Why population heterogeneity matters for modelling infectious diseases.

Area of Science:

• Epidemiology
• Computational modeling
• Public health

Background:

• The COVID-19 pandemic exposed significant disparities in infectious disease burden across various sociodemographic groups in the United States.
• Observed variations in case incidence, mortality, and disease burden across racial, ethnic, sex, age, and geographic strata necessitate advanced modeling approaches.

Purpose of the Study:

• To address challenges in incorporating fine-grained sociodemographic data and exposure risks into infectious disease models.
• To demonstrate how detailed agent-based modeling can reveal disparities in disease spread and burden among different demographic groups.

Main Methods:

• Utilized EpiCast, a large-scale agent-based model for respiratory pathogen spread in the US.
• Incorporated drivers of exposure risk and detailed sociodemographic data to simulate transmission dynamics.
• Analyzed how differences in infection rates emerge across households, workplaces, and schools.

Main Results:

• Demonstrated that embedding population heterogeneity into models reveals uneven predicted disease burden among racial groups.
• Identified factors such as household size and workplace exposure risk as key drivers of these disparities.
• Showcased how differences in infection rates manifest across demographic groups in various settings.

Conclusions:

• Detailed agent-based models can capture complex dynamics driving infectious disease heterogeneities.
• These models are crucial for understanding and addressing uneven disease burden across sociodemographic groups.
• Findings can inform policy intervention design for future pandemic preparedness and response.