南盟国家COVID-19的流行趋势:预测建模和分析

T. R. Lamichhane, M. Ghimire, S. Bhatt, R. K. Joshi
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引用次数: 0

摘要

本文旨在整合南盟国家新型冠状病毒每日病例;利用logistic模型预测印度、巴基斯坦、孟加拉国、尼泊尔、斯里兰卡、阿富汗、马尔代夫和不丹的疫情趋势。根据2020年1月23日至2021年5月31日南盟国家COVID-19流行病学数据分析了冠状病毒病例的近期趋势。通过logistic曲线与累计病例的拟合,计算出各国COVID-19的最终规模、增长率参数和拐点。COVID-19每日病例的图形模式表明,其在南盟国家的第二波影响比第一波更具破坏性。logistic模型很好地描述了这些国家COVID-19病例的增长趋势,其决定系数大于0.96。预测第二波感染的最终规模最大的是印度,为1980万,增长率参数为0.08,感染时间为68天,而预测的最终规模最小的是阿富汗,为0.041万,增长率参数为0.06,感染时间为71天。如果当前这种传染病的情况保持不变,逻辑模型有助于预测一个国家感染病例的轨迹。此外,它还有助于政府制定政策决定和必要行动,以控制COVID-19在南亚地区的传播。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The epidemic trend of COVID-19 in SAARC countries: a predictive modelling and analysis
This paper aims to integrate novel coronavirus daily cases in SAARC countries; India, Pakistan, Bangladesh, Nepal, Sri Lanka, Afghanistan, Maldives and Bhutan to forecast the epidemic trend of COVID-19 by using logistic model. The recent trend of coronavirus cases were analyzed from the COVID-19 epidemiological data for SAARC countries from 23 January 2020 to 31 May 2021. The final size, growth rate parameter and point of inflection of COVID-19 for each countries were calculated by fitting the logistic curve with the cumulative cases. The graphical patterns of COVID-19 daily cases reflect that its second wave impact is more devastating than the first wave in SAARC countries. The increasing trend of COVID-19 cases in these countries was well described by logistic model with coefficient of determination greater than 0.96. The predictive final size of the second wave infections is maximum for India which is 19.8 million with growth rate parameter of 0.08 and inflection time of 68 days whereas the predictive final size is minimum for Afghanistan which is 0.041 million with growth rate parameter of 0.06 and inflection time of 71 days. The logistic model is helpful in predicting the trajectory of the infected cases in a country if the current scenario of this type of infectious disease remains same. Also, it helps the government to frame policy decisions and necessary actions that controls the transmission of COVID-19 in the South Asian region.
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