SARS-CoV-2在大学课堂中的传播。

IF 2 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
William Ruth, Richard Lockhart
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引用次数: 1

摘要

我们在西蒙弗雷泽大学的真实课堂网络上调查了SARS-CoV-2的传播动力学。在一个学期的过程中,通过许多参数设置模拟爆发,包括将班级移动到一定的在线规模阈值以上。采用回归树分析疾病参数对仿真输出的影响。我们发现,需要积极的班级规模阈值策略来降低大规模爆发的风险,并且有症状的个体传播是爆发规模的关键驱动因素。这些发现为其他机构设计控制策略以及为疾病监测确定优先事项和分配资源提供了指导。补充资料:在线版本提供补充资料,网址为10.1007/s13721-022-00375-1。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

SARS-CoV-2 transmission in university classes.

SARS-CoV-2 transmission in university classes.

SARS-CoV-2 transmission in university classes.

SARS-CoV-2 transmission in university classes.

We investigate transmission dynamics for SARS-CoV-2 on a real network of classes at Simon Fraser University. Outbreaks are simulated over the course of one semester across numerous parameter settings, including moving classes above certain size thresholds online. Regression trees are used to analyze the effect of disease parameters on simulation outputs. We find that an aggressive class size thresholding strategy is required to mitigate the risk of a large outbreak, and that transmission by symptomatic individuals is a key driver of outbreak size. These findings provide guidance for designing control strategies at other institutions, as well as setting priorities and allocating resources for disease monitoring.

Supplementary information: The online version contains supplementary material available at 10.1007/s13721-022-00375-1.

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来源期刊
CiteScore
5.40
自引率
4.30%
发文量
43
期刊介绍: NetMAHIB publishes original research articles and reviews reporting how graph theory, statistics, linear algebra and machine learning techniques can be effectively used for modelling and analysis in health informatics and bioinformatics. It aims at creating a synergy between these disciplines by providing a forum for disseminating the latest developments and research findings; hence, results can be shared with readers across institutions, governments, researchers, students, and the industry. The journal emphasizes fundamental contributions on new methodologies, discoveries and techniques that have general applicability and which form the basis for network based modelling, knowledge discovery, knowledge sharing and decision support to the benefit of patients, healthcare professionals and society in traditional and advanced emerging settings, including eHealth and mHealth .
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