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Herbert C, Wang B, Lin H, Yan Y, Hafer N, Pretz C, Stamegna P, Wright C, Suvarna T, Harman E, Schrader S, Nowak C, Kheterpal V, Orvek E, Wong S, Zai A, Barton B, Gerber BS, Lemon SC, Filippaios A, Gibson L, Greene S, Colubri A, Achenbach C, Murphy R, Heetderks W, Manabe YC, O’Connor L, Fahey N, Luzuriaga K, Broach J, Roth K, McManus DD, Soni A. Performance of and Severe Acute Respiratory Syndrome Coronavirus 2 Diagnostics Based on Symptom Onset and Close Contact Exposure: An Analysis From the Test Us at Home Prospective Cohort Study. Open Forum Infect Dis 2024; 11:ofae304. [PMID: 38911947 PMCID: PMC11191649 DOI: 10.1093/ofid/ofae304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Accepted: 05/29/2024] [Indexed: 06/25/2024] Open
Abstract
Background Understanding changes in diagnostic performance after symptom onset and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exposure within different populations is crucial to guide the use of diagnostics for SARS-CoV-2. Methods The Test Us at Home study was a longitudinal cohort study that enrolled individuals across the United States between October 2021 and February 2022. Participants performed paired antigen-detection rapid diagnostic tests (Ag-RDTs) and reverse-transcriptase polymerase chain reaction (RT-PCR) tests at home every 48 hours for 15 days and self-reported symptoms and known coronavirus disease 2019 exposures immediately before testing. The percent positivity for Ag-RDTs and RT-PCR tests was calculated each day after symptom onset and exposure and stratified by vaccination status, variant, age category, and sex. Results The highest percent positivity occurred 2 days after symptom onset (RT-PCR, 91.2%; Ag-RDT, 71.1%) and 6 days after exposure (RT-PCR, 91.8%; Ag-RDT, 86.2%). RT-PCR and Ag-RDT performance did not differ by vaccination status, variant, age category, or sex. The percent positivity for Ag-RDTs was lower among exposed, asymptomatic than among symptomatic individuals (37.5% (95% confidence interval [CI], 13.7%-69.4%) vs 90.3% (75.1%-96.7%). Cumulatively, Ag-RDTs detected 84.9% (95% CI, 78.2%-89.8%) of infections within 4 days of symptom onset. For exposed participants, Ag-RDTs detected 94.0% (95% CI, 86.7%-97.4%) of RT-PCR-confirmed infections within 6 days of exposure. Conclusions The percent positivity for Ag-RDTs and RT-PCR tests was highest 2 days after symptom onset and 6 days after exposure, and performance increased with serial testing. The percent positivity of Ag-RDTs was lowest among asymptomatic individuals but did not differ by sex, variant, vaccination status, or age category.
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Affiliation(s)
- Carly Herbert
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
- University of Massachusetts Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Biqi Wang
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
- Division of Health System Science, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Honghuang Lin
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
- Division of Health System Science, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Yi Yan
- Division of Microbiology, OHT7 Office of Product Evaluation and Quality, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Nathaniel Hafer
- University of Massachusetts Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
- Program in Molecular Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Caitlin Pretz
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Pamela Stamegna
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Colton Wright
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | | | | | | | | | | | - Elizabeth Orvek
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Steven Wong
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Adrian Zai
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Bruce Barton
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Ben S Gerber
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Stephenie C Lemon
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Andreas Filippaios
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Laura Gibson
- Division of Infectious Disease, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Sharone Greene
- Division of Infectious Disease, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Andres Colubri
- Department of Microbiology and Physiological Systems, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Chad Achenbach
- Division of Infectious Disease, Department of Medicine, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Robert Murphy
- Division of Infectious Disease, Department of Medicine, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - William Heetderks
- National Institute of Biomedical Imaging and Bioengineering, NIH, via contract with Kelly Services, Bethesda, Maryland, USA
| | - Yukari C Manabe
- Division of Infectious Disease, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Laurel O’Connor
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Nisha Fahey
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
- Department of Pediatrics, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Katherine Luzuriaga
- University of Massachusetts Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
- Program in Molecular Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - John Broach
- University of Massachusetts Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Kristian Roth
- Division of Microbiology, OHT7 Office of Product Evaluation and Quality, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - David D McManus
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
- Division of Health System Science, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
- Division of Cardiology, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Apurv Soni
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
- Division of Health System Science, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
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Li Y, Jiang X, Qiu Y, Gao F, Xin H, Li D, Qin Y, Li Z. Latent and incubation periods of Delta, BA.1, and BA.2 variant cases and associated factors: a cross-sectional study in China. BMC Infect Dis 2024; 24:294. [PMID: 38448822 PMCID: PMC10916204 DOI: 10.1186/s12879-024-09158-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 02/20/2024] [Indexed: 03/08/2024] Open
Abstract
