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.

鉴于病毒的抗原特性，人类行为是疾病传播的唯一最重要的决定因素。与疾病传播相关的人类行为，如社交距离，戴面罩或无症状时进行测试，取决于一系列因素，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。使用决策模型来按时间和子群体定制这些干预措施可以确保效率（例如，低成本），有效性（例如，减少住院次数）和公平（例如，公平获取药品）。该项目的总体目标是将公共卫生干预措施驱动的行为改变纳入流行病学数学模型，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的呼吸系统疾病，并以COVID-19为例来验证该方法并量化其影响。所提出的方法可以推广到政策制定者针对行为改变的其他应用，例如药物依从性。在目标1中，研究人员将通过作用机制（即，能力、机会和动机）。将考虑九种类型的政策干预（教育，说服，激励，胁迫，限制，培训，轻推，建模和启用）与两种类型的预防行为-人际保护（即，社交距离，戴口罩）和服务利用率（即，测试，疫苗接种）。实证工作包括对减少COVID-19传播的干预措施进行动态荟萃分析，并辅以德尔菲方法。研究人员将开发一个在线工具，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量作为基于代理的建模的输入。目标1的结果将通过将自适应人类行为集成到基于代理的模型（ABM）中来实现。然而，现实的ABM与大量的代理类型和复杂的行为和社会过程是计算密集型的模拟，分析棘手的，可能是不可推广的。这些缺陷可能会阻碍对ABM的全面分析和验证，从而妨碍在大流行期间将其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，从ABM中导出常微分方程（ODE）模型。研究人员还引入了新的正则化技术，该技术结合了生物物理约束，为决策者提供了可解释的结果。这些ODE模型和学习的函数形式近似于干预措施对推动疾病传播的行为和社会过程的影响，将用于目标3，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）数学科学处（DMS）和社会和经济科学处（SES）共同资助。该奖项反映了NSF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。