Geographical surveillance of COVID-19: Diagnosed cases and death in the United States

  1. Raid Amin
  2. Terri Hall
  3. Jacob Church
  4. Daniela Schlierf
  5. Martin Kulldorff

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Abstract

Background

COVID-19 is a new coronavirus that has spread from person to person throughout the world. Geographical disease surveillance is a powerful tool to monitor the spread of epidemics and pandemic, providing important information on the location of new hot-spots, assisting public health agencies to implement targeted approaches to minimize mortality.

Methods

County level data from January 22-April 28 was downloaded from USAfacts.org to create heat maps with ArcMap™ for diagnosed COVID-19 cases and mortality. The data was analyzed using spatial and space-time scan statistics and the SaTScan™ software, to detect geographical cluster with high incidence and mortality, adjusting for multiple testing. Analyses were adjusted for age. While the spatial clusters represent counties with unusually high counts of COVID-19 when averaged over the time period January 22-April 20, the space-time clusters allow us to identify groups of counties in which there exists a significant change over time.

Results

There were several statistically significant COVID-19 clusters for both incidence and mortality. Top clusters with high rates included the areas in and around New York City, New Orleans and Chicago, but there were also several small rural clusters. Top clusters for a recent surge in incidence and mortality included large parts of the Midwest, the Mid-Atlantic Region, and several smaller areas in and around New York and New England.

Conclusions

Spatial and space-time surveillance of COVID-19 can be useful for public health departments in their efforts to minimize mortality from the disease. It can also be applied to smaller regions with more granular data.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.22.20110155: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    RandomizationP-values were calculated by fitting the Gumbel distribution to 999 random data sets generated under the null hypothesis of equal risk throughout the country. [10] The Prospective Space-Time Scan Statistic: To detect recent increases, a prospective space-time cluster analysis was performed for both COVID-19 cases and for mortality.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    The diagnosis and death data have different strength and weaknesses. The diagnosis data has the major weakness, in that the rates both depend on the presence of the disease as well as on the amount of testing. For a cluster to be detected, both are required. Hence, the analysis may have missed high incidence areas with low levels of testing. The mortality data is less dependent on testing, and it is reassuring that the two data sets produce somewhat similar results. One advantage of the diagnosis analyses is that it provides earlier indications about COVID-19 activity since the disease is typically diagnosed before death. An even earlier indication of COVID-19 activity may be syndromic data on influenza like illness, and spatial scan statistics have also been applied to such data. [12] It is popular to compare COVID-19 mortality between countries and regions, but most such comparisons do not adjust for age, which is critical since COVID-19 is highly age dependent. It is also critical to note that some countries and regions are further along to reach herd immunity, and it is not necessarily the areas with fewer deaths that will have the fewest deaths by the time the pandemic is over. This study applies modern disease surveillance methodology in addition to using choropleth maps that clearly show the relative ranking of counties’ virus cases and deaths. While the descriptive choropleth maps give an overall view of the contiguous US regarding the relative standings of counties f...

    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

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  2. Version published to 10.1101/2020.05.22.20110155v1 on medRxiv