estimation