Review of human behavior integration in COVID-19 modeling studies

Area of Science:

• Epidemiology
• Mathematical Modeling
• Behavioral Science

Background:

• Human behavior significantly impacts infectious disease transmission.
• Integrating behavioral dynamics into epidemiological models is crucial, especially for understanding policy responses during pandemics like COVID-19.

Purpose of the Study:

• To review and analyze COVID-19 modeling studies.
• To assess the extent and nature of behavioral components incorporated into these models.
• To identify gaps in behavioral integration within epidemiological modeling.

Main Methods:

• Systematic review of 276 COVID-19 modeling studies published between February 2020 and February 2023.
• Extraction of key study characteristics, focusing on behavioral aspects and data sources.
• Synthesis of identified behavioral factors into distinct categories.

Main Results:

• Only 38% of reviewed studies incorporated human behavior, often limited to single factors (e.g., compliance, mobility).
• Behavioral factors were categorized into six main groups.
• Mechanistic modeling was prevalent (92%), but only 34% of studies utilized databases for behavioral modeling.

Conclusions:

• A significant gap exists in incorporating comprehensive behavioral components into COVID-19 epidemiological models.
• Limited use of databases for modeling behavior may reduce accuracy in reflecting real-world dynamics.
• Future epidemiological models require deeper integration with behavioral sciences for improved accuracy and predictive power.