RAPID: COVID-19 Behavior, Perception, and Control Across Geographic and Economic Gradients

新冠肺炎是一种由最近发现的新型冠状病毒引起的呼吸道疾病。自2019年在武汉发现中国以来，新冠肺炎已经在全球引发了200多万起病例。它已经蔓延到全球，包括许多没有足够医疗基础设施的脆弱国家。人们已经尝试了许多不同的应对措施，包括社交距离、学校和活动关闭，以及旅行禁令。该项目将开发数学模型来解决三个基本问题：1)有效保护需要多少参与和协调控制？2)什么独立控制努力可以弥补缺乏协调以实现有效保护？以及3)社区人口结构、社会经济状况和卫生保健基础设施如何影响结果？该项目旨在为各级(地方、区域、国家、国际)疾病控制政策的协调提供信息，以帮助减少目前和未来的暴发。该项目将通过一种新的风险感知驱动的传染病模型，促进对控制努力影响的基本理解，并预测哪些驱动公众对社区层面控制努力的需求可能导致潜在有害的长期决策。该项目将对两名博士生进行与疾病动力学和传播的数学建模相关的技术培训。新冠肺炎正在权衡和实施许多缓解方案，在不同的行政级别做出不同的决定，包括替代隔离战略和不同程度的“封锁”。所有这些决定都伴随着对风险的不同看法。PIs将开发和分析疾病传播模型，其中纳入了各种因素，包括公众对风险的感知、住院人口的年龄结构以及从当地社区到整个国家的不同尺度的空间结构。这些模型将被用来探索社区人口结构、社会经济条件和卫生保健基础设施的影响，以及这些因素如何在不同的社会和经济环境下影响控制努力。有了这些模型的结果，政策制定者和决策者可以考虑他们所管理的社区的特定特征的影响，作为更广泛的公共卫生努力网络的贡献者，并选择最佳缓解步骤。这项工作将为疾病控制政策的协调提供信息，以直接遏制正在进行的疫情。该项目的结果，虽然是专门为新冠肺炎病毒控制战略量身定做的，但将适用于未来任何新的传染病爆发。该奖项的资金来自CARE法案补充资金分配给MP。该奖项反映了美国国家科学基金会的法定使命，并已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。

COVID-19 is a respiratory disease caused by a recently discovered, novel coronavirus. Since its discovery in Wuhan, China, in 2019, COVID-19 has already led to over 2 million cases globally. It has spread globally, including to many vulnerable countries without adequate healthcare infrastructures. Many different responses have been tried, including social distancing, school and event closings, and travel bans. This project will develop mathematical models to address three fundamental questions: 1) how much participation and coordinated control is needed for effective protection? 2) what independent control efforts can compensate for lack of coordination to achieve effective protection? and 3) how do community population demographics, socioeconomic conditions, and health care infrastructure impact outcomes? This project aims to inform coordination of disease control policies at all scales (local, regional, national, international) to aid in curtailing the ongoing and future outbreaks. This project will advance fundamental understanding of the impacts of control efforts via a new risk perception-driven infectious disease model, and predict which drivers of public demand for community-level control efforts might lead to potentially harmful long-term decisions. The project will involve training two doctoral students in techniques related to mathematical modeling of disease dynamics and spread. Many mitigation options are being weighed and implemented for COVID-19, with different decisions made at different administrative levels, including alternative quarantine strategies and different degrees of “lockdown”. All of these decisions come with different perceptions of risk. The PIs will develop and analyze disease transmission models that incorporate various factors including public perception of risk, age-structure with a hospitalized population, and spatial structure with different scales spanning local communities to an entire country. These models will be used to explore impacts of community population demographics, socioeconomic conditions, and health care infrastructures, and how these factors impact control efforts under different social and economic settings. With the results of these models, policy- and decision-makers can consider the impacts of specific features of the communities under their administration as contributors to a broader network of public health efforts and choose the optimal mitigation steps. This work will inform coordination of disease control policies to curtail the ongoing outbreak directly. The results of this project, while tailored specifically to inform COVID-19 virus control strategies, will be applicable to any novel infectious disease outbreak in the future.This award is being funded by the CARES Act supplemental funds allocated to MPS.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.