COVID-19 case management: The policy model in Morocco context

  1. Issam Bennis

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Abstract

Background

Based on the updated scientific evidence around SARS-CoV-2 diagnostic, health policymakers had to consider that many decisions could enhance or limit the success of the overall COVID-19 control strategy. The purpose of this study is to share alternative COVID-19 case management based on the updated international knowledge.

Methods

This study presents the main information about COVID-19 case management in Morocco from March to October 2020. The NVivo qualitative model content analysis was used to compare and prioritize health decisions with updated scientific evidence.

Results

The lack of molecular diagnostic accuracy using the interpretation of cycles quantification values, was targeted only by allowing all private laboratories to do RT qPCR. However, there is an urgent need for standardisation with accurate molecular SARS-CoV-2 thermocyclers and kits that notify systematic cycles quantification and do more tests per days to control the spread effectively. A predictive tree of the cycle’s range is presented following three steps: 1) the initial clinical definition, 2) the molecular confirmation, 3) and the diagnostic follows up results of the RT qPCR up to 28 days after the onset. At the same time, the seasonal vaccination against influenza and pneumonia could help to reduce COVID-19 deaths.

Conclusions

Until an available SARS-CoV-2 specific vaccine and/or curative effective treatment, updated control strategy in Morocco and similar context countries require to target population living in highly COVID-19 epidemic cities or areas by mass testing with the right interpretation of PCR values changes, associated to seasonal vaccination to foster the immunity.

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  1. ScreenIT

    SciScore for 10.1101/2020.11.07.20227603: (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
    The trends were analysed in Excel Microsoft Software and presented by figures.
    Excel Microsoft
    suggested: None

    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 10.1101/2020.11.07.20227603v1 on medRxiv