BACKGROUND The latent and incubation periods characterize the transmission of infectious viruses and are the basis for the development of outbreak prevention and control strategies. However, systematic studies on the latent period and associated factors with the incubation period for SAS-CoV-2 variants are still lacking. We inferred the two durations of Delta, BA.1, and BA.2 cases and analyzed the associated factors. METHODS The Delta, BA.1, and BA.2 (and its lineages BA.2.2 and BA.2.76) cases with clear transmission chains and infectors from 10 local SAS-CoV-2 epidemics in China were enrolled. The latent and incubation periods were fitted by the Gamma distribution, and associated factors were analyzed using the accelerated failure time model. RESULTS The mean latent period for 672 Delta, 208 BA.1, and 677 BA.2 cases was 4.40 (95%CI: 4.24 ~ 4.63), 2.50 (95%CI: 2.27 ~ 2.76), and 2.58 (95%CI: 2.48 ~ 2.69) days, respectively, with 85.65% (95%CI: 83.40 ~ 87.77%), 97.80% (95%CI: 96.35 ~ 98.89%), and 98.87% (95%CI: 98.40 ~ 99.27%) of them starting to shed viruses within 7 days after exposure. In 405 Delta, 75 BA.1, and 345 BA.2 symptomatic cases, the mean latent period was 0.76, 1.07, and 0.79 days shorter than the mean incubation period [5.04 (95%CI: 4.83 ~ 5.33), 3.42 (95%CI: 3.00 ~ 3.89), and 3.39 (95%CI: 3.24 ~ 3.55) days], respectively. No significant difference was observed in the two durations between BA.1 and BA.2 cases. After controlling for the sex, clinical severity, vaccination history, number of infectors, the length of exposure window and shedding window, the latent period [Delta: exp(β) = 0.81, 95%CI: 0.66 ~ 0.98, p = 0.034; Omicron: exp(β) = 0.82, 95%CI: 0.71 ~ 0.94, p = 0.004] and incubation period [Delta: exp(β) = 0.69, 95%CI: 0.55 ~ 0.86, p < 0.001; Omicron: exp(β) = 0.83, 95%CI: 0.72 ~ 0.96, p = 0.013] were significantly shorter in 18 ~ 49 years but did not change significantly in ≥ 50 years compared with 0 ~ 17 years. CONCLUSION Pre-symptomatic transmission can occur in Delta, BA.1, and BA.2 cases. The latent and incubation periods between BA.1 and BA.2 were similar but shorter compared with Delta. Age may be associated with the latent and incubation periods of SARS-CoV-2.
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Affiliation(s)
- Yu Li
- Division of Infectious Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Xinli Jiang
- Division of Infectious Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Yan Qiu
- Division of Infectious Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Feng Gao
- Division of Infectious Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Hualei Xin
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, Hong Kong, China
- School of Population Medicine and Public Health, Chinese Academy of Medical Science (CAMS), Peking Union Medical College (PUMC), No. 9, Dongdan Santiao, Dongcheng District, Beijing, 100730, China
| | - Dan Li
- Division of Infectious Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Ying Qin
- Division of Infectious Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Zhongjie Li
- Division of Infectious Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
- School of Population Medicine and Public Health, Chinese Academy of Medical Science (CAMS), Peking Union Medical College (PUMC), No. 9, Dongdan Santiao, Dongcheng District, Beijing, 100730, China.
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Xu X, Wu Y, Kummer AG, Zhao Y, Hu Z, Wang Y, Liu H, Ajelli M, Yu H. Assessing changes in incubation period, serial interval, and generation time of SARS-CoV-2 variants of concern: a systematic review and meta-analysis. BMC Med 2023; 21:374. [PMID: 37775772 PMCID: PMC10541713 DOI: 10.1186/s12916-023-03070-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 09/05/2023] [Indexed: 10/01/2023] Open
Abstract
BACKGROUND After the first COVID-19 wave caused by the ancestral lineage, the pandemic has been fueled from the continuous emergence of new SARS-CoV-2 variants. Understanding key time-to-event periods for each emerging variant of concern is critical as it can provide insights into the future trajectory of the virus and help inform outbreak preparedness and response planning. Here, we aim to examine how the incubation period, serial interval, and generation time have changed from the ancestral SARS-CoV-2 lineage to different variants of concern. METHODS We conducted a systematic review and meta-analysis that synthesized the estimates of incubation period, serial interval, and generation time (both realized and intrinsic) for the ancestral lineage, Alpha, Beta, and Omicron variants of SARS-CoV-2. RESULTS Our study included 280 records obtained from 147 household studies, contact tracing studies, or studies where epidemiological links were known. With each emerging variant, we found a progressive shortening of each of the analyzed key time-to-event periods, although we did not find statistically significant differences between the Omicron subvariants. We found that Omicron BA.1 had the shortest pooled estimates for the incubation period (3.49 days, 95% CI: 3.13-4.86 days), Omicron BA.5 for the serial interval (2.37 days, 95% CI: 1.71-3.04 days), and Omicron BA.1 for the realized generation time (2.99 days, 95% CI: 2.48-3.49 days). Only one estimate for the intrinsic generation time was available for Omicron subvariants: 6.84 days (95% CrI: 5.72-8.60 days) for Omicron BA.1. The ancestral lineage had the highest pooled estimates for each investigated key time-to-event period. We also observed shorter pooled estimates for the serial interval compared to the incubation period across the virus lineages. When pooling the estimates across different virus lineages, we found considerable heterogeneities (I2 > 80%; I2 refers to the percentage of total variation across studies that is due to heterogeneity rather than chance), possibly resulting from heterogeneities between the different study populations (e.g., deployed interventions, social behavior, demographic characteristics). CONCLUSIONS Our study supports the importance of conducting contact tracing and epidemiological investigations to monitor changes in SARS-CoV-2 transmission patterns. Our findings highlight a progressive shortening of the incubation period, serial interval, and generation time, which can lead to epidemics that spread faster, with larger peak incidence, and harder to control. We also consistently found a shorter serial interval than incubation period, suggesting that a key feature of SARS-CoV-2 is the potential for pre-symptomatic transmission. These observations are instrumental to plan for future COVID-19 waves.
