Generalized contact matrices allow integrating socioeconomic variables into epidemic models

Area of Science:

• Epidemiology
• Mathematical Biology
• Public Health

Background:

• Socioeconomic status (SES) variables like income, ethnicity, and education influence disease transmission dynamics.
• Traditional epidemic models often stratify social contacts solely by age and interaction contexts, overlooking SES.
• This oversight can lead to inaccurate predictions of infectious disease spread.

Purpose of the Study:

• To introduce and analyze generalized contact matrices that incorporate multiple stratification dimensions, including SES.
• To demonstrate the impact of including SES on epidemic modeling and outcome predictions.
• To highlight the necessity of integrating SES into infectious disease models for enhanced accuracy.

Main Methods:

• Development and application of generalized contact matrices stratifying individuals across multiple dimensions (e.g., age, context, SES).
• Mathematical proof of a lower-bound theorem showing potential underestimation of the basic reproductive number when SES is ignored.
• Utilizing synthetic and empirical data to validate the enhanced modeling approach.

Main Results:

• Disregarding SES dimensions in contact matrices can lead to underestimation of the basic reproductive number.
• Generalized contact matrices effectively capture behavioral variations, such as differential adherence to nonpharmaceutical interventions across SES groups.
• Models incorporating SES variables provide a more accurate representation of epidemic outcomes and dynamics.

Conclusions:

• Integrating socioeconomic status into epidemic models via generalized contact matrices is crucial for accurate disease spread prediction.
• Neglecting SES can result in significant misrepresentations of epidemic dynamics and public health intervention effectiveness.
• This research advocates for the inclusion of socioeconomic and other relevant demographic dimensions in future epidemic modeling efforts.