Logistic growth modelling of COVID-19 proliferation in China and its international implications
Logistic growth modelling of COVID-19 proliferation in China and its international implications
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DOI：
10.1016/j.ijid.2020.04.085
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发表时间：
2020-07-01
影响因子：
8.4
通讯作者：
Shen, Christopher Y.
中科院分区：
文献类型：
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作者：
Shen, Christopher Y.
随着2019冠状病毒病（COVID-19）大流行在全球范围内的持续扩散，本文分享了在中国省和国家层面对疫情进行建模的结果。本文检验了logistic增长模型的适用性，对COVID-19大流行和其他传染病的研究具有启示意义。方法：采用NLS（非线性最小二乘法）方法，以中国多个地区和其他选定国家的每日新增COVID-19病例为例，估计差异化logistic增长函数的参数。该估计基于2020年1月20日至2020年3月13日的训练数据。随后进行限制性检验，检验指定参数在不同地区或国家之间是否相同，并进行残差诊断。使用2020年3月14日至2020年4月18日的测试数据对模型的拟合优度进行检验。结果：本文提出的模型非常好地拟合了整个中国、其11个选定的省市和其他两个国家（韩国和伊朗）的时间序列数据，并提供了关键参数的估计。该研究拒绝了原假设，即中国10个选定的湖北省以外省份以及韩国和伊朗之间的疫情增长率相同。该研究发现，该模型没有为处于疫情早期阶段的国家提供可靠的估计。此外，本研究表明，在同一非线性曲线的不同部分之间进行比较时，R-2值可能会发生变化和误导。此外，研究还发现部分省份和国家的残差存在异方差和正序列相关。结论：研究结果表明，该模型有可能有助于制定更好的公共卫生政策来抗击COVID-19。该模型提供了一个简单的逻辑框架，用于回顾性分析已经出现最大病例扩散的地区的疫情。基于统计结果，本研究还概述了建模中的某些挑战及其对结果的影响。(C) 2020作者。由爱思唯尔有限公司代表国际传染病学会出版。
Objective: As the coronavirus disease 2019 (COVID-19) pandemic continues to proliferate globally, this paper shares the findings of modelling the outbreak in China at both provincial and national levels. This paper examines the applicability of the logistic growth model, with implications for the study of the COVID-19 pandemic and other infectious diseases.Methods: An NLS (Non-Linear Least Squares) method was employed to estimate the parameters of a differentiated logistic growth function using new daily COVID-19 cases in multiple regions in China and in other selected countries. The estimation was based upon training data from January 20, 2020 to March 13, 2020. A restriction test was subsequently implemented to examine whether a designated parameter was identical among regions or countries, and the diagnosis of residuals was also conducted. The model's goodness of fit was checked using testing data from March 14, 2020 to April 18, 2020.Results: The model presented in this paper fitted time-series data exceedingly well for thewhole of China, its eleven selected provinces and municipalities, and two other countries-South Korea and Iran-and provided estimates of key parameters. This study rejected the null hypothesis that the growth rates of outbreaks were the same among ten selected non-Hubei provinces in China, as well as between South Korea and Iran. The study found that the model did not provide reliable estimates for countries that were in the early stages of outbreaks. Furthermore, this study concured that the R-2 values might vary and mislead when compared between different portions of the same non-linear curve. In addition, the study identified the existence of heteroskedasticity and positive serial correlation within residuals in some provinces and countries.Conclusions: The findings suggest that there is potential for this model to contribute to better public health policy in combatting COVID-19. The model does so by providing a simple logistic framework for retrospectively analyzing outbreaks in regions that have already experienced a maximal proliferation in cases. Based upon statistical findings, this study also outlines certain challenges in modelling and their implications for the results. (C) 2020 The Author(s). Published by Elsevier Ltd on behalf of International Society for Infectious Diseases.