RAISE: IHBEM: Understanding and Predicting Behavioral Responses to Epidemic Risks and Control Policies: Implications for Epidemiological Models and Policy Design

RAISE：IHBEM：理解和预测对流行病风险和控制政策的行为反应：对流行病学模型和政策设计的影响

• 批准号: 2230119
• 负责人: David Finnoff
• 金额: $ 99.16万
• 依托单位: University of Wyoming
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2230119&HistoricalAwards=false
• 关键词: RAISE; IHBEM; Understanding; Predicting; Behavioral

The global COVID-19 pandemic has revealed how critically the success of public health measures depends on understanding human behavioral responses to both infection risks and policy recommendations. Core mathematical epidemiological models have provided useful insight about pandemic risks, but typically do not account for the wide variety of people’s responses to the risk from the disease and the ways these responses shape ongoing transmission. Behavior affects disease spread: nature affects people, and people affect nature—connecting the natural and human systems in feedback loops that determine the course of outbreaks. The multi-disciplinary research team will bridge disciplines, toolkits, and data to advance knowledge about these feedbacks between human behaviors and infectious disease outcomes. The PIs will extend mathematical epidemiological models by including heterogeneity in behavioral responses to risks and social norms (e.g., mask/vaccines acceptance) involving feedback from both aggregate public health outcomes and the diverse responses from individuals. The models will be informed by data from three countries with unique characteristics representing health risks, public policies, information sources, and government trust: the United States, Norway, and Sweden. Real-world data will be supplemented by data collected in controlled settings—through surveys and behavioral laboratory experiments—to gain a deeper understanding of the individual factors and social processes that shape people’s responses to epidemic risks. The project will improve the abilities of epidemiological models to predict both disease outcomes and the economic impacts of public health regulations or guidelines, enhancing the capacity of public policy-makers to design and evaluate epidemic control measures during future outbreaks.This project will develop and estimate behavioral reaction functions that can be included in systems of ordinary differential equations (ODEs) that comprise most epi-models, with the goal of better informing policy design during novel epidemics. The PIs will focus on two overarching research questions: Q1 How do people’s behavioral reactions influence the evolution of an infectious disease outbreak through feedbacks on pathogen spread? and Q2 How are people’s behaviors during an outbreak moderated by the pronounced uncertainties and frequent policy changes that are characteristic of novel epidemics? To address these questions, the PIs will: (i) develop a new epidemiological-behavioral system of coupled ODEs, (ii) use observational, survey, and experimental data and methods to estimate people’s reactions to epidemic risks and top-down control policies, (iii) integrate our empirical findings into our new epi-model, and (iv) use their parameterized epi-model to conduct retrospective and prospective policy simulations and comparisons. In addition, the PIs will use observational data from three developed countries that undertook distinct policy approaches to the on-going COVID-19 pandemic: the United States, Norway, and Sweden. Moreover, the PIS will design surveys and behavioral laboratory experiments to gain a deeper understanding of responses to risk in contexts that are similar to novel epidemics. This multi-method approach will provide opportunities to test hypotheses about the mechanisms that underlie associations in the observational data and examine the external validity of the survey and laboratory studies.This project is jointly funded 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).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.

全球新冠肺炎疫情揭示了公共卫生措施的成功在多大程度上取决于理解人类对感染风险和政策建议的行为反应。核心数学流行病学模型提供了有关大流行风险的有用见解，但通常没有考虑到人们对疾病风险的各种反应，以及这些反应影响持续传播的方式。行为影响疾病传播：自然影响人，人影响自然--在决定疫情爆发过程的反馈环路中连接自然和人类系统。多学科研究团队将在学科、工具包和数据之间架起桥梁，以促进对人类行为和传染病结果之间的这些反馈的了解。PIs将扩展数学流行病学模型，纳入对风险和社会规范(例如，口罩/疫苗接受度)的行为反应的异质性，涉及公共卫生总体结果的反馈和个人的不同反应。这些模型将以来自三个国家的数据为依据，这些国家具有独特的特征，代表健康风险、公共政策、信息来源和政府信任：美国、挪威和瑞典。真实世界的数据将得到在受控环境中收集的数据的补充-通过调查和行为实验室实验-以更深入地了解塑造人们对流行病风险的反应的个人因素和社会过程。该项目将提高流行病学模型预测疾病结果和公共卫生法规或指南的经济影响的能力，增强公共政策制定者在未来暴发期间设计和评估疫情控制措施的能力。该项目将开发和评估可包括在组成大多数EPI模型的常微分方程组(ODE)系统中的行为反应函数，目的是在新疫情期间更好地为政策设计提供信息。PI将集中在两个主要的研究问题上：Q1人们的行为反应如何通过对病原体传播的反馈影响传染病爆发的演变？第二个问题是，人们在疫情爆发期间的行为如何受到新流行病特有的明显不确定性和频繁的政策变化的影响？为了解决这些问题，PIs将：(I)开发一种新的流行病学-行为耦合系统，(Ii)使用观察、调查和实验数据和方法来估计人们对流行病风险和自上而下控制政策的反应，(Iii)将我们的经验结果整合到我们的新的EPI模型中，以及(Iv)使用他们的参数化EPI模型来进行回溯性和前瞻性的政策模拟和比较。此外，PIs将使用来自三个发达国家的观测数据，这三个国家对正在进行的新冠肺炎大流行采取了不同的政策方法：美国、挪威和瑞典。此外，PIS将设计调查和行为实验室实验，以更深入地了解在类似于新流行病的背景下对风险的反应。这种多方法方法将提供机会来测试关于观测数据中关联的机制的假设，并检查调查和实验室研究的外部有效性。该项目由数学和物理科学局(MPS)的数学科学部(DMS)和社会、行为和经济科学局(SBE)的社会和经济科学部(SES)联合资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。