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Medical Journal of the Islamic Republic of Iran logoLink to Medical Journal of the Islamic Republic of Iran
. 2022 Dec 19;36:155. doi: 10.47176/mjiri.36.155

Epidemiologic Parameters for COVID-19: A Systematic Review and Meta-Analysis

Neda Izadi 1, Niloufar Taherpour 2, Yaser Mokhayeri 3, Sahar Sotoodeh Ghorbani 1, Khaled Rahmani 4, Seyed Saeed Hashemi Nazari 5,*
PMCID: PMC9832936  PMID: 36654849

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.

Keywords: epidemiologic parameters, R0; serial interval, doubling time, case fatality rate

↑What is “already known” in this topic:

COVID-19 is a highly contagious disease that has spread significantly worldwide. Numerous strategies and parameter values have been documented in the reports from various countries on the epidemiological characteristics of the COVID-19 pandemic.

→What this article adds:

The results of this study showed that the pooled estimate for R0 was 2.99 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, incubation period, growth rate, and case fatality rate (CFR) were 2.99, 4.45, 4.14, 4.24 days, 0.38%, and 3.29%, for COVID-19, respectively.

Introduction

Coronaviruses are a group of RNA viruses that cause diseases among humans and animals (1). The latest of coronavirus types as a novel coronavirus that was named severe acute respiratory syndrome coronavirus 2 (SARS-Cov2) or COVID-19 occurred in Wuhan, China, in December 2019 with a human outbreak (2).

The World Health Organization (WHO) declared the outbreak to be a public health emergency and international concern and recognized it as a pandemic on March 11, 2020 (3). COVID-19 has spread widely in the world and is prevalent in different countries such as China, Italy, United States, France, Spain, Iran, and Germany, with 2,833,697 cases and 197,354 deaths and 807,469 recovered until April 24 2020 worldwide (4). The main rout of transmission of COVID-19 is based on human-to-human transmission via either respiratory droplets, saliva, or close contacts with infected people or aerosol generation procedures during the clinical care of COVID-19 patients (5).

Most COVID-19 infected people (80.9%) are with mild to moderate respiratory syndromes, old people or patients with underlying diseases such as diabetes, cardiovascular disease, cancer, immune deficiency, and respiratory diseases are more at risk to develop the severe (13.8%) and critical (4.7%) form of the disease (6, 7).

Knowledge regarding epidemiological characteristics and parameters of the infectious diseases such as incubation period (time from exposure to the agent until the first symptoms develop), serial interval (duration between symptom onset of a primary case and symptom onset of its secondary cases), basic reproduction number (R0) (the transmission potential of a disease), and other epidemiologic parameters is important for modelling and estimating epidemic trends and also implementing and evaluating preventive procedures (8-11).

With regard to COVID-19 pandemic parameters, there are many reports from different countries in the world. For example, about 25.6% to 51.7% of patients have been reported to be asymptomatic or with mild symptoms ( 12) and 25% to 30% of them have been admitted to the intensive care unit for medical care (13). The case-fatality rate was reported in China and other countries among old patients to be 6% (range: 4%-11%) and 2.3% in all ages (13, 14). Furthermore, the median incubation period was reported as 5 to 6 days (2-14 ranges) from the WHO, while in China the incubation period was reported up to 24 days (15, 16). Also, according to different mathematical models, R0 was reported about 6.47 (range, 1.66-10) in China, 2.6 in South Korea, and 4.7 in Iran (17-19).

Thus, according to the reports from different countries about epidemiological characteristics of the COVID-19 pandemic, different methods and values of parameters have been observed. Thus, to estimate and forecast the spread of the disease efficiently, we need acceptable and real values for each parameter. The present study was conducted to provide a systematic assessment and estimation of parameters related to COVID-19. This evaluation will help researchers with better prediction and estimation of current epidemic trends.

Methods

This is a systematic review and meta-analysis to determine the epidemiologic parameters for COVID-19.

Search Strategy

To find relevant studies, a comprehensive literature search of the Web of Science, Medline (PubMed), Scopus, and Google Scholar was performed for observational studies published electronically from early December 2019 up to 23 March 2020.

Two researchers independently searched studies. In the search strategy, English keywords (MeSH termas) and probable combination of them were used. Epidemiologic parameters in infectious diseases are combination of some specific keywords and definitions such as basic reproduction number (R0), serial interval, incubation period, doubling time, growth rate, case-fatality rate, mortality rate, and onset of symptom to hospitalization. These keywords with the Boolean operators ("OR" and "AND") were combined in search process.

The terms of search strategies were according to the following keywords: ("novel coronavirus" OR "2019-nCov" OR "COVID-19" OR " SARS-CoV-2") AND ("basic reproduction number" OR "basic reproductive rate" OR "case fatality rate" OR "case fatality ratio" OR "mortality rate" OR "doubling time" OR "growth rate" OR "incubation period" OR "onset of symptom to hospitalization"). Moreover, for comprehensive assessment of available evidences, grey literatures such as web-based nonpeer review studies were searched in this topic as well.

Study Selection

We included studies in accordance with the PRISMA guidelines and standard meta-analysis methods. All of theextracted articles were screened independently by 2 researchers. The abstracts and full texts of the articles were reviewed, duplicate studies were excluded, andrelevant articles were selected for data extraction.

Inclusion and Exclusion Criteria

The COVID-19 epidemiologic parameters of interest were provided by all epidemiological study designs (observational studies), including peer-reviewed and nonpeer-reviewed articles. In addition, irrelevant studies, letters, news, and studies that did not report epidemiologic parameters were excluded.

Screening and Data Extraction

All articles were reviewed independently by 4 researchers and information was extracted using a designed checklist (Appendix 1). Extracted items were the first author, year and month of publication, duration of the study, location of the study, type of parameters, point estimate, or mean/median and its confidence interval for epidemiological parameters, and the review status of articles (peer-reviewed or not).

