Abstract：
The role of schools in the spread of SARS-CoV-2 is controversial, with some claiming they are an important driver of the pandemic and others arguing that transmission in schools is negligible. School cluster reports that have been collected in various jurisdictions are a source of data about transmission in schools. These reports consist of the name of a school, a date, and the number of students known to be infected. We provide a simple model for the frequency and size of clusters in this data, based on random arrivals of index cases at schools who then infect their classmates with a highly variable rate, fitting the overdispersion evident in the data. We fit our model to reports from four Canadian provinces, providing estimates of mean and dispersion for cluster size, as well as the distribution of the instantaneous transmission parameter β, whilst factoring in imperfect ascertainment. According to our model with parameters estimated from the data, in all four provinces (i) more than 65% of non-index cases occur in the 20% largest clusters, and (ii) reducing instantaneous transmission rate and the number of contacts a student has at any given time are effective in reducing the total number of cases, whereas strict bubbling (keeping contacts consistent over time) does not contribute much to reduce cluster sizes. We predict strict bubbling to be more valuable in scenarios with substantially higher transmission rates.
During the COVID-19 pandemic, public health officials promoted social distancing as a way to reduce SARS-CoV-2 transmission. The goal of social distancing is to reduce the number, proximity, and duration of face-to-face interactions between people. To achieve this, people shifted many activities online or canceled events outright. In education, some schools closed and shifted to online learning, while others continued classes in person with safety precautions. Better information about SARS-CoV-2 transmission in schools could help public health officials to make decisions of what activities to keep in person and when to suspend classes. If safety measures lower transmission in schools considerably, then closing schools may not be worth online education\'s social, educational, and economic costs. However, if transmission of SARS-CoV-2 in schools remains high despite measures, closing schools may be essential, despite the costs. Tupper et al. used data about COVID-19 cases in children attending in-person school in four Canadian provinces between 2020 and 2021 to fit a computer model of school transmission. On average, their analysis shows that one infected person in a school leads to between two and three further cases. Most of the time, no more students are infected, indicating that normally infection clusters are small; and only rarely does one infected person set off a large outbreak. The model also showed that measures to reduce transmission, like masking or small class sizes, were more effective than interventions such as keeping students with the same cohort all day (bubbling). Tupper et al. caution that their findings apply to the variants of SARS-CoV-2 circulating in Canada during the 2020-2021 school year, and may not apply to newer, highly transmissible strains like Omicron. However, the model could always be adapted to assess school or workplace transmission of more recent strains of SARS-CoV-2, and more generally of other diseases. Thus, Tupper et al. provide a new approach to estimating the rate of disease transmission and comparing the impact of different prevention strategies.
During the COVID-19 pandemic, public health officials promoted social distancing as a way to reduce SARS-CoV-2 transmission. The goal of social distancing is to reduce the number, proximity, and duration of face-to-face interactions between people. To achieve this, people shifted many activities online or canceled events outright. In education, some schools closed and shifted to online learning, while others continued classes in person with safety precautions. Better information about SARS-CoV-2 transmission in schools could help public health officials to make decisions of what activities to keep in person and when to suspend classes. If safety measures lower transmission in schools considerably, then closing schools may not be worth online education\'s social, educational, and economic costs. However, if transmission of SARS-CoV-2 in schools remains high despite measures, closing schools may be essential, despite the costs. Tupper et al. used data about COVID-19 cases in children attending in-person school in four Canadian provinces between 2020 and 2021 to fit a computer model of school transmission. On average, their analysis shows that one infected person in a school leads to between two and three further cases. Most of the time, no more students are infected, indicating that normally infection clusters are small; and only rarely does one infected person set off a large outbreak. The model also showed that measures to reduce transmission, like masking or small class sizes, were more effective than interventions such as keeping students with the same cohort all day (bubbling). Tupper et al. caution that their findings apply to the variants of SARS-CoV-2 circulating in Canada during the 2020-2021 school year, and may not apply to newer, highly transmissible strains like Omicron. However, the model could always be adapted to assess school or workplace transmission of more recent strains of SARS-CoV-2, and more generally of other diseases. Thus, Tupper et al. provide a new approach to estimating the rate of disease transmission and comparing the impact of different prevention strategies.
