Rate Estimation and Identification of COVID-19 Infections: Towards Rational Policy Making During Early and Late Stages of Epidemics

  1. Richard Beigel
  2. Simon Kasif

Reviewed by

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

Pandemics have a profound impact on our world, causing loss of life, affecting our culture and historically shaping our genetics. The response to a pandemic requires both resilience and imagination. It has been clearly documented that obtaining an accurate estimate and trends of the actual infection rate and mortality risk are very important for policy makers and medical professionals. One cannot estimate mortality rates without an accurate assessment of the number of infected individuals in the population. This need is also aligned with identifying the infected individuals so they can be properly treated, monitored and tracked. However, accurate estimation of the infection rate, locally, geographically and nationally is important independently. These infection rate estimates can guide policy makers at both state, national or world level to achieve a better management of risk to society. The decisions facing policy makers are very different during early stages of an emerging epidemic where the infection rate is low, middle stages where the rate is rapidly climbing, and later stages where the epidemic curve has flattened to a low and relatively sustainable rate. In this paper we provide relatively efficient pooling methods to both estimate infection rates and identify infected individuals for populations with low infection rates. These estimates may provide significant cost reductions for testing in rural communities, third world countries and other situations where the cost of testing is expensive or testing is not widely available. As we prepare for the second wave of the pandemic this line of work may provide new solutions for both the biomedical community and policy makers at all levels.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.05.22.20110585: (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: 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.

  2. Version published to 10.1101/2020.05.22.20110585 on medRxiv