Quality Assessment of Studies

To assess the quality of the included peer-reviewed and nonpeer-reviewed articles, 2 authors separately assessed the quality of the studies using the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist as a scale for assessing the quality of observational studies. The STROBE includes 22 questions about methodology, aim of study, study design, and frame of original article. Finally, we scored the quality of the study as high if its rating was at least 70% (score of 16 out of 22), medium if its rating was at least 55% (12 out of 22), and poor if its rating was less than 55% (lower 12 out of 22). After that, studies with high and medium quality were included in the analysis. Given that there is a possibility of error in nonpeer review studies, we have analyzed this group of studies separately, regardless of the quality score of these studies.

Statistical Analysis

The “Metan” command was used to apply a fixed or random effects model based on Cochran’s Q-test results or a large Higgins and Thompson’s I2 value. Forest plots were used for graphical description of the results. Cumulative meta-analysis was used to examine the R0 trend during different months. However, due to the small number of months in this study, this part was removed from the analysis and results.

In studies that mortality rate was reported, because the denominator was confirmed cases, it was considered a CFR. In addition, for studies that reported the median and interquartile range (IQR), the median was consideredequivalent to the mean and the IQR was converted to standard deviation using the “IQR/1.35” formula. Finally, publication bias was examined using the Begg and Egger test. Stata 14 was used for all statistical analyses. Satistical significance was set at P < 0.05.

Results

Having assessed the quality of relevant studies, 76 observational studies up to March 23, 2020, were included in this study (Figure 1). The majority of studies were done in Wuhan, China. Detailed information of the eligiblestudies and their characteristics are presented in Appendix 1 (12, 17, 18, 20-92).

Figure 1.

Flow diagram of the study selection process including publications for the epidemiologic parameters for COVID-19

Figure 1

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The Overall Basic Reproductive Number (R0) by Country and Peer Review Status

Total: The overall R0 was 2.99 (95% CI, 2.71-3.27) for COVID-19 (Table 1).

Table 1. Overall Estimation of Epidemiologic Parameters for COVID-19.

Parameters No. of studies Estimate 95% CI P for Heterogeneity I2(%)
Basic Reproductive Number (R0) Overall 69 2.99 2.71-3.27 <0.001 99.3
Korea 1 2.6 2.5-2.7 - -
China 57 3.23 2.92-3.55 <0.001 99.1
Singapore 6 1.19 1.07-1.3 <0.001 82.2
Iran 2 3.6 a 3.1-4.09 0.99 -
Japan 3 2.35 2.1-2.6 0.007 80.1
Peer Review 13 2.75 2.25-3.24 <0.001 99.4
Not Peer Review 56 3.08 2.73-3.43 <0.001 99.3
Growth Rate (%) Overall 5 0.38 0.2-0.55 <0.001 97.7
Symptom onset to Hospitalization (day) Overall 6 5.09 2.15-8.02 0.03 53
Incubation Period (day) Overall 22 4.24 3.03-5.44 0.02 35
Peer Review 18 4.03 2.72-5.33 0.01 41
Non Peer Review 4 5.82a 2.91-8.74 0.76 16
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a Fixed effect model

Country: The overall R0 was 3.23, 1.19, 3.6, and 2.35 for China, Singapore, Iran, and Japan, respectively (Table 1).

Peer Review Status: The overall R0 was 2.75 and 3.08 for peer-reviewed and nonpeer-reviewed articles, respectively (Table 1).

Overall Serial Interval (SI) by Country and Peer Review Status

Total: The overall SI was 4.45 days (95% CI, 4.03-4.87) for COVID-19.

Country: Using the random effect model, the overall SI was 4.46 and 4.64 days for China and Singapore, respectively (Figure 2).

Figure 2.

Overall serial interval (SI) for COVID-19 by country

Figure 2

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Peer Review Status: The overall SI was 5.3 and 4.39 days for peer-reviewed and nonpeer-reviewed articles, respectively (Figure 3).

Figure 3.

Overall serial interval (SI) for COVID-19 by peer review status

Figure 3

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Overall Doubling Time by Peer-review Status

Total: The overall doubling time was 4.14 days (95% CI, 2.67-5.62) for COVID-19.

Peer-review Status: The overall doubling time was 3.33 and 4.64 days for peer-reviewed and non-peer reviewed articles, respectively (Figure 4).

Figure 4.

Overall doubling time for COVID-19 by peer review status

Figure 4

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Overall Incubation Period by Peer-review Status

Total: The overall incubation periodwas 4.24 days (95% CI, 3.03-5.44) for COVID-19.

Peer-review Status: The overall incubation periodwas 4.03 and 5.82 days for peer-reviewed and nonpeer-reviewed articles, respectively (Table 1).

Overall Estimation for Other Epidemiologic Parameters

The overall estimation for the growth rate and the case fatality rate for COVID-19 was 0.38% and 3.29%, respectively (Table 1 & Figure 5). In addition, the overall time from symptom onset to hospitalization was 5.09 days for COVID-19 (Table 1).

Figure 5.

Overall case fatality rate (CRF) for COVID-19

Figure 5

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Trend of R0 for COVID-19

Based on the cumulative meta-analysis, the trend of R0 had been increasing at first and, then, decreasing in March.

Assessment of Publication Bias

The Begg and/or Egger tests indicated no publication bias in the parameters of R0, serial interval, doubling time, and incubation period (P > 0.05).

