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

DETER项目将收集数据集，这些数据集可以将病毒传播研究从二维绘图练习转变为非常详细的三维传播模型，以便更好地为社区提供所需的信息，以改进疾病跟踪、社区传播预测和预防性消毒策略。该项目将提供离开医疗机构时与人类行为相关的新型数据，从而可以创建更多的局部疾病传播模型。该项目将跟踪人类的行为，包括人们去哪里（比如公交车、咖啡店），以及他们与环境的身体互动方式（比如他们触摸了什么，接触了多长时间）。该项目将立即公开数据，这些数据可能对基于病毒的疫情建模至关重要，包括预测当前COVID-19大流行期间进一步的社区传播。在美国，超过四分之三的COVID-19病例是由社区传播造成的。然而，目前的模型没有考虑局部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。DETER项目将提供此类数据，并展示新的三维方法来了解社区层面的风险。DETER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普遍性，以及手与建筑环境互动的程度和类型。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设施上的触摸行为来回答。该项目将提供一个可转移的框架和数据整合战略，可将其纳入各种三维模型和方案，从而帮助研究人员和当地社区掌握预测社区传播的更好方法。该奖项反映了美国国家科学基金会的法定使命，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。

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.