加拿大政府控制新冠肺炎疫情的政策评估

IF 0.7 Q3 STATISTICS & PROBABILITY Statistical Theory and Related Fields Pub Date : 2023-04-28 DOI:10.1080/24754269.2023.2201108
Mengyao Chen, Yuehua Wu, B. Jin
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引用次数: 0

摘要

在本文中,我们调查了加拿大新冠肺炎疫情,并评估了加拿大政府控制新冠肺炎疫情的政策。2020年1月25日,安大略省报告了第一例新冠肺炎病例。从那时起,到目前为止,已经有超过一百万例病例。在此期间,联邦、省和地方政府实施了法规和政策,以控制疫情。为了评估这些政府政策,可以通过分析新冠肺炎的感染率、感染期和繁殖数量来完成,我们引入了一个扩展的易感-非易感感染-移动(SEIR)模型来解决问题,并使用迭代滤波器集成调整卡尔曼滤波器(IF-EAKF)算法进行模型推理。我们首先根据各省的政策强度将时间段划分为几个阶段,将2020年3月4日至2020年10月31日的时间段分为三个时间阶段:爆发阶段、严格政策执行阶段和省级重新开放阶段。然后,我们使用IF-EAKF算法来获得模型参数的估计值。我们发现,第二阶段的感染率低于第一和第三阶段。我们还讨论了在第三波疫情中,在不同政策强度和不同政策持续时间下,新冠肺炎新增病例的数量。
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Evaluation of the Canadian government policies on controlling the COVID-19 outbreaks
In this paper, we investigate the COVID-19 pandemic in Canada and evaluate the Canadian government policies on controlling COVID-19 outbreaks. The first case of COVID-19 was reported in Ontario on 25 January 2020. Since then, there have been over million cases by now. During this time period, the federal, provincial and local governments have implemented regulations and policies in order to control the pandemic. To evaluate these government policies, which may be done by analysing the infection rate, infection period and reproductive number of COVID-19, we approach the problem by introducing an extended susceptible-exposed-infectious-removed (SEIR) model and conduct the model inference by using the iterated filter ensemble adjustment Kalman filter (IF-EAKF) algorithm. We first divide the time period into phases according to the policy intensities in each province by segmenting the time period from 4 March 2020 to 31 October 2020 into three time phases: the exploding phase, the strict policy implementation phase, and the provincial reopening phase. We then use IF-EAKF algorithm to obtain the estimates of the model parameters. We show that the infection rate in the second phase is lower than that in both first and third phases. We also discuss the number of new COVID-19 cases under different policy intensities and different policy durations in the third wave of the pandemic.
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来源期刊
CiteScore
0.90
自引率
20.00%
发文量
21
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