IHBEM: Data-driven integration of behavior change interventions into epidemiological models using equation learning

IHBEM：使用方程学习将行为改变干预措施以数据驱动的方式整合到流行病学模型中

• 批准号: 2327836
• 负责人: Osman Ozaltin
• 金额: $ 76万
• 依托单位: North Carolina 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: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2327836&HistoricalAwards=false
• 关键词: IHBEM; Data; driven; integration; behavior

Given the antigenic characteristics of a virus, human behavior is the single most important determinant of disease transmission. Human behaviors relevant to disease spread such as social distancing, wearing face coverings, or testing when asymptomatic depend on a host of factors including risk perceptions, physical ability as well as the availability of resources and opportunities. Policy interventions by health agencies or other decision makers can impact these factors to alter human behaviors. Using decision models to tailor these interventions by time and sub-population can ensure efficiency (e.g., low cost), effectiveness (e.g., less hospitalizations), and equity (e.g., fairness in access to pharmaceuticals). The overall goal of this project is to incorporate behavior change driven by public health interventions into mathematical epidemiological models to inform decision making and policy evaluation during infectious disease outbreaks. The investigators consider respiratory diseases in general, and use COVID-19 as an example to validate the approach and quantify impact. The proposed methods can be generalized to other applications where policy makers target behavior change, such as medication adherence.In Aim 1, the investigators will trace the impact of policy interventions on infection-preventive behaviors through mechanisms of action (i.e., capability, opportunity, and motivation). Nine types of policy interventions will be considered (education, persuasion, incentives, coercion, restriction, training, nudging, modeling, and enablement) in relation to two types of preventive behavior – interpersonal protection (i.e., social distancing, wearing a face mask) and service utilization (i.e., testing, vaccination). The empirical work involves a dynamic meta-analysis of interventions to reduce the spread of COVID-19, supplemented by Delphi methods. The investigators will develop an online tool that will enable researchers to contribute to the meta-analysis and use the resultant weighted-average effect sizes as inputs for agent-based modeling. The results of Aim 1 will be operationalized by integrating adaptive human behaviors into an agent-based model (ABM). However, realistic ABMs with a large number of agent types and complex behavioral and social processes are computationally intensive to simulate, analytically intractable, and may not be generalizable. These drawbacks may inhibit the comprehensive analysis and validation of ABMs and thereby prevent their utilization for decision- and policy-making during a pandemic. Thus, in Aim 2, the investigators propose an equation learning framework to derive ordinary differential equation (ODE) models from ABMs. The investigators also introduce novel regularization techniques that incorporate biophysical constraints to provide interpretable results for decision-makers. These ODE models and the learned functional forms approximating the impact of interventions on behavioral and social processes that drive disease spread will be used in Aim 3 to inform policies through bilevel optimization models.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.

鉴于病毒的抗原特征，人类行为是疾病传播的最重要的决定因素。与疾病传播相关的人类行为，例如社交距离、戴口罩或无症状时进行检测，取决于许多因素，包括风险认知、身体能力以及资源和机会的可用性。卫生机构或其他决策者的政策干预可以影响这些因素，从而改变人类行为。使用决策模型按时间和亚人群定制这些干预措施可以确保效率（例如，低成本）、有效性（例如，减少住院治疗）和公平（例如，获得药品的公平性）。该项目的总体目标是将公共卫生干预措施驱动的行为变化纳入数学流行病学模型中，为传染病爆发期间的决策和政策评估提供信息。研究人员考虑了一般呼吸系统疾病，并以 COVID-19 为例来验证该方法并量化影响。所提出的方法可以推广到决策者以行为改变为目标的其他应用，例如药物依从性。在目标 1 中，研究人员将通过行动机制（即能力、机会和动机）追踪政策干预对感染预防行为的影响。将考虑与两类预防行为——人际保护（即保持社交距离、戴口罩）和服务利用（即检测、疫苗接种）相关的九种类型的政策干预措施（教育、说服、激励、强制、限制、培训、助推、建模和赋能）。实证工作涉及对减少 COVID-19 传播的干预措施进行动态荟萃分析，并辅以德尔菲法。研究人员将开发一种在线工具，使研究人员能够为荟萃分析做出贡献，并使用所得的加权平均效应大小作为基于主体的建模的输入。目标 1 的结果将通过将适应性人类行为集成到基于代理的模型 (ABM) 中来实现。然而，具有大量代理类型以及复杂的行为和社会过程的现实 ABM 需要大量的计算来模拟，分析起来很困难，并且可能无法推广。这些缺点可能会阻碍对反导措施的全面分析和验证，从而阻碍其在大流行期间用于决策和政策制定。因此，在目标 2 中，研究人员提出了一个方程学习框架，用于从 ABM 导出常微分方程 (ODE) 模型。研究人员还引入了新颖的正则化技术，该技术结合了生物物理约束，为决策者提供可解释的结果。这些 ODE 模型和学习的函数形式近似于驱动疾病传播的行为和社会过程干预措施的影响，将在目标 3 中使用，通过双层优化模型为政策提供信息。该项目由数学和物理科学局 (MPS) 的数学科学部 (DMS) 和社会、行为和经济科学局的社会和经济科学部 (SES) 共同资助 (SBE)。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。