RAISE: IHBEM: Modeling Dynamic Disease-Behavior Feedbacks for Improved Epidemic Prediction and Response

RAISE：IHBEM：对动态疾病行为反馈进行建模以改进流行病预测和应对

• 批准号: 2229996
• 负责人: Lauren Gardner
• 金额: $ 99.99万
• 依托单位: Johns Hopkins University
• 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: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229996&HistoricalAwards=false
• 关键词: RAISE; IHBEM; Modeling; Dynamic; Disease

Epidemiological models inform policymakers about how infectious diseases like COVID-19 may spread through the population. These predictions guide the allocation of health resources and interventions to reduce disease spread or mitigate its burdens. While these models incorporate information about how an infection is transmitted, the disease course in infected individuals, and the effects of interventions like vaccines, they rarely capture how individuals make behavior decisions or how these choices respond to an epidemic. As individuals face different economic and health circumstances, the population will not uniformly respond to the epidemic or policy interventions. This omission materially affects the accuracy of these models and by extension, the effectiveness of policies deployed to combat the disease and its impacts. To address this limitation, this project brings together expertise in epidemiology, mathematical biology, systems engineering, economics, and decision science at Johns Hopkins University to develop a new integrated modeling framework that combines traditional epidemiological models of disease spread with economic models of individual decision-making. The three most significant benefits of this new approach are: 1) Improving future epidemic forecasts for existing and new emerging infections; 2) Allowing for a cost-benefit analysis of disease mitigation policies that reflects changes in human behavior and economic outputs; 3) Helping predict the disparate impact of an epidemic and mitigation policies across socioeconomic groups facing different health-wealth tradeoffs. The broader impacts of the project include dissemination of research results to broad audience and training of public health practitioners. The tools to understand the complex dynamic interactions between human behavior and pathogens during disease emergence, dissemination, and control are lacking. Current epidemiological models generally do not endogenize individual behaviors, while agent-based models from economics that have this feature miss critical aspects of disease transmission and progression. The goals of this study are to: 1) More accurately predict disease spread and health outcomes during an outbreak, epidemic, or pandemic; 2) Enable multi-objective policy design by simultaneously quantifying both the disease burden and economic costs of proposed policies, allowing for the evaluation of both economic and health policies; and 3) Evaluate heterogeneity and equity by quantifying the distributional impacts of disease burden and economic cost across socio-demographic and risk groups. To address these goals, this project brings together a multi-disciplinary team at Johns Hopkins—with expertise in epidemiology, mathematical biology, systems engineering, economics, and decision science—to develop a novel integrated mathematical framework that combines mechanistic models of infectious disease dynamics with economic models of human behavior. This framework is designed to capture behavioral responses to both the epidemic state and policies in place, and the effect of individual-level behavioral responses on the trajectory of the disease within a population.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.

流行病学模型为政策制定者提供了关于COVID-19等传染病如何在人群中传播的信息。这些预测指导卫生资源的分配和干预措施，以减少疾病传播或减轻其负担。虽然这些模型包含了有关感染如何传播的信息，感染个体的疾病过程以及疫苗等干预措施的影响，但它们很少捕捉到个体如何做出行为决定或这些选择如何应对流行病。由于个人面临不同的经济和健康状况，人口对疫情或政策干预的反应不会一致。这一遗漏严重影响了这些模型的准确性，进而影响了为防治这一疾病及其影响而采取的政策的有效性。为了解决这一限制，该项目汇集了流行病学，数学生物学，系统工程，经济学和决策科学在约翰霍普金斯大学的专业知识，开发一个新的综合建模框架，结合传统的流行病学模型的疾病传播与经济模型的个人决策。这种新方法的三个最重要的好处是：1）改善对现有和新出现的感染的未来流行病预测; 2）允许对反映人类行为和经济产出变化的疾病缓解政策进行成本效益分析; 3）帮助预测流行病和缓解政策对面临不同健康-财富权衡的社会经济群体的不同影响。该项目的更广泛影响包括向广大受众传播研究成果和培训公共卫生从业人员。缺乏了解疾病发生、传播和控制过程中人类行为与病原体之间复杂动态相互作用的工具。目前的流行病学模型通常不会将个人行为内生化，而具有这一特征的经济学基于主体的模型则忽略了疾病传播和进展的关键方面。本研究的目标是：1）更准确地预测疾病爆发、流行或大流行期间的疾病传播和健康结果; 2）通过同时量化拟议政策的疾病负担和经济成本，实现多目标政策设计，从而对经济和健康政策进行评估;通过量化疾病负担和经济成本在社会人口和风险群体中的分布影响，评估异质性和公平性。为了实现这些目标，该项目汇集了约翰霍普金斯大学的多学科团队，他们具有流行病学，数学生物学，系统工程，经济学和决策科学的专业知识，以开发一种新的综合数学框架，将传染病动力学的机械模型与人类行为的经济模型相结合。该框架旨在捕捉对流行状态和政策的行为反应，以及个人行为反应对人群中疾病轨迹的影响。该项目由数学和物理科学局（MPS）数学科学处（DMS）和社会科学局社会和经济科学处（SES）共同资助，行为和经济科学（SBE）。该奖项反映了NSF的法定使命，并被认为是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的支持。