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

这项研究通过整合数学模型与社会和行为科学来增强传染病模型范式，从而为流行病学模型做出贡献。从为了应对感染风险而采取的社交距离和戴口罩，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。在该项目中，为实现合并行为模型和流行病学模型的愿景采取了基础步骤。这一系列研究产生了流行病学模型，该模型代表了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) 下的数学科学部 (DMS) 以及社会、行为和经济科学局 (SBE) 下的社会和经济科学部 (SES) 联合资助。这项研究的三个具体目标是：(i) 模拟人类行为，重点关注五种行为结构：互动（例如流动性）、遵守预防措施（例如使用口罩）、愿意疫苗接种、风险认知和依从性疲劳，所有这些都将个人决策或政府政策与疫情动态联系起来； (ii) 将这些人类行为机制整合到疾病模型中，以便内生地预测它们； (iii) 分析流行病预测所产生的变化及其对基于模型的政策建议的影响，这些建议涉及疫苗优先顺序、经济与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用于估计和验证的各种统计方法的基础上。建模方法和验证技术依赖于有关人类行为和 COVID-19 大流行期间疾病传播的多个数据源。在建立和验证行为-疾病耦合模型后，该研究的中心假设得到了检验：在长期预测任务中，包含人类行为变化的模型在长期预测任务中优于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，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.