Estimating data-driven coronavirus disease 2019 mitigation strategies for safe university reopening

被引:2
|
作者
Yang, Qihui [1 ]
Gruenbacher, Don M. [1 ]
Scoglio, Caterina M. [1 ]
机构
[1] Kansas State Univ, Dept Elect & Comp Engn, Manhattan, KS USA
基金
美国国家科学基金会;
关键词
COVID-19; SARS-CoV-2; agent-based model; non-pharmaceutical interventions; social contact; vaccination;
D O I
10.1098/rsif.2021.0920
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
After one pandemic year of remote or hybrid instructional modes, universities struggled with plans for an in-person autumn (fall) semester in 2021. To help inform university reopening policies, we collected survey data on social contact patterns and developed an agent-based model to simulate the spread of severe acute respiratory syndrome coronavirus 2 in university settings. Considering a reproduction number of R-0 = 3 and 70% immunization effectiveness, we estimated that at least 80% of the university population immunized through natural infection or vaccination is needed for safe university reopening with relaxed non-pharmaceutical interventions (NPIs). By contrast, at least 60% of the university population immunized through natural infection or vaccination is needed for safe university reopening when NPIs are adopted. Nevertheless, attention needs to be paid to large-gathering events that could lead to infection size spikes. At an immunization coverage of 70%, continuing NPIs, such as wearing masks, could lead to a 78.39% reduction in the maximum cumulative infections and a 67.59% reduction in the median cumulative infections. However, even though this reduction is very beneficial, there is still a possibility of non-negligible size outbreaks because the maximum cumulative infection size is equal to 1.61% of the population, which is substantial.
引用
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页数:9
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