Sheikh Taslim Ali1,2, Amy Yeung1, Songwei Shan1,2, Lin Wang3, Huizhi Gao1, Zhanwei Du1,2, Xiao-Ke Xu4, Peng Wu1,2, Eric H Y Lau1,2, Benjamin J Cowling1,2. 1. World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region, China. 2. Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China. 3. Department of Genetics, University of Cambridge, Cambridge, United Kingdom. 4. College of Information and Communication Engineering, Dalian Minzu University, Dalian, China.
Abstract
BACKGROUND: Estimates of the serial interval distribution contribute to our understanding of the transmission dynamics of coronavirus disease 2019 (COVID-19). Here, we aimed to summarize the existing evidence on serial interval distributions and delays in case isolation for COVID-19. METHODS: We conducted a systematic review of the published literature and preprints in PubMed on 2 epidemiological parameters, namely, serial intervals and delay intervals relating to isolation of cases for COVID-19 from 1 January 2020 to 22 October 2020 following predefined eligibility criteria. We assessed the variation in these parameter estimates using correlation and regression analysis. RESULTS: Of 103 unique studies on serial intervals of COVID-19, 56 were included, providing 129 estimates. Of 451 unique studies on isolation delays, 18 were included, providing 74 estimates. Serial interval estimates from 56 included studies varied from 1.0 to 9.9 days, while case isolation delays from 18 included studies varied from 1.0 to 12.5 days, which were associated with spatial, methodological, and temporal factors. In mainland China, the pooled mean serial interval was 6.2 days (range, 5.1-7.8) before the epidemic peak and reduced to 4.9 days (range, 1.9-6.5) after the epidemic peak. Similarly, the pooled mean isolation delay related intervals were 6.0 days (range, 2.9-12.5) and 2.4 days (range, 2.0-2.7) before and after the epidemic peak, respectively. There was a positive association between serial interval and case isolation delay. CONCLUSIONS: Temporal factors, such as different control measures and case isolation in particular, led to shorter serial interval estimates over time. Correcting transmissibility estimates for these time-varying distributions could aid mitigation efforts.
BACKGROUND: Estimates of the serial interval distribution contribute to our understanding of the transmission dynamics of coronavirus disease 2019 (COVID-19). Here, we aimed to summarize the existing evidence on serial interval distributions and delays in case isolation for COVID-19. METHODS: We conducted a systematic review of the published literature and preprints in PubMed on 2 epidemiological parameters, namely, serial intervals and delay intervals relating to isolation of cases for COVID-19 from 1 January 2020 to 22 October 2020 following predefined eligibility criteria. We assessed the variation in these parameter estimates using correlation and regression analysis. RESULTS: Of 103 unique studies on serial intervals of COVID-19, 56 were included, providing 129 estimates. Of 451 unique studies on isolation delays, 18 were included, providing 74 estimates. Serial interval estimates from 56 included studies varied from 1.0 to 9.9 days, while case isolation delays from 18 included studies varied from 1.0 to 12.5 days, which were associated with spatial, methodological, and temporal factors. In mainland China, the pooled mean serial interval was 6.2 days (range, 5.1-7.8) before the epidemic peak and reduced to 4.9 days (range, 1.9-6.5) after the epidemic peak. Similarly, the pooled mean isolation delay related intervals were 6.0 days (range, 2.9-12.5) and 2.4 days (range, 2.0-2.7) before and after the epidemic peak, respectively. There was a positive association between serial interval and case isolation delay. CONCLUSIONS: Temporal factors, such as different control measures and case isolation in particular, led to shorter serial interval estimates over time. Correcting transmissibility estimates for these time-varying distributions could aid mitigation efforts.
Authors: Blanca Elena Guerrero Daboin; Italla Maria Pinheiro Bezerra; Tassiane Cristina Morais; Isabella Portugal; Jorge de Oliveira Echeimberg; André Evaristo Marcondes Cesar; Matheus Paiva Emidio Cavalcanti; Lucas Cauê Jacintho; Rodrigo Daminello Raimundo; Khalifa Elmusharaf; Carlos Eduardo Siqueira; Luiz Carlos de Abreu Journal: Int J Environ Res Public Health Date: 2022-01-20 Impact factor: 3.390
Authors: Wenrui Li; Katia Bulekova; Brian Gregor; Laura F White; Eric D Kolaczyk Journal: Philos Trans A Math Phys Eng Sci Date: 2022-08-15 Impact factor: 4.019
Authors: Min Kang; Hualei Xin; Jun Yuan; Sheikh Taslim Ali; Zimian Liang; Jiayi Zhang; Ting Hu; Eric Hy Lau; Yingtao Zhang; Meng Zhang; Benjamin J Cowling; Yan Li; Peng Wu Journal: Euro Surveill Date: 2022-03