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Wu Y, Kang L, Guo Z, Liu J, Liu M, Liang W. Incubation Period of COVID-19 Caused by Unique SARS-CoV-2 Strains: A Systematic Review and Meta-analysis. JAMA Netw Open 2022; 5:e2228008. [PMID: 35994285 PMCID: PMC9396366 DOI: 10.1001/jamanetworkopen.2022.28008] [Citation(s) in RCA: 165] [Impact Index Per Article: 82.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
IMPORTANCE Several studies were conducted to estimate the average incubation period of COVID-19; however, the incubation period of COVID-19 caused by different SARS-CoV-2 variants is not well described. OBJECTIVE To systematically assess the incubation period of COVID-19 and the incubation periods of COVID-19 caused by different SARS-CoV-2 variants in published studies. DATA SOURCES PubMed, EMBASE, and ScienceDirect were searched between December 1, 2019, and February 10, 2022. STUDY SELECTION Original studies of the incubation period of COVID-19, defined as the time from infection to the onset of signs and symptoms. DATA EXTRACTION AND SYNTHESIS Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline, 3 reviewers independently extracted the data from the eligible studies in March 2022. The parameters, or sufficient information to facilitate calculation of those values, were derived from random-effects meta-analysis. MAIN OUTCOMES AND MEASURES The mean estimate of the incubation period and different SARS-CoV-2 strains. RESULTS A total of 142 studies with 8112 patients were included. The pooled incubation period was 6.57 days (95% CI, 6.26-6.88) and ranged from 1.80 to 18.87 days. The incubation period of COVID-19 caused by the Alpha, Beta, Delta, and Omicron variants were reported in 1 study (with 6374 patients), 1 study (10 patients), 6 studies (2368 patients) and 5 studies (829 patients), respectively. The mean incubation period of COVID-19 was 5.00 days (95% CI, 4.94-5.06 days) for cases caused by the Alpha variant, 4.50 days (95% CI, 1.83-7.17 days) for the Beta variant, 4.41 days (95% CI, 3.76-5.05 days) for the Delta variant, and 3.42 days (95% CI, 2.88-3.96 days) for the Omicron variant. The mean incubation was 7.43 days (95% CI, 5.75-9.11 days) among older patients (ie, aged over 60 years old), 8.82 days (95% CI, 8.19-9.45 days) among infected children (ages 18 years or younger), 6.99 days (95% CI, 6.07-7.92 days) among patients with nonsevere illness, and 6.69 days (95% CI, 4.53-8.85 days) among patients with severe illness. CONCLUSIONS AND RELEVANCE The findings of this study suggest that SARS-CoV-2 has evolved and mutated continuously throughout the COVID-19 pandemic, producing variants with different enhanced transmission and virulence. Identifying the incubation period of different variants is a key factor in determining the isolation period.
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Affiliation(s)
- Yu Wu
- Department of Epidemiology and Biostatics, School of Public Health, Peking University, Beijing, China
| | - Liangyu Kang
- Department of Epidemiology and Biostatics, School of Public Health, Peking University, Beijing, China
| | - Zirui Guo
- Department of Epidemiology and Biostatics, School of Public Health, Peking University, Beijing, China
| | - Jue Liu
- Department of Epidemiology and Biostatics, School of Public Health, Peking University, Beijing, China
| | - Min Liu
- Department of Epidemiology and Biostatics, School of Public Health, Peking University, Beijing, China
| | - Wannian Liang
- Vanke School of Public Health, Tsinghua University, Beijing, China
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Modeling Hospital Resource Management during the COVID-19 Pandemic: An Experimental Validation. ECONOMETRICS 2021. [DOI: 10.3390/econometrics9040038] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
One of the main challenges posed by the healthcare crisis generated by COVID-19 is to avoid hospital collapse. The occupation of hospital beds by patients diagnosed by COVID-19 implies the diversion or suspension of their use for other specialities. Therefore, it is useful to have information that allows efficient management of future hospital occupancy. This article presents a robust and simple model to show certain characteristics of the evolution of the dynamic process of bed occupancy by patients with COVID-19 in a hospital by means of an adaptation of Kaplan-Meier survival curves. To check this model, the evolution of the COVID-19 hospitalization process of two hospitals between 11 March and 15 June 2020 is analyzed. The information provided by the Kaplan-Meier curves allows forecasts of hospital occupancy in subsequent periods. The results shows an average deviation of 2.45 patients between predictions and actual occupancy in the period analyzed.
