Evaluating COVID-19 Mitigation Strategies in Schools with a Spatially-Explicit Agent-Based Model of Infection Dynamics

该项目将开发和完善一个模拟建模系统，以帮助学校和学区评估控制学生COVID-19感染的干预措施的效果，首先将重点放在加利福尼亚州圣地亚哥县的学校。目标是帮助个别学校根据其独特情况选择最有效的策略，特别是每所学校的人口、教室布局和通风、社区感染率和校车通勤。互动模型将允许学校和地区层面的决策者评估药物干预措施（如疫苗接种和检测）和非药物安全措施（包括社交距离、戴口罩、限制不同学生群体之间的互动、减少教室占用和改善通风）的潜在影响。找到这些措施的最佳组合对于学校安全开学至关重要，这对于改善儿童教育和发展以及让家长重返工作岗位至关重要。由于学龄儿童（其中许多人没有接种疫苗）受到新的和更危险的COVID-19变种的不成比例的影响，这项模拟服务将为我们在圣地亚哥县和全国其他地方的学校和学区一级的决策者中的合作伙伴提供急需的见解。学校感染模型模拟了学生、教师和工作人员（建模为人类代理人）在典型的学校活动中相互作用时的气溶胶和飞沫传播，包括课堂教学、自助餐厅午餐、休息和乘坐校车。当一个健康的人接近被感染的、无症状的人或在通风不良的空间里呆很长时间时，他接触病毒的可能性就会增加。该模型考虑了房间布局和通风的空间信息，并模拟了每隔5分钟的学校日程安排。该项目以小学的成功原型为基础，将把该模式扩展到初中和高中，以及具有不同社会经济和人口特征的社区。此外，它还可以模拟体育活动的感染动态，并分析不同COVID-19变体下的感染模式。通过科学门户访问该模型将使学校管理人员、研究人员、教师和学生能够在超级计算机上运行模拟，然后将不同场景下的模型输出可视化并进行比较。与原型阶段一样，该项目将让本科生参与模型开发的各个方面，并向公共卫生和教育专家以及公众展示结果——这是他们作为从事教育和卫生公平等具有社会影响主题的数据科学家的培训的重要组成部分。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。

This project will develop and refine a simulation modeling system to help schools and school districts evaluate the effects of interventions to control COVID-19 infections among students, focusing first on schools in San Diego County, CA. The goal is to help individual schools choose the most effective strategies for their unique circumstances, particularly each school’s population, classroom layouts and ventilation, neighborhood infection rates, and school bus commutes. The interactive model will allow decision makers at the school and district levels to evaluate the potential impacts of both pharmaceutical interventions, like vaccination and testing, and non-pharmaceutical safety measures including social distancing, mask-wearing, limiting interactions between different student cohorts, decreasing classroom occupancy, and improving ventilation. Finding the best combination of such measures is central to safe school re-opening, which is critical for improving child education and development and allowing parents to return to work. As school-aged children, many of whom are not vaccinated, are disproportionately affected by new and more dangerous COVID-19 variants, this simulation service will provide much-needed insights to our partners among decision-makers at the school and district level in San Diego County and elsewhere in the nation. The school infection model simulates aerosol and droplet transmission as students, teachers, and staff (modeled as human agents) interact during typical school day activities, including classroom instruction, cafeteria lunch, recess, and traveling on a school bus. The likelihood that a healthy agent is exposed to the virus increases as they come near infected, asymptomatic agents or spend significant time in poorly ventilated spaces. The model considers spatial information about room layouts and ventilation, and simulates school day schedules down to 5-minute intervals. Building on a successful prototype for elementary schools, this project will extend the model to middle and high schools and to neighborhoods with different socio-economic and demographic characteristics. Additionally, it will enable simulation of infection dynamics for athletics activities and analysis of infection patterns under different COVID-19 variants. Making the model accessible via a science gateway will let school administrators, researchers, teachers, and students run simulations on a supercomputer, then visualize and compare model outputs under different scenarios. As in the prototype phase, the project will engage undergraduate students in all aspects of model development, and in presenting results to public health and education experts and to the general public – an essential component of their training as data scientists working on societally-impactful topics like education and health equity.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.