摘要：
学校在SARS-CoV-2传播中的作用是有争议的,一些人声称他们是大流行的重要驱动因素，另一些人则认为学校的传播可以忽略不计。在各个司法管辖区收集的学校集群报告是有关学校传输的数据来源。这些报告包括一所学校的名称，一个日期,以及已知被感染的学生人数。我们为这些数据中集群的频率和大小提供了一个简单的模型，基于随机到达学校的索引病例，然后以高度可变的比率感染同学，拟合数据中明显的过度分散。我们将模型与加拿大四个省的报告相匹配，提供集群大小的均值和离差估计，以及瞬时传输参数β的分布，同时考虑不完善的确定。根据我们的模型，从数据中估计出参数，在所有四个省（i），超过65％的非指标病例发生在20％最大的集群中，（ii）降低瞬时传输速率和学生在任何给定时间的联系人数量可有效减少病例总数，而严格冒泡（保持联系人随着时间的推移保持一致）对减少集群大小没有太大贡献。我们预测，在传输速率高得多的情况下，严格的冒泡将更有价值。
在COVID-19大流行期间，公共卫生官员提倡社会距离，以减少SARS-CoV-2传播。社交距离的目标是减少数量，接近度,以及人与人之间面对面互动的持续时间。为了实现这一点，人们在网上转移了许多活动，或者直接取消了活动。在教育方面,一些学校关闭并转向在线学习，而其他人则亲自采取安全预防措施继续上课。有关SARS-CoV-2在学校传播的更好信息可以帮助公共卫生官员决定亲自进行哪些活动以及何时停课。如果安全措施大大降低了学校的传播，那么关闭学校可能不值得在线教育的社交，教育,和经济成本。然而,尽管采取措施，如果SARS-CoV-2在学校中的传播仍然很高，关闭学校可能是必不可少的，尽管成本。Tupper等人。使用了有关2020年至2021年间加拿大四个省亲自上学的儿童中COVID-19病例的数据，以符合学校传播的计算机模型。平均而言,他们的分析表明，一所学校的一名感染者导致了两到三起病例。大多数时候,没有更多的学生被感染，表明通常感染集群很小；一个感染者很少引发大规模爆发。该模型还表明，减少传播的措施，像掩蔽或小班大小，比干预措施更有效，比如让学生整天都在同一个队列(冒泡)。Tupper等人。注意他们的发现适用于2020-2021学年在加拿大传播的SARS-CoV-2变体，可能不适用于较新的，像Omicron这样的高传染性菌株。然而,该模型始终适用于评估近期SARS-CoV-2菌株的学校或工作场所传播，以及更广泛的其他疾病。因此，Tupper等人。提供了一种新的方法来估计疾病传播率和比较不同预防策略的影响。
在COVID-19大流行期间，公共卫生官员提倡社会距离，以减少SARS-CoV-2传播。社交距离的目标是减少数量，接近度,以及人与人之间面对面互动的持续时间。为了实现这一点，人们在网上转移了许多活动，或者直接取消了活动。在教育方面,一些学校关闭并转向在线学习，而其他人则亲自采取安全预防措施继续上课。有关SARS-CoV-2在学校传播的更好信息可以帮助公共卫生官员决定亲自进行哪些活动以及何时停课。如果安全措施大大降低了学校的传播，那么关闭学校可能不值得在线教育的社交，教育,和经济成本。然而,尽管采取措施，如果SARS-CoV-2在学校中的传播仍然很高，关闭学校可能是必不可少的，尽管成本。Tupper等人。使用了有关2020年至2021年间加拿大四个省亲自上学的儿童中COVID-19病例的数据，以符合学校传播的计算机模型。平均而言,他们的分析表明，一所学校的一名感染者导致了两到三起病例。大多数时候,没有更多的学生被感染，表明通常感染集群很小；一个感染者很少引发大规模爆发。该模型还表明，减少传播的措施，像掩蔽或小班大小，比干预措施更有效，比如让学生整天都在同一个队列(冒泡)。Tupper等人。注意他们的发现适用于2020-2021学年在加拿大传播的SARS-CoV-2变体，可能不适用于较新的，像Omicron这样的高传染性菌株。然而,该模型始终适用于评估近期SARS-CoV-2菌株的学校或工作场所传播，以及更广泛的其他疾病。因此，Tupper等人。提供了一种新的方法来估计疾病传播率和比较不同预防策略的影响。