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Cheng C, Zhang D, Dang D, Geng J, Zhu P, Yuan M, Liang R, Yang H, Jin Y, Xie J, Chen S, Duan G. The incubation period of COVID-19: a global meta-analysis of 53 studies and a Chinese observation study of 11 545 patients. Infect Dis Poverty 2021; 10:119. [PMID: 34535192 PMCID: PMC8446477 DOI: 10.1186/s40249-021-00901-9] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 09/02/2021] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND The incubation period is a crucial index of epidemiology in understanding the spread of the emerging Coronavirus disease 2019 (COVID-19). In this study, we aimed to describe the incubation period of COVID-19 globally and in the mainland of China. METHODS The searched studies were published from December 1, 2019 to May 26, 2021 in CNKI, Wanfang, PubMed, and Embase databases. A random-effect model was used to pool the mean incubation period. Meta-regression was used to explore the sources of heterogeneity. Meanwhile, we collected 11 545 patients in the mainland of China outside Hubei from January 19, 2020 to September 21, 2020. The incubation period fitted with the Log-normal model by the coarseDataTools package. RESULTS A total of 3235 articles were searched, 53 of which were included in the meta-analysis. The pooled mean incubation period of COVID-19 was 6.0 days (95% confidence interval [CI] 5.6-6.5) globally, 6.5 days (95% CI 6.1-6.9) in the mainland of China, and 4.6 days (95% CI 4.1-5.1) outside the mainland of China (P = 0.006). The incubation period varied with age (P = 0.005). Meanwhile, in 11 545 patients, the mean incubation period was 7.1 days (95% CI 7.0-7.2), which was similar to the finding in our meta-analysis. CONCLUSIONS For COVID-19, the mean incubation period was 6.0 days globally but near 7.0 days in the mainland of China, which will help identify the time of infection and make disease control decisions. Furthermore, attention should also be paid to the region- or age-specific incubation period.
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Affiliation(s)
- Cheng Cheng
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, Zhengzhou, 450001, Henan, People's Republic of China
| | - DongDong Zhang
- Department of Nutrition and Food Hygiene, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, Zhengzhou, 450001, Henan, People's Republic of China
| | - Dejian Dang
- Infection Prevention and Control Department, The Fifth Affiliated Hospital of Zhengzhou University, No.3 Kangfuqian Street, Zhengzhou, 450052, Henan, People's Republic of China
| | - Juan Geng
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, Zhengzhou, 450001, Henan, People's Republic of China
| | - Peiyu Zhu
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, Zhengzhou, 450001, Henan, People's Republic of China
| | - Mingzhu Yuan
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, Zhengzhou, 450001, Henan, People's Republic of China
| | - Ruonan Liang
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, Zhengzhou, 450001, Henan, People's Republic of China
| | - Haiyan Yang
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, Zhengzhou, 450001, Henan, People's Republic of China
| | - Yuefei Jin
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, Zhengzhou, 450001, Henan, People's Republic of China
| | - Jing Xie
- Henan Key Laboratory of Molecular Medicine, Zhengzhou University, No. 100 Kexue Avenue, Zhengzhou, 450001, Henan, People's Republic of China
- Centre for Biostatistics and Clinical Trials (BaCT), Peter MacCallum Cancer Centre, No. 305 Grattan Street, Melbourne, 3000, Victoria, Australia
| | - Shuaiyin Chen
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, Zhengzhou, 450001, Henan, People's Republic of China.
| | - Guangcai Duan
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, No. 100 Kexue Avenue, Zhengzhou, 450001, Henan, People's Republic of China.
- Henan Key Laboratory of Molecular Medicine, Zhengzhou University, No. 100 Kexue Avenue, Zhengzhou, 450001, Henan, People's Republic of China.
