Literature DB >> 32191173

Serial Interval of COVID-19 among Publicly Reported Confirmed Cases.

Zhanwei Du, Xiaoke Xu, Ye Wu, Lin Wang, Benjamin J Cowling, Lauren Ancel Meyers.   

Abstract

We estimate the distribution of serial intervals for 468 confirmed cases of coronavirus disease reported in China as of February 8, 2020. The mean interval was 3.96 days (95% CI 3.53-4.39 days), SD 4.75 days (95% CI 4.46-5.07 days); 12.6% of case reports indicated presymptomatic transmission.

Entities:  

Keywords:  2019 novel coronavirus disease; COVID-19; China; SARS-CoV-2; Wuhan; coronavirus; epidemiology; respiratory infections; serial interval; severe acute respiratory syndrome coronavirus 2; viruses; zoonoses

Mesh:

Year:  2020        PMID: 32191173      PMCID: PMC7258488          DOI: 10.3201/eid2606.200357

Source DB:  PubMed          Journal:  Emerg Infect Dis        ISSN: 1080-6040            Impact factor:   6.883


Key aspects of the transmission dynamics of coronavirus disease (COVID-19) remain unclear (). The serial interval of COVID-19 is defined as the time duration between a primary case-patient (infector) having symptom onset and a secondary case-patient (infectee) having symptom onset (). The distribution of COVID-19 serial intervals is a critical input for determining the basic reproduction number (R0) and the extent of interventions required to control an epidemic (). To obtain reliable estimates of the serial interval, we obtained data on 468 COVID-19 transmission events reported in mainland China outside of Hubei Province during January 21–February 8, 2020. Each report consists of a probable date of symptom onset for both the infector and infectee, as well as the probable locations of infection for both case-patients. The data include only confirmed cases compiled from online reports from 18 provincial centers for disease control and prevention (https://github.com/MeyersLabUTexas/COVID-19). Fifty-nine of the 468 reports indicate that the infectee had symptoms earlier than the infector. Thus, presymptomatic transmission might be occurring. Given these negative-valued serial intervals, COVID-19 serial intervals seem to resemble a normal distribution more than the commonly assumed gamma or Weibull distributions (,), which are limited to positive values (Appendix). We estimate a mean serial interval for COVID-19 of 3.96 (95% CI 3.53–4.39) days, with an SD of 4.75 (95% CI 4.46–5.07) days, which is considerably lower than reported mean serial intervals of 8.4 days for severe acute respiratory syndrome () to 14.6 days () for Middle East respiratory syndrome. The mean serial interval is slightly but not significantly longer when the index case is imported (4.06 [95% CI 3.55–4.57] days) versus locally infected (3.66 [95% CI 2.84–4.47] days), but slightly shorter when the secondary transmission occurs within the household (4.03 [95% CI 3.12–4.94] days) versus outside the household (4.56 [95% CI 3.85–5.27] days) (Figure). Combining these findings with published estimates for the early exponential growth rate COVID-19 in Wuhan (), we estimate an R0 of 1.32 (95% CI 1.16–1.48) (), which is lower than published estimates that assume a mean serial interval exceeding 7 days (,).
Figure

Estimated serial interval distribution for coronavirus disease (COVID-19) based on 468 reported transmission events, China, January 21–February 8, 2020. A) All infection events (N = 468) reported across 93 cities of mainland China as of February 8, 2020; B) the subset infection events (n = 122) in which both the infector and infectee were infected in the reporting city (i.e., the index patient’s case was not an importation from another city). Gray bars indicate the number of infection events with specified serial interval, and blue lines indicate fitted normal distributions. Negative serial intervals (left of the vertical dotted lines) suggest the possibility of COVID-19 transmission from asymptomatic or mildly symptomatic case-patients.