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Affiliation(s)
- Xiangyanyu Xu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Yanpeng Wu
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China
| | - Allisandra G Kummer
- Laboratory of Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
| | - Yuchen Zhao
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China
| | - Zexin Hu
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China
| | - Yan Wang
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Hengcong Liu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Marco Ajelli
- Laboratory of Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA.
| | - Hongjie Yu
- School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China.
- Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China.
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4
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Alpers R, Kühne L, Truong HP, Zeeb H, Westphal M, Jäckle S. Evaluation of the EsteR Toolkit for COVID-19 Decision Support: Sensitivity Analysis and Usability Study. JMIR Form Res 2023; 7:e44549. [PMID: 37368487 DOI: 10.2196/44549] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 05/14/2023] [Accepted: 05/15/2023] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND During the COVID-19 pandemic, local health authorities were responsible for managing and reporting current cases in Germany. Since March 2020, employees had to contain the spread of COVID-19 by monitoring and contacting infected persons as well as tracing their contacts. In the EsteR project, we implemented existing and newly developed statistical models as decision support tools to assist in the work of the local health authorities. OBJECTIVE The main goal of this study was to validate the EsteR toolkit in two complementary ways: first, investigating the stability of the answers provided by our statistical tools regarding model parameters in the back end and, second, evaluating the usability and applicability of our web application in the front end by test users. METHODS For model stability assessment, a sensitivity analysis was carried out for all 5 developed statistical models. The default parameters of our models as well as the test ranges of the model parameters were based on a previous literature review on COVID-19 properties. The obtained answers resulting from different parameters were compared using dissimilarity metrics and visualized using contour plots. In addition, the parameter ranges of general model stability were identified. For the usability evaluation of the web application, cognitive walk-throughs and focus group interviews were conducted with 6 containment scouts located at 2 different local health authorities. They were first asked to complete small tasks with the tools and then express their general impressions of the web application. RESULTS The simulation results showed that some statistical models were more sensitive to changes in their parameters than others. For each of the single-person use cases, we determined an area where the respective model could be rated as stable. In contrast, the results of the group use cases highly depended on the user inputs, and thus, no area of parameters with general model stability could be identified. We have also provided a detailed simulation report of the sensitivity analysis. In the user evaluation, the cognitive walk-throughs and focus group interviews revealed that the user interface needed to be simplified and more information was necessary as guidance. In general, the testers rated the web application as helpful, especially for new employees. CONCLUSIONS This evaluation study allowed us to refine the EsteR toolkit. Using the sensitivity analysis, we identified suitable model parameters and analyzed how stable the statistical models were in terms of changes in their parameters. Furthermore, the front end of the web application was improved with the results of the conducted cognitive walk-throughs and focus group interviews regarding its user-friendliness.
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Affiliation(s)
- Rieke Alpers
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Lisa Kühne
- Department of Prevention and Evaluation, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
| | - Hong-Phuc Truong
- Fraunhofer Institute for Industrial Mathematics ITWM, Kaiserslautern, Germany
| | - Hajo Zeeb
- Department of Prevention and Evaluation, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany
| | - Max Westphal
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Sonja Jäckle
- Fraunhofer Institute for Digital Medicine MEVIS, Lübeck, Germany
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Wong KY, Zhou Q, Hu T. Semiparametric regression analysis of doubly-censored data with applications to incubation period estimation. LIFETIME DATA ANALYSIS 2023; 29:87-114. [PMID: 35831702 PMCID: PMC9281361 DOI: 10.1007/s10985-022-09567-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Accepted: 06/28/2022] [Indexed: 06/15/2023]
Abstract
The incubation period is a key characteristic of an infectious disease. In the outbreak of a novel infectious disease, accurate evaluation of the incubation period distribution is critical for designing effective prevention and control measures . Estimation of the incubation period distribution based on limited information from retrospective inspection of infected cases is highly challenging due to censoring and truncation. In this paper, we consider a semiparametric regression model for the incubation period and propose a sieve maximum likelihood approach for estimation based on the symptom onset time, travel history, and basic demographics of reported cases. The approach properly accounts for the pandemic growth and selection bias in data collection. We also develop an efficient computation method and establish the asymptotic properties of the proposed estimators. We demonstrate the feasibility and advantages of the proposed methods through extensive simulation studies and provide an application to a dataset on the outbreak of COVID-19.