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Gaythorpe KAM, Bhatia S, Mangal T, Unwin HJT, Imai N, Cuomo-Dannenburg G, Walters CE, Jauneikaite E, Bayley H, Kont MD, Mousa A, Whittles LK, Riley S, Ferguson NM. Children's role in the COVID-19 pandemic: a systematic review of early surveillance data on susceptibility, severity, and transmissibility. Sci Rep 2021; 11:13903. [PMID: 34230530 PMCID: PMC8260804 DOI: 10.1038/s41598-021-92500-9] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 06/10/2021] [Indexed: 02/07/2023] Open
Abstract
SARS-CoV-2 infections have been reported in all age groups including infants, children, and adolescents. However, the role of children in the COVID-19 pandemic is still uncertain. This systematic review of early studies synthesises evidence on the susceptibility of children to SARS-CoV-2 infection, the severity and clinical outcomes in children with SARS-CoV-2 infection, and the transmissibility of SARS-CoV-2 by children in the initial phases of the COVID-19 pandemic. A systematic literature review was conducted in PubMed. Reviewers extracted data from relevant, peer-reviewed studies published up to July 4th 2020 during the first wave of the SARS-CoV-2 outbreak using a standardised form and assessed quality using the NIH Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. For studies included in the meta-analysis, we used a random effects model to calculate pooled estimates of the proportion of children considered asymptomatic or in a severe or critical state. We identified 2775 potential studies of which 128 studies met our inclusion criteria; data were extracted from 99, which were then quality assessed. Finally, 29 studies were considered for the meta-analysis that included information of symptoms and/or severity, these were further assessed based on patient recruitment. Our pooled estimate of the proportion of test positive children who were asymptomatic was 21.1% (95% CI: 14.0-28.1%), based on 13 included studies, and the proportion of children with severe or critical symptoms was 3.8% (95% CI: 1.5-6.0%), based on 14 included studies. We did not identify any studies designed to assess transmissibility in children and found that susceptibility to infection in children was highly variable across studies. Children's susceptibility to infection and onward transmissibility relative to adults is still unclear and varied widely between studies. However, it is evident that most children experience clinically mild disease or remain asymptomatically infected. More comprehensive contact-tracing studies combined with serosurveys are needed to quantify children's transmissibility relative to adults. With children back in schools, testing regimes and study protocols that will allow us to better understand the role of children in this pandemic are critical.
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Affiliation(s)
- Katy A M Gaythorpe
- MRC Centre for Global Infectious Disease Analysis and WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK.
| | - Sangeeta Bhatia
- MRC Centre for Global Infectious Disease Analysis and WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Tara Mangal
- MRC Centre for Global Infectious Disease Analysis and WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - H Juliette T Unwin
- MRC Centre for Global Infectious Disease Analysis and WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Natsuko Imai
- MRC Centre for Global Infectious Disease Analysis and WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Gina Cuomo-Dannenburg
- MRC Centre for Global Infectious Disease Analysis and WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Caroline E Walters
- MRC Centre for Global Infectious Disease Analysis and WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Elita Jauneikaite
- MRC Centre for Global Infectious Disease Analysis and WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Helena Bayley
- Department of Physics, University of Oxford, Oxford, UK
| | - Mara D Kont
- MRC Centre for Global Infectious Disease Analysis and WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Andria Mousa
- MRC Centre for Global Infectious Disease Analysis and WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Lilith K Whittles
- MRC Centre for Global Infectious Disease Analysis and WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Steven Riley
- MRC Centre for Global Infectious Disease Analysis and WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
| | - Neil M Ferguson
- MRC Centre for Global Infectious Disease Analysis and WHO Collaborating Centre for Infectious Disease Modelling, Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK
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Melo Costa M, Benoit N, Dormoi J, Amalvict R, Gomez N, Tissot-Dupont H, Million M, Pradines B, Granjeaud S, Almeras L. Salivette, a relevant saliva sampling device for SARS-CoV-2 detection. J Oral Microbiol 2021; 13:1920226. [PMID: 33986939 PMCID: PMC8098750 DOI: 10.1080/20002297.2021.1920226] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 04/12/2021] [Accepted: 04/19/2021] [Indexed: 12/22/2022] Open
Abstract
Background: The gold standard for COVID-19 diagnosis relies on quantitative reverse-transcriptase polymerase-chain reaction (RT-qPCR) from nasopharyngeal swab (NPS) specimens, but NPSs present several limitations. The simplicity, low invasive and possibility of self-collection of saliva imposed these specimens as a relevant alternative for SARS-CoV-2 detection. However, the discrepancy of saliva test results compared to NPSs made of its use controversial. Here, we assessed Salivettes®, as a standardized saliva collection device, and compared SARS-CoV-2 positivity on paired NPS and saliva specimens. Methods: A total of 303 individuals randomly selected among those investigated for SARS-CoV-2 were enrolled, including 30 (9.9%) patients previously positively tested using NPS (follow-up group), 90 (29.7%) mildly symptomatic and 183 (60.4%) asymptomatic. Results: The RT-qPCR revealed a positive rate of 11.6% (n = 35) and 17.2% (n = 52) for NPSs and saliva samples, respectively. The sensitivity and specificity of saliva samples were 82.9% and 91.4%, respectively, using NPS as reference. The highest proportion of discordant results concerned the follow-up group (33.3%). Although the agreement exceeded 90.0% in the symptomatic and asymptomatic groups, 17 individuals were detected positive only in saliva samples, with consistent medical arguments. Conclusion Saliva collected with Salivette® was more sensitive for detecting symptomatic and pre-symptomatic infections.
