Estimation of undetected symptomatic and asymptomatic cases of COVID‐19 infection and prediction of its spread in the USA

  1. Ashutosh Mahajan
  2. Ravi Solanki
  3. Namitha Sivadas

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

The reported COVID‐19 cases in the United States of America have crossed over 10 million and a large number of infected cases are undetected whose estimation can be done if country‐wide antibody testing is performed. In this study, we estimate this undetected fraction of the population by a modeling and simulation approach. We employ an epidemic model SIPHERD in which three categories of infection carriers, symptomatic, purely asymptomatic, and exposed are considered with different transmission rates that are taken dependent on the social distancing conditions, and the detection rate of the infected carriers is taken dependent on the tests done per day. The model is first validated for Germany and South Korea and then applied for prediction of the total number of confirmed, active and dead, and daily new positive cases in the United States. Our study predicts the possible outcomes of the infection if social distancing conditions are relaxed or kept stringent. We estimate that around 30.1 million people are already infected, and in the absence of any vaccine, 66.2 million (range: 64.3–68.0) people, or 20% (range: 19.4–20.5) of the population will be infected by mid‐February 21 if social distancing conditions are not made stringent. We find the infection‐to‐fatality ratio to be 0.65% (range: 0.63–0.67).

Article activity feed

  1. Version published to 10.1002/jmv.26897
  2. ScreenIT

    SciScore for 10.1101/2020.06.21.20136580: (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
    fmincon returns the set of parameters within the given range, which minimizes the COST function defined above.
    COST
    suggested: (COST, RRID:SCR_014098)
    The model and the optimization scheme is implemented in MATLAB.
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)

    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.

    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.

  3. Version published to 10.1101/2020.06.21.20136580v1 on medRxiv