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Affiliation(s)
- Kin Yau Wong
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
| | - Qingning Zhou
- Department of Mathematics and Statistics, The University of North Carolina at Charlotte, Fretwell 335L, 9201 University City Blvd., Charlotte, NC, 28223, USA.
| | - Tao Hu
- School of Mathematical Sciences, Capital Normal University, Beijing, People's Republic of China
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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: 137] [Impact Index Per Article: 68.5] [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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7
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Huang S, Li J, Dai C, Tie Z, Xu J, Xiong X, Hao X, Wang Z, Lu C. Incubation period of coronavirus disease 2019: new implications for intervention and control. INTERNATIONAL JOURNAL OF ENVIRONMENTAL HEALTH RESEARCH 2022; 32:1707-1715. [PMID: 33818217 DOI: 10.1080/09603123.2021.1905781] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 03/15/2021] [Indexed: 06/12/2023]
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.
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Affiliation(s)
| | - Jing Li
- Institute of NBC Defense of PLA, Beijing, China
| | - Chengguqiu Dai
- Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zihan Tie
- Institute of NBC Defense of PLA, Beijing, China
| | - Jiazhao Xu
- Institute of NBC Defense of PLA, Beijing, China
| | - Xiang Xiong
- Institute of NBC Defense of PLA, Beijing, China
| | - Xingjie Hao
- Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhongyi Wang
- Institute of Biotechnology, Academy of Military Medical Sciences, Beijing, China
| | - Chan Lu
- XiangYa School of Public Health, Central South University, Changsha, China
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8
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Achangwa C, Park H, Ryu S. Incubation period of wild type of SARS-CoV-2 infections by age, gender, and epidemic periods. Front Public Health 2022; 10:905020. [PMID: 35968429 PMCID: PMC9363879 DOI: 10.3389/fpubh.2022.905020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 06/29/2022] [Indexed: 01/08/2023] Open
Abstract
Background The incubation period of the coronavirus disease 2019 (COVID-19) is estimated to vary by demographic factors and the COVID-19 epidemic periods. Objective This study examined the incubation period of the wild type of SARS-CoV-2 infections by the different age groups, gender, and epidemic periods in South Korea. Methods We collected COVID-19 patient data from the Korean public health authorities and estimated the incubation period by fitting three different distributions, including log-normal, gamma, and Weibull distributions, after stratification by gender and age groups. To identify any temporal impact on the incubation period, we divided the study period into two different epidemic periods (Period-1: 19 January−19 April 2020 and Period-2: 20 April−16 October 2020), and assessed for any differences. Results We identified the log-normal as the best-fit model. The estimated median incubation period was 4.6 (95% CI: 3.9–4.9) days, and the 95th percentile was 11.7 (95% CI: 10.2–12.2) days. We found that the incubation period did not differ significantly between males and females (p = 0.42), age groups (p = 0.60), and the two different epidemic periods (p = 0.77). Conclusions The incubation period of wild type of SARS-CoV-2 infection during the COVID-19 pandemic 2020, in South Korea, does not likely differ by age group, gender and epidemic period.
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Affiliation(s)
- Chiara Achangwa
- Department of Preventive Medicine, Konyang University College of Medicine, Daejeon, South Korea
| | - Huikyung Park
- Department of Preventive Medicine, Konyang University College of Medicine, Daejeon, South Korea
| | - Sukhyun Ryu
- Department of Preventive Medicine, Konyang University College of Medicine, Daejeon, South Korea
- Myunggok Medical Research Institute, Konyang University College of Medicine, Daejeon, South Korea
- *Correspondence: Sukhyun Ryu
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9
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Liu X, Wang L, Ma X, Wang J, Wu L. Modeling the effect of age on quantiles of the incubation period distribution of COVID-19. BMC Public Health 2021; 21:1762. [PMID: 34579681 PMCID: PMC8474900 DOI: 10.1186/s12889-021-11761-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 09/09/2021] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND The novel coronavirus SARS-CoV-2 (coronavirus disease 2019, COVID-19) has caused serious consequences on many aspects of social life throughout the world since the first case of pneumonia with unknown etiology was identified in Wuhan, Hubei province in China in December 2019. Note that the incubation period distribution is key to the prevention and control efforts of COVID-19. This study aimed to investigate the conditional distribution of the incubation period of COVID-19 given the age of infected cases and estimate its corresponding quantiles from the information of 2172 confirmed cases from 29 provinces outside Hubei in China. METHODS We collected data on the infection dates, onset dates, and ages of the confirmed cases through February 16th, 2020. All the data were downloaded from the official websites of the health commission. As the epidemic was still ongoing at the time we collected data, the observations subject to biased sampling. To address this issue, we developed a new maximum likelihood method, which enables us to comprehensively study the effect of age on the incubation period. RESULTS Based on the collected data, we found that the conditional quantiles of the incubation period distribution of COVID-19 vary by age. In detail, the high conditional quantiles of people in the middle age group are shorter than those of others while the low quantiles did not show the same differences. We estimated that the 0.95-th quantile related to people in the age group 23 ∼55 is less than 15 days. CONCLUSIONS Observing that the conditional quantiles vary across age, we may take more precise measures for people of different ages. For example, we may consider carrying out an age-dependent quarantine duration in practice, rather than a uniform 14-days quarantine period. Remarkably, we may need to extend the current quarantine duration for people aged 0 ∼22 and over 55 because the related 0.95-th quantiles are much greater than 14 days.
