Spatial Individual‐Level Models for Transmission Dynamics of Seasonal Infectious Diseases

Seasonality plays a crucial role in the transmission dynamics of many infectious diseases, contributing to periodic fluctuations in disease incidence. The previously developed geographically dependent individual-level model (GD-ILM) has been effective in modeling infectious diseases, but does not incorporate seasonal effects, limiting its ability to capture seasonal trends. In this study, we extend the GD-ILM by introducing a seasonally varying transmission component, allowing the model to account for periodic fluctuations in infection risk. Our approach integrates a seasonally forced infection kernel to model periodic changes in transmission rates over time, leading to a novel spatiotemporal kernel. To facilitate efficient and reliable parameter estimation in this high-dimensional setting, we employ the Monte Carlo expectation conditional maximization algorithm. We apply our model to individual-level influenza A data from Manitoba, Canada, examining spatial and seasonal infection patterns to identify high-risk regions and periods, and thus informing targeted intervention strategies. The proposed model's performance is further validated through comprehensive simulation studies. Simulation results confirm that models omitting seasonal components lead to biased spatial parameter estimates under various disease prevalence conditions. To support reproducibility and practical application, we developed the SeasEpi R package publicly available on the comprehensive R archive network (CRAN), which implements the seasonal GD-ILM framework and provides tools for model fitting, simulation, and evaluation. The seasonal GD-ILM offers a more accurate framework for modeling infectious disease transmission by integrating both spatial and seasonal dynamics. It supports more accurate risk assessment and enhances public health responses by enabling timely and location-specific interventions based on seasonal transmission patterns.