Modelling scenarios of the epidemic of COVID-19 in Canada.
Modelling scenarios of the epidemic of COVID-19 in Canada.
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DOI：
10.14745/ccdr.v46i06a08
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发表时间：
2020-06-04
期刊：
影响因子：
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通讯作者：
Wu, Jianhong
中科院分区：
文献类型：
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作者：
Ogden, Nick H;Fazil, Aamir;Wu, Jianhong
背景技术背景：严重急性呼吸综合征病毒2（SARS-CoV-2），可能是一种蝙蝠来源的冠状病毒，于2019年底在中国从野生动物传播到人类，表现为一种呼吸道疾病。2019冠状病毒病（COVID-19）最初在中国境内传播，然后在全球范围内传播，导致大流行。目的：本文描述了COVID-19的一般预测模型，以及加拿大公共卫生署为模拟非药物干预（NPI）对加拿大人群中SARS-CoV-2传播的影响以支持公共卫生决策所做的努力。方法：两种建模方法，1）基于代理的模型和2）确定性房室模型的广泛目标，描述和研究的概要说明使用Analytica 5.3软件开发的模型。结果：如果不干预，超过70%的加拿大人口可能会被感染。非药物干预措施的使用强度不足以使流行病消亡，将发病率降低到50%或更低，流行病持续时间更长，高峰期更低。如果过早取消非营利机构，疫情可能反弹，导致受影响人口的高百分比（超过70%）。如果NPIs的应用强度足够高，导致流行病的消亡，攻击率可以降低到1%和25%之间的population.CONCLUSION：应用NPIs的强度足够高，导致流行病的消亡似乎是首选。解除中断性NPI（如关闭）必须伴随着对其他NPI的增强，以防止新的引入，并识别和控制任何新的传输链。
BACKGROUND: Severe acute respiratory syndrome virus 2 (SARS-CoV-2), likely a bat-origin coronavirus, spilled over from wildlife to humans in China in late 2019, manifesting as a respiratory disease. Coronavirus disease 2019 (COVID-19) spread initially within China and then globally, resulting in a pandemic.OBJECTIVE: This article describes predictive modelling of COVID-19 in general, and efforts within the Public Health Agency of Canada to model the effects of non-pharmaceutical interventions (NPIs) on transmission of SARS-CoV-2 in the Canadian population to support public health decisions.METHODS: The broad objectives of two modelling approaches, 1) an agent-based model and 2) a deterministic compartmental model, are described and a synopsis of studies is illustrated using a model developed in Analytica 5.3 software.RESULTS: Without intervention, more than 70% of the Canadian population may become infected. Non-pharmaceutical interventions, applied with an intensity insufficient to cause the epidemic to die out, reduce the attack rate to 50% or less, and the epidemic is longer with a lower peak. If NPIs are lifted early, the epidemic may rebound, resulting in high percentages (more than 70%) of the population affected. If NPIs are applied with intensity high enough to cause the epidemic to die out, the attack rate can be reduced to between 1% and 25% of the population.CONCLUSION: Applying NPIs with intensity high enough to cause the epidemic to die out would seem to be the preferred choice. Lifting disruptive NPIs such as shut-downs must be accompanied by enhancements to other NPIs to prevent new introductions and to identify and control any new transmission chains.