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Affiliation(s)
- Xiaohui Liu
- School of Statistics, Jiangxi University of Finance and Economics, No.169, East Shuanggang Road, Nanchang, Jiangxi Province, 330013 China
- Research Center of Applied Statistics, Jiangxi University of Finance and Economics, No.169, East Shuanggang Road, Nanchang, Jiangxi Province, 330013 China
| | - Lei Wang
- National Engineering Laboratory for Educational Big Data, Central China Normal University, NO.152 Luoyu Road, Wuhan, Hubei Province, 430079 China
| | - Xiansi Ma
- School of Statistics, Jiangxi University of Finance and Economics, No.169, East Shuanggang Road, Nanchang, Jiangxi Province, 330013 China
- Research Center of Applied Statistics, Jiangxi University of Finance and Economics, No.169, East Shuanggang Road, Nanchang, Jiangxi Province, 330013 China
| | - Jiewen Wang
- School of Statistics, Jiangxi University of Finance and Economics, No.169, East Shuanggang Road, Nanchang, Jiangxi Province, 330013 China
- Research Center of Applied Statistics, Jiangxi University of Finance and Economics, No.169, East Shuanggang Road, Nanchang, Jiangxi Province, 330013 China
| | - Liwen Wu
- Department of Neurology, Hunan children’s Hospital, No.86 Ziyuan Road, Changsha, Hunan Province, 410000 China
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10
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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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11
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You Y, Yang X, Hung D, Yang Q, Wu T, Deng M. Asymptomatic COVID-19 infection: diagnosis, transmission, population characteristics. BMJ Support Palliat Care 2021:bmjspcare-2020-002813. [PMID: 34330791 DOI: 10.1136/bmjspcare-2020-002813] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Revised: 02/26/2021] [Accepted: 03/27/2021] [Indexed: 12/19/2022]
Abstract
A novel coronavirus first discovered in late December 2019 has spread to many countries around the world. An increasing number of asymptomatic patients have been reported and their ability to spread the virus has been proven. This brings major challenges to the control of the transmission. The discovery and control of asymptomatic patients with COVID-19 are the key issues in future epidemic prevention and recovery. In this narrative review, we summarise the existing knowledge about asymptomatic patients and put forward detection methods that are suitable for finding such patients. Besides, we compared the characteristics and transmissibility of asymptomatic patients in different populations in order to find the best screening, diagnosis and control measures for different populations. Comprehensive preventive advice is also provided to prevent the spread of infection from asymptomatic patients.
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Affiliation(s)
- Yaxian You
- Department of Biochemistry and Molecular Biology & Hunan Province Key Laboratory of Basic and Applied Hematology, School of Life Sciences, Central South University, Changsha, Hunan, China
- Xiangya School of Medicine, Central South University, Changsha, Hunan, China
| | - Xinyuan Yang
- Department of Biochemistry and Molecular Biology & Hunan Province Key Laboratory of Basic and Applied Hematology, School of Life Sciences, Central South University, Changsha, Hunan, China
- Xiangya School of Medicine, Central South University, Changsha, Hunan, China
| | - Dongni Hung
- Department of Biochemistry and Molecular Biology & Hunan Province Key Laboratory of Basic and Applied Hematology, School of Life Sciences, Central South University, Changsha, Hunan, China
- Xiangya School of Medicine, Central South University, Changsha, Hunan, China
| | - Qianxi Yang
- Department of Biochemistry and Molecular Biology & Hunan Province Key Laboratory of Basic and Applied Hematology, School of Life Sciences, Central South University, Changsha, Hunan, China
- Xiangya School of Medicine, Central South University, Changsha, Hunan, China
| | - Ting Wu
- Department of Biochemistry and Molecular Biology & Hunan Province Key Laboratory of Basic and Applied Hematology, School of Life Sciences, Central South University, Changsha, Hunan, China
- Department of Cardiovascular Medicine, The Third Xiangya Hospita, Central South University, Changsha, Hunan, China
| | - Meichun Deng
- Department of Biochemistry and Molecular Biology & Hunan Province Key Laboratory of Basic and Applied Hematology, School of Life Sciences, Central South University, Changsha, Hunan, China
- Xiangya School of Medicine, Central South University, Changsha, Hunan, China
- Hunan Key Laboratory of Animal Models for Human Diseases, Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, China
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12
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Xin H, Wong JY, Murphy C, Yeung A, Ali ST, Wu P, Cowling BJ. The incubation period distribution of coronavirus disease 2019 (COVID-19): a systematic review and meta-analysis. Clin Infect Dis 2021; 73:2344-2352. [PMID: 34117868 DOI: 10.1093/cid/ciab501] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Indexed: 11/14/2022] Open
Abstract
Incubation period is an important parameter to inform quarantine period and to study transmission dynamics of infectious diseases. We conducted a systematic review and meta-analysis on published estimates of the incubation period distribution of COVID-19, and showed that the pooled median of the point estimates of the mean, median and 95 th percentile for incubation period are 6.3 days (range: 1.8 to 11.9 days), 5.4 days (range: 2.0 to 17.9 days) and 13.1 days (range: 3.2 to 17.8 days) respectively. Estimates of the mean and 95 th percentile of the incubation period distribution were considerably shorter before the epidemic peak in China compared to after the peak, and variation was also noticed for different choices of methodological approach in estimation. Our findings implied that corrections may be needed before directly applying estimates of incubation period into control of or further studies on emerging infectious diseases.
