Behavioral change models for infectious disease transmission: a systematic review (2020-2025)

TL;DR

This systematic review analyzes post-2020 infectious disease models that incorporate human behavioral adaptations, highlighting their theoretical foundations, modeling approaches, and how they influence disease transmission and control strategies.

Contribution

It provides a comprehensive taxonomy of behavioral modeling approaches, emphasizing the integration of psychosocial constructs and guiding future theory-informed epidemiological models.

Findings

COVID-19 dominates recent models (73%)

Most models use compartmental ODE frameworks (81%)

Behavioral effects mainly modify contact and transmission rates

Abstract

Background: Human behavior shapes infectious disease dynamics, yet its integration into transmission models remains fragmented. Recent epidemics, particularly COVID-19, highlight the need for models capturing adaptation to perceived risk, social influence, and policy signals. This review synthesizes post-2020 models incorporating behavioral adaptation, examines their theoretical grounding, and evaluates how behavioral constructs modify transmission, vaccination, and compliance. Methods: Following PRISMA guidelines, we searched Scopus and PubMed (2020-2025), screening 1,274 records with citation chaining. We extracted data on disease context, country, modeling framework, behavioral mechanisms (prevalence-dependent, policy/media, imitation/social learning), and psychosocial constructs (personal threat, coping appraisal, barriers, social norms, cues to action). A total of 216 studies met inclusion criteria. Results: COVID-19 accounted for 73% of studies. Most used compartmental ODE models (81%) and focused on theoretical or U.S. settings. Behavioral change was mainly reactive: 47% applied prevalence-dependent feedback, 25% included awareness/media dynamics, and 19% relied on exogenous policy triggers. Game-theoretic or social learning approaches were rare (less or equal than 5%). Behavioral effects primarily modified contact or transmission rates (91%). Psychosocial constructs were unevenly represented: cues to action (n=159) and personal threat (n=145) dominated, whereas coping appraisal (n=82), barriers (n=36), and social norms (n=25) were less common. Conclusions: We propose a taxonomy structured by behavioral drivers, social scale, and memory to clarify dominant paradigms and their empirical basis. Mapping models to psychosocial constructs provides guidance for more theory-informed and data grounded-integration of behavioral processes in epidemiological modeling.