Discussion

In this secondary analysis, we aimed to calculate the pooled estimate of some epidemiological parameters of COVID-19; namely, basic reproductive number (R0), serial interval, doubling time, incubation period, growth rate, CFR, and time from symptom onset to hospitalization. Overall, the estimates were 2.99, 4.45 days, 4.14 days, 4.24 days, 0.38%, 3.29%, and 5.09 days in the same order. The pooled estimated values may differ from the pooled reported values from other studies. This variation is expected because factors such as place of sampling, the sample size, surveillance system, and quality of reported data from countries in emergency condition, and type of data analysis may affect these values. For example, R0 variations to some extent might be due to different methods calculations, including exponential growth method, maximum likelihood, and Bayesian time-dependent method (93-95). The pooled estimated R0 in this study was nearly accordant with the pooled estimation found by Alimohamadi et al in 2020. (R0 = 3.32 (95% CI, 2.81 to 3.82) (96).

According to our results, the pooled estimate of CFR 3.29% (95% CI, 2.78-3.81) is lower than SARS-CoV (97) and MERS-CoV (98). Health control policies, medical standard, and detection rate could affect CFR (35). Moreover, the CFR estimate in the early phase of the epidemic might be biased (overestimated). Usually in the early phase, some subclinical cases and patients with mild symptoms may not be detected (detection bias) (99, 100).

The pooled estimate of incubation period in 22 studies was 4.24 days (95% CI, 3.03, 5.44), while in a study of Jie Li et al the pooled mean incubation period in 7 studies was 5.3 days (95% CI, 4.5-6.0) (101). A valid and precise estimate of incubation period has a pivotal role for duration of quarantine (50). Indeed, understanding the incubation period is beneficial for surveillance and control methods, as well as modeling and monitoring operations (102).

Our estimate for overall doubling time—time for a given quantity to double in size or number at a constant growth rate—was 4.14 days (95% CI, 2.67, 5.62). This estimation was in accordance with the study of Zhang et al in 2020 (103). The doubling time has an important implication for predicting epidemic. Generally, social distancing, quarantine, and active surveillance are needed to reduce transmission and extend the doubling time (104). Moreover, the authors tried to estimate pooled measures for the growth rate and the serial interval. These 2 epidemiological parameters are used to estimate the reproduction number (105). In this study, the serial interval was calculated as 4.45 (95% CI, 4.03-4.87). In addition, the pooled serial interval of COVID-19 obtained in this study was shorter than the pooled serial interval in study of Rai et al (5.19 (95% CI, 4.37, 6.02) (106).

As a limitation, all 76 studies (except for 1, Mirjam E Kretzschmar et al) (107) have been conducted in Asia, particularly in Wuhan, China. Some epidemiological parameters in Europe, Africa, and the United States could be different based on control strategies. Hence, distribution of these epidemiological parameters could be more global. Future studies to calculate more generalized pooled estimates, using studies all over the world is recommended.

Conclusion

The epidemiological characteristics of COVID-19 as an emerging disease may be revealed by calculating the pooled estimate of the disease's epidemiological parameters, paving the way for health policymakers to consider additional control measures.

Conflict of Interests

The authors declare that they have no competing interests.

Acknowledgment

The authors would like to appreciate all those researchers who helped in conducting this study.

Ethical Approval

The ethical approval is granted by the ethics committee of the school of Public Health and Neuroscience Research Center (PHNS), Shahid-Beheshti University of Medical Sciences (SBMU), Tehran, Iran (IR.SBMU.PHNS.REC.1399.009).

Authors’ Contributions

N.I.was involved in design, data analysis, and participated as a reviewer on the topic. Also, she designed tools for the data extraction.N.T.performed an independent systematic literature search, wrote the first manuscript version, and participated as a reviewer on the topic.Y.M.wrote the first manuscript version and participated as a reviewer on the topic. S.S.GH. performed an independent systematic literature search and participated as a reviewer on the topic. KH.R. participated in project administration. S.S.H.N. as a supervisor, directed every step of the review, revised the results, and versions of the manuscript. All authors read and approved the final version of manuscript.

Appendix 1.

Description of eligible studies reporting the epidemiologic parameters for COVID-19.

