Data-Driven Modeling to Improve Understanding of Human Behavior, Mobility, and Disease Spread

疾病动态模型是用于预测随时间推移的病例和死亡人数以及在决策者准备和应对传染病暴发时为其提供支持的重要工具。然而，尽管取得了重大进展，许多模型仍然缺乏对人类行为和流动性的现实表现，这是疾病传播的关键驱动因素。如果不考虑人类行为的复杂性，模型做出准确预测的能力是有限的，特别是在更长的时间范围内。该项目研究将现实的人类行为和流动性纳入疾病传播模型，以1)更好地解释人类应对疾病爆发的不同方式，2)改进对传染病传播的预测，以及3)帮助制定最有效的缓解政策。研究人员使用公开可用的数据，以便可以在美国任何县或州快速部署模型，以预测和缓解未来传染性呼吸道疾病(例如新冠肺炎、季节性流感、麻疹和天花)的爆发。这些模型可能会提供更及时和更准确的预测，帮助公众、关键机构和政策制定者预测即将发生的事情，并为基于证据的政策制定提供支持。该项目将为早期职业研究人员提供职业发展机会，并为有抱负的科学家暑期实习计划中的博士后研究人员、研究生、本科生和高中生提供培训机会。研究人员将使用数据驱动的方法来解释疾病爆发时行为反应的时空变化。他们假设，地区变量，如平均收入、年龄、政治倾向与行为反应的空间模式相关，并将利用非常大的数据集来挖掘这些变量与观察到的行为反应之间的关联规则，包括社交距离、呆在家里的行为、口罩使用和疫苗接受程度。这些关联规则将被用来开发一种新的建模框架，该框架捕捉人类对疾病反应的时空变化。所提出的建模框架将以弗吉尼亚州费尔法克斯县为例，对新冠肺炎的传播进行模拟。这一框架还将被用于指令性分析，以便在未来爆发传染病时找到最佳行动方案。研究人员将模拟和优化旨在减缓疾病传播和将社会经济影响降至最低的政策措施。这一优化将结合自动优化工具与政策、流行病学、健康地理和心理学研究人员的专业知识。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。

Models of disease dynamics are important tools used to predict the numbers of cases and deaths over time and to support policymakers as they prepare for and respond to infectious disease outbreaks. However, despite significant advances, many models still lack realistic representations of human behavior and mobility, which are key drivers of disease spread. Without accounting for the complexity of human behavior, models are limited in their ability to make accurate predictions, especially over longer time horizons. This project investigates the inclusion of realistic human behavior and mobility in models of disease spread to 1) better explain the different ways that humans respond to disease outbreaks, 2) improve predictions of infectious disease spread, and 3) help to prescribe the most effective mitigation policies. The investigators use publicly available data so that models can be rapidly deployed for any county or state in the U.S. to predict and mitigate future outbreaks of infectious respiratory diseases (e.g., COVID-19, seasonal influenza, measles, and smallpox). These models may provide more timely and accurate predictions to help the general public, key institutions, and policymakers anticipate what is to come and provide support for evidence-based policy making. This project will support professional development opportunities for early-career researchers and training opportunities for a postdoctoral researcher, graduate, undergraduate, and high school students in the Aspiring Scientists Summer Internship program.The researchers will use a data-driven approach to explain the spatio-temporal variations in the behavioral response to a disease outbreak. They hypothesize that regional variables such as average income, age, political leaning are associated with spatial patterns of behavioral response, and will leverage very large data sets to mine association rules between such variables and observed behavioral response, including social distancing, stay-at-home behavior, mask usage, and vaccine acceptance. These association rules will be used to develop a novel modeling framework that captures spatio-temporal variations of human response to disease. The proposed modeling framework will be implemented to simulate the spread of COVID-19 using Fairfax County, VA, as a case study. This framework also will be leveraged for prescriptive analytics to find the best course of action in the event of future infectious disease outbreaks. The researchers will simulate and optimize policy measures aimed at mitigating disease spread and minimizing socio-economic impact. This optimization will combine automatic optimization tools with the expertise of researchers in policy, epidemiology, health geography, and psychology.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.