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Izadi N, Taherpour N, Mokhayeri Y, Sotoodeh Ghorbani S, Rahmani K, Hashemi Nazari SS. Epidemiologic Parameters for COVID-19: A Systematic Review and Meta-Analysis. Med J Islam Repub Iran 2022; 36:155. [PMID: 36654849 PMCID: PMC9832936 DOI: 10.47176/mjiri.36.155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Indexed: 12/24/2022] Open
Abstract
Background: The World Health Organization (WHO) declared the coronavirus disease 2019 (COVID-19) outbreak to be a public health emergency and international concern and recognized it as a pandemic. This study aimed to estimate the epidemiologic parameters of the COVID-19 pandemic for clinical and epidemiological help. Methods: In this systematic review and meta-analysis study, 4 electronic databases, including Web of Science, PubMed, Scopus, and Google Scholar were searched for the literature published from early December 2019 up to 23 March 2020. After screening, we selected 76 articles based on epidemiological parameters, including basic reproduction number, serial interval, incubation period, doubling time, growth rate, case-fatality rate, and the onset of symptom to hospitalization as eligibility criteria. For the estimation of overall pooled epidemiologic parameters, fixed and random effect models with 95% CI were used based on the value of between-study heterogeneity (I2). Results: A total of 76 observational studies were included in the analysis. The pooled estimate for R0 was 2.99 (95% CI, 2.71-3.27) for COVID-19. The overall R0 was 3.23, 1.19, 3.6, and 2.35 for China, Singapore, Iran, and Japan, respectively. The overall serial interval, doubling time, and incubation period were 4.45 (95% CI, 4.03-4.87), 4.14 (95% CI, 2.67-5.62), and 4.24 (95% CI, 3.03-5.44) days for COVID-19. In addition, the overall estimation for the growth rate and the case fatality rate for COVID-19 was 0.38% and 3.29%, respectively. Conclusion: The epidemiological characteristics of COVID-19 as an emerging disease may be revealed by computing the pooled estimate of the epidemiological parameters, opening the door for health policymakers to consider additional control measures.
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Affiliation(s)
- Neda Izadi
- Department of Epidemiology, School of Public Health and Safety, Shahid
Beheshti University of Medical Sciences, Tehran, Iran
| | - Niloufar Taherpour
- Prevention of Cardiovascular Disease Research Center, Shahid Beheshti
University of Medical Sciences, Tehran, Iran
| | - Yaser Mokhayeri
- Cardiovascular Research Center, Shahid Rahimi Hospital, Lorestan
University of Medical Sciences, Khorramabad, Iran
| | - Sahar Sotoodeh Ghorbani
- Department of Epidemiology, School of Public Health and Safety, Shahid
Beheshti University of Medical Sciences, Tehran, Iran
| | - Khaled Rahmani
- Liver and Digestive Research Center, Research Institute for Health
Development, Kurdistan University of Medical Sciences, Sanandaj, Iran
| | - Seyed Saeed Hashemi Nazari
- Prevention of Cardiovascular Disease Research Center, Department of
Epidemiology, School of Public Health and Safety, Shahid Beheshti University of
Medical Sciences, Tehran, Iran, Corresponding author:Seyed Saeed
Hashemi Nazari,
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Challen R, Brooks-Pollock E, Tsaneva-Atanasova K, Danon L. Meta-analysis of the severe acute respiratory syndrome coronavirus 2 serial intervals and the impact of parameter uncertainty on the coronavirus disease 2019 reproduction number. Stat Methods Med Res 2022; 31:1686-1703. [PMID: 34931917 PMCID: PMC9465543 DOI: 10.1177/09622802211065159] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The serial interval of an infectious disease, commonly interpreted as the time between the onset of symptoms in sequentially infected individuals within a chain of transmission, is a key epidemiological quantity involved in estimating the reproduction number. The serial interval is closely related to other key quantities, including the incubation period, the generation interval (the time between sequential infections), and time delays between infection and the observations associated with monitoring an outbreak such as confirmed cases, hospital admissions, and deaths. Estimates of these quantities are often based on small data sets from early contact tracing and are subject to considerable uncertainty, which is especially true for early coronavirus disease 2019 data. In this paper, we estimate these key quantities in the context of coronavirus disease 2019 for the UK, including a meta-analysis of early estimates of the serial interval. We estimate distributions for the serial interval with a mean of 5.9 (95% CI 5.2; 6.7) and SD 4.1 (95% CI 3.8; 4.7) days (empirical distribution), the generation interval with a mean of 4.9 (95% CI 4.2; 5.5) and SD 2.0 (95% CI 0.5; 3.2) days (fitted gamma distribution), and the incubation period with a mean 5.2 (95% CI 4.9; 5.5) and SD 5.5 (95% CI 5.1; 5.9) days (fitted log-normal distribution). We quantify the impact of the uncertainty surrounding the serial interval, generation interval, incubation period, and time delays, on the subsequent estimation of the reproduction number, when pragmatic and more formal approaches are taken. These estimates place empirical bounds on the estimates of most relevant model parameters and are expected to contribute to modeling coronavirus disease 2019 transmission.
