Literature DB >> 32987515

Predicting the cumulative number of cases for the COVID-19 epidemic in China from early data.

Zhi Hua Liu1, Pierre Magal2,3, Ousmane Seydi4, Glenn Webb5.   

Abstract

We model the COVID-19 coronavirus epidemic in China. We use early reported case data to predict the cumulative number of reported cases to a final size. The key features of our model are the timing of implementation of major public policies restricting social movement, the identification and isolation of unreported cases, and the impact of asymptomatic infectious cases.

Entities:  

Keywords:  corona virus ; epidemic mathematical model ; isolation ; public closings ; quarantine ; reported and unreported cases

Mesh：

Year:  2020        PMID： 32987515     DOI： 10.3934/mbe.2020172

Source DB:  PubMed          Journal:  Math Biosci Eng        ISSN： 1547-1063            Impact factor:   2.080


  34 in total

1.  SI epidemic model applied to COVID-19 data in mainland China.

Authors:  J Demongeot; Q Griette; P Magal
Journal:  R Soc Open Sci       Date:  2020-12-02       Impact factor: 2.963

2.  Evaluating short-term forecasting of COVID-19 cases among different epidemiological models under a Bayesian framework.

Authors:  Qiwei Li; Tejasv Bedi; Christoph U Lehmann; Guanghua Xiao; Yang Xie
Journal:  Gigascience       Date:  2021-02-19       Impact factor: 6.524

3.  The relative power of individual distancing efforts and public policies to curb the COVID-19 epidemics.

Authors:  Cécile Aubert; Emmanuelle Augeraud-Véron
Journal:  PLoS One       Date:  2021-05-07       Impact factor: 3.240

4.  Dynamics of epidemic spreading on connected graphs.

Authors:  Christophe Besse; Grégory Faye
Journal:  J Math Biol       Date:  2021-04-16       Impact factor: 2.259

5.  A mathematical model for the spread of COVID-19 and control mechanisms in Saudi Arabia.

Authors:  Mostafa Bachar; Mohamed A Khamsi; Messaoud Bounkhel
Journal:  Adv Differ Equ       Date:  2021-05-14

6.  Transmission dynamics of SARS-CoV-2: A modeling analysis with high-and-moderate risk populations.

Authors:  Salihu S Musa; Isa A Baba; Abdullahi Yusuf; Tukur A Sulaiman; Aliyu I Aliyu; Shi Zhao; Daihai He
Journal:  Results Phys       Date:  2021-05-19       Impact factor: 4.476

7.  Mathematical modeling of the COVID-19 pandemic with intervention strategies.

Authors:  Subhas Khajanchi; Kankan Sarkar; Jayanta Mondal; Kottakkaran Sooppy Nisar; Sayed F Abdelwahab
Journal:  Results Phys       Date:  2021-05-06       Impact factor: 4.476

8.  Analyzing and forecasting COVID-19 pandemic in the Kingdom of Saudi Arabia using ARIMA and SIR models.

Authors:  Khaled Ali Abuhasel; Mosaad Khadr; Mohammed M Alquraish
Journal:  Comput Intell       Date:  2020-10-05       Impact factor: 2.142

9.  Estimating the Prevalence of Asymptomatic COVID-19 Cases and Their Contribution in Transmission - Using Henan Province, China, as an Example.

Authors:  Chunyu Li; Yuchen Zhu; Chang Qi; Lili Liu; Dandan Zhang; Xu Wang; Kaili She; Yan Jia; Tingxuan Liu; Daihai He; Momiao Xiong; Xiujun Li
Journal:  Front Med (Lausanne)       Date:  2021-06-23

10.  Estimating the effects of asymptomatic and imported patients on COVID-19 epidemic using mathematical modeling.

Authors:  Tingzhe Sun; Dan Weng
Journal:  J Med Virol       Date:  2020-05-10       Impact factor: 20.693
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