I-Corps: A spatiotemporal simulation system to predict COVID-19 case trajectories in schools

这个i-Corps项目的更广泛的影响/商业潜力是使大学校园和学校系统以及新冠肺炎危机后以校园为背景的公司和其他组织能够安全地重新开放。由于新冠肺炎的突然爆发，全球大学校园和学校系统关闭，以减缓疾病的传播。尽管在很大程度上成功地控制了新冠肺炎的传播，但学校关闭除了直接的经济影响外，还给学生、教职员工带来了健康问题，包括身体和心理压力。该项目开发了一个系统，以基于科学数据和信息的具体政策更好地支持学校重新开学。潜在产品可以模拟新冠肺炎案例的轨迹，为潜在客户提供科学监控校园状况的可靠方法。该产品具有在全球范围内使用的潜力，也可以用于管理具有集中聚集地点设置的大型活动。这个i-Corps项目基于一个时空模拟/预测系统，该系统是为了解决因新冠肺炎大流行而导致的紧急校园重新开放问题而开发的。该系统整合了对时空基础设施的调查，包括大数据、云计算、分析和社区外联。人类行为模拟、校园时空动态和公共卫生数据被结合起来预测可能的病毒病例轨迹。这种新颖的时空模拟集成了基于代理的人员流动和公共卫生风险以及控制策略(如口罩强制要求、教室大小限制、测试和跟踪以及疫苗接种状态)，以得出准确的预测。该系统已经使用来自几个大学校园的数据进行了验证。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 broader impact/commercial potential of this I-Corps project is to enable safe reopening of college campuses and school systems, as well as companies and other organizations with a campus setting after the COVID-19 crisis. Due to the sudden outbreak of COVID-19, college campuses and school systems were shut down globally to slow down the spread of the disease. Despite being largely successful in controlling the spread of COVID-19, school closings caused health problems, including physical and psychological stresses, for students, staff, and faculty in addition to direct economic impacts. This project develops a system to better support school re-openings with specific policies based on scientific data and information. The potential product can simulate the trajectory of COVID-19 cases and provide potential customers with a reliable method to scientifically monitor the status of their campuses. This product has the potential to be used globally and also can be used to manage large events with a focused gathering site setting.This I-Corps project is based on a spatiotemporal simulation/prediction system developed to address the urgent campus reopening questions caused by the COVID-19 pandemic. The system integrates investigation on spatiotemporal infrastructure including big data, cloud computing, analytics, and community outreach. Human behavior simulation, campus spatiotemporal dynamics, and public health data are combined to predicate possible viral case trajectories. The novel spatiotemporal simulation integrates agent-based people movement and public health risks as well as control policies (such as mask mandates, classroom size restrictions, test and tracking, and vaccination status) to derive accurate predictions. The system has been validated using data from several college campuses.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.