RAISE: IHBEM: Mathematical Formulations of Human Behavior Change in Epidemic Models

这项研究有助于流行病学建模，通过整合数学建模和社会和行为科学，以加强传染病建模范式。从社交距离和戴口罩以应对感知的感染风险，到经济压力下非药物干预措施的变化，人类的反应改变了COVID-19大流行的结果。在这个项目中，采取了基础性的步骤，以实现合并行为和流行病学模型的愿景。这一系列研究导致流行病学模型，代表人类行为和疾病在相互关联的结构中的传播，有助于更准确地预测流行病，并加强对社会福祉产生重大影响的政策制定。 该项目由数学和物理科学局（MPS）数学科学部（DMS）和社会、行为和经济科学局（SBE）社会和经济科学部（SES）共同资助。该研究的三个具体目标是：（i）模拟人类行为，重点是五种行为结构：互动（例如，流动性），遵守预防措施（例如，口罩使用），接种疫苗的意愿，风险感知和依从性疲劳，所有这些都是将个人决策或政府政策与疫情动态联系起来的纽带;（ii）将这些人类行为机制整合到疾病模型中，以便它们被内生地预测;以及（iii）分析流行病预测的变化及其对基于模型的疫苗优先级政策建议的影响，经济-公共卫生权衡，以及新的正常流行状态的出现。在数学上，系统动力学方法建立在常微分方程模型和各种统计方法的估计和验证。建模方法和验证技术依赖于COVID-19大流行期间人类行为和疾病传播的多个数据源。在建立和验证耦合行为-疾病模型后，测试了研究的中心假设：在长期预测任务中，包含人类行为变化的模型的内在表现优于那些缺乏这种反馈机制的模型，并提供独特的政策建议。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

This research contributes to epidemiological modeling by integrating mathematical modeling and social and behavioral sciences to enhance the infectious disease modeling paradigm. From social distancing and mask-wearing in response to perceived risk of infection to changes in non-pharmaceutical Interventions under economic pressures, human responses altered the COVID-19 pandemic outcomes. In this project, foundational steps are taken toward the vision of merging behavioral and epidemiological models. This line of research leads to epidemiological models that represent human behavior and the spread of the disease in interconnected structures, contribute to more accurate forecasting of an epidemic, and enhance policy-making with major impacts on societal well-being. This project is funded jointly by the Division of Mathematical Sciences (DMS) in the Directorate of Mathematical and Physical Sciences (MPS) and the Division of Social and Economic Sciences (SES) in the Directorate of Social, Behavioral, and Economic Sciences (SBE).The three specific objectives of this research are: (i) Model human behavior with a focus on five behavioral constructs: interactions (e.g., mobility), compliance with preventive measures (e.g., mask use), willingness to vaccinate, risk perception, and adherence fatigue, all of which are at the nexus connecting personal decision or government policies to outbreak dynamics; (ii) Integrate these human behavior mechanisms into disease models so that they are projected endogenously; and (iii) Analyze the resulting changes in epidemic forecasting as well as their implications on model-based policy recommendations regarding vaccine prioritization, economic-public health tradeoffs, and emergence of a new normal endemic state. Mathematically, the system dynamics approach builds on ordinary differential equation models and various statistical methods for estimation and validation. The modeling approach and validation techniques rely on multiple data sources on human behavior and the spread of the disease during the COVID-19 pandemic. Upon building and validating coupled behavior-disease models, the central hypothesis of the study is tested: Models that incorporate human behavioral changes endogenously outperform those that lack such feedback mechanisms in long-term forecasting tasks, and offer distinct policy recommendations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.