ID Author YOP Mon Start Date End Date Country/City N Explanation Parameter Point Est LCI UCI Unit Mean SD Median IQR Q1-Q3
1 Kaiyuan Sun(20) 2020 Feb 13-Jan 31-Jan China 507 Onset of symptom to hospitalization day 2 0-5
Incubation period day 4.5 3-5.5
R0 2.5-2.8
2 Biao Tang(17) 2020 Feb 31-Dec 15-Jan China-Wuhan 11081 Incubation period day 7 1.7
R0 6.47 5.71 7.23
Generation time day 6
3 Biao Tang(18) 2020 Mar 10-Jan 23-Jan Mainland-China Serial interval day 5 3
reported Cumulative confirmed cases 80651 count
predicted Cumulative confirmed cases 600000 count
Initial R0 (mainland-china) R0 3.8 3.5 4.2
Initial R0 (Guangdong) R0 3 2.6 3.3
Initial R0 (south Korea) R0 2.6 2.5 2.7
4 Sany Tang(21) 2020 Feb 10-Jan 15-Feb Shaanxi-China Illness onset to medical visit day 3.43
Importation to illness onset of disease day 2.38
Medical visit to confirmation day 3.05
longest Incubation period 19 day
Serial interval day 7.4 3.4
R0 1.48 0.98
5 Amna Tariq(22) 2020 Mar 23-Jan 05-Mar Singapore Generation time day 4.41 3.17
per day Number of new cases 2.5 count
Reporting delay 7.6 6.6 8.5 day
Cumulative case 294.4 101.1 1239.7 count
Effective R 0.9 0.7 1
R0 0.7 0.5 1
Dispersion parameter 0.4 0.1 inf count
6 Sijia Tian(23) 2020 Feb 20-Jan 10-Feb Beijing-China 262 Incubation period day 6.7
Contact to illness onset day 6.7 5.2
Illness onset to medical visit day 4.5 3.7
Days from visit hospital to define day 2.1 1.9
Percentage of hospitalization 81.7 %
Percentage of discharge 17.2 %
0.9 %
7 Lauren C.Tindale( 24) 2020 Mar 19-Jan 26-Feb Singapore 93 Incubation period 7.1 6.1 8.3 day
Serial interval 4.56 2.69 6.42 day
R0 1.97 1.45 2.48
percentage of discharge 66.7 %
Exposure to onset of symptoms day 6.6 4.8
Hospitalization after symptom onset day 5.9 5.1
Length of hospitalization day 13.3 6
Pre-symptomatic transmission day 2.55
Growth rate 0.15 count
Doubling time 6.6 day
21-Jan 26-Feb Tianjin-China 135 Incubation period 9 7.92 10.2 day
Serial interval 4.22 3.43 5.01 day
R0 1.87 1.65 2.09
Exposure to onset of symptoms day 5.4 4.5
Percentage of discharge 48.1 %
Confirmed after symptom onset day 5.2 4.2
Pre-symptomatic transmission day 2.89
Percentage of deaths 2.2 %
8 Ashleigh R. Tuite( 25) 2020 Feb 18-Nov 24-Feb China/ Wuhan R0 2.3
Serial interval 7 day
9 Dawei Wang(26) 2020 Feb 01-Jan 28-Jan China/ Wuhan 138 Onset of symptoms to hospitalization day 7 4-8
Onset of dyspnea day 5 1-10
Onset of ARDS day 8 6-12
Onset of symptom to ICU admission day 10 6-12
Percentage of hospitalization 12.3 %
Admitted to the ICU 26 %
Hospital admission to ICU admission day 1 0-3
10 Wang Meng(27) 2020 Mar 19-Mar 21-Mar China (excluding Hubei) R0 2.82 0.11
predicted Cumulative case count 14408 429
11 Wang Wenbao(28) 2020 Mar 14-Jan 23-Jan China (8 provinces) per 10000 Incidence rate 34 25.3 42.9 count
Cumulative case 5586 4156 7048 count
R0 3.38 3.25 3.48
Incubation period day
Case fatality rate$ 3.06 %
12 Wang Ying(29) 2020 Mar 15-Dec 29-Feb China/ Wuhan R0 3.49 3.42 3.58
R0 2.95 2.86 3.03
13 Joseph T Wu(30) 2020 Mar 15-Dec 29-Feb Mainland-China Case fatality rate 1.4 0.9* 2.1* %
Fatality rate 11 %
Cumulative cases 79394 count
R0 1.94 1.83* 2.06*
Serial interval 7 5.8* 8.1* day
Illness onset to death day 20 10
Doubling time 5.2 4.6* 6.1* day
14 Joseph T Wu(31) 2020 Jan 31-Dec 28-Jan China/ Wuhan Doubling time 6.4 5.8* 7.1* day
Cumulative cases 75815 37304* 130330* count
R0 2.68 2.47* 2.86*
15 Peng Wu(32) 2020 Jan 10-Jan 21-Jan China/ Wuhan 136 Fatality rate 14 3.9 32 %
700 R0 0.3 0.17 0.44
16 Tianmin Xu(33) 2020 Mar 23-Jan 18-Feb Changzou-China 15 Incubation period day 8 4-10
17 Incubation period day 8 4-11
19 Incubation period day 12 9-14
17 Xiao-Wei Xu(34) 2020 Feb 10-Jan 26-Jan Zhejiang-China 62 Incubation period day 4 3-5
Onset of symptoms to hospitalization day 2 1-4
Percentage of discharge 2 %
Admitted to ICU 2 %
Death 0 %
Onset of ARDS 2 %
18 Shu Yang(35) 2020 Feb 10-Jan 03-Feb whole mainland China 32020 Case fatality rate 2.1 2.05 2.14 %
mainland China excluding Hubei Case fatality rate 0.15 0.12 0.18 %
Hubei excluding Wuhan Case fatality rate 1.41 1.38 1.45 %
Wuhan Case fatality rate 5.25 4.98 5.51 %
19 Chong You(36) 2020 Feb 19-Jan 05-Feb China 5405 Serial interval day 4.41 3.17 4 2-6
Infectious period day 10.91 3.95 11 8-13
21-Jan 28-Jan China SIR method R0 5.4 4.5 6.2
29-Jan 05-Feb R0 2.3 2.1 2.5
