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基于数据驱动的传染病模型在评估马来西亚大型集会和后续流动管制令对 COVID-19 传播影响中的应用。

A data driven change-point epidemic model for assessing the impact of large gathering and subsequent movement control order on COVID-19 spread in Malaysia.

机构信息

School of Mathematical and Computer Sciences, Heriot-Watt University Malaysia, Putrajaya, Wilayah Persekutuan Putrajaya, Malaysia.

School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, Scotland, United Kingdom.

出版信息

PLoS One. 2021 May 27;16(5):e0252136. doi: 10.1371/journal.pone.0252136. eCollection 2021.

DOI:10.1371/journal.pone.0252136
PMID:34043676
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8158983/
Abstract

The second wave of COVID-19 in Malaysia is largely attributed to a four-day mass gathering held in Sri Petaling from February 27, 2020, which contributed to an exponential rise of COVID-19 cases in the country. Starting from March 18, 2020, the Malaysian government introduced four consecutive phases of a Movement Control Order (MCO) to stem the spread of COVID-19. The MCO was implemented through various non-pharmaceutical interventions (NPIs). The reported number of cases reached its peak by the first week of April and then started to reduce, hence proving the effectiveness of the MCO. To gain a quantitative understanding of the effect of MCO on the dynamics of COVID-19, this paper develops a class of mathematical models to capture the disease spread before and after MCO implementation in Malaysia. A heterogeneous variant of the Susceptible-Exposed-Infected-Recovered (SEIR) model is developed with additional compartments for asymptomatic transmission. Further, a change-point is incorporated to model disease dynamics before and after intervention which is inferred based on data. Related statistical analyses for inference are developed in a Bayesian framework and are able to provide quantitative assessments of (1) the impact of the Sri Petaling gathering, and (2) the extent of decreasing transmission during the MCO period. The analysis here also quantitatively demonstrates how quickly transmission rates fall under effective NPI implementation within a short time period. The models and methodology used provided important insights into the nature of local transmissions to decision makers in the Ministry of Health, Malaysia.

摘要

马来西亚的第二波 COVID-19 疫情主要归因于 2020 年 2 月 27 日在斯里 Petaling 举行的为期四天的大规模集会,这导致马来西亚 COVID-19 病例呈指数级增长。从 2020 年 3 月 18 日开始,马来西亚政府实施了四轮连续的行动控制令(MCO)以阻止 COVID-19 的传播。MCO 通过各种非药物干预(NPI)实施。报告的病例数在 4 月的第一周达到峰值,然后开始减少,这证明了 MCO 的有效性。为了定量了解 MCO 对 COVID-19 动态的影响,本文开发了一类数学模型来捕捉马来西亚 MCO 实施前后疾病的传播。开发了具有无症状传播附加隔室的 Susceptible-Exposed-Infected-Recovered (SEIR) 模型的异质变体。此外,还结合了一个变化点来模拟干预前后的疾病动态,该变化点是根据数据推断出来的。用于推理的相关统计分析是在贝叶斯框架中进行的,能够定量评估 (1) Sri Petaling 集会的影响,以及 (2) MCO 期间传播减少的程度。这里的分析还定量地证明了在短时间内通过有效实施 NPI 可以多快降低传播速度。所使用的模型和方法为马来西亚卫生部的决策者提供了对本地传播性质的重要见解。

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