A mathematical model to estimate percentage secondary infections from margin of error of diagnostic sensitivity: Useful tool for regulatory agencies to assess the risk of propagation due to false negative outcome of diagnostics

  1. P Azhahianambi
  2. K Karthik
  3. RP Aravindh Babu
  4. TMA Senthil kumar
  5. C Balachandran
  6. G Dhinakar Raj

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Abstract

False negative outcome of a diagnosis is one the major reasons for the dissemination of the diseases with high risk of propagation. Diagnostic sensitivity and the margin of error determine the false negative outcome of the diagnosis. A mathematical model had been developed to estimate the mean % secondary infections based on the margin of error of diagnostic sensitivity, % prevalence and R 0 value. This model recommends a diagnostic test with diagnostic sensitivity ≥ 96% and at least 92% lower bound limit of the 95% CI or ≤ 4% margin of error for a highly infectious diseases like COVID-19 to curb the secondary transmission of the infection due to false negative cases. Positive relationship was found between mean % secondary infection and margin of error of sensitivity suggesting greater the margin of error of a diagnostic test sensitivity, higher the number of secondary infections in a population due to false negative cases. Negative correlation was found between number of COVID-19 test kits (>90% sensitivity) with regulatory approval and margin of error (R= −0.92, p =0.023) suggesting lesser the margin of error of a diagnostic test, higher the chances of getting approved by the regulatory agencies. However, there are no specific regulatory standards available for margin of error of the diagnostic sensitivity of COVID-19 diagnostic tests. Highly infectious disease such as COVID-19, certainly need specific regulatory standards on margin of error or 95% CI of the diagnostic sensitivity to curb the dissemination of the disease due to false negative cases and our model can be used to set the standards such as sensitivity, margin of error or lower bound limit of 95% CI.

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    SciScore for 10.1101/2021.01.29.21250804: (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

    Antibodies
    SentencesResources
    COVID-19 diagnostics data: The data on antibody, antigen and Nucleic Acid Amplification Test (NAAT) based COVID-19 diagnostic kits, diagnostic sensitivity and specificity of the kits, margin of error and regulatory approval status of the COVID-19 diagnostic kits were obtained from finddx.com.
    antibody , antigen and Nucleic Acid Amplification Test ( NAAT
    suggested: None
    Software and Algorithms
    SentencesResources
    2, GraphPad prism 7.0 and MS excel.
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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.

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  2. Version published to 10.1101/2021.01.29.21250804 on medRxiv