Literature DB >> 33818217

Incubation period of coronavirus disease 2019: new implications for intervention and control.

Shunxiang Huang1, Jing Li1, Chengguqiu Dai2, Zihan Tie1, Jiazhao Xu1, Xiang Xiong1, Xingjie Hao2, Zhongyi Wang3, Chan Lu4.   

Abstract

The COVID-19 pandemic has been causing serious disasters to mankind. The incubation period is a key parameter for epidemic control and also an important basis for epidemic prediction, but its distribution law remains unclear. This paper analyzed the epidemiological information of 787 confirmed non-Wuhan resident cases, and systematically studied the characteristics of the incubation period of COVID-19 based on the interval-censored data estimation method. The results show that the incubation period of COVID-19 approximately conforms to the Gamma distribution with a mean value of 7.8 (95%CI:7.4-8.5) days and a median value of 7.0 (95%CI:6.7-7.3) days. The incubation period was positively correlated with age and negatively correlated with disease severity. Female cases presented a slightly higher incubation period than that of males. The proportion of infected persons who developed symptoms within 14 days was 91.6%. These results are of great significance to the prevention and control of the COVID-19 pandemic.

Entities:  

Keywords:  Coronavirus disease 2019; covid-19; incubation period; interval-censored; intervention and control; pandemic

Mesh:

Year:  2021        PMID: 33818217     DOI: 10.1080/09603123.2021.1905781

Source DB:  PubMed          Journal:  Int J Environ Health Res        ISSN: 0960-3123            Impact factor:   3.411


  6 in total

1.  A survey on the correlation between PM2.5 concentration and the incidence of suspected and positive cases of COVID-19 referred to medical centers: A case study of Tehran.

Authors:  Fallah Hashemi; Lori Hoepner; Farahnaz Soleimani Hamidinejad; Alireza Abbasi; Sima Afrashteh; Mohammad Hoseini
Journal:  Chemosphere       Date:  2022-04-19       Impact factor: 8.943

2.  The incubation period of COVID-19: a global meta-analysis of 53 studies and a Chinese observation study of 11 545 patients.

Authors:  Cheng Cheng; DongDong Zhang; Dejian Dang; Juan Geng; Peiyu Zhu; Mingzhu Yuan; Ruonan Liang; Haiyan Yang; Yuefei Jin; Jing Xie; Shuaiyin Chen; Guangcai Duan
Journal:  Infect Dis Poverty       Date:  2021-09-17       Impact factor: 4.520

3.  Incubation period, clinical and lung CT features for early prediction of COVID-19 deterioration: development and internal verification of a risk model.

Authors:  Hongbing Peng; Chao Hu; Wusheng Deng; Lingmei Huang; Yushan Zhang; Baowei Luo; Xingxing Wang; Xiaodan Long; Xiaoying Huang
Journal:  BMC Pulm Med       Date:  2022-05-12       Impact factor: 3.320

4.  Incubation Period of COVID-19 Caused by Unique SARS-CoV-2 Strains: A Systematic Review and Meta-analysis.

Authors:  Yu Wu; Liangyu Kang; Zirui Guo; Jue Liu; Min Liu; Wannian Liang
Journal:  JAMA Netw Open       Date:  2022-08-01

5.  Spatial heterogeneity affects predictions from early-curve fitting of pandemic outbreaks: a case study using population data from Denmark.

Authors:  Mathias L Heltberg; Christian Michelsen; Emil S Martiny; Lasse Engbo Christensen; Mogens H Jensen; Tariq Halasa; Troels C Petersen
Journal:  R Soc Open Sci       Date:  2022-09-14       Impact factor: 3.653

6.  Modeling the effect of age on quantiles of the incubation period distribution of COVID-19.

Authors:  Xiaohui Liu; Liwen Wu; Lei Wang; Xiansi Ma; Jiewen Wang
Journal:  BMC Public Health       Date:  2021-09-27       Impact factor: 3.295

  6 in total

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