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Affiliation(s)
- Robert Challen
- EPSRC Centre for Predictive Modelling in Healthcare, 3286University of Exeter, UK
- 7852Somerset NHS Foundation Trust, UK
- Joint Universities Pandemic and Epidemiological Research (JUNIPER) consortium, UK
| | - Ellen Brooks-Pollock
- Joint Universities Pandemic and Epidemiological Research (JUNIPER) consortium, UK
- 152331Bristol Medical School, Population Health Sciences, 1980University of Bristol, UK
| | - Krasimira Tsaneva-Atanasova
- EPSRC Centre for Predictive Modelling in Healthcare, 3286University of Exeter, UK
- 522468The Alan Turing Institute, British Library, UK
- Data Science Institute, 151756College of Engineering, Mathematics and Physical Sciences, 3286University of Exeter, UK
| | - Leon Danon
- 152331Bristol Medical School, Population Health Sciences, 1980University of Bristol, UK
- 522468The Alan Turing Institute, British Library, UK
- Data Science Institute, 151756College of Engineering, Mathematics and Physical Sciences, 3286University of Exeter, UK
- Department of Engineering Mathematics, 1980University of Bristol, UK
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Litwin T, Timmer J, Berger M, Wahl-Kordon A, Müller MJ, Kreutz C. Preventing COVID-19 outbreaks through surveillance testing in healthcare facilities: a modelling study. BMC Infect Dis 2022; 22:105. [PMID: 35093012 PMCID: PMC8800405 DOI: 10.1186/s12879-022-07075-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 01/14/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Surveillance testing within healthcare facilities provides an opportunity to prevent severe outbreaks of coronavirus disease 2019 (COVID-19). However, the quantitative impact of different available surveillance strategies and their potential to decrease the frequency of outbreaks are not well-understood. METHODS We establish an individual-based model representative of a mental health hospital yielding generalizable results. Attributes and features of this facility were derived from a prototypical hospital, which provides psychiatric, psychosomatic and psychotherapeutic treatment. We estimate the relative reduction of outbreak probability for three test strategies (entry test, once-weekly test and twice-weekly test) relative to a symptom-based baseline strategy. Based on our findings, we propose determinants of successful surveillance measures. RESULTS Entry Testing reduced the outbreak probability by 26%, additionally testing once or twice weekly reduced the outbreak probability by 49% or 67% respectively. We found that fast diagnostic test results and adequate compliance of the clinic population are mandatory for conducting effective surveillance. The robustness of these results towards uncertainties is demonstrated via comprehensive sensitivity analyses. CONCLUSIONS We conclude that active testing in mental health hospitals and similar facilities considerably reduces the number of COVID-19 outbreaks compared to symptom-based surveillance only.
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Affiliation(s)
- Tim Litwin
- Institute of Medical Biometry and Statistics (IMBI), Faculty of Medicine and Medical Center, University of Freiburg, 79104, Freiburg, Germany.
- Freiburg Center for Data Analysis and Modelling (FDM), University of Freiburg, 79104, Freiburg, Germany.
