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.

该项目将开发和完善模拟建模系统，以帮助学校和学区评估控制学生中 COVID-19 感染的干预措施的效果，首先关注加利福尼亚州圣地亚哥县的学校，目标是帮助个别学校选择合适的方案。针对其独特情况，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，制定最有效的策略。交互式模型将使学校和学区的决策者能够评估药物干预措施的潜在影响，例如疫苗接种和检测以及非药物安全措施包括保持社交距离、戴口罩、限制不同学生群体之间的互动、减少教室占用率和改善通风，这对于学校安全重新开放至关重要，这对于改善儿童教育和发展至关重要。由于学龄儿童（其中许多人没有接种疫苗）受到新的、更危险的 COVID-19 变体的影响尤为严重，因此这项模拟服务将为我们的决策者合作伙伴提供急需的见解。在圣地亚哥的学校和地区层面县和全国其他地方的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）在典型的学校日常活动（包括课堂教学、自助餐厅午餐、课间休息和在学校旅行）中互动时的气溶胶和飞沫传播。当健康的感染者接近感染者、无症状的感染者或在通风不良的空间中呆很长时间时，他们接触病毒的可能性就会增加。该模型考虑了有关房间布局和通风的空间信息，并模拟了学校的日程安排。该项目以小学的成功原型为基础，将模型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，它将能够模拟体育活动和运动的动态感染。分析不同 COVID-19 变体下的感染模式。通过科学网关访问该模型将使学校管理人员、研究人员、教师和学生在超级计算机上运行模拟，然后可视化并比较不同场景下的模型输出。阶段，该项目将在各个方面吸引本科生模型开发的过程，以及向公共卫生和教育专家以及公众展示结果的过程——这是他们作为数据科学家从事教育和健康公平等具有社会影响力的主题的培训的重要组成部分。该奖项的法定使命是，并被认为是值得的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来获得支持。