RAPID: Collaborative: Location Privacy Preserving COVID-19 Symptom Map Construction via Mobile Crowdsourcing for Proactive Constrained Resource Allocation

最近发生的新冠肺炎大流行给包括美国在内的世界许多国家带来了公共卫生危机。由于目前没有治疗COVID-19的药物，最具挑战性的问题是如何有效地将有限的医疗资源分配到下一个潜在爆发的社区或地区，以便我们能够阻止甚至防止病毒的进一步传播。目前依靠人们去看医生或去检测站收集感染数据的反应性努力可能无法有效和及时地发挥作用，因为这些地方的覆盖范围有限，成本高，并且人们在这些地方拥挤的暴露风险增加。该项目通过移动众包开发了一份细粒度和位置隐私保护的covid - 19症状地图（CSM），其中症状可能包括发烧、咳嗽或呼吸短促。拟议的CSM在确定潜在爆发地区和促进主动和动态分配医疗保健资源方面具有前景。同时，位置隐私保护功能将保护移动众包参与者免受偏见或歧视，鼓励他们为公共利益而参与。该项目涉及移动众包、公共卫生科学、数据分析、隐私和社会科学等方面的协同努力。教育和外联活动的目的是增加妇女和少数民族对科学和工程的参与。该项目开发了一个跨学科框架，涉及位置隐私保护、移动众包、数据分析、社会科学和公共卫生科学，并包含一个包含四个相互关联的重点的研究计划。首先，提出了一种新的位置差分隐私保护机制——差分私有六边形分层地理空间索引系统（DPH3），该机制能够很好地在地图中表现物理社区结构，保障移动众包参与者的位置差分隐私，并在地图覆盖和精度方面为CSM建设提供较高的移动众包实用性。其次，设计基于DPH3和近似算法的覆盖感知众包参与者招募方案，在保证众包覆盖的同时，保护参与者的位置差异隐私。第三，利用病毒传播模型作为普通克里格法的先验知识，利用鲁棒估计减轻DPH3所增加的位置隐私保护噪声的影响，构建质量保证的CSM。第四，从社会科学的角度出发，采用战略沟通的方式促进社区参与，激发社区成员主动寻求和分享新冠肺炎症状信息，促进社区信息共享所需的众包，增强社区成员的权能。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 recent pandemic of COVID-19 has caused a public health crisis to US and many other countries in the world. Since there is currently no medication to treat COVID-19, the most challenging question is how to effectively allocate constrained healthcare resources to the next potential outburst communities or areas, so that we can halt or even prevent the virus' further spreading. The current reactive efforts of relying on people to visit their doctors or testing stations for collecting infection data may not work in an efficient and timely manner due to the limited coverage, high cost, and increased exposure risk for people to be congested at those places. This project develops a fine-grained and location privacy-preserving COVID19 symptom map (CSM) via mobile crowdsourcing, where the symptoms may include fever, cough, or shortness of breath. The proposed CSM is promising in its ability to identify the potential outbreak areas and facilitate the proactive and dynamic allocation of healthcare resources. Meanwhile, the location privacy preserving feature will protect the mobile crowdsourcing participants from bias or discrimination, encouraging them to participate for the public good. The project involves synergized efforts from mobile crowdsourcing, public health science, data analytics, privacy, and social science. Education and outreach activities are designed to increase the participation of women and minority in science and engineering. This project develops an interdisciplinary framework for location privacy preservation, mobile crowdsourcing, data analytics, social science, and public health science, and contains a research plan with four interconnected thrusts. First, a novel location differential privacy preservation mechanism named differentially private hexagonal hierarchical geospatial indexing system (DPH3) will be developed, which can well represent physical community structure in the map, guarantee mobile crowdsourcing participants’ location differential privacy, and provide high mobile crowdsourcing utility for CSM construction in terms of map coverage and accuracy. Second, a coverage-aware crowdsourcing participant recruitment scheme based on DPH3 and approximation algorithm will be designed to guarantee the crowdsensing coverage while preserving participants' location differential privacy. Third, a quality-assured CSM will be constructed by using virus propagation model as the prior knowledge for detrending in ordinary kriging and robust estimation for mitigating the impact of location privacy preserving noises added by DPH3. Fourth, a strategic communication approach to community engagement from the perspective of social science will be used to motivate community members’ activeness in information seeking and sharing about COVID-19 symptoms, facilitate crowdsourcing needed for the CSM, as well as empower the community members.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.