PIPP Phase I: Mobility Analysis for Pandemic Prevention Strategies (MAPPS)

传染病自然会随着人的流动而发生。一旦出现传染性疾病，人口流动和社会混合模式是其传播的主要原因。因此，了解人类流动和社会互动的模式对于确定大流行病传播的可能性至关重要。 这些信息对于制定以科学为基础的有针对性的干预措施以阻止或减缓新病原体的传播也至关重要。此外，了解人类如何跨越空间和时间并相互作用，其意义不仅限于预防大流行病。该项目将提供一个框架，用于收集、捕捉、管理和分析复杂的、多尺度的人类运动和社会互动数据，这些数据可以用于了解各种紧迫问题，包括气候变化、野生动物跟踪、救灾、管理和城市规划。一个功能齐全、准确的大流行预测计算系统可以挽救数百万人的生命，节省数十亿美元的医疗费用。随着布朗大学流行病预防战略流动性分析中心的成立，该项目旨在：对流动性和社会混合的现有数据进行分类、组织和综合;开发新的创新工具来衡量流动性和社会混合;并利用这些信息开发和推广旨在预测和预防未来流行病的数学模型。 关于这些问题的现有数据将有系统地收集、编目和整理，存入一个联合的公开数据库，而新的数据将通过有针对性的试点项目产生。此外，该项目旨在开发一种新的设备，可能是可穿戴的，能够测量人类的流动性，社会混合和生物识别。研究团队将开发一系列复杂、灵活的数学模型，利用这些数据预测新的大流行病的出现，描述流行病动态，评估不同干预策略的有效性和影响，并为预防和控制卫生政策决策提供信息。 这项工作将重点关注道德和数据隐私。 重点突出的专题研讨会将汇集国内和国际专家，目的是确定该项目提出的技术和伦理问题的创新解决方案。 最后，布朗大学校园内的综合活动将作为类似数据收集和分析的模板：目标是绘制大学的整个社交网络，利用这些信息更好地了解社会混合模式，并利用这些信息探索各种可能减轻或消除病原体传播的干预措施。 该奖项得到了跨部门的大流行预防阶段预测情报（PIPP）计划的支持，该计划由生物科学（BIO），计算机信息科学与工程（CISE），工程（ENG）和社会部门共同资助。行为与经济科学（SBE）该奖项反映了NSF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。

Infectious diseases naturally follow the movement of people. Once a transmissible disease emerges, human mobility and patterns of social mixing are largely responsible for its spread. Therefore, understanding patterns of human mobility and social interaction is essential for identifying the potential for pandemic spread. This information is also critically important for developing science-based targeted interventions aimed at stopping or slowing the spread of a new pathogen. In addition, understanding how humans move across space and time and interact with each other has broad implications beyond pandemic prevention. This project will provide a framework to collect, capture, curate and analyze complex, multi-scale human movement and social interaction data that can be adapted to understand a wide variety of pressing problems including climate change, tracking of wildlife, disaster relief, management and urban planning. A fully functioning, accurate computational system for pandemic prediction could save millions of lives and billions of dollars in health care costs. With the establishment of the Center for Mobility Analysis for Pandemic Prevention Strategies (MAPPS) at Brown University, this project aims to: categorize, organize and synthesize existing data on mobility and social mixing; develop new and innovative tools for measuring mobility and social mixing; and use that information to develop and populate mathematical models aimed at predicting and preventing future pandemics. Existing data on these issues will be systematically collected, catalogued and organized in a federated, publicly available database, while new data will be generated through targeted pilot projects. In addition, the project aims to develop a new device, possibly wearable, that is capable of measuring human mobility, social mixing and biometrics. The research team will develop a series of complex, flexible mathematical models that use these data to predict the emergence of new pandemics, describe epidemic dynamics, evaluate the effectiveness and impact of different intervention strategies, and inform health policy decisions for prevention and control. The work will be infused with a strong focus on ethics and data privacy. Focused, thematic workshops will bring together national and international experts with the aim of identifying innovative solutions to the technical and ethical issues raised by this project. Finally, an integrative activity across the Brown University campus will serve as a template for similar data collection and analysis: the goals are to map the entire social network at the University, to use this information to better understand patterns of social mixing and to use that information to explore a variety of interventions that could potentially mitigate or eliminate pathogenic spread. This award is supported by the cross-directorate Predictive Intelligence for Pandemic Prevention Phase I (PIPP) program, which is jointly funded by the Directorates for Biological Sciences (BIO), Computer Information Science and Engineering (CISE), Engineering (ENG), and Social, Behavioral and Economic Sciences (SBE).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.