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This study introduces a seasonal component to a disease model, improving predictions of infectious disease spread. The enhanced model accurately captures seasonal patterns, crucial for targeted public health interventions.

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Public Health

Background:

  • Seasonality significantly impacts infectious disease transmission dynamics.
  • Existing geographically dependent individual-level models (GD-ILM) lack seasonal components, limiting their predictive accuracy.
  • Accurate modeling of seasonal trends is vital for understanding and controlling disease outbreaks.

Purpose of the Study:

  • To extend the GD-ILM by incorporating a seasonally varying transmission component.
  • To develop a novel spatiotemporal kernel integrating spatial and seasonal infection dynamics.
  • To improve the accuracy of infectious disease modeling for public health applications.

Main Methods:

  • Integration of a seasonally forced infection kernel into the GD-ILM.
  • Application of the Monte Carlo expectation conditional maximization algorithm for parameter estimation.
  • Validation using individual-level influenza A data from Manitoba, Canada, and simulation studies.

Main Results:

  • The seasonal GD-ILM accurately captures spatial and seasonal infection patterns.
  • Omitting seasonal components leads to biased spatial parameter estimates.
  • The model identifies high-risk regions and periods for targeted interventions.

Conclusions:

  • The seasonal GD-ILM provides a more accurate framework for modeling infectious disease transmission.
  • Integrating spatial and seasonal dynamics enhances risk assessment and public health responses.
  • The developed SeasEpi R package supports reproducibility and practical application of the model.