High COVID-19 transmission potential associated with re-opening universities can be mitigated with layered interventions.
High COVID-19 transmission potential associated with re-opening universities can be mitigated with layered interventions.
复制标题
与重新开放的大学相关的高共价传播潜力可以通过分层干预来减轻。
DOI：
10.1038/s41467-021-25169-3
复制
发表时间：
2021-08-17
影响因子：
16.6
通讯作者：
Danon L
中科院分区：
文献类型：
--
作者：
Brooks-Pollock E;Christensen H;Trickey A;Hemani G;Nixon E;Thomas AC;Turner K;Finn A;Hickman M;Relton C;Danon L
由于复杂的社交网络和潜在的无症状传播，控制COVID-19在大学的传播带来了挑战。我们开发了一个随机传输模型的基础上现实的混合模式和评估替代缓解策略。我们预测，对于合理的模型参数，如果无症状病例的传染性是有症状病例的一半，那么在没有额外控制措施的情况下，15%（98%预测区间：6-35%）的学生可能在第一学期被感染。一年级学生是传播的主要驱动力，感染率最高，主要是由于社区居住。孤立地说，减少面对面的教学是被认为最有效的干预措施，然而，分层的多种干预措施可以将感染率降低75%。每两周或更频繁的大规模测试需要影响传输，并且不是考虑的最有效的选择。我们的研究结果表明，应考虑在大学环境中采取额外的疫情控制措施。在COVID-19限制措施之后，大学重新向学生开放，由于大量的社会接触和无症状传播的可能性，可能会增加传播。在这里，作者使用一个数学模型与社会联系数据来估计重新开放一个典型的非校园为基础的大学在英国的影响。
Controlling COVID-19 transmission in universities poses challenges due to the complex social networks and potential for asymptomatic spread. We developed a stochastic transmission model based on realistic mixing patterns and evaluated alternative mitigation strategies. We predict, for plausible model parameters, that if asymptomatic cases are half as infectious as symptomatic cases, then 15% (98% Prediction Interval: 6–35%) of students could be infected during the first term without additional control measures. First year students are the main drivers of transmission with the highest infection rates, largely due to communal residences. In isolation, reducing face-to-face teaching is the most effective intervention considered, however layering multiple interventions could reduce infection rates by 75%. Fortnightly or more frequent mass testing is required to impact transmission and was not the most effective option considered. Our findings suggest that additional outbreak control measures should be considered for university settings. Reopening of universities to students following COVID-19 restrictions risks increased transmission due to high numbers of social contacts and the potential for asymptomatic transmission. Here, the authors use a mathematical model with social contact data to estimate the impacts of reopening a typical non-campus based university in the UK.
登录
查看更多内容
影响因子：
158.5
作者：
Li, Qun;Guan, Xuhua;Feng, Zijian
通讯作者：
Feng, Zijian
影响因子：
4.6
作者：
Nixon E;Trickey A;Christensen H;Finn A;Thomas A;Relton C;Montgomery C;Hemani G;Metz J;Walker JG;Turner K;Kwiatkowska R;Sauchelli S;Danon L;Brooks-Pollock E
通讯作者：
Brooks-Pollock E
DOI：
10.1098/rspb.2013.1037
发表时间：
2013-08-22
期刊：
Proceedings. Biological sciences
影响因子：
--
作者：
Danon L;Read JM;House TA;Vernon MC;Keeling MJ
通讯作者：
Keeling MJ
影响因子：
16.6
作者：
Bi Q;Lessler J;Eckerle I;Lauer SA;Kaiser L;Vuilleumier N;Cummings DAT;Flahault A;Petrovic D;Guessous I;Stringhini S;Azman AS;SEROCoV-POP Study Group
通讯作者：
SEROCoV-POP Study Group
影响因子：
4.2
作者：
Gilmore, A;Jones, G;Stuart, JM
通讯作者：
Stuart, JM