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

在这个项目中，流行病数量的估计和干预效果的评估，通过将现实的行为反馈机制模型进行改进。 机械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以及预测病例和死亡轨迹的有力工具。然而，缺乏经验数据的社会接触模式及其时间变化，阻碍了这些模型的进展和应用。具体而言，缺乏急性感染者及其密切接触者（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体收集的。因此，急性感染的传播模型往往依赖于简单化的假设，即感染者会完美地自我隔离，或者接触模式在整个传染期内保持不变。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。为了解决这个问题，在这个项目中，急性感染者和他们的密切接触者之间的接触模式的时间变化进行了测量。 该项目的长期效益包括建立传染病建模能力，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，采用基于临床的方法招募美国急性呼吸道感染和急性胃肠炎病例以及这些病例的家庭成员。使用在线接触日记在两周内捕获其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而变化。 这些病例的家庭成员被招募来评估密切接触者在暴露后如何改变他们的行为。这使得开发的传输动态模型（易感染的恢复模型和适当的阐述），这是结构化和参数化的基础上，在这项研究中调查的行为现实主义。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。关键模型参数的估计以及干预措施的影响如何不同（即，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。

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.