Estimates of serial interval for COVID-19: A systematic review and meta-analysis

• Published in: Clinical epidemiology and global health Vol. 9; pp. 157 - 161
• Main Authors: Rai, Balram, Shukla, Anandi, Dwivedi, Laxmi Kant
• Format: Journal Article
• Language: English
• Published: India Elsevier B.V 01.01.2021; Published by Elsevier, a division of RELX India, Pvt. Ltd on behalf of INDIACLEN; Elsevier
• Subjects: COVID-19; Epidemiology; Meta-analysis; Serial interval; Systematic review; COVID-19; Systematic review; Serial interval; Epidemiology; Meta-analysis
• ISSN: 2213-3984; 2452-0918; 2213-3984
• DOI: 10.1016/j.cegh.2020.08.007

On 11th 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. The present study aimed at the qualitative and quantitative synthesis of the currently available evidence for the serial interval of COVID-19. 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 I2 statistic, Cochran's Q test, Egger's test, and Beggs's test. 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 I2 statistic. There is no potential bias introduced in the meta-analysis due to small study effects. 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.