Estimated serial interval distribution for coronavirus disease (COVID-19) based on 468 reported transmission events, China, January 21–February 8, 2020. A) All infection events (N = 468) reported across 93 cities of mainland China as of February 8, 2020; B) the subset infection events (n = 122) in which both the infector and infectee were infected in the reporting city (i.e., the index patient’s case was not an importation from another city). Gray bars indicate the number of infection events with specified serial interval, and blue lines indicate fitted normal distributions. Negative serial intervals (left of the vertical dotted lines) suggest the possibility of COVID-19 transmission from asymptomatic or mildly symptomatic case-patients. These estimates reflect reported symptom onset dates for 752 case-patients from 93 cities in China, who range in age from 1 to 90 years (mean 45.2 years, SD 17.21 years). Recent analyses of putative COVID-19 infector–infectee pairs from several countries have indicated average serial intervals of 4.0 days (95% CI 3.1–4.9 days; n = 28; unpub. data, H. Nishiura et al., unpub. data, https://doi.org/10.1101/2020.02.03.20019497), 4.4 days (95% CI 2.9–6.7 days, n = 21; S. Zhao et al., unpub. data, https://doi.org/10.1101/2020.02.21.20026559], and 7.5 days (95% CI 5.3–19, n = 6; 8). Whereas none of these studies report negative serial intervals in which the infectee had symptoms before the infector, 12.6% of the serial intervals in our sample were negative. We note 4 potential sources of bias. First, the data are restricted to online reports of confirmed cases and therefore might be biased toward more severe cases in areas with a high-functioning healthcare and public health infrastructure. The rapid isolation of such case-patients might have prevented longer serial intervals, potentially shifting our estimate downward compared with serial intervals that might be observed in an uncontrolled epidemic. Second, the distribution of serial intervals varies throughout an epidemic; the time between successive cases contracts around the epidemic peak (). A susceptible person is likely to become infected more quickly if they are surrounded by 2 infected persons instead of 1. Because our estimates are based primarily on transmission events reported during the early stages of outbreaks, we do not explicitly account for such compression and interpret the estimates as basic serial intervals at the outset of an epidemic. However, if some of the reported infections occurred amid growing clusters of cases, then our estimates might reflect effective (compressed) serial intervals that would be expected during a period of epidemic growth. Third, the identity of each infector and the timing of symptom onset were presumably based on individual recollection of past events. If recall accuracy is impeded by time or trauma, case-patients might be more likely to attribute infection to recent encounters (short serial intervals) over past encounters (longer serial intervals). In contrast, the reported serial intervals might be biased upward by travel-related delays in transmission from primary case-patients that were infected in Wuhan or another city before returning home. If their infectious period started during travel, then we might be unlikely to observe early transmission events with shorter serial intervals. The mean serial interval is slightly higher for the 218 of 301 unique infectors reported to have imported cases. Given the heterogeneity in type and reliability of these sources, we caution that our findings should be interpreted as working hypotheses regarding the infectiousness of COVID-19, requiring further validation. The potential implications for COVID-19 control are mixed. Although our lower estimates for R0 suggest easier containment, the large number of reported asymptomatic transmission events is concerning.

Appendix

Additional information about serial interval of COVID-19 among publicly reported confirmed cases.
  9 in total

1.  Transmission dynamics and control of severe acute respiratory syndrome.

Authors:  Marc Lipsitch; Ted Cohen; Ben Cooper; James M Robins; Stefan Ma; Lyn James; Gowri Gopalakrishna; Suok Kai Chew; Chorh Chuan Tan; Matthew H Samore; David Fisman; Megan Murray
Journal:  Science       Date:  2003-05-23       Impact factor: 47.728

2.  A note on generation times in epidemic models.

Authors:  Ake Svensson
Journal:  Math Biosci       Date:  2006-11-09       Impact factor: 2.144

3.  How generation intervals shape the relationship between growth rates and reproductive numbers.

Authors:  J Wallinga; M Lipsitch
Journal:  Proc Biol Sci       Date:  2007-02-22       Impact factor: 5.349

4.  Generation interval contraction and epidemic data analysis.

Authors:  Eben Kenah; Marc Lipsitch; James M Robins
Journal:  Math Biosci       Date:  2008-02-29       Impact factor: 2.144

5.  Outbreaks of Middle East Respiratory Syndrome in Two Hospitals Initiated by a Single Patient in Daejeon, South Korea.

Authors:  Sun Hee Park; Yeon-Sook Kim; Younghee Jung; Soo Young Choi; Nam-Hyuk Cho; Hye Won Jeong; Jung Yeon Heo; Ji Hyun Yoon; Jacob Lee; Shinhye Cheon; Kyung Mok Sohn
Journal:  Infect Chemother       Date:  2016-06-30

6.  The estimation of SARS incubation distribution from serial interval data using a convolution likelihood.

Authors:  Anthony Y C Kuk; Stefan Ma
Journal:  Stat Med       Date:  2005-08-30       Impact factor: 2.373

7.  Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia.

Authors:  Qun Li; Xuhua Guan; Peng Wu; Xiaoye Wang; Lei Zhou; Yeqing Tong; Ruiqi Ren; Kathy S M Leung; Eric H Y Lau; Jessica Y Wong; Xuesen Xing; Nijuan Xiang; Yang Wu; Chao Li; Qi Chen; Dan Li; Tian Liu; Jing Zhao; Man Liu; Wenxiao Tu; Chuding Chen; Lianmei Jin; Rui Yang; Qi Wang; Suhua Zhou; Rui Wang; Hui Liu; Yinbo Luo; Yuan Liu; Ge Shao; Huan Li; Zhongfa Tao; Yang Yang; Zhiqiang Deng; Boxi Liu; Zhitao Ma; Yanping Zhang; Guoqing Shi; Tommy T Y Lam; Joseph T Wu; George F Gao; Benjamin J Cowling; Bo Yang; Gabriel M Leung; Zijian Feng
Journal:  N Engl J Med       Date:  2020-01-29       Impact factor: 176.079

8.  Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study.

