EpiGeoPop: a tool for developing spatially accurate country-level epidemiological models.

Area of Science:

• Epidemiology
• Computational Biology
• Geographic Information Science

Background:

• Mathematical models are essential for understanding infectious disease dynamics and informing public health policy.
• Agent-based models (ABMs) offer detailed insights into disease spread by incorporating population heterogeneity and spatial factors.
• Current ABM setup can be complex and time-consuming, particularly in integrating realistic spatial data.

Purpose of the Study:

• To introduce EpiGeoPop, a tool designed to streamline the creation of spatially accurate population data for ABMs.
• To demonstrate the critical impact of incorporating precise spatial details into ABM simulations of disease outbreaks.
• To highlight the utility of EpiGeoPop in facilitating the use of real-world spatial data in epidemiological modeling.

Main Methods:

• Development of EpiGeoPop for rapid generation of country-wide, spatially accurate population configurations.
• Utilisation of Epiabm, a modular ABM derived from CovidSim, to conduct disease outbreak simulations.
• Integration of multiple international datasets to showcase the application of spatially detailed ABMs.

Main Results:

• EpiGeoPop significantly reduces the complexity and time required for setting up spatially detailed ABMs.
• Simulations using Epiabm with accurate spatial data reveal crucial differences in predicted disease spread patterns.
• The study validates the importance of high-resolution spatial data for reliable ABM outputs in infectious disease modeling.

Conclusions:

• EpiGeoPop enhances the accessibility and accuracy of agent-based modeling for infectious disease research.
• Accurate spatial data integration is critical for robust predictions and effective intervention strategies in disease outbreak simulations.
• The developed tools facilitate more realistic and impactful epidemiological modeling using diverse international data sources.