21-Jan 28-Jan Hubei R0 5.5 4.2 6.8
29-Jan 05-Feb R0 2.8 2.5 3.1
21-Jan 28-Jan Other R0 5.1 3.9 6.3
29-Jan 05-Feb R0 1.2 1.1 1.4
21-Jan 28-Jan Beijing R0 2.3 1.1 3.8
29-Jan 05-Feb R0 2.1 1 3.3
21-Jan 28-Jan Shanghai R0 2.4 1 3.8
29-Jan 05-Feb R0 1.2 0.7 2
21-Jan 28-Jan Guangdong R0 3.7 2.7 4.9
29-Jan 05-Feb R0 1.2 0.8 1.8
21-Jan 28-Jan Zhejiang R0 5 3.3 7
29-Jan 05-Feb R0 1 0.4 1.7
21-Jan 28-Jan Hun R0 5.3 4.3 7
29-Jan 05-Feb R0 1.3 1 1.8
21-Jan 28-Jan Hen R0 6.4 3.5 10.2
29-Jan 05-Feb R0 1.5 1.1 2
20 Jasper Fuk-Woo Chan(37) 2020 Jan 10-Jan 15-Jan China/ Wuhan 6 Onset of symptom to sample collection 7 day
Onset of symptom to sample collection 6 day
Onset of symptom to sample collection 9 day
Onset of symptom to sample collection 10 day
Onset of symptom to sample collection 7 day
Onset of symptom to hospitalization 7 day
Onset of symptom to hospitalization 6 day
Onset of symptom to hospitalization 9 day
Onset of symptom to hospitalization 10 day
Onset of symptom to hospitalization 7 day
21 Choujun Zhan(38) 2020 Mar 19-Feb 06-Mar South Korea Confirmed cases 7313 count
Italy Confirmed cases 5883 count
Iran Confirmed cases 5823 count
Iran Infected cases count 14450 6244
Tehran Infected cases count 2498 566
Zanjan Infected cases count 1695 92
Lombardi-Italy Infected cases count 4784 788
Emelia Romagna-Italy Infected cases count 1555 360
Daegu-South Korea Infected cases count 7619 2096
Seoul-South Korea Infected cases count 1287 197
Italy Fatality rate 4 %
22 Bo Zhang(39) 2020 Feb 08-Dec 22-Jan China/ Wuhan 1568 Infected cases 4508 count
R0 3.6
13-Feb early April China/ Wuhan Infected cases 42073 41673 42475 count
Number of deaths 2179 2088 2270 count
10-Jan 22-Jan China/ Hubei R0 0.67
Infected cases 7138 count
13-Feb early April China/ Hubei R0 3.4
Infected cases 21342 21057 21629 count
Number of deaths 633 585 683 count
13-Feb early April China excluding Hubei R0 0.59
Infected cases 13384 13158 13612 count
Death 107 87 128 count
R0 0.63
23 Lianglu Zhang (40) 2020 Feb 22-Jan 12-Feb China/ Wuhan after intervention R0 1.44 1.4-1.47
Incubation period day 3 3-7.2
24 Sheng Zhang(41) 2020 Feb 17-Feb 26-Feb Japan (Princess ship) maximum likelihood R0 2.28 2.06 2.52
Cumulative case 1514 1384 1656 count
25 Zhanwei Du(42) 2020 21-Jan 08-Feb China 468 Serial interval 3.96 3.53 4.39 count 4.75
Asymptomatic patients 12.6 %
26 Hongxin Zhao(43) 2020 Feb 29-Jan 02-Feb 5 countries (korea, Germany,France,singapore,Japan 1916 Infection rate 1.1 0.4 3.1 %
Infected cases 110000 40000 310000 count
27 Shi Zhao(12) 2020 Feb 20-Jan 20-Feb Japan (Princess ship) 634 Cumulative cases 3066 2046 3441 count
R0 2.2 2.1 2.4
Dispersion parameter 44 6 88 count
Doubling time 4.6 3 9.3 day
Asymptomatic patients 25.6-51.7 %
28 Shi Zhao(44) 2020 Jan 10-Jan 24-Jan China/ Wuhan (8-fold) exp growth R0 2.24 1.96 2.55
(0-fold) exp growth R0 3.58 2.89 4.39
29 Shi Zhao(45) 2020 Feb 01-Dec 24-Jan Mainland China 41 Under reported cases 469 403 540 count
R0 2.56 2.49 2.63
30 Fei Zhou(46) 2020 Mar 29-Dec 31-Jan China/ Wuhan (2 hospitals) 191 ICU admission 26 %
ICU length of stay day 8 4-12
Hospital length of stay day 11 7-14
Illness onset to hospitalization day 11 8-14
Illness onset to dyspnea day 7 4-9
Illness onset to ARDS day 12 8-15
Illness onset to ICU day 12 8-15
Illness onset to Death-discharge day 21 17-25
31 Guopeng Zhou(47) 2020 Feb first day 50th day China/ Wuhan 141427709 Cumulative cases count 2868.7 1739
51th day 70th day Cumulative cases count 52185.4 31621.4
71 th day 90 th day Cumulative cases count 913396.5 559099.9
first day 90thday R0 2.2 1.4 3.9
Incubation period 7.5 5.3 19 day 3.4
31-Dec 18-Feb ICU admission 5 %
32 Tao Zhou(48) 2020 Feb 25-Jan China/ Wuhan 3440 Northeastern University Reports R0 2.8-3.3
People's Daily Reports R0 3.2-3.9
Asymptomatic patients 5.1 %
33 Cleo Astassopoulou( 49) 2020 12-Feb 11th of Jan 10th of Feb Hubei/China 11-16 Jan R0 4.8 3.35 6.27
11-17 Jan R0 4.6 3.56 5.65
11-18 Jan R0 5.14 4.25 6.04
11-19 Jan R0 6.09 5.02 7.16
11-20 Jan R0 7.09 5.84 8.35
Nov 16-Feb 10 (Based on the SIRD simulator) R0 2.5
Case fatality rate$ 2.94 2.89 3 %
Recovery rate 0.05 0.045 0.055
Recovery time 20 18 22 day
Infection rate 0.199 0.197 0.2
Forecast to Feb 29 Expected number of Infected cases 140000 70000 290000
Forecast to Feb 29 Expected number of recovered population 60000 33000 95000
