Individual level modeling of infectious disease transmission with reinfection dynamics: Application to Tuberculosis...

Recent advancements in stochastic modeling of infectious disease transmission have increasingly incorporated spatial factors, enhancing the accuracy of disease spread predictions and public health interventions. For many infectious diseases, reinfection is a key factor that impacts disease dynamics, epidemic progression, prevalence, and control efforts, complicating management strategies. Accurately incorporating reinfection into disease modeling is essential for developing effective interventions. This study expands upon previously proposed Geographically Dependent Individual Level Models (GD-ILMs) of infectious diseases by integrating them within a Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS) compartmental framework, termed GD-ILM SEIRS, to consider reinfection. A Monte Carlo Expectation Conditional Maximization algorithm was employed to estimate the parameters of the model. The GD-ILM SEIRS was applied to Tuberculosis data from Manitoba, Canada, covering the period from 2011 to 2018. It considers spatial dependencies, along with individual and regional risk factors influencing susceptibility to initial infection, reinfection, and infectivity. An analysis of Manitoba's health authority districts highlights specific risk factors related to susceptibility to initial infection, reinfection, and infectivity. Additionally, the fitted model enables calculation of infection probabilities at high-resolution geographic scales. The results allow for targeted interventions and optimized resource allocation by detecting high-risk areas and vulnerable populations to reduce transmission rates, prevent reinfection, and enhance health outcomes in Manitoba. Moreover, a simulation study across various grid configurations demonstrates the model's effectiveness in estimating parameters. This study highlights the need to integrate reinfection dynamics into infectious disease models to strengthen the impact of public health interventions and disease control strategies.