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Affiliation(s)
- Monique Melo Costa
- Unité Parasitologie Et Entomologie, Département Microbiologie Et Maladies Infectieuses, Institut De Recherche Biomédicale Des Armées, Marseille, France
- Aix Marseille Univ, IRD, SSA, AP-HM, VITROME, Marseille, France
- IHU Méditerranée Infection, Marseille, France
| | - Nicolas Benoit
- Unité Parasitologie Et Entomologie, Département Microbiologie Et Maladies Infectieuses, Institut De Recherche Biomédicale Des Armées, Marseille, France
- Aix Marseille Univ, IRD, SSA, AP-HM, VITROME, Marseille, France
- IHU Méditerranée Infection, Marseille, France
- Centre National De Référence Du Paludisme, Marseille, France
| | - Jerome Dormoi
- Unité Parasitologie Et Entomologie, Département Microbiologie Et Maladies Infectieuses, Institut De Recherche Biomédicale Des Armées, Marseille, France
- Aix Marseille Univ, IRD, SSA, AP-HM, VITROME, Marseille, France
- IHU Méditerranée Infection, Marseille, France
| | - Remy Amalvict
- Unité Parasitologie Et Entomologie, Département Microbiologie Et Maladies Infectieuses, Institut De Recherche Biomédicale Des Armées, Marseille, France
- Aix Marseille Univ, IRD, SSA, AP-HM, VITROME, Marseille, France
- IHU Méditerranée Infection, Marseille, France
- Centre National De Référence Du Paludisme, Marseille, France
| | - Nicolas Gomez
- Unité Parasitologie Et Entomologie, Département Microbiologie Et Maladies Infectieuses, Institut De Recherche Biomédicale Des Armées, Marseille, France
- Aix Marseille Univ, IRD, SSA, AP-HM, VITROME, Marseille, France
- IHU Méditerranée Infection, Marseille, France
| | - Hervé Tissot-Dupont
- Aix Marseille Univ, IRD, SSA, AP-HM, VITROME, Marseille, France
- IHU Méditerranée Infection, Marseille, France
| | - Matthieu Million
- Aix Marseille Univ, IRD, SSA, AP-HM, VITROME, Marseille, France
- IHU Méditerranée Infection, Marseille, France
| | - Bruno Pradines
- Unité Parasitologie Et Entomologie, Département Microbiologie Et Maladies Infectieuses, Institut De Recherche Biomédicale Des Armées, Marseille, France
- Aix Marseille Univ, IRD, SSA, AP-HM, VITROME, Marseille, France
- IHU Méditerranée Infection, Marseille, France
- Centre National De Référence Du Paludisme, Marseille, France
| | - Samuel Granjeaud
- CRCM Integrative Bioinformatics Platform, Centre De Recherche En Cancérologie De Marseille, INSERM, U1068, Institut Paoli-Calmettes, CNRS, UMR7258, Aix-Marseille Université UM 105, Marseille, France
| | - Lionel Almeras
- Unité Parasitologie Et Entomologie, Département Microbiologie Et Maladies Infectieuses, Institut De Recherche Biomédicale Des Armées, Marseille, France
- Aix Marseille Univ, IRD, SSA, AP-HM, VITROME, Marseille, France
- IHU Méditerranée Infection, Marseille, France
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Geng MJ, Wang LP, Ren X, Yu JX, Chang ZR, Zheng CJ, An ZJ, Li Y, Yang XK, Zhao HT, Li ZJ, He GX, Feng ZJ. Risk factors for developing severe COVID-19 in China: an analysis of disease surveillance data. Infect Dis Poverty 2021; 10:48. [PMID: 33845915 PMCID: PMC8040359 DOI: 10.1186/s40249-021-00820-9] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 03/05/2021] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND COVID-19 has posed an enormous threat to public health around the world. Some severe and critical cases have bad prognoses and high case fatality rates, unraveling risk factors for severe COVID-19 are of significance for predicting and preventing illness progression, and reducing case fatality rates. Our study focused on analyzing characteristics of COVID-19 cases and exploring risk factors for developing severe COVID-19. METHODS The data for this study was disease surveillance data on symptomatic cases of COVID-19 reported from 30 provinces in China between January 19 and March 9, 2020, which included demographics, dates of symptom onset, clinical manifestations at the time of diagnosis, laboratory findings, radiographic findings, underlying disease history, and exposure history. We grouped mild and moderate cases together as non-severe cases and categorized severe and critical cases together as severe cases. We compared characteristics of severe cases and non-severe cases of COVID-19 and explored risk factors for severity. RESULTS The total number of cases were 12 647 with age from less than 1 year old to 99 years old. The severe cases were 1662 (13.1%), the median