On the 30 January 2020, the WHO declared a public health emergency of international concern due to the coronavirus disease 2019 (COVID-19). Social restrictions with different efficiencies were put in place to avoid transmission. Students living in student accommodation constitute an interesting group to test restrictions because they share living places, workplaces and daily routines, which are key factors in the transmission. In this paper, we present a new geospatial agent-based simulation model to explore the transmission of COVID-19 between students living in Newcastle University accommodation and the efficiency of simulated restrictions (e.g., facemask, lockdown, self-isolation). Results showed that facemasks could reduce infection peak by 30% if worn by all students; an early lockdown could keep 65% of the students safe in the best case; self-isolation could keep 86% of the students safe; while the combination of these measures could prevent disease in 95% of students in the best case-scenario. Spatial analyses showed that the most dangerous places were those where many students interact for a long time, such as faculties and accommodation. The developed ABM could help university managers to respond to current and future epidemics and plan effective responses to keep safe as many students as possible.

中文翻译：

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用于模拟大学生 COVID-19 传播的基于 3D 代理的行人运动模型

2020 年 1 月 30 日，世界卫生组织宣布因 2019 年冠状病毒病（COVID-19）为国际关注的突发公共卫生事件。实施了不同效率的社会限制以避免传播。住在学生宿舍的学生构成了一个有趣的测试限制的群体，因为他们共享生活场所、工作场所和日常生活，这些都是传播的关键因素。在本文中，我们提出了一种新的基于地理空间代理的模拟模型，以探索 COVID-19 在住在纽卡斯尔大学住宿的学生之间的传播以及模拟限制（例如，口罩、锁定、自我隔离）的效率。结果表明，如果所有学生都戴口罩，可以将感染高峰降低30%；在最好的情况下，提前封锁可以保证 65% 的学生安全；自我隔离可以保证 86% 的学生安全；而这些措施的结合可以在最好的情况下预防 95% 的学生的疾病。空间分析表明，最危险的地方是许多学生长时间互动的地方，例如学院和住宿。开发的 ABM 可以帮助大学管理人员应对当前和未来的流行病，并计划有效的应对措施，以确保尽可能多的学生安全。