RAPID: DETER: Developing Epidemiology mechanisms in Three-dimensions to Enhance Response

威慑项目将收集数据集，将病毒传播研究从二维测绘工作转变为高度详细的三维传播模型，以更好地为社区配备改进疾病跟踪、社区传播预测和预防性消毒策略所需的信息。该项目将提供与人类离开医疗机构时的行为相关的新型数据，这将允许创建更本地化的疾病传播模型。该项目将根据人们去哪里(如公交车、咖啡馆)以及他们如何与环境进行身体互动(即他们触摸的东西和接触了多长时间)来跟踪人类行为。该项目将立即公开数据，这些数据可能对基于病毒的爆发建模至关重要，包括预测在当前的新冠肺炎大流行期间进一步的社区传播。美国超过四分之三的新冠肺炎病例是由社区传播造成的。然而，目前的模型没有考虑本地化行为来预测病毒传播或在个性化环境及其周围社区内的传播程度。威慑项目将提供这样的数据，并展示了解社区层面风险的新的三维方法。威慑项目调查了在访问医疗机构后，人类行为在目的地选择方面的概括性程度，以及与建筑环境的手部互动的程度和类型。这些问题将通过跟踪离开医疗机构的个人并记录在公共交通和公共住宿中的触摸行为来回答。该项目将提供一个可移植的框架和数据集成策略，可用于各种三维模型和方案，帮助研究人员和当地社区配备更好的方法来预测基于社区的传播。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。

The DETER project will collect data sets that can transform the study of virus transmission from two-dimensional mapping exercises into highly detailed, three-dimensional propagation models to better equip communities with the information they need for improved disease tracking, community-transmission prediction, and preventative disinfection strategies. The project will provide new types of data related to human behavior when leaving healthcare facilities that will allow more localized disease transmission models to be created. The project will track human behavior in terms of where people go (e.g. bus, coffee shop) and how they physically interact with the environment (i.e. what they touch and for how long). The project will immediately make publicly available data that could be critical for modeling virus-based outbreaks including predicting further community transmission during the current COVID-19 pandemic.Community-transmission is responsible for over three-quarters of the COVID-19 cases in the US. Yet, current models do not consider localized behavior to predict virus transmission or the extent of propagation within individualized settings and their surrounding communities. The DETER project will provide such data and demonstrate new three-dimensional means to understand community-level risk. The DETER project investigates how generalizable human behavior is in terms of destination selection after visiting a healthcare facility and the extent and types of hand-based interaction with the built environment. These questions will be answered through tracking individuals when leaving healthcare facilities and recording touch-based behaviors on public transportation and public accommodations. The project will provide a transferable framework and a data integration strategy that can be adopted into a wide variety of three-dimensional models and schema that will help equip researchers and local communities with better methods for predicting community-based transmission.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.