Forecast to Feb 29 Expected number of Death cases 16000 9000 29000
11-16 Jan R0 4.15 2.92 5.38
11-17 Jan R0 3.98 3.11 4.85
11-18 Jan R0 4.39 3.67 5.11
11-19 Jan R0 5.15 4.3 6.01
11-20 Jan R0 6.01 4.93 7.08
Nov 16-Feb 10 (Based on the SIRD simulator) R0 2.64
Case fatality rate$ 0.58 0.57 0.59 %
Recovery rate 0.08 0.073 0.088
Recovery time 12 11 13 day
Infection rate 0.227 0.224 0.229
Forecast to Feb 29 Expected number of Infected cases 1000000 330000 2200000
Forecast to Feb 29 Expected number of recovered population 580000 230000 960000
Forecast to Feb 29 Expected number of Death cases 19000 7000 35000
34 Jantien A Backer ( 50) 2020 06-Feb 20-Jan 28-Jan Wuhan, China 88 Weibull Incubation period day 6.4 2.3 6.4
Gamma Incubation period day 6.5 2.6 6.1
Lognormal Incubation period day 6.8 3.4 6.1
35 David Baud(51) 2020 12-Mar 01-Mar China 79968 China Case fatality rate$ 5.6 5.4 5.8 %
Outside of China 7169 Outside of China Case fatality rate$ 15.2 12.5 17.9 %
Global 87137 Global mortality rates Case fatality rate$ 5.7 5.5 5.9 %
36 Zhidong Cao(52) 2020 China Effective reproduction number 3.37 4.77 4.08 0.36
Fatality rate 6.5 %
Average infectious period <2.3 day
37 Tian-Mu Chen(53) 2020 28-Feb 7 Dec, 2019 1 Jan, 2020 China R0 3.58
38 Matteo Chinazzi(54) 2020 07-Feb China R0 2.4 2.2 2.6
Doubling time measured 4.6 4.2 5.1
On Jan 22, 2020, the projected, no travel restrictions for Mainland China excluding Wuhan Median number of cases 3491 1924 7360
On Jan 22, 2020, the projected, in Wuhan Median number of cases 58956 40760 87471
Median ascertainment rate of detecting an infected % 19.59 14.36-35.58
39 Yi Chen Chong(55) 2020 15-Feb China R0 4.29
40 Ilaria Dorigatti(56) 2020 10-Feb China 26 China: Parametric model fitted to publicly reported number of cases and Deaths in Hubei as of 5th Feb, assuming exponential growth at rate 0.14/day Case fatality ratio 18 11* 81* %
Outside mainland China:Parametric model fitted to reported traveller cases up to 8th Feb using both Death and recovery outcomes and inferring latest possible dates of onset in traveller cases Case fatality ratio 5.1 1.1* 38* %
Outside mainland China:Parametric model fitted to reported traveller cases up to 8th Feb using only Death outcome and inferring latest possible unreported dates of onset in traveller cases Case fatality ratio 5.6 2* 85* %
Outside mainland China:Kaplan-Meier-like non-parametric model fitted to reported traveller cases up to 8th Feb using both Death and recovery outcomes Case fatality ratio 1.2 0.9 26 %
all infections (asymptomatic or symptomatic): Scaling CASE FATALITY RATE estimate for Hubei for the level of infection under-ascertainment estimated from infection prevalence detected in repatriation flights, assuming infected individuals test positive for 14 days Case fatality ratio 0.9 0.5 4 %
all infections (asymptomatic or symptomatic): As previous row, but assuming infected individuals test positive for 7 days Case fatality ratio 0.8 0.4 3 %
Onset-to-recovery 18* 83* day 22.2 0.45
Onset-to-Death 18* 82* day 22.3 0.42
41 Mirjam E Kretzschmar(57) 2020 Mar Netherlands optimistic baseline scenario R0 2.5
realistic scenario Effective reproduction number 1.4
realistic scenario Exponential growth rate 0.05 %
optimistic baseline scenario Exponential growth rate 0.127 %
optimistic baseline scenario Doubling time 5.5 day
realistic scenario Doubling time 14.4 day
Infectious period 10 day
Latent period 4 6 day
Incubation period 3 7.2 day 6.54 2.3
42 Toshikazu kuniya(58) 2020 Mar Feb Mar Japan (range 2.1-5.1) R0 2.6 2.4 2.8
43 Alessia Lai(59) 2020 Feb Feb Feb China R0 2.6
44 Hien Lau(60) 2020 Mar Jan Feb China Doubling time 2 1.9 2.6 day
Doubling time 4 3.5 4.3 day
45 Stephen A Lauer(61) 2020 Mar Jan Feb China 181 Incubation period 4.5 5.8 day 5.1
46 Char Leung(62) 2020 Mar Jan Feb China Incubation period day 1.7
Incubation period day 7.5
Incubation period day 1.8
Incubation period day 7.2
Incubation period day 1.7
Incubation period day 7.2
47 Qun Li(63) 2020 Jan Dec Jan China 425 Incubation period 4.1 7 day 5.2
Doubling time 7.4 4.2 14 day
Serial interval 5.3 19 day 7.5
R0 2.2 1.4 3.9
Growth rate 0.1 0.05 0.16 %
Time from symptom onset to hospitalization 12.5 10.3 14.8 day 9.1
48 Tao Liu(64) 2020 Jan Dec Jan China 830 Time from symptom onset to isolation 2.9 day
Proportion of symptomatic that die 0.03 %
