Sentinel Event Surveillance to Estimate Total Sars-CoV-2 Infections, United States

This article has been Reviewed by the following groups

Read the full article See related articles

Abstract

Human infections with a novel coronavirus (SARS-CoV-2) were first identified via syndromic surveillance in December of 2019 in Wuhan China. Since identification, infections (coronavirus disease-2019; COVID-19) caused by this novel pathogen have spread globally, with more than 180,000 confirmed cases as of March 16, 2020. Effective public health interventions, including social distancing, contact tracing, and isolation/quarantine rely on the rapid and accurate identification of confirmed cases. However, testing capacity (having sufficient tests and laboratory throughput) to support these non-pharmaceutical interventions remains a challenge for containment and mitigation of COVID-19 infections.

We undertook a sentinel event strategy (where single health events signal emerging trends) to estimate the incidence of COVID-19 in the US. Data from a recent national conference, the Conservative Political Action Conference, (CPAC) near Washington, DC and from the outbreak in Wuhan, China were used to fit a simple exponential growth model to estimate the total number of incident SARS-CoV-2 infections in the United States on March 1, 2020, and to forecast subsequent infections potentially undetected by current testing strategies. Our analysis and forecasting estimates a total of 54,100 SARS-CoV-2 infections (80 % CI 5,600 to 125,300) have occurred in the United States to March 12, 2020.

Our forecast predicts that a very substantial number of infections are undetected, and without extensive and far-reaching non-pharmaceutical interventions, the number of infections should be expected to grow at an exponential rate.

Article activity feed

  1. SciScore for 10.1101/2020.03.17.20037648: (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: Thank you for sharing your code and data.


    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.

    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.