Literature DB >> 32391240

Early Prediction of the 2019 Novel Coronavirus Outbreak in the Mainland China Based on Simple Mathematical Model.

Linhao Zhong1, Lin Mu2,3, Jing Li3,4, Jiaying Wang3,4, Zhe Yin3,4, Darong Liu3,4.   

Abstract

The 2019 novel coronavirus (2019-nCoV) outbreak has been treated as a Public Health Emergency of International Concern by the World Health Organization. This work made an early prediction of the 2019-nCoV outbreak in China based on a simple mathematical model and limited epidemiological data. Combing characteristics of the historical epidemic, we found part of the released data is unreasonable. Through ruling out the unreasonable data, the model predictions exhibit that the number of the cumulative 2019-nCoV cases may reach 76,000 to 230,000, with a peak of the unrecovered infectives (22,000-74,000) occurring in late February to early March. After that, the infected cases will rapidly monotonically decrease until early May to late June, when the 2019-nCoV outbreak will fade out. Strong anti-epidemic measures may reduce the cumulative infected cases by 40%-49%. The improvement of medical care can also lead to about one-half transmission decrease and effectively shorten the duration of the 2019-nCoV.

Entities:  

Keywords:  Epidemic transmission; infection rate; mathematical model; novel coronavirus; prediction; removal rate

Year:  2020        PMID: 32391240      PMCID: PMC7176026          DOI: 10.1109/ACCESS.2020.2979599

Source DB:  PubMed          Journal:  IEEE Access        ISSN: 2169-3536            Impact factor:   3.367


  31 in total

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Journal:  IEEE Access       Date:  2020-09-25       Impact factor: 3.367

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Journal:  IEEE Access       Date:  2020-09-07       Impact factor: 3.367

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4.  Ranking of Importance Measures of Tweet Communities: Application to Keyword Extraction From COVID-19 Tweets in Japan.

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5.  Early prediction of coronavirus disease epidemic severity in the contiguous United States based on deep learning.

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Journal:  Results Phys       Date:  2021-05-08       Impact factor: 4.476

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Journal:  Transbound Emerg Dis       Date:  2021-04-20       Impact factor: 4.521

7.  Accurate detection of Covid-19 patients based on Feature Correlated Naïve Bayes (FCNB) classification strategy.

Authors:  Nehal A Mansour; Ahmed I Saleh; Mahmoud Badawy; Hesham A Ali
Journal:  J Ambient Intell Humaniz Comput       Date:  2021-01-15

8.  COVID-19 outbreak in India: an SEIR model-based analysis.

Authors:  Debashis Saikia; Kalpana Bora; Madhurjya P Bora
Journal:  Nonlinear Dyn       Date:  2021-06-04       Impact factor: 5.022

9.  Deming least square regressed feature selection and Gaussian neuro-fuzzy multi-layered data classifier for early COVID prediction.

Authors:  Rathnamma V Mydukuri; Suresh Kallam; Rizwan Patan; Fadi Al-Turjman; Manikandan Ramachandran
Journal:  Expert Syst       Date:  2021-03-26       Impact factor: 2.812

10.  Can medical practitioners rely on prediction models for COVID-19? A systematic review.

Authors:  Erfan Shamsoddin
Journal:  Evid Based Dent       Date:  2020-09
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