Incubation period day 4.8
R0 2.9 2.32 3.63
49 Jiaqiang Liao(65) 2020 Mar Jan Feb China 46 Incubation period 4.4 9.6 day 6.6
Serial interval 1.9 0.4 6.2 day
50 Qiushi Lin(66) 2020 Feb Dec Jan China Cumulative case count 4090 3975 4206 count
Cumulative case count 56833 55242 58449 count
Latent period day 3
Infectious period day 5
51 Natalie Linton(67) 2020 Feb Jan Feb China Time from hospitalization to Death 8.3 6.4 10.5 day
Time from symptom onset to Death 13.8 11.8 16 day
Incubation period 4.6 3.3 5.7 day
Incubation period 5 4.1 5.8 day
Time from symptom onset to hospitalization 2.7 1.6 4.1 day
52 Tao Liu(68) 2020 Feb Jan Feb China nationwide Doubling time 2.4 day
Wuhan Doubling time 2.8 day
Guangdong Doubling time 3.6 day
nationwide R0 4.5 4.4 4.6
Wuhan R0 4.4 4.3 4.6
53 Kenji Mizumoto(69) 2020 Feb Jan Feb China Effective reproduction number 3.24 3.16 3.32 num
Proportion of symptomatic that die 0.0406 %
R0 7.05 6.11 8.18
Cumulative case count 983006 759175 1296258 count
54 Kamalich Muniz-Rodriguez(70) 2020 Mar Feb Feb Iran R0 3.6 3.2 4.2
SI: mean=4.41; sd=3.17 R0 3.58 1.29 8.46
Doubling time 1.2 1.05 1.44 day
Doubling time 2.4 day
Growth rate 0.85 0.69 1 %
55 Hiroshi Nishiura(71) 2020 Feb Jan Feb Japan 565 Ascertainment rate 9.2 5 20 %
Serial interval day 7.5
56 Hiroshi Nishiura(72) 2020 Mar Feb Feb China Serial interval 4 3.1* 4.9* day
Serial interval 4.6 3.5* 5.9* day
57 Ryosuke Omori(73) 2020 Mar Feb Japan Ascertainment rate 0.44 0.37 0.5 %
58 Sang Woo Park(74) 2020 Feb Feb China R0 2.9 2.1 4.5
59 Liangrong Peng(75) 2020 Feb Feb China Latent period 2 day
Mainland Quarantine time 6.6 day
Hubei Quarantine time 7.2 day
Wuhan Quarantine time 7.4 day
Beijing Quarantine time 5.7 day
Shanghai Quarantine time 5.6 day
60 Rachael Pung(76) 2020 Mar Feb Feb China 36 Incubation period day 4 3-6
Serial interval 3 8 day
Time from symptom onset to hospitalization day 4 3-6
61 Guo-Qing Qian(77) 2020 Mar Feb Feb China 91 Incubation period day 6 3-8
62 Jomar F Rabajante( 78) 2020 Feb Feb Philippine R0 2
Infectious period 14 day
63 Jonathan M Read(79) 2020 Feb Jan China Infectious period 3.6 3.6 3.6 day
R0 3.8 3.6 4
Ascertainment rate 5.1 4.8 5.5 %
64 Julien Riou(80) 2020 Jan Dec Jan Wuhan Dispersion rate 0.54 %
R0 2.2
65 Steven Sanche(81) 2020 Feb Dec Feb China Time from hospitalization to Death 11.2 8.7 14.9 day
Time from hospitalization to discharge 11.5 8 17.3 %
Growth rate 0.29 0.21 0.37 %
Growth rate 0.14 0.12 0.15 day
Incubation period 4.2 3.5 5.1 day
66 Mingwang Shen(82) 2020 Jan Jan China R0 4.71 4.5 4.92
67 Eunha Shim(83) 2020 Mar Jan Feb South Korea Effective reproduction number 1.5 1.4 1.6 num
Growth rate 0.6 0.5 0.7 %
68 Yaqing Fang(84) 2020 Mar 20-Jan 29-Feb China - Wuhan 291 20-Jan R0 2.47
437 21-Jan R0 2.56
560 22-Jan R0 2.67
805 23-Jan R0 2.81
1230 24-Jan R0 2.92
1892 25-Jan R0 2.98
2635 26-Jan R0 3.1
4371 27-Jan R0 3.14
5761 28-Jan R0 3.17
7439 29-Jan R0 3.19
9331 30-Jan R0 3.2
11315 31-Jan R0 3.2
13775 01-Feb R0 3.19
16400 02-Feb R0 3.17
19414 03-Feb R0 3.15
22974 04-Feb R0 3.13
26334 05-Feb R0 3.11
29017 06-Feb R0 3.09
31774 07-Feb R0 3.06
33738 08-Feb R0 3.03
35982 09-Feb R0 2.98
37626 10-Feb R0 2.94
38800 11-Feb R0 2.89
52526 12-Feb R0 2.9
55748 13-Feb R0 2.87
56873 14-Feb R0 2.84
57416 15-Feb R0 2.8
57934 16-Feb R0 2.77
58016 17-Feb R0 2.74
57805 18-Feb R0 2.7
56303 19-Feb R0 2.67
54965 20-Feb R0 2.64
53284 21-Feb R0 2.61
51606 22-Feb R0 2.57
49824 23-Feb R0 2.54
47672 24-Feb R0 2.51
45604 25-Feb R0 2.47
43258 26-Feb R0 2.44
39919 27-Feb R0 2.41
37414 28-Feb R0 2.37
35329 29-Feb R0 2.34
69 Ganyani Tapiwa(85) 2020 Mar 27-Feb Singapore Incubation period mean:5.2 - SD:2.8 Generation interval 5.2 3.78 6.78 day 1.72
Incubation period mean:5.2 - SD:2.8 Serial interval 5.21 -3.35 13.94 day 4.32
Tianjin/ China Incubation period mean:5.2 - SD:2.8 Generation interval 3.95 3.01 4.91 day 1.51
Incubation period mean:5.2 - SD:2.8 Serial interval 3.95 -4.47 12.51 day 4.24
Singapore Incubation period mean:6.4 - SD:2.3 Generation interval 5.29 3.89 6.77 day 2.08
Incubation period mean:6.4 - SD:2.3 Serial interval 5.29 -2.13 13.16 day 3.86
Incubation period mean:4.8 - SD:2.6 Generation interval 5.19 3.82 6.74 day 1.77
Incubation period mean:4.8 - SD:2.6 Serial interval 5.19 -2.86 13.45 day 4.08
Tianjin/ China Incubation period mean:6.4 - SD:2.3 Generation interval 4.02 3.11 5 day 2.29
