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

RAISE：IHBEM：流行病模型中人类行为变化的数学公式

• 批准号: 2229819
• 负责人: Navid Ghaffarzadegan
• 金额: $ 89万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229819&HistoricalAwards=false
• 关键词: RAISE; IHBEM; Mathematical; Formulations; Human

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.

这项研究通过整合数学建模以及社会和行为科学来增强传染病建模范式来有助于流行病学建模。从对感知的感染风险响应社会疏远和戴面具的戴，到经济压力下的非药物干预措施的变化，人类的反应改变了共同的19个大流行成果。在这个项目中，基本步骤是朝着合并行为和流行病学模型的愿景。这一研究导致了代表人类行为和疾病在相互联系的结构中的传播的流行病学模型，有助于对流行病的更准确的预测，并增强对社会福祉的重大影响的政策制定。 该项目是由数学科学（DMS）在数学和身体科学局（MPS）（MPS）和社会和经济科学（SES）的划分的共同资助的（例如，面具使用），愿意接种，风险感知和依从性疲劳的意愿，所有这些都在连接个人决策或政府政策以爆发动态的Nexus中； （ii）将这些人类行为机制整合到疾病模型中，以便内源性预测； （iii）分析流行病预测的结果变化，以及它们对基于模型的政策建议，疫苗优先次序，经济公共健康权衡以及出现新的正常地方性状态的影响。从数学上讲，系统动力学方法建立在普通的微分方程模型以及各种估计和验证的统计方法上。建模方法和验证技术依赖于关于人类行为的多个数据源以及在19009年大流行期间疾病的传播。在构建和验证耦合行为疾病模型后，对研究的中心假设进行了测试：结合人类行为变化的模型内源性变化胜过那些在长期预测任务中缺乏此类反馈机制的人，并提供独特的政策建议，并提供独特的政策建议。该奖项反映了NSF的法定任务，并通过评估了基金会的范围来反映支持者的支持，并已被评估了基础的范围。