A Novel Indicator for COVID-19 Pandemic Assessment and Comparison

  1. Kanchan Mukherjee

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Abstract

Introduction

In an increasingly globalized world, no country can remain immune to the effect of the COVID-19 pandemic. The pandemic has exposed the need for effective public health surveillance in the interest of global health security. However, current indicators are limited in doing a comparative intercountry assessment and comparison because of variation in testing rates and reporting standards. Hence, this study attempts at addressing this gap.

Methods

The study proposes incremental change in cases per testing rate (ICTR) as an indicator for doing cross country comparison of the pandemic progress. The equation for calculating this indicator is explained in this study. This is followed by measuring its strength of association and predictive power for determining change in SARS-CoV-2 cases in five countries (USA, UK, India, Pakistan and Bangladesh).

Results and discussion

ICTR was found to have a significantly higher strength of association and predictive power (than the existing indicator-test positivity rate) for determining change in cases over different time periods. Using ICTR, cross country comparison was done for the five countries for15 months to draw deeper insights into the progress of the pandemic.

Conclusions

The study finds ICTR to be a suitable indicator for intercountry comparison and intracountry monitoring of the pandemic, which would be useful for global COVID-19 surveillance.

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  1. ScreenIT

    SciScore for 10.1101/2021.10.28.21265596: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power AnalysisTo test whether ICTR is a better measure than TPR for inter country comparison, the study used regression analysis to test the strength of association and predictive power of these two indicators with the change in number of SARS-CoV-2 cases, and compared the respective R, R2, F and p-values.

    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: We detected the following sentences addressing limitations in the study:
    Study limitations: The study restricts the analysis to only five countries and hence future studies using ICTR can check its validity and implications for larger set of countries.

    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.

    Results from scite Reference Check: We found no unreliable references.

    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 10.1101/2021.10.28.21265596v1 on medRxiv