- Institute of Physics, University of Freiburg, 79104, Freiburg, Germany.
| | - Jens Timmer
- Freiburg Center for Data Analysis and Modelling (FDM), University of Freiburg, 79104, Freiburg, Germany
- Institute of Physics, University of Freiburg, 79104, Freiburg, Germany
- Centre for Integrative Biological Signalling Studies (CIBSS), University of Freiburg, 79104, Freiburg, Germany
| | - Mathias Berger
- Department of Psychiatry and Psychotherapy, Medical Center, Faculty of Medicine, University of Freiburg, 79104, Freiburg, Germany
| | | | - Matthias J Müller
- Oberberg Group, 10117, Berlin, Germany
- Faculty of Medicine, Justus-Liebig-University Giessen, 35392, Giessen, Germany
| | - Clemens Kreutz
- Institute of Medical Biometry and Statistics (IMBI), Faculty of Medicine and Medical Center, University of Freiburg, 79104, Freiburg, Germany
- Freiburg Center for Data Analysis and Modelling (FDM), University of Freiburg, 79104, Freiburg, Germany
- Centre for Integrative Biological Signalling Studies (CIBSS), University of Freiburg, 79104, Freiburg, Germany
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Tang C, Wang T, Zhang P. Functional data analysis: An application to COVID-19 data in the United States in 2020. QUANTITATIVE BIOLOGY 2022. [DOI: 10.15302/j-qb-022-0300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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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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Alene M, Yismaw L, Assemie MA, Ketema DB, Gietaneh W, Birhan TY. Serial interval and incubation period of COVID-19: a systematic review and meta-analysis. BMC Infect Dis 2021; 21:257. [PMID: 33706702 PMCID: PMC7948654 DOI: 10.1186/s12879-021-05950-x] [Citation(s) in RCA: 119] [Impact Index Per Article: 39.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 03/02/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Understanding the epidemiological parameters that determine the transmission dynamics of COVID-19 is essential for public health intervention. Globally, a number of studies were conducted to estimate the average serial interval and incubation period of COVID-19. Combining findings of existing studies that estimate the average serial interval and incubation period of COVID-19 significantly improves the quality of evidence. Hence, this study aimed to determine the overall average serial interval and incubation period of COVID-19. METHODS We followed the PRISMA checklist to present this study. A comprehensive search strategy was carried out from international electronic databases (Google Scholar, PubMed, Science Direct, Web of Science, CINAHL, and Cochrane Library) by two experienced reviewers (MAA and DBK) authors between the 1st of June and the 31st of July 2020. All observational studies either reporting the serial interval or incubation period in persons diagnosed with COVID-19 were included in this study. Heterogeneity across studies was assessed using the I2 and Higgins test. The NOS adapted for cross-sectional studies was used to evaluate the quality of studies. A random effect Meta-analysis was employed to determine the pooled estimate with 95% (CI). Microsoft Excel was used for data extraction and R software was used for analysis. RESULTS We combined a total of 23 studies to estimate the overall mean serial interval of COVID-19. The mean serial interval of COVID-19 ranged from 4. 2 to 7.5 days. Our meta-analysis showed that the weighted pooled mean serial interval of COVID-19 was 5.2 (95%CI: 4.9-5.5) days. Additionally, to pool the mean incubation period of COVID-19, we included 14 articles. The mean incubation period of COVID-19 also ranged from 4.8 to 9 days. Accordingly, the weighted pooled mean incubation period of COVID-19 was 6.5 (95%CI: 5.9-7.1) days. CONCLUSIONS This systematic review and meta-analysis showed that the weighted pooled mean serial interval and incubation period of COVID-19 were 5.2, and 6.5 days, respectively. In this study, the average serial interval of COVID-19 is shorter than the average incubation period, which suggests that substantial numbers of COVID-19 cases will be attributed to presymptomatic transmission.
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Affiliation(s)
- Muluneh Alene
- Department of Public Health, Debre Markos University, Debre Markos, Ethiopia
| | - Leltework Yismaw
- Department of Public Health, Debre Markos University, Debre Markos, Ethiopia
| | | | | | - Wodaje Gietaneh
- Department of Public Health, Debre Markos University, Debre Markos, Ethiopia
| | - Tilahun Yemanu Birhan
- Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Science, University of Gondar, Gondar, Ethiopia
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