Published June 25, 2024 | Version 0.0
Software Open

Generalized Contact Matrices Allow Integrating Socio-economic Variables into Epidemic Models

  • 1. Department of Network and Data Science, Central European University, Vienna, Austria
  • 2. ISI Foundation, Turin, Italy
  • 3. Department of Sociology and Social Research, University of Trento, Trento, Italy
  • 4. National Laboratory for Health Security, HUN-REN Rényi Institute of Mathematics, Budapest, Hungary
  • 5. School of Mathematical Sciences, Queen Mary University of London, UK

Description

Variables related to socio-economic status (SES), including income, ethnicity, and education shape contact structures and impact the spread of infectious diseases. However, these factors are often overlooked in epidemic models, which typically stratify social contacts by age and interaction contexts. Here, we introduce and study generalized contact matrices that stratify contacts across multiple dimensions. We demonstrate a lower-bound theorem proving that disregarding additional dimensions, besides age and context, might lead to an underestimation of the basic reproductive number. By using SES variables in both synthetic and empirical data, we illustrate how generalized contact matrices enhance epidemic models, capturing variations in behaviors such as heterogeneous levels of adherence to non-pharmaceutical interventions among demographic groups. Moreover, we highlight the importance of integrating SES traits into epidemic models, as neglecting them might lead to substantial misrepresentation of epidemic outcomes and dynamics. Our research contributes to the efforts aiming at incorporating socio-economic and other dimensions into epidemic modeling.

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