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Affiliation(s)
- Hualei Xin
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Jessica Y Wong
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Caitriona Murphy
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Amy Yeung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Sheikh Taslim Ali
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.,Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, New Territories, Hong Kong
| | - Peng Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.,Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, New Territories, Hong Kong
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.,Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, New Territories, Hong Kong
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13
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How to address SARS-CoV-2 airborne transmission to ensure effective protection of healthcare workers? A review of the literature. Infect Dis Now 2021; 51:410-417. [PMID: 34029759 PMCID: PMC8139431 DOI: 10.1016/j.idnow.2021.05.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 05/12/2021] [Indexed: 12/09/2022]
Abstract
SARS-CoV-2 mainly infects the respiratory tract, and presents significantly higher active replication in the upper airways. To remain viable and infectious, the SARS-CoV-2 virion must be complete and integral, which is not easily demonstrated in the environment by positive reverse transcriptase PCR results. Real-life conditions in healthcare settings may be conducive to SARS-CoV-2 RNA dissemination in the environment but without evidence of its viability and infectiveness in air. Theoretically, SARS-CoV-2 shedding and dissemination nonetheless appears to be air-mediated, and a distinction between “air” and “droplet” transmission is too schematic to reflect the reality of the respiratory particles emitted by patients, between which a continuum exists. Airborne transmission is influenced by numerous environmental conditions that are not transposable between different viral agents and situations in healthcare settings or in the community. Even though international guidelines on “droplet” versus “air” precautions and personal protective equipment (surgical versus respirator masks) are under discussion, the existing literature underscores the effectiveness of “droplet” precautions as a means of protecting healthcare workers. Differentiation in guidelines between healthcare venues, community settings and, more generally, confined environments is of paramount importance, especially insofar as it underlines the abiding pandemic-related need for systematic mask wearing by the general population.
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14
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McAloon CG, Wall P, Griffin J, Casey M, Barber A, Codd M, Gormley E, Butler F, McV Messam LL, Walsh C, Teljeur C, Smyth B, Nolan P, Green MJ, O'Grady L, Culhane K, Buckley C, Carroll C, Doyle S, Martin J, More SJ. Estimation of the serial interval and proportion of pre-symptomatic transmission events of COVID- 19 in Ireland using contact tracing data. BMC Public Health 2021; 21:805. [PMID: 33906635 PMCID: PMC8076671 DOI: 10.1186/s12889-021-10868-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 04/16/2021] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND The serial interval is the period of time between the onset of symptoms in an infector and an infectee and is an important parameter which can impact on the estimation of the reproduction number. Whilst several parameters influencing infection transmission are expected to be consistent across populations, the serial interval can vary across and within populations over time. Therefore, local estimates are preferable for use in epidemiological models developed at a regional level. We used data collected as part of the national contact tracing process in Ireland to estimate the serial interval of SARS-CoV-2 infection in the Irish population, and to estimate the proportion of transmission events that occurred prior to the onset of symptoms. RESULTS After data cleaning, the final dataset consisted of 471 infected close contacts from 471 primary cases. The median serial interval was 4 days, mean serial interval was 4.0 (95% confidence intervals 3.7, 4.3) days, whilst the 25th and 75th percentiles were 2 and 6 days respectively. We found that intervals were lower when the primary or secondary case were in the older age cohort (greater than 64 years). Simulating from an incubation period distribution from international literature, we estimated that 67% of transmission events had greater than 50% probability of occurring prior to the onset of symptoms in the infector. CONCLUSIONS Whilst our analysis was based on a large sample size, data were collected for the primary purpose of interrupting transmission chains. Similar to other studies estimating the serial interval, our analysis is restricted to transmission pairs where the infector is known with some degree of certainty. Such pairs may represent more intense contacts with infected individuals than might occur in the overall population. It is therefore possible that our analysis is biased towards shorter serial intervals than the overall population.
