学校与家庭网络对荷兰学生COVID-19感染的影响：一项使用人群级登记数据的研究
The Impact of School and Family Networks on COVID-19 Infections Among Dutch Students: A Study Using Population-Level Registry Data
AI总结：
本研究利用荷兰人群级登记数据匹配学生对，比较家庭、学校及地理邻近接触，发现家庭传播风险远高于学校，且风险差异主要来自未观测的个体与学校因素。
摘要：
了解不同社会互动的影响是改进流行病模型的关键。在此，我们使用广泛的登记数据——包括PCR检测结果和人群级网络——来调查2020年6月至2021年10月期间荷兰学校、家庭和其他社会接触对SARS-CoV-2传播的影响。我们根据学生是否就读同一所或不同的小学（2020年）和中学（2021年）及其地理邻近性来匹配学生对，从而分离并比较潜在SARS-CoV-2传播的不同情境。随后，我们计算了时间相关感染的概率，即两名学生在14天内均检测呈阳性的概率。我们的结果突显，与学校环境相比，家庭和家族传播在SARS-CoV-2扩散中的相对重要性。居住在同一家庭的兄弟姐妹和父母—子女对发生时间相关感染的概率为22.6%—23.2%，居住在不同家庭的家庭成员为4.7%—7.9%。相比之下，居住在附近但未就读同一所小学或中学的学生对的时间相关感染概率为0.52%，就读不同中学但曾就读同一所小学的学生对为0.66%，就读同一所中学的学生对为1.65%。最后，我们使用多层回归分析考察个体、学校和地理因素如何促成传播风险。我们发现，传播概率的最大差异源于未观测到的个体因素（60%）和学校层面因素（35%）。只有很小一部分（3%）可归因于学生的地理邻近性，或学校规模、教派或学校所在地区的收入中位数。
英文摘要：
Understanding the impact of different social interactions is key to improving epidemic models. Here, we use extensive registry data -- including PCR test results and population-level networks -- to investigate the impact of school, family, and other social contacts on SARS-CoV-2 transmission in the Netherlands (June 2020--October 2021). We isolate and compare different contexts of potential SARS-CoV-2 transmission by matching pairs of students based on their attendance at the same or different primary school (in 2020) and secondary school (in 2021) and their geographic proximity. We then calculated the probability of temporally associated infections -- i.e. the probability of both students testing positive within a 14-day period. Our results highlight the relative importance of household and family transmission in the spread of SARS-CoV-2 compared to school settings. The probability of temporally associated infections for siblings and parent-child pairs living in the same household was 22.6--23.2\%, and 4.7--7.9\% for family members living in different household. In contrast, the probability of temporally associated infections was 0.52\% for pairs of students living nearby but not attending the same primary or secondary school, 0.66\% for pairs attending different secondary schools but having attended the same primary school, and 1.65\% for pairs attending the same secondary school. Finally, we used multilevel regression analyses to examine how individual, school, and geographic factors contribute to transmission risk. We found that the largest differences in transmission probabilities were due to unobserved individual (60\%) and school-level (35\%) factors. Only a small proportion (3\%) could be attributed to geographic proximity of students or to school size, denomination, or the median income of the school area.
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