IHBEM: Modeling the Impact of Behavioral Feedback on the Transmission of Acute Infectious Diseases

在这个项目中，通过将现实的行为反馈融入到机械模型中，改进了流行病数量的估计和干预影响的评估。机械论数学模型是了解传染病流行病学、评估各种控制措施的影响、预测病例和死亡轨迹的有力工具。然而，缺乏关于社会接触模式及其时间变化的经验数据，阻碍了这些模型的进步和应用。具体地说，缺乏来自急性感染患者及其密切接触者(如家庭成员)的社会接触数据，因为行为数据传统上是从健康个人那里收集的。因此，急性感染的传播模型通常依赖于简单的假设，即感染者完全隔离自己，或者接触模式在整个感染期保持不变。模型结构和参数化中的这些不切实际的假设可能会给结果带来偏差。为了解决这一问题，本项目测量了急性感染者及其密切接触者之间接触模式的时间变化。该项目的长期效益包括建立传染病模型的能力，从而为决策者和公共卫生官员提供更知情的决策工具，以制定干预措施。为了实现研究目标，以临床为基础的方法招募了患有急性呼吸道感染和急性胃肠炎的美国病例及其家庭成员。使用在线接触日记在两周的时间内捕获他们接触模式的每日变化，并评估这些模式如何随疾病严重程度的变化而变化。这些病例的家庭成员被招募来评估密切接触者在接触后如何改变他们的行为。这使得传播动力学模型(易感-感染-恢复模型和适当的阐述)的开发成为可能，这些模型是基于本研究中调查的行为现实主义而结构化和参数化的。SARS-CoV-2被用作急性呼吸道感染的例子，轮状病毒被用作急性胃肠炎的例子。在将行为反馈纳入这些机械模型后，对关键模型参数的估计以及干预措施的影响如何不同(即，改进)进行了量化。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

In this project, the estimation of epidemic quantities and the assessment of intervention impacts are improved by incorporating realistic behavioral feedback into mechanistic models. Mechanistic mathematical models are powerful tools to understand the epidemiology of infectious diseases, evaluate the impact of various control measures, and forecast the trajectory of cases and deaths. Lack of empirical data on social contact patterns and their temporal variation, however, has hindered progress and application of these models. Specifically, there is a dearth of social contact data from individuals with acute infections and their close contacts, such as household members, as behavior data have traditionally been collected from healthy individuals. Consequently, transmission models for acute infections often rely on simplistic assumptions that infected individuals perfectly isolate themselves or that contact patterns remain unchanged throughout the infectious period. These unrealistic assumptions in model structure and parameterization can introduce bias into the results. To address this issue, in this project the temporal changes in contact patterns among individuals with acute infection and their close contacts is measured. Long-term benefits of the project include the building of capacity in infectious disease modeling, thus providing decision makers and public health officials with more informed decision-making tools to develop interventions. To achieve research objectives, clinic-based approaches are employed to recruit U.S. cases with acute respiratory infections and acute gastroenteritis and household members of these cases. Daily changes in their contact patterns are captured over a two-week period using an online contact diary and how these patterns vary by disease severity is assessed. Household members of these cases are recruited to evaluate how close contacts modify their behaviors following exposure. This enables development of transmission dynamic models (susceptible-infectious-recovered models and appropriate elaborations), which are structured and parameterized based on the behavioral realism investigated in this study. SARS-CoV-2 is used as an example of acute respiratory infection and rotavirus is used as an example of acute gastroenteritis. How the estimation of key model parameters as well as the impact of interventions differ (i.e., improve) after incorporating behavioral feedback into these mechanistic models is quantified.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.