key: cord-342822-d7jx06mh authors: Izadi, N.; Taherpour, N.; Mokhayeri, Y.; Sotoodeh Ghorbani, S.; Rahmani, K.; Hashemi Nazari, S. S. title: The epidemiologic parameters for COVID-19: A Systematic Review and Meta-Analysis date: 2020-05-06 journal: nan DOI: 10.1101/2020.05.02.20088385 sha: doc_id: 342822 cord_uid: d7jx06mh Introduction: The World Health Organization (WHO) declared the outbreak to be a public health emergency and international concern and recognized it as a pandemic. The aim of this study was to estimate the epidemiologic parameters of novel coronavirus (COVID-19) pandemic for clinical and epidemiological help. Methods: Four electronic databases including Web of Science, Medline (PubMed), Scopus and Google Scholar were searched for literature published from early December 2019 up to 23 March 2020. The "metan" command was used to perform a fixed or random effects analysis. Cumulative meta-analysis was performed using the "metacum" command. Results: Totally 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, incubation period were 4.45, 4.14 and 4.24 days for COVID-19. In addition, the overall estimation for growth rate and case fatality rate for COVID-19 were 0.38% and 3.29%, respectively. Conclusion: Calculating the pooled estimate of the epidemiological parameters of COVID-19 as an emerging disease, could reveal epidemiological features of the disease that consequently pave the way for health policy makers to think more about control strategies. Keywords: Epidemiologic Parameters; R0; Serial Interval; Doubling Time; Case Fatality Rate;COVID-19 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 11 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 disease are more at risk to develop sever (13.8%) and critical (4.7%) 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 (R 0 ) (the transmission potential of a disease) and other epidemiologic parameters is important for modelling and estimation of epidemic trends and also implementation and evaluation of preventive procedures (8) (9) (10) (11) . . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 6, 2020. . https://doi.org/10.1101/2020.05.02.20088385 doi: medRxiv preprint About 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 25-30% of them have been admitted to ICU for medical care (13) . Case-fatality rate was reported in China and other countries among old patients 6% (4-11% ranges) and 2.3 % in all ages (13, 14) . Furthermore, the median incubation period was reported as So, for efficient estimation and forecasting of disease spreading, we need acceptable and real values of 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. The current study is a systematic review and meta-analysis to determine the epidemiologic parameters for COVID-19. To find relevant studies, a comprehensive literature search of the Web of Science, Medline . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 6, 2020. Consistent with PRISMA guidelines, the standard meta-analysis techniques, we included studies. All of the extracted articles independently were screened by two researchers. Abstract and full text of the articles were reviewed and duplicated studies were excluded and then relevant articles were selected for data extraction. All epidemiological studies designs (observational studies) including peer reviewed or not peer reviewed articles that provided the epidemiologic parameters of interest regarding the novel corona virus were included. In addition, irrelevant studies, letters and news and studies that didn't report epidemiologic parameters were excluded. All articles were reviewed independently by four researchers and information was extracted using designed checklist (Appendix 1). Extracted items were name of the first author, years and month of article published, duration of the study, location of study conduction, type of . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 6, 2020. . https://doi.org/10.1101/2020.05.02.20088385 doi: medRxiv preprint parameters, point estimate or mean/median and its confidence interval for epidemiological parameters and review status of articles (peer-reviewed or not). To assess the quality of included the peer-reviewed and not peer reviewed articles, we used the STROBE quality assessment scale for observational studies. We assessed the quality of all studies and finally, studies with high and medium quality were included in the analyses. 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 I 2 value. Forest plots were used for graphical description of the results. Also, the "metacum" command was used for cumulative meta-analysis to determine trend of basic reproductive number (R 0 ). In studies that mortality rate was reported, because of the denominator was confirmed cases, it was considered a case fatality rate (CFR). In addition, for studies that reported the median and interquartile range (IQR), the median was considered equivalent to the mean and the IQR was converted to standard deviation using the "IQR/1.35" formula. Stata 14 was used for all statistical analyses. Having assessed the quality of relevant studies, 76 observational studies up to 23 March, 2020 were included in this study (Follow diagram). The majority of studies were done in Wuhan, China. Detailed information of the eligible studies and their characteristics has been shown in Appendix 1 (12,17,18,20-92). -The overall basic reproductive number (R 0 ) by country and peer review status Total: The overall R 0 was 2.99 (95% CI: 2.71-3.27) for COVID-19 ( Table 1) . . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 6, 2020. . https://doi.org/10.1101/2020.05.02.20088385 doi: medRxiv preprint Country: The overall R 0 was 3.23, 1.19, 3.6 and 2.35 for China, Singapore, Iran and Japan, respectively ( Table 1) . The overall R 0 was 2.75 and 3.08 for peer reviewed and not peer reviewed articles, respectively ( Table 1) . -The 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 random effect model, the overall SI was 4.46 and 4.64 days for China and Singapore, respectively (Error! Reference source not found.). Peer review status: The overall SI was 5.3 and 4.39 days for peer reviewed and not peer reviewed articles, respectively (Error! Reference source not found.2). -The 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 not peer reviewed articles, respectively (Error! Reference source not found.3). -The overall incubation period by peer review status Total: The overall incubation period was 4.24 days (95% CI: 3.03-5.44) for COVID-19. Peer review status: The overall incubation period was 4.03 and 5.82 days for peer reviewed and not peer reviewed articles, respectively ( Table 1) . The overall estimation for growth rate and case fatality rate for COVID-19 were 0.38% and 3.29%, respectively (Table 1 & Fig 4) . In addition, the overall time from symptom onset to hospitalization was 5.09 days for COVID-19 ( Table 1) . . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 6, 2020. Based on the cumulative meta-analysis, the trend of R 0 had been increasing at first and, then, decreasing in March. Records screened based on title and abstract (n =111) . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 6, 2020. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 6, 2020. . https://doi.org/10.1101/2020.05.02.20088385 doi: medRxiv preprint reviewed some published papers. Therefore, we tried to calculate the pooled parameters by peer review status. It should be noted that, R 0 variations to some extent might be due to different methods calculations including exponential growth method, maximum likelihood, and Bayesian timedependent method (93) (94) (95) . According to our results the pooled estimate of CFR 3.29% (95% CI: 2.78-3.81) is lower than SARS-CoV(96) and MERS-CoV (97) . Health control policies, medical standard, and detection rate could affect CFR (35). Moreover, 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) (98, 99) . Pooled estimate of incubation period using 22 studies was 4.24 days (95% CI: 3.03, 5.44). Valid and precise estimate of incubation period has a pivotal role for duration of quarantine (50) . In fact, knowledge about incubation period is useful for surveillance and control approaches, also modeling and monitoring activities (100). 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). The doubling time has an important implication for predicting epidemic. Generally, social distancing, quarantine, and active surveillance are needed to reduce transmission and consequently extend the doubling time (101) . Moreover, the authors tried to estimate pooled measures for growth rate and serial interval. These two epidemiological parameters are used to estimate reproduction number (102) . As a limitation, all 76 studies (except for one, Mirjam E Kretzschmar et al) (103) have been conducted in Asia, particularly in Wuhan, China. Some epidemiological parameters in Europe, Africa, and America could be different based on control strategies. Hence, distribution of these . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 6, 2020. . https://doi.org/10.1101/2020.05.02.20088385 doi: medRxiv preprint epidemiological parameters could be more globally. Future studies to calculate more generalized pooled estimates, using studies all over the world, would be recommended. Calculating the pooled estimate of the epidemiological parameters of COVID-19 as an emerging disease, could reveal epidemiological features of the disease that consequently pave the way for health policy makers to think more about control strategies. We would like to appreciate all those researchers who helped us to conduct this study. This study was supported by School of Public Health and Safety, Shahid Beheshti University of Medical Sciences grant number 23149. The funding agency did not play any role in the planning, conduct, and reporting or in the decision to submit the paper for publication. The authors declare that they have no competing interests. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 6, 2020. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 6, 2020. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. 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