age of severe cases was 57 years [Inter-quartile range(IQR): 46-68] and the median age of non-severe cases was 43 years (IQR: 32-54). The risk factors for severe COVID-19 were being male [adjusted odds ratio (aOR) = 1.3, 95% CI: 1.2-1.5]; fever (aOR = 2.3, 95% CI: 2.0-2.7), cough (aOR = 1.4, 95% CI: 1.2-1.6), fatigue (aOR = 1.3, 95% CI: 1.2-1.5), and chronic kidney disease (aOR = 2.5, 95% CI: 1.4-4.6), hypertension (aOR = 1.5, 95% CI: 1.2-1.8) and diabetes (aOR = 1.96, 95% CI: 1.6-2.4). With the increase of age, risk for the severity was gradually higher [20-39 years (aOR = 3.9, 95% CI: 1.8-8.4), 40-59 years (aOR = 7.6, 95% CI: 3.6-16.3), ≥ 60 years (aOR = 20.4, 95% CI: 9.5-43.7)], and longer time from symtem onset to diagnosis [3-5 days (aOR = 1.4, 95% CI: 1.2-1.7), 6-8 days (aOR = 1.8, 95% CI: 1.5-2.1), ≥ 9 days(aOR = 1.9, 95% CI: 1.6-2.3)]. CONCLUSIONS Our study showed the risk factors for developing severe COVID-19 with large sample size, which included being male, older age, fever, cough, fatigue, delayed diagnosis, hypertension, diabetes, chronic kidney diasease, early case identification and prompt medical care. Based on these factors, the severity of COVID-19 cases can be predicted. So cases with these risk factors should be paid more attention to prevent severity.
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Affiliation(s)
- Meng-Jie Geng
- Division of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Li-Ping Wang
- Division of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Xiang Ren
- Division of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jian-Xing Yu
- National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zhao-Rui Chang
- Division of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Can-Jun Zheng
- Division of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zhi-Jie An
- National Immunization Program, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yu Li
- Division of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Xiao-Kun Yang
- Division of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Hong-Ting Zhao
- Division of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zhong-Jie Li
- Division of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China.
| | - Guang-Xue He
- National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
| | - Zi-Jian Feng
- Division of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China.
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7
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Dhouib W, Maatoug J, Ayouni I, Zammit N, Ghammem R, Fredj SB, Ghannem H. The incubation period during the pandemic of COVID-19: a systematic review and meta-analysis. Syst Rev 2021; 10:101. [PMID: 33832511 PMCID: PMC8031340 DOI: 10.1186/s13643-021-01648-y] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 03/22/2021] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND The aim of our study was to determine through a systematic review and meta-analysis the incubation period of COVID-19. It was conducted based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA). Criteria for eligibility were all published population-based primary literature in PubMed interface and the Science Direct, dealing with incubation period of COVID-19, written in English, since December 2019 to December 2020. We estimated the mean of the incubation period using meta-analysis, taking into account between-study heterogeneity, and the analysis with moderator variables. RESULTS This review included 42 studies done predominantly in China. The mean and median incubation period were of maximum 8 days and 12 days respectively. In various parametric models, the 95th percentiles were in the range 10.3-16 days. The highest 99th percentile would be as long as 20.4 days. Out of the 10 included studies in the meta-analysis, 8 were conducted in China, 1 in Singapore, and 1 in Argentina. The pooled mean incubation period was 6.2 (95% CI 5.4, 7.0) days. The heterogeneity (I2 77.1%; p < 0.001) was decreased when we included the study quality and the method of calculation used as moderator variables (I2 0%). The mean incubation period ranged from 5.2 (95% CI 4.4 to 5.9) to 6.65 days (95% CI 6.0 to 7.2). CONCLUSIONS This work provides additional evidence of incubation period for COVID-19 and showed that it is prudent not to dismiss the possibility of incubation periods up to 14 days at this stage of the epidemic.
