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

使用基于空间显式代理的感染动态模型评估学校的 COVID-19 缓解策略

• 批准号: 2139740
• 负责人: Ilya Zaslavsky
• 金额: $ 25万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2139740&HistoricalAwards=false
• 关键词: Evaluating; COVID; 19; Mitigation; Strategies

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.

该项目将开发并完善模拟建模系统，以帮助学校和学区评估控制学生之间的COVID-19感染的影响，首先关注加利福尼亚州圣地亚哥县的学校。目的是帮助各个学校为其独特情况选择最有效的策略，尤其是每个学校的人口，教室的布局和通风，邻里感染率和校车通勤。交互式模型将允许学校和地区级别的决策者评估药物干预措施（如疫苗和测试）的潜在影响，以及非药物安全措施，包括社交距离，戴面具，戴面具，限制不同学生同伙之间的互动，减少课堂职业和改善频静脉感。找到此类测量的最佳组合对于安全学校重新开放至关重要，这对于改善儿童教育和发展并允许父母重返工作岗位至关重要。由于许多未接种疫苗的学龄儿童受到新的和更危险的Covid-19变体的影响不成比例，因此，这种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地区层的决策者中的合作伙伴提供急需的见解。学校感染模型在典型的上学日活动中模拟了学生，老师和员工（以人类特工为模型），包括课堂说明，自助餐厅午餐，休息和乘坐校车旅行，以模拟气溶胶和液滴传播。健康剂暴露于病毒的可能性随着感染，不对称剂或在通风良好的空间中花费大量时间而增加。该模型考虑了有关房间布局和通风的空间信息，并模拟了学校日的时间表至5分钟的间隔。该项目以成功的小学原型为基础，将模型扩展到中学和高中以及具有不同社会经济和人口特征的社区。此外，它将能够模拟运动活动的感染动力学，并在不同的COVID-19变体下对感染模式进行分析。通过科学网关使该模型访问将使学校管理员，研究人员，教师和学生在超级计算机上运行模拟，然后在不同情况下可视化和比较模型输出。与原型阶段一样，该项目将与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和公众呈现成果，这是他们培训的重要组成部分，作为从事社会影响和健康公平的数据科学家（例如教育和健康公平）的数据，这些奖项（如NSF的法定任务）反映了通过评估范围的范围的构成者的构成师的范围，并具有范围的范围。