A Tempo-geographic Analysis of Global COVID-19 Epidemic Outside of China

  1. Huipeng Liao
  2. Gifty Marley
  3. Yafei Si
  4. Zaisheng Wang
  5. Yewei Xie
  6. Cheng Wang
  7. Weiming Tang

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Background

Understanding the global epidemic trends, geographic distribution, and transmission patterns of COVID-19 contribute to providing timely information for the global response of the epidemic. This study aims to understand the global pandemic geospatial patterns and trends and identify new epicenters requiring urgent attention.

Methods

Data on COVID-19 between 31 st Dec. 2019 and 14 th Mar. 2020 was included. The epidemic trend was analyzed using joinpoint regressions; the growth of affected countries was by descriptive analysis; and the global distribution and transmission trend by spatial analysis. Findings: The number of new cases in the regions outside of China slowly increased before 24 th Feb. and rapidly accelerated after 24 th Feb. Compared to China, other affected countries experienced a longer duration of a slow increase at the early stage and rapid growth at the latter stages. The first apparent increase in the number of affected countries occurred from 23 rd Jan to 1 st Feb, and the second apparent increase started from 25 th Feb. The fist COVID-19 cases reported by countries from 28 th Feb. were mainly imported from Europe. The geographic distribution changed from single-center (13 th Jan. - 20 th Feb.) to multi-centers pattern (20 th Feb. – 14 th Mar.). More countries were affected with COVID-19 and developed local transmission.

Interpretation

The joinpoint regression and geospatial analysis indicated a multi-center pandemic of COVID-19. Strategies to prevent the new multiple centers as well as prevent ongoing transmission are needed.

Funding

NIH.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.03.20.20039602: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The spatial data were derived from GADM version 3.6.[10] All the data were publicly available.
    GADM
    suggested: None
    The geographic outputs were generated by ArcGIS 10□2 software (Esri Inc, Redlands, California).
    ArcGIS
    suggested: (ArcGIS for Desktop Basic, RRID:SCR_011081)

    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:
    This study has some limitations. First, the data collected were from publicly available datasets. They lacked detailed epidemiological information as well as the detailed geographic information on patients for further assessing the potential driving forces as well as more detailed geographic patterns of the pandemic. Secondly, our analysis is lagged by a time gap between when a suspected case identification and case confirmation, but it does provide time-sensitive and evidence-based information to aid in further response to the epidemic. Finally, due to constraints of detection capability and technique, the underreporting of cases especially in developing countries may result in underestimation of the study.

    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 10.1101/2020.03.20.20039602v2 on medRxiv
  3. Version published to 10.1101/2020.03.20.20039602v1 on medRxiv