Incubation period mean:6.4 - SD:2.3 Serial interval 4.02 -4.83 13.45 day 3.98
Incubation period mean:4.8 - SD:2.6 Generation interval 3.95 3.05 4.93 day 1.75
Incubation period mean:4.8 - SD:2.6 Serial interval 3.95 -4.6 12.73 day 4.07
Singapore mean:5.2 - SD:2.8- allowing SI negative Generation interval 3.86 2.22 5.6 day 2.65
mean:5.2 - SD:2.8- allowing SI negative Serial interval 3.86 -5.15 13.88 day 4.76
Tianjin/ China mean:5.2 - SD:2.8- allowing SI negative Generation interval 2.9 1.85 4.12 day 2.86
mean:5.2 - SD:2.8- allowing SI negative Serial interval 2.9 -6.12 13.47 day 4.88
Singapore mean:5.2 - SD:2.8- using GI- baseline R0 1.27 1.19 1.36
mean:5.2 - SD:2.8- using SI- baseline R0 1.25 1.17 1.34
mean:5.2 - SD:2.8- using GI- all negative SI R0 1.19 1.1 1.28
mean:5.2 - SD:2.8- using SI- all negative SI R0 1.17 1.08 1.26
Tianjin/ China mean:5.2 - SD:2.8- using GI- baseline R0 1.59 1.42 1.78
mean:5.2 - SD:2.8- using SI- baseline R0 1.41 1.26 1.58
mean:5.2 - SD:2.8- using GI- all negative SI R0 1.32 1.18 1.51
mean:5.2 - SD:2.8- using SI- all negative SI R0 1.17 1.05 1.34
Singapore mean:5.2 - SD:2.8-baseline Proportion of pre-symptomatic transmission 48 32 67 %
Tianjin/ China mean:5.2 - SD:2.8-baseline Proportion of pre-symptomatic transmission 62 50 76 %
Singapore mean:5.2 - SD:2.8-all negative SI Proportion of pre-symptomatic transmission 66 45 84 %
Tianjin/ China mean:5.2 - SD:2.8-all negative SI Proportion of pre-symptomatic transmission 77 65 87 %
70 Guan Wei-Jie(86) 2020 Mar 29-Jan China 1099 Incubation period 4 day 4 5 2
Duration of hospitalization day 12.8 12
71 Slav W. Hermanowicz(87) 2020 Feb 16-Jan 08-Feb China 62 17-Jan R0 1.38
121 18-Jan R0 1.95
198 19-Jan R0 1.64
291 20-Jan R0 1.47
440 21-Jan R0 1.51
571 22-Jan R0 1.3
830 23-Jan R0 1.45
1287 24-Jan R0 1.55
1975 25-Jan R0 1.53
2744 26-Jan R0 1.39
4515 27-Jan R0 1.65
5974 28-Jan R0 1.32
7711 29-Jan R0 1.29
9692 30-Jan R0 1.26
11860 31-Jan R0 1.22
14380 01-Feb R0 1.21
17307 02-Feb R0 1.2
20467 03-Feb R0 1.18
24324 04-Feb R0 1.19
28018 05-Feb R0 1.15
31161 06-Feb R0 1.11
31774 07-Feb R0 1.02
33738 08-Feb R0 1.06
72 Zhiliang Hu(88) 2020 28-Jan 09-Feb Jiangsu Province, China 24 Median communicable period 21 24 day
73 Xuan Jiang(89) 2020 Feb Incubation period 4.9 4.4 5.5 day
74 Sung-mok Jung(90) 2020 Feb 08-Dec 24-Jan China scenario 1: exponential growth started from the assumed illness onset date of index case,(8 Dec) Case fatality rate 5.3 3.5 7.5 %
scerio2: all parameters are variable, and calculation begins on the date the first exported case was observed (i.e., 13 Jan 2020) Case fatality rate 8.4 5.3 12.3 %
scenario 1: exponential growth started from the assumed illness onset date of index case,(8 Dec) R0 2.1 2 2.2
scerio2: all parameters are variable, and calculation begins on the date the first exported case was observed (i.e., 13 Jan 2020) R0 3.2 2.7 3.7
scenario 1: exponential growth started from the assumed illness onset date of index case,(8 Dec) Cumulative incidence 6924 4885 9211 count
scerio2: all parameters are variable, and calculation begins on the date the first exported case was observed (i.e., 13 Jan 2020) Cumulative incidence 19289 10901 30158 count
75 Moran Ki(91) 2020 Feb 20-Jan Korea 28 Incubation period 3.9 day 3.9 3
Serial interval 6.6 day 6.6 4
Symptoms onset to diagnosis 5.2 day 5.2 4
Symptoms onset to quarantine or isolation 4.3 day 4.3 3
Diagnosis to discharge 13 day 13 12.5
total Poisson R0 0.48 0.25 0.84
total binomial R0 0.48 0.28 0.69
first generation (n=9) Poisson R0 0.56 0.26 1.07
first generation (n=9) binomial R0 0.56 0.3 0.8
second generation (n=3) Poisson R0 0.33 0.07 0.97
second generation (n=3) binomial R0 0.33 0.07 0.7
76 Weier Wang(92) 2020 Jan 1-Dec 26-Jan China 41 10-Jan Case fatality rate$ 2.44 %
440 21-Jan Case fatality rate$ 2.05 %
571 22-Jan Case fatality rate$ 2.98 %
830 23-Jan Case fatality rate$ 3.01 %
1287 24-Jan Case fatality rate$ 3.19 %
1975 25-Jan Case fatality rate$ 2.84 %
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Cite this article as : Izadi N, Taherpour N, Mokhayeri Y, Sotoodeh Ghorbani S, Rahmani Kh, Hashemi Nazari SS. Epidemiologic Parameters for COVID-19: A Systematic Review and Meta-Analysis. Med J Islam Repub Iran. 2022 (19 Dec);36:155. https://doi.org/10.47176/mjiri.36.155

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