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Affiliation(s)
- Conor G McAloon
- School of Veterinary Medicine, University College Dublin, Belfield, Dublin 4, Ireland.
| | - Patrick Wall
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, Belfield, Dublin 4, Ireland
| | - John Griffin
- Centre for Veterinary Epidemiology and Risk Analysis, School of Veterinary Medicine, University College Dublin, Belfield, Dublin, Ireland
| | - Miriam Casey
- Centre for Veterinary Epidemiology and Risk Analysis, School of Veterinary Medicine, University College Dublin, Belfield, Dublin, Ireland
| | - Ann Barber
- Centre for Veterinary Epidemiology and Risk Analysis, School of Veterinary Medicine, University College Dublin, Belfield, Dublin, Ireland
| | - Mary Codd
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, Belfield, Dublin 4, Ireland
| | - Eamonn Gormley
- School of Veterinary Medicine, University College Dublin, Belfield, Dublin 4, Ireland
| | - Francis Butler
- School of Biosystems and Food Engineering, University College Dublin, Belfield, Dublin 4, Ireland
| | - Locksley L McV Messam
- School of Veterinary Medicine, University College Dublin, Belfield, Dublin 4, Ireland
| | - Cathal Walsh
- Department of Mathematics and Statistics, University of Limerick, Limerick, Ireland
| | - Conor Teljeur
- Health Information and Quality Authority, George's Court, Dublin 7, Ireland
| | - Breda Smyth
- Department of Public Health, Health Service Executive West, Galway, Ireland
| | - Philip Nolan
- National University of Ireland Maynooth, Kildare, Ireland
| | - Martin J Green
- School of Veterinary Medicine and Science, University of Nottingham, Nottingham, UK
| | - Luke O'Grady
- School of Veterinary Medicine, University College Dublin, Belfield, Dublin 4, Ireland
- School of Veterinary Medicine and Science, University of Nottingham, Nottingham, UK
| | - Kieran Culhane
- Central Statistics Office, Ardee Road, Rathmines, Dublin, Ireland
| | - Claire Buckley
- COVID-19 Contact Management Programme, Health Service Executive, Dublin, Ireland
- School of Public Health, University College Cork, Cork, Ireland
| | - Ciara Carroll
- COVID-19 Contact Management Programme, Health Service Executive, Dublin, Ireland
| | - Sarah Doyle
- COVID-19 Contact Management Programme, Health Service Executive, Dublin, Ireland
| | - Jennifer Martin
- COVID-19 Contact Management Programme, Health Service Executive, Dublin, Ireland
| | - Simon J More
- School of Veterinary Medicine, University College Dublin, Belfield, Dublin 4, Ireland
- Centre for Veterinary Epidemiology and Risk Analysis, School of Veterinary Medicine, University College Dublin, Belfield, Dublin, Ireland
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15
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Li Q, Zhao C. A Review of the Current Status of Clinical Management of COVID-19 in the Elderly. Med Sci Monit 2021; 27:e930278. [PMID: 33833211 PMCID: PMC8043417 DOI: 10.12659/msm.930278] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Accepted: 01/28/2021] [Indexed: 12/15/2022] Open
Abstract
The high infectivity and severity of SARS-CoV-2 infection (COVID-19), and our limited understanding of the biology of the novel coronavirus, as well as the lack of an effective treatment for COVID-19, have created a global pandemic. Those most likely to become seriously ill with COVID-19 are adults, especially the elderly and those who are already weak or sick. At present, a specific drug for treatment of COVID-19 has not been developed. This, combined with the typical coexistence of a variety of chronic diseases in elderly patients, makes treatment challenging at present. In addition, for elderly patients, COVID-19 isolation measures during the epidemic can easily lead to psychological problems. Thus, how to manage elderly patients has become a focus of social attention in the current circumstances. This article reviews the effects of COVID-19 and makes management suggestions for elderly patients during this epidemic period. In addition to the elderly, critically ill people are also highly susceptible to this novel coronavirus. For elderly COVID-19 patients, antiviral therapy, immune regulation, and even auxiliary respiratory therapy can be given after a comprehensive evaluation of the disease. With the approval and use of COVID-19 vaccines, it is reasonable to expect that we can conquer SARS-CoV-2.
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Affiliation(s)
- Qiaoyun Li
- Graduate School, Qinghai University, Xining, Qinghai, P.R. China
| | - Chengyu Zhao
- Department of Geriatrics, Affiliated Hospital of Qinghai University, Xining, Qinghai, P.R. China
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16
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In pursuit of the right tail for the COVID-19 incubation period. Public Health 2021; 194:149-155. [PMID: 33915459 PMCID: PMC7997403 DOI: 10.1016/j.puhe.2021.03.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 02/24/2021] [Accepted: 03/09/2021] [Indexed: 01/08/2023]
Abstract
Definition of the incubation period for COVID-19 is critical for implementing quarantine and thus infection control. Whereas the classical definition relies on the time from exposure to time of first symptoms, a more practical working definition is the time from exposure to time of first live virus excretion. For COVID-19, average incubation period times commonly span 5–7 days which are generally longer than for most typical other respiratory viruses. There is considerable variability reported however for the late right-hand statistical distribution. A small but yet epidemiologically important subset of patients may have the late end of the incubation period extend beyond the 14 days that is frequently assumed. Conservative assumptions of the right tail end distribution favor safety, but pragmatic working modifications may be required to accommodate high rates of infection and/or healthcare worker exposures. Despite the advent of effective vaccines, further attention and study in these regards are warranted. It is predictable that vaccine application will be associated with continued confusion over protection and its longevity. Measures for the application of infectivity will continue to be extremely relevant.
