Literature DB >> 32113197

[Dynamic basic reproduction number based evaluation for current prevention and control of COVID-19 outbreak in China].

L L Huang1, S P Shen2, P Yu3, Y Y Wei2.   

Abstract

Objective: To evaluate the current status of the prevention and control of coronavirus disease (COVID-19) outbreak in China, establish a predictive model to evaluate the effects of the current prevention and control strategies, and provide scientific information for decision-making departments.
Methods: Based on the epidemic data of COVID-19 openly accessed from national health authorities, we estimated the dynamic basic reproduction number R(0)(t) to evaluate the effects of the current COVID-19 prevention and control strategies in all the provinces (municipalities and autonomous regions) as well as in Wuhan and the changes in infectivity of COVID-19 over time.
Results: For the stability of the results, 24 provinces (municipality) with more than 100 confirmed COVID-19 cases were included in the analysis. At the beginning of the outbreak, the R(0)(t) showed unstable trend with big variances. As the strengthening of the prevention and control strategies, R(0)(t) began to show a downward trend in late January, and became stable in February. By the time of data analysis, 18 provinces (municipality) (75%) had the R(0)(t)s less than 1. The results could be used for the decision making to free population floating conditionally. Conclusions: Dynamic R(0)(t) is useful in the evaluation of the change in infectivity of COVID-19, the prevention and control strategies for the COVID-19 outbreak have shown preliminary effects, if continues, it is expected to control the COVID-19 outbreak in China in near future.

Entities:  

Keywords:  Coronavirus disease; Dynamic basic reproduction number; Statistical prediction

Mesh:

Year:  2020        PMID: 32113197     DOI: 10.3760/cma.j.cn112338-20200209-00080

Source DB:  PubMed          Journal:  Zhonghua Liu Xing Bing Xue Za Zhi        ISSN: 0254-6450


  7 in total

1.  Therapeutic capability of five active compounds in typical African medicinal plants against main proteases of SARS-CoV-2 by computational approach.

Authors:  Oluwasayo Peter Abodunrin; Olayinka Fisayo Onifade; Abayomi Emmanuel Adegboyega
Journal:  Inform Med Unlocked       Date:  2022-05-23

2.  Sociodemographic, clinical and laboratory factors on admission associated with COVID-19 mortality in hospitalized patients: A retrospective observational study.

Authors:  Mario Rivera-Izquierdo; María Del Carmen Valero-Ubierna; Juan Luis R-delAmo; Miguel Ángel Fernández-García; Silvia Martínez-Diz; Arezu Tahery-Mahmoud; Marta Rodríguez-Camacho; Ana Belén Gámiz-Molina; Nicolás Barba-Gyengo; Pablo Gámez-Baeza; Celia Cabrero-Rodríguez; Pedro Antonio Guirado-Ruiz; Divina Tatiana Martín-Romero; Antonio Jesús Láinez-Ramos-Bossini; María Rosa Sánchez-Pérez; José Mancera-Romero; Miguel García-Martín; Luis Miguel Martín-delosReyes; Virginia Martínez-Ruiz; Pablo Lardelli-Claret; Eladio Jiménez-Mejías
Journal:  PLoS One       Date:  2020-06-25       Impact factor: 3.240

3.  Invisible spread of SARS-CoV-2.

Authors:  Nian Xiong; Tao Wang; Zhicheng Lin
Journal:  Lancet Infect Dis       Date:  2020-04-08       Impact factor: 25.071

4.  The potential effects of widespread community transmission of SARS-CoV-2 infection in the World Health Organization African Region: a predictive model.

Authors:  Joseph Waogodo Cabore; Humphrey Cyprian Karamagi; Hillary Kipruto; James Avoka Asamani; Benson Droti; Aminata Binetou Wahebine Seydi; Regina Titi-Ofei; Benido Impouma; Michel Yao; Zabulon Yoti; Felicitas Zawaira; Prosper Tumusiime; Ambrose Talisuna; Francis Chisaka Kasolo; Matshidiso R Moeti
Journal:  BMJ Glob Health       Date:  2020-05

5.  A classification of countries and regions by degree of the spread of coronavirus based on statistical criteria.

Authors:  Antoni Wilinski; Eryk Szwarc
Journal:  Expert Syst Appl       Date:  2021-02-01       Impact factor: 6.954

6.  Epidemiological characteristics of COVID-19 clusters in Hainan, China.

Authors:  Sha Xiao; Yunru Liu; Fang Liu; Hanxi Zhang; Fan Zhang; Lu Wang
Journal:  Medicine (Baltimore)       Date:  2021-10-22       Impact factor: 1.817

7.  In Silico Discovery of Candidate Drugs against Covid-19.

Authors:  Claudia Cava; Gloria Bertoli; Isabella Castiglioni
Journal:  Viruses       Date:  2020-04-06       Impact factor: 5.048

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.