Authors:  Joseph T Wu; Kathy Leung; Gabriel M Leung
Journal:  Lancet       Date:  2020-01-31       Impact factor: 79.321

9.  Epidemiological research priorities for public health control of the ongoing global novel coronavirus (2019-nCoV) outbreak.

Authors:  Benjamin J Cowling; Gabriel M Leung
Journal:  Euro Surveill       Date:  2020-02-11
  9 in total
  215 in total

Review 1.  Epidemiological Measures in the Context of the COVID-19 Pandemic.

Authors:  Emilio Gianicolo; Nicola Riccetti; Maria Blettner; André Karch
Journal:  Dtsch Arztebl Int       Date:  2020-05-08       Impact factor: 5.594

2.  Projecting demand for critical care beds during COVID-19 outbreaks in Canada.

Authors:  Affan Shoukat; Chad R Wells; Joanne M Langley; Burton H Singer; Alison P Galvani; Seyed M Moghadas
Journal:  CMAJ       Date:  2020-04-08       Impact factor: 8.262

3.  Slight reduction in SARS-CoV-2 exposure viral load due to masking results in a significant reduction in transmission with widespread implementation.

Authors:  Bryan T Mayer; Joshua T Schiffer; Ashish Goyal; Daniel B Reeves; Niket Thakkar; Mike Famulare; E Fabián Cardozo-Ojeda
Journal:  Sci Rep       Date:  2021-06-04       Impact factor: 4.996

4.  Transmission dynamics and control of two epidemic waves of SARS-CoV-2 in South Korea.

Authors:  Sukhyun Ryu; Sheikh Taslim Ali; Eunbi Noh; Dasom Kim; Eric H Y Lau; Benjamin J Cowling
Journal:  BMC Infect Dis       Date:  2021-05-26       Impact factor: 3.090

5.  Impact of reduction of susceptibility to SARS-CoV-2 on epidemic dynamics in four early-seeded metropolitan regions.

Authors:  Thomas J Barrett; Karen C Patterson; Timothy M James; Peter Krüger
Journal:  Sci Rep       Date:  2021-06-09       Impact factor: 4.379

6.  Key questions for modelling COVID-19 exit strategies.

Authors:  Robin N Thompson; T Déirdre Hollingsworth; Valerie Isham; Daniel Arribas-Bel; Ben Ashby; Tom Britton; Peter Challenor; Lauren H K Chappell; Hannah Clapham; Nik J Cunniffe; A Philip Dawid; Christl A Donnelly; Rosalind M Eggo; Sebastian Funk; Nigel Gilbert; Paul Glendinning; Julia R Gog; William S Hart; Hans Heesterbeek; Thomas House; Matt Keeling; István Z Kiss; Mirjam E Kretzschmar; Alun L Lloyd; Emma S McBryde; James M McCaw; Trevelyan J McKinley; Joel C Miller; Martina Morris; Philip D O'Neill; Kris V Parag; Carl A B Pearson; Lorenzo Pellis; Juliet R C Pulliam; Joshua V Ross; Gianpaolo Scalia Tomba; Bernard W Silverman; Claudio J Struchiner; Michael J Tildesley; Pieter Trapman; Cerian R Webb; Denis Mollison; Olivier Restif
Journal:  Proc Biol Sci       Date:  2020-08-12       Impact factor: 5.349

7.  Physical distancing implementation, ambient temperature and Covid-19 containment: An observational study in the United States.

Authors:  Cui Guo; Shin Heng Teresa Chan; Changqing Lin; Yiqian Zeng; Yacong Bo; Yumiao Zhang; Shakhaoat Hossain; Jimmy W M Chan; David W Yeung; Alexis K H Lau; Xiang Qian Lao
Journal:  Sci Total Environ       Date:  2021-05-21       Impact factor: 7.963

8.  Using a household-structured branching process to analyse contact tracing in the SARS-CoV-2 pandemic.

Authors:  Martyn Fyles; Elizabeth Fearon; Christopher Overton; Tom Wingfield; Graham F Medley; Ian Hall; Lorenzo Pellis; Thomas House
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2021-05-31       Impact factor: 6.237

9.  Demographic and territorial characteristics of COVID-19 cases and excess mortality in the European Union during the first wave.

Authors:  Anne Goujon; Fabrizio Natale; Daniela Ghio; Alessandra Conte
Journal:  J Popul Res (Canberra)       Date:  2021-05-29

10.  Estimates of regional infectivity of COVID-19 in the United Kingdom following imposition of social distancing measures.

Authors:  Robert Challen; Krasimira Tsaneva-Atanasova; Martin Pitt; Tom Edwards; Luke Gompels; Lucas Lacasa; Ellen Brooks-Pollock; Leon Danon
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2021-05-31       Impact factor: 6.237

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