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Affiliation(s)
- Wafa Dhouib
- Department of Epidemiology and Preventive Medicine, University of Sousse, Sousse, Tunisia.
| | - Jihen Maatoug
- Department of Epidemiology and Preventive Medicine, University of Sousse, Sousse, Tunisia
| | - Imen Ayouni
- Department of Epidemiology and Preventive Medicine, University of Sousse, Sousse, Tunisia
| | - Nawel Zammit
- Department of Epidemiology and Preventive Medicine, University of Sousse, Sousse, Tunisia
| | - Rim Ghammem
- Department of Epidemiology and Preventive Medicine, University of Sousse, Sousse, Tunisia
| | - Sihem Ben Fredj
- Department of Epidemiology and Preventive Medicine, University of Sousse, Sousse, Tunisia
| | - Hassen Ghannem
- Department of Epidemiology and Preventive Medicine, University of Sousse, Sousse, Tunisia
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Comber L, Walsh KA, Jordan K, O'Brien KK, Clyne B, Teljeur C, Drummond L, Carty PG, De Gascun CF, Smith SM, Harrington P, Ryan M, O'Neill M. Alternative clinical specimens for the detection of SARS-CoV-2: A rapid review. Rev Med Virol 2020; 31:e2185. [PMID: 33091200 DOI: 10.1002/rmv.2185] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 10/08/2020] [Accepted: 10/10/2020] [Indexed: 01/22/2023]
Abstract
The collection of nasopharyngeal swabs to test for the presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an invasive technique with implications for patients and clinicians. Alternative clinical specimens from the upper respiratory tract may offer benefits in terms of collection, comfort and infection risk. The objective of this review was to synthesise the evidence for detection of SARS-CoV-2 ribonucleic acid (RNA) using reverse transcription polymerase chain reaction (RT-PCR) tested saliva or nasal specimens compared with RT-PCR tested nasopharyngeal specimens. Searches were conducted in PubMed, Embase, Europe PMC and NHS evidence from December 2019 to 20 July 2020. Eighteen studies were identified; 12 for saliva, four for nasal and two included both specimen types. For saliva-based studies, the proportion of saliva samples testing positive relative to all positive samples in each study ranged from 82.9% to 100%; detection in nasopharyngeal specimens ranged from 76.7% to 100%; positive agreement between specimens for overall detection ranged from 65.4% to 100%. For nasal-based studies, the proportion of nasal swabs testing positive relative to all positive samples in each study ranged from 81.9% to 100%; detection in nasopharyngeal specimens ranged from 70% to 100%; positive agreement between specimens for overall detection ranged from 62.3% to 100%. The results indicate an inconsistency in the detection of SARS-CoV-2 RNA in the specimen types included, often with neither the index nor the reference of interest detecting all known cases. Depending on the test environment, these clinical specimens may offer a viable alternative to standard. However, at present the evidence is limited, of variable quality, and relatively inconsistent.
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Affiliation(s)
- Laura Comber
- Health Technology Assessment Directorate, Health Information and Quality Authority, Dublin, Ireland
| | - Kieran A Walsh
- Health Technology Assessment Directorate, Health Information and Quality Authority, Dublin, Ireland
| | - Karen Jordan
- Health Technology Assessment Directorate, Health Information and Quality Authority, Dublin, Ireland
| | - Kirsty K O'Brien
- Health Technology Assessment Directorate, Health Information and Quality Authority, Dublin, Ireland
| | - Barbara Clyne
- Health Technology Assessment Directorate, Health Information and Quality Authority, Dublin, Ireland.,Department of General Practice, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Conor Teljeur
- Health Technology Assessment Directorate, Health Information and Quality Authority, Dublin, Ireland
| | - Linda Drummond
- Health Technology Assessment Directorate, Health Information and Quality Authority, Dublin, Ireland
| | - Paul G Carty
- Health Technology Assessment Directorate, Health Information and Quality Authority, Dublin, Ireland
| | - Cillian F De Gascun
- National Virus Reference Laboratory, University College Dublin, Dublin, Ireland
| | - Susan M Smith
- Department of General Practice, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Patricia Harrington
- Health Technology Assessment Directorate, Health Information and Quality Authority, Dublin, Ireland
| | - Máirín Ryan
- Health Technology Assessment Directorate, Health Information and Quality Authority, Dublin, Ireland.,Department of Pharmacology & Therapeutics, Trinity College Dublin, Dublin, Ireland
| | - Michelle O'Neill
- Health Technology Assessment Directorate, Health Information and Quality Authority, Dublin, Ireland
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