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。与疾病传播相关的人类行为，例如社交疏远，戴脸部覆盖或测试，何时不对称取决于许多因素，包括风险感知，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能会影响这些因素改变人类行为。使用决策模型根据时间和子人口量身定制这些干预措施可以确保有效性（例如，低成本），有效性（例如，住院时间更少）和权益（例如，使用药品的公平性）。该项目的总体目标是通过公共卫生干预措施将行为改变驱动力纳入数学流行病学模型，以告知在传染病暴发期间决策和政策评估。研究人员一般认为呼吸道疾病，并以Covid-19为例，以验证该方法并量化影响。所提出的方法可以推广到其他应用程序，在其他应用程序中，政策制定者的目标改变，例如药物依从性。在AIM 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。将考虑九种类型的政策干预措施（教育，说服力，激励措施，胁迫，限制，训练，训练，建模和支持）与两种类型的预防行为 - 人际交往保护（即社交距离，戴口罩，戴上面具）和服务利用（即测试，接种）。经验工作涉及对干预措施的动态荟萃分析，以减少由Delphi方法补充的Covid-19的扩散。调查人员将开发一种在线工具，该工具将使研究人员能够为荟萃分析做出贡献，并使用由此产生的加权平均效应大小作为基于代理建模的输入。 AIM 1的结果将通过将自适应人类行为整合到基于代理的模型（ABM）中来实现。但是，具有大量代理类型以及复杂的行为和社会过程的现实ABM在计算密集程度上可以模拟，分析性棘手，并且可能无法推广。这些缺点可能会抑制对ABM的全面分析和验证，从而阻止其在大流行期间的决策和决策制定。在AIM 2中，研究人员提出了一个方程学习框架，以从ABM中得出普通的微分方程（ODE）模型。研究人员还引入了新型调节技术，这些技术结合了生物物理约束，为决策者提供了可解释的结果。 These ODE models and the learned functional forms Approximate 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 Sc​​iences (SES) in the Directorate of Social, Behavioral and Economic Sc​​iences （SBE）。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来评估，被认为是宝贵的支持。