COVID-19Predict – Predicting Pandemic Trends

  1. Jürgen Bosch
  2. Austin Wilson
  3. Karthik O’Neil
  4. Peter A. Zimmerman

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Abstract

Background

Given the global public health importance of the COVID-19 pandemic, data comparisons that predict on-going infection and mortality trends across national, state and county-level administrative jurisdictions are vitally important. We have designed a COVID-19 dashboard with the goal of providing concise sets of summarized data presentations to simplify interpretation of basic statistics and location-specific current and short-term future risks of infection.

Methods

We perform continuous collection and analyses of publicly available data accessible through the COVID-19 dashboard hosted at Johns Hopkins University (JHU github). Additionally, we utilize the accumulation of cases and deaths to provide dynamic 7-day short-term predictions on these outcomes across these national, state and county administrative levels.

Findings

COVID-19Predict produces 2,100 daily predictions [or calculations] on the state level (50 States x3 models x7 days x2 cases and deaths) and 131,964 (3,142 Counties x3 models x7 days x2 cases and deaths) on the county level. To assess how robust our models have performed in making short-term predictions over the course of the pandemic, we used available case data for all 50 U.S. states spanning the period January 20 - August 16 2020 in a retrospective analysis. Results showed a 3.7% to −0.2% mean error of deviation from the actual case predictions to date.

Interpretation

Our transparent methods and admin-level visualizations provide real-time data reporting and forecasts related to on-going COVID-19 transmission allowing viewers (individuals, health care providers, public health practitioners and policy makers) to develop their own perspectives and expectations regarding public life activity decisions.

Funding

Financial resources for this study have been provided by Case Western Reserve University.

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  1. ScreenIT

    SciScore for 10.1101/2020.09.09.20191593: (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 analysis of historical data: All statistical analysis for this manuscript was carried out using GraphPad Prism 8.4.3.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)

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

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Both approaches emphasize that estimating the number of infected individuals is made very challenging for COVID-19 tracking because (1) the high number of SARS-CoV-2-infected individuals who are asymptomatic and do not get tested and (2) limitations to diagnostic testing. Overall, we have observed a range in aCFR among the 50 states from 0.718 to 8.45 and a range among the 3,142 counties from 0 to 83.6. We interpret the ratio of the aCFR to represent a multiplier to more accurately predict the number of SARSCoV-2-infected individuals in an administrative level when compared with more broadly calculated CFRs (current CFRs calculated by the United States CDC = 0.68%, median international CFR - 0.27%)20,22. Therefore, if the current aCFR is 6.8%, this would be 10-fold higher than the CDC calculated CFR and 25.1-fold higher than the international CFR. This may suggest that given 1,000 reported cases within the admin level the actual number of cases could be between 10,000 and 24,000. Alternatively, a local aCFR that is higher than expected, could signal the emergence of new, more serious cases or indicate that a local health care delivery system was failing its patient population. Further evaluation of the data, the analytical summaries, and the 7-day forecasting methods has been performed in developing COVID-19Predict to assess the variability of the predictions and observed outcomes (Figure 2, Supplemental Figures S1, Supplemental Table 1). To do so we performed identical calcu...

    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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  2. Version published to 10.1101/2020.09.09.20191593v1 on medRxiv