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

流行病学模型为决策者提供了关于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、受感染个体的病程以及疫苗等干预措施效果的信息，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，人们不会对流行病或政策干预作出统一的反应。这种遗漏严重影响了这些模型的准确性，进而影响了为防治该疾病及其影响而部署的政策的有效性。为了解决这一限制，该项目汇集了约翰霍普金斯大学流行病学、数学生物学、系统工程、经济学和决策科学方面的专业知识，开发了一个新的综合建模框架，将疾病传播的传统流行病学模型与个人决策的经济模型相结合。这种新方法的三个最显著的好处是：1)改进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减轻疾病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。了解疾病出现、传播和控制期间人类行为与病原体之间复杂的动态相互作用的工具是缺乏的。目前的流行病学模型通常不将个人行为内因化，而具有这一特征的基于主体的经济学模型遗漏了疾病传播和进展的关键方面。本研究的目标是：1)在爆发、流行或大流行期间更准确地预测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，从而能够对经济和卫生政策进行评估；3)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来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一个多学科团队，他们拥有流行病学、数学生物学、系统工程、经济学和决策科学方面的专业知识，开发了一个新的综合数学框架，将传染病动力学的机制模型与人类行为的经济模型结合起来。该框架旨在捕捉对流行病状况和现有政策的行为反应，以及个人层面的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理事会（MPS）的数学科学部（DMS）和社会、行为和经济科学理事会（SBE）的社会和经济科学部（SES）共同资助。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

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.