» Articles » PMID: 32869006

Estimates of Serial Interval for COVID-19: A Systematic Review and Meta-analysis

Overview
Specialty Public Health
Date 2020 Sep 2
PMID 32869006
Citations 54
Authors
Affiliations
Soon will be listed here.
Abstract

Background: On 11 March 2020, the World Health Organization declared COVID-19 as Pandemic. The estimation of transmission dynamics in the initial days of the outbreak of any infectious disease is crucial to control its spread in a new area. The serial interval is one of the significant epidemiological measures that determine the spread of infectious disease. It is the time interval between the onset of symptoms in the primary and secondary case.

Objective: The present study aimed at the qualitative and quantitative synthesis of the currently available evidence for the serial interval of COVID-19.

Methodology: Data on serial intervals were extracted from 11 studies following a systematic review. A meta-analysis was performed to estimate the pooled estimate of the serial interval. The heterogeneity and bias in the included studies were tested by various statistical measures and tests, including I statistic, Cochran's Q test, Egger's test, and Beggs's test.

Result: The pooled estimate for the serial interval was 5.40 (5.19, 5.61) and 5.19 (4.37, 6.02) days by the fixed and random effects model, respectively. The heterogeneity between the studies was found to be 89.9% by I statistic. There is no potential bias introduced in the meta-analysis due to small study effects.

Conclusion: The present review provides sufficient evidence for the estimate of serial interval of COVID-19, which can help in understanding the epidemiology and transmission of the disease. The information on serial interval can be useful in developing various policies regarding contact tracing and monitoring community transmission of COVID-19.

Citing Articles

Retrospective estimation of the time-varying effective reproduction number for a COVID-19 outbreak in Shenyang, China: An observational study.

Li P, Wen L, Sun B, Sun W, Chen H Medicine (Baltimore). 2024; 103(22):e38373.

PMID: 39259088 PMC: 11142808. DOI: 10.1097/MD.0000000000038373.


Analysis of the impact of COVID-19 variants and vaccination on the time-varying reproduction number: statistical methods.

Jang G, Kim J, Lee Y, Son C, Ko K, Lee H Front Public Health. 2024; 12:1353441.

PMID: 39022412 PMC: 11253806. DOI: 10.3389/fpubh.2024.1353441.


Variability in the serial interval of COVID-19 in South Korea: a comprehensive analysis of age and regional influences.

Lee H, Lee G, Kim T, Kim S, Kim H, Lee S Front Public Health. 2024; 12:1362909.

PMID: 38515590 PMC: 10955094. DOI: 10.3389/fpubh.2024.1362909.


Monitoring the reproductive number of COVID-19 in France: Comparative estimates from three datasets.

Bonaldi C, Fouillet A, Sommen C, Levy-Bruhl D, Paireau J PLoS One. 2023; 18(10):e0293585.

PMID: 37906577 PMC: 10617725. DOI: 10.1371/journal.pone.0293585.


Analysis of the effectiveness of non-pharmaceutical interventions on influenza during the Coronavirus disease 2019 pandemic by time-series forecasting.

Kim H, Min K, Cho S BMC Infect Dis. 2023; 23(1):717.

PMID: 37875817 PMC: 10594831. DOI: 10.1186/s12879-023-08640-y.


References
1.
Bi Q, Wu Y, Mei S, Ye C, Zou X, Zhang Z . Epidemiology and transmission of COVID-19 in 391 cases and 1286 of their close contacts in Shenzhen, China: a retrospective cohort study. Lancet Infect Dis. 2020; 20(8):911-919. PMC: 7185944. DOI: 10.1016/S1473-3099(20)30287-5. View

2.
Lu H, Stratton C, Tang Y . Outbreak of pneumonia of unknown etiology in Wuhan, China: The mystery and the miracle. J Med Virol. 2020; 92(4):401-402. PMC: 7166628. DOI: 10.1002/jmv.25678. View

3.
Liu Y, Gayle A, Wilder-Smith A, Rocklov J . The reproductive number of COVID-19 is higher compared to SARS coronavirus. J Travel Med. 2020; 27(2). PMC: 7074654. DOI: 10.1093/jtm/taaa021. View

4.
Assiri A, McGeer A, Perl T, Price C, Al Rabeeah A, Cummings D . Hospital outbreak of Middle East respiratory syndrome coronavirus. N Engl J Med. 2013; 369(5):407-16. PMC: 4029105. DOI: 10.1056/NEJMoa1306742. View

5.
Kenah E, Lipsitch M, Robins J . Generation interval contraction and epidemic data analysis. Math Biosci. 2008; 213(1):71-9. PMC: 2365921. DOI: 10.1016/j.mbs.2008.02.007. View