Comparison of spatiotemporal characteristics of the COVID-19 and SARS outbreaks in mainland China

  1. Xi Zhang
  2. Huaxiang Rao
  3. Yuwan Wu
  4. Yubei Huang
  5. Hongji Dai

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Abstract

Background: Both coronavirus disease 2019 (COVID-19) and severe acute respiratory syndrome (SARS) are caused by coronaviruses and have infected people in China and worldwide. We aimed to investigate whether COVID-19 and SARS exhibited similar spatial and temporal features at provincial level in mainland China. Methods: The number of people infected by COVID-19 and SARS were extracted from daily briefings on newly confirmed cases during the epidemics, as of Mar. 4, 2020 and Aug. 3, 2003, respectively. We depicted spatiotemporal patterns of the COVID-19 and SARS epidemics using spatial statistics such as Moran’s I and the local indicators of spatial association (LISA). Results: Compared to SARS, COVID-19 had a higher overall incidence. We identified 3 clusters (predominantly located in south-central China; the highest RR=135.08, 95% CI: 128.36-142.08) for COVID-19 and 4 clusters (mainly in Northern China; the highest RR=423.51, 95% CI: 240.96-722.32) for SARS. Fewer secondary clusters were identified after the "Wuhan lockdown". The LISA cluster map detected a significantly high-low (Hubei) and low-high spatial clustering (Anhui, Hunan, and Jiangxi, in Central China) for COVID-19. Two significant high-high (Beijing and Tianjin) and low-high (Hebei) clusters were detected for SARS. Conclusions: COVID-19 and SARS outbreaks exhibited distinct spatiotemporal clustering patterns at the provincial levels in mainland China, which may be attributable to changes in social and demographic factors, local government containment strategies or differences in transmission mechanisms.

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  1. Version published to 10.21203/rs.3.rs-25761/v3 on Research Square
  2. Version published to 10.1186/s12879-020-05537-y
  3. Version published to 10.21203/rs.3.rs-25761/v2 on Research Square
  4. ScreenIT

    SciScore for 10.1101/2020.03.23.20034058: (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

    Software and Algorithms
    SentencesResources
    Statistical analyses: We used ArcGIS software v10.2.2 (ESRI Inc., Redlands, CA, USA) to depict the spatial distribution and perform global and local spatial autocorrelation analyses.
    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.

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  5. Version published to 10.21203/rs.3.rs-25761/v1 on Research Square
  6. Version published to 10.1101/2020.03.23.20034058v1 on medRxiv