Serial Interval and Generation Interval for Imported and Local Infectors, Respectively, Estimated Using Reported Contact-Tracing Data of COVID-19 in China

  1. Menghui Li
  2. Kai Liu
  3. Yukun Song
  4. Ming Wang
  5. Jinshan Wu

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

The emerging virus, COVID-19, has caused a massive outbreak worldwide. Based on the publicly available contact-tracing data, we identified 509 transmission chains from 20 provinces in China and estimated the serial interval (SI) and generation interval (GI) of COVID-19 in China. Inspired by different possible values of the time-varying reproduction number for the imported cases and the local cases in China, we divided all transmission events into three subsets: imported (the zeroth generation) infecting 1st-generation locals, 1st-generation locals infecting 2nd-generation locals, and other transmissions among 2+. The corresponding SI (GI) is respectively denoted as SI 1 0 ( GI 1 0 ), SI 2 1 ( GI 2 1 ), and SI 3 + 2 + ( GI 3 + 2 + ). A Bayesian approach with doubly interval-censored likelihood is employed to fit the distribution function of the SI and GI. It was found that the estimated SI 1 0 = 6 . 52   ( 95 %   CI : 5 . 96 - 7 . 13 ) , SI 2 1 = 6 . 01   ( 95 % CI : 5 . 44 - 6 . 64 ) , SI 3 + 2 + = 4 . 39   ( 95 %   CI : 3 . 74 - 5 . 15 ) , and GI 1 0 = 5 . 47   ( 95 %   CI : 4 . 57 - 6 . 45 ) , GI 2 1 = 5 . 01   ( 95 %   CI : 3 . 58 - 7 . 06 ) , GI 3 + 2 + = 4 . 25   ( 95 %   CI : 2 . 82 - 6 . 23 ) . Thus, overall both SI and GI decrease when generation increases.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.04.15.20065946: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    There are several limitations to this study. Our data is restricted to online reports from only 10 provinces in China. The content of epidemiological investigation reports from different provinces varies a lot. Many case reports do not have exposure date and infector ID, which are quite crucial in epidemics modeling. Thus, while admitting this limitation, here we also call for designing/utilizing a standard format of the case reports, countrywide, or even worldwide. Our sample size, especially on generation interval, is still very small. Thus, our results GI are not as reliable as the ones on SI.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.3389/fpubh.2020.577431
  3. Version published to 10.1101/2020.04.15.20065946v1 on medRxiv