Covid-19 transmission modelling of students returning home from university
Covid-19 transmission modelling of students returning home from university
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DOI：
10.1080/20476965.2020.1857214
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发表时间：
2021-01-20
期刊：
影响因子：
1.8
通讯作者：
Woolley, Thomas E.
中科院分区：
文献类型：
--
作者：
Harper, Paul R.;Moore, Joshua W.;Woolley, Thomas E.
我们提供了一个开源模型来估计潜在感染新冠肺炎的学生从大学回到私人家中与其他居住者一起导致的继发感染人数。使用蒙特卡罗方法和来自英国来源的数据，我们预测一个有感染力的学生平均会感染0.94个其他家庭成员。或者，根据经验，每个受感染的学生将产生(略少于)一次家庭内继发感染。所有返校学生的中学病例总数取决于他们离开校园回国时每个学生群体中的病毒流行率。虽然该估计方法具有较好的通用性和稳健性，但估计结果对输入数据比较敏感。我们提供了MatLab代码和一个有用的在线应用程序()，可用于根据本地参数值估计二次感染的数量。这可以在世界范围内用于支持政策制定。
We provide an open-source model to estimate the number of secondary Covid-19 infections caused by potentially infectious students returning from university to private homes with other occupants. Using a Monte-Carlo method and data derived from UK sources, we predict that an infectious student would, on average, infect 0.94 other household members. Or, as a rule of thumb, each infected student would generate (just less than) one secondary within-household infection. The total number of secondary cases for all returning students is dependent on the virus prevalence within each student population at the time of their departure from campus back home. Although the proposed estimation method is general and robust, the results are sensitive to the input data. We provide Matlab code and a helpful online app () that can be used to estimate numbers of secondary infections based on local parameter values. This can be used worldwide to support policy making.