📝 Original Info
- Title: Behavioral change models for infectious disease transmission: a systematic review (2020-2025)
- ArXiv ID: 2602.16633
- Date: 2026-02-18
- Authors: ** 논문에 명시된 저자 정보가 제공되지 않았습니다. 실제 논문에서 확인하시기 바랍니다. **
📝 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.
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Social and behavioral factors are critical to the emergence, spread and containment of human disease and act as key determinants of the course, duration and outcomes of outbreaks. 1,[2][3][4] Recent large-scale epidemics, such as COVID-19, have reinforced the need for models that better integrate social and behavioral dynamics and theories to reflect the realities of affected communities. 5,6 Individuals and communities play a vital role in reducing transmission during outbreak response by maintaining preventive behaviors. 7,8 The feasibility and acceptability of adopting recommended health behaviors are influenced by complex elements, including the individual's perception of the disease threat, their level of trust in governing authorities and other information sources, and their physical, financial, and social capacity to voluntarily take action. 7,9 Ultimately, incorporating these factors into disease models is expected to improve their predictive accuracy and enable the design of more effective response measures and policies.
Modeling approaches for integrated behavior often augment classical epidemiological frameworks with mechanisms to capture dynamic behavioral shifts, which is defined as behavior changing endogenously as a function of another timedependent variable within the model. 10 In a review of COVID-19 models incorporating adaptive (endogenous) behavior, three main approaches were identified: feedback loops (72% of studies), game theory/utility theory (27%), and information/opinion spread (9%). 11 The feedback loop approach uses the prevalence of a disease outcome (such as cases or deaths) to stimulate a change in behavior within the model, sometimes leading to the projection of periodic waves of infection. 6 Game-theoretic methods analyze strategic interactions by assuming rational decision-makers who weigh the costs and benefits or maximize utility. 12 In these models, individuals reduce contacts to balance the benefits of social interaction against the perceived risk and costs associated with contracting the disease. Finally, models focused on information/opinion spread (or “coupled contagion”) simulate how an individual’s actions are influenced by the transmission of attitudes or awareness through social networks or social media. 13 These processes capture how opinions-which may or may not reflect the objective epidemic state-can propagate independently and lead to less rational behavior, such as fear-driven isolation or the spread of misinformation that increases infection rates.
A primary challenge in integrated modeling is the lack of robust social, behavioral, and operational data, which forces modelers to rely on simplifying assumptions. 3 Furthermore, a review found that many existing modeling assumptions reflected a limited understanding of the interplay between social and behavioral factors. 11,14 Modelers often face the complex task of developing computationally tractable formulations that harness data and theory on complex social phenomena while maintaining parsimony. Several established and growing body of psychosocial research provides theoretical grounding for these modeling efforts. The Health Belief Model (HBM) 15 , Theory of Planned Behavior (TPB) 16 , and Protection Motivation Theory (PMT) 17 are among the most widely applied frameworks to explain the social, cognitive and emotional drivers of protective actions. These theories underscore the role of perceived susceptibility, perceived severity, self-efficacy, and social norms in shaping behavioral intention and compliance. Yet, significant heterogeneity persists in how behavior change is incorporated across models, with no standardized criteria to guide the choice of mechanisms, functional forms or theoretical grounding 10 . The motivation of our study is thus to suggest a more structured framework with clarified specific mechanisms for integrating behavioral science into epidemiological modeling. 18,19 In this context, our review systematically examines post-2020 infectious disease models that incorporate behavioral adaptation mechanisms, focusing on how they incorporate human responses to an epidemic through several distinct mechanisms that modify transmission pathways or care-seeking behavior. We classify these models according to their underlying behavioral constructs, adaptation mechanisms, and impacts on model parameters and structure. Building on this classification, we propose a unifying taxonomy organized around behavioral drivers, social scale, and memory structure to highlight dominant modeling paradigms, evaluate their empirical grounding, and recommend pathways for more rigorous integration of behavioral science into epidemiological modeling. By mapping models to established psychosocial constructs, our approach offers a novel framework for guiding future behavior-adaptation model formulation, comparative evaluation, and data collection strategies.
Search Strategy. A systematic search was conducted on Nov
Reference
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