尼日利亚新冠肺炎流行和病死率的Gompertz和Logistic增长模型比较研究

Nnamdi E., Amaeze O.G.
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引用次数: 0

摘要

本研究模拟了2020年2月至2022年7月尼日利亚Covid-19大流行的患病率和死亡率。本文对两个著名的人口增长模型Gompertz和Logistic进行了比较研究。本研究的数据来自Our World indata网站OWID (https//www.ourworldindata.org)。采用赤池信息准则(Akaike Information Criterion, AIC)和贝叶斯信息准则(Bayesian Information Criterion, BIC)对模型的性能进行比较,并考虑了各模型收敛前的迭代次数和收敛容忍度。该研究表明,与对尼日利亚累积covid-19病例和累积covid-19相关死亡进行建模的逻辑增长模型相比,Gompertz人口增长模型提供了更好的拟合。从这些模型中,我们获得了大流行的重要特征,如增长率和渐近线。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparative Study of the Gompertz and Logistic Growth Models on the Prevalence and Fatality of Covid-19 Pandemic in Nigeria
This study models the prevalence and fatality of the Covid-19 pandemic in Nigeria from February 2020 to July 2022. It is a comparative study of two prominent models: The Gompertz and Logistic population growth models. The data for this study was obtained from the website of Our World in Data, OWID (https//www.ourworldindata.org). The Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) were employed to compare the performance of the models, and the number of iterations before convergence and convergence tolerance for each model was also put into consideration. The study revealed that the Gompertz population growth model provides a better fit compared to the logistic growth in modelling the cumulative covid-19 cases and cumulative covid-19-related deaths in Nigeria. From the models, we obtained important features of the pandemic, such as the growth rate and asymptotes.
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