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的研究从二维映射练习转化为高度详细的三维传播模型，以更好地为社区提供改进疾病跟踪，社区传播预测和预防性消毒策略所需的信息。该项目将提供与离开医疗机构时的人类行为相关的新型数据，从而创建更多本地化的疾病传播模型。该项目将跟踪人们的行为，包括人们去哪里（例如公共汽车，咖啡店）以及他们如何与环境进行物理交互（即他们接触什么以及持续多久）。 该项目将立即公开可用的数据，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大流行期间的进一步社区传播。社区传播是美国超过四分之三的COVID-19病例的原因。然而，目前的模型不考虑本地化的行为来预测病毒的传播或传播的程度在个性化的设置和他们周围的社区。DETER项目将提供此类数据，并展示了解社区一级风险的新的三维手段。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可推广性，以及与建筑环境进行基于手的交互的程度和类型。这些问题将通过跟踪离开医疗机构的个人以及记录公共交通和公共住宿上基于触摸的行为来回答。该项目将提供一个可转移的框架和数据集成策略，可以采用到各种各样的三维模型和模式，这将有助于装备研究人员和当地社区更好的方法来预测社区为基础的transmission.This奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的知识价值和更广泛的影响审查标准。

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.