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17
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Küchenhoff H, Günther F, Höhle M, Bender A. Analysis of the early COVID-19 epidemic curve in Germany by regression models with change points. Epidemiol Infect 2021; 149:e68. [PMID: 33691815 PMCID: PMC7985895 DOI: 10.1017/s0950268821000558] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 03/04/2021] [Accepted: 03/05/2021] [Indexed: 12/13/2022] Open
Abstract
We analysed the coronavirus disease 2019 epidemic curve from March to the end of April 2020 in Germany. We use statistical models to estimate the number of cases with disease onset on a given day and use back-projection techniques to obtain the number of new infections per day. The respective time series are analysed by a trend regression model with change points. The change points are estimated directly from the data. We carry out the analysis for the whole of Germany and the federal state of Bavaria, where we have more detailed data. Both analyses show a major change between 9 and 13 March for the time series of infections: from a strong increase to a decrease. Another change was found between 25 March and 29 March, where the decline intensified. Furthermore, we perform an analysis stratified by age. A main result is a delayed course of the pandemic for the age group 80 + resulting in a turning point at the end of March. Our results differ from those by other authors as we take into account the reporting delay, which turned out to be time dependent and therefore changes the structure of the epidemic curve compared to the curve of newly reported cases.
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Affiliation(s)
| | - Felix Günther
- Statistical Consulting Unit StaBLab, LMU Munich, Germany
- Department of Genetic Epidemiology, University of Regensburg, Germany
| | - Michael Höhle
- Department of Mathematics, Stockholm University, Sweden
| | - Andreas Bender
- Statistical Consulting Unit StaBLab, LMU Munich, Germany
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18
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Hoshino K, Maeshiro T, Nishida N, Sugiyama M, Fujita J, Gojobori T, Mizokami M. Transmission dynamics of SARS-CoV-2 on the Diamond Princess uncovered using viral genome sequence analysis. Gene 2021; 779:145496. [PMID: 33588037 PMCID: PMC7880849 DOI: 10.1016/j.gene.2021.145496] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 01/26/2021] [Accepted: 02/03/2021] [Indexed: 12/13/2022]
Abstract
An outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) occurred aboard the Diamond Princess cruise ship between her January 20 departure and late February 2020. Here, we used phylodynamic analyses to investigate the transmission dynamics of SARS-CoV-2 during the outbreak. Using a Bayesian coalescent-based method, the estimated mean nucleotide substitution rate of 240 SARS-CoV-2 whole-genome sequences was approximately 7.13 × 10−4 substitutions per site per year. Population dynamics and the effective reproductive number (Re) of SARS-CoV-2 infections were estimated using a Bayesian framework. The estimated origin of the outbreak was January 21, 2020. The infection spread substantially before quarantine on February 5. The Re peaked at 6.06 on February 4 and gradually declined to 1.51, suggesting that transmission continued slowly even after quarantine. These findings highlight the high transmissibility of SARS-CoV-2 and the need for effective measures to control outbreaks in confined settings.
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Affiliation(s)
- Kunikazu Hoshino
- Department of Infectious, Respiratory, and Digestive Medicine, University of the Ryukyus, 207 Uehara, Nishihara, Okinawa 903-0215, Japan; Genome Medical Sciences Project, National Center for Global Health and Medicine, 1-7-1 Kohnodai, Ichikawa, Chiba 272-8516, Japan.
| | - Tatsuji Maeshiro
- Department of Infectious, Respiratory, and Digestive Medicine, University of the Ryukyus, 207 Uehara, Nishihara, Okinawa 903-0215, Japan.
| | - Nao Nishida
- Genome Medical Sciences Project, National Center for Global Health and Medicine, 1-7-1 Kohnodai, Ichikawa, Chiba 272-8516, Japan.
| | - Masaya Sugiyama
- Genome Medical Sciences Project, National Center for Global Health and Medicine, 1-7-1 Kohnodai, Ichikawa, Chiba 272-8516, Japan.
| | - Jiro Fujita
- Department of Infectious, Respiratory, and Digestive Medicine, University of the Ryukyus, 207 Uehara, Nishihara, Okinawa 903-0215, Japan.
| | - Takashi Gojobori
- Computational Bioscience Research Center, Biological and Environmental Sciences and Engineering, King Abdullah University of Science and Technology, 4700 King Abdullah University of Science and Technology, 23955-6900, Saudi Arabia.
| | - Masashi Mizokami
- Genome Medical Sciences Project, National Center for Global Health and Medicine, 1-7-1 Kohnodai, Ichikawa, Chiba 272-8516, Japan.
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19
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Kong T. More studies showing longer COVID‐19 incubation period in older adults and questioning the appropriate times for quarantine and contact tracing. Aging Med (Milton) 2020; 3:278-279. [PMID: 33363265 PMCID: PMC7753659 DOI: 10.1002/agm2.12137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 10/28/2020] [Accepted: 10/28/2020] [Indexed: 11/09/2022] Open
Affiliation(s)
- Tak‐kwan Kong
- Department of Medicine & Therapeutics Prince of Wales HospitalThe Chinese University of Hong Kong Hong Kong SAR China
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