Molecular Evidence of Coinfection with Acute Respiratory Viruses and High Prevalence of SARS-CoV-2 among Patients Presenting Flu-Like Illness in Bukavu City, Democratic Republic of Congo

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Abstract

The coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with clinical manifestation cases that are almost similar to those of common respiratory viral infections. This study determined the prevalence of SARS-CoV-2 and other acute respiratory viruses among patients with flu-like symptoms in Bukavu city, Democratic Republic of Congo. We screened 1352 individuals with flu-like illnesses seeking treatment in 10 health facilities. Nasopharyngeal swab specimens were collected to detect SARS-CoV-2 using real-time reverse transcription-polymerase chain reaction (RT-PCR), and 10 common respiratory viruses were detected by multiplex reverse transcription-polymerase chain reaction assay. Overall, 13.9% (188/1352) of patients were confirmed positive for SARS-CoV-2. Influenza A 5.6% (56/1352) and Influenza B 0.9% (12/1352) were the most common respiratory viruses detected. Overall, more than two cases of the other acute respiratory viruses were detected. Frequently observed symptoms associated with SARS-CoV-2 positivity were shivering (47.8%; OR = 1.8; CI: 0.88–1.35), cough (89.6%; OR = 6.5, CI: 2.16–28.2), and myalgia and dizziness (59.7%; OR = 2.7; CI: 1.36–5.85). Moreover, coinfection was observed in 12 (11.5%) specimens. SARS-CoV-2 and influenza A were the most cooccurring infections, accounting for 33.3% of all positive cases. This study demonstrates cases of COVID-19 infections cooccurring with other acute respiratory infections in Bukavu city during the ongoing outbreak of COVID-19. Therefore, testing for respiratory viruses should be performed in all patients with flu-like symptoms for effective surveillance of the transmission patterns in the COVID-19 affected areas for optimal treatment and effective disease management.

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  1. SciScore for 10.1101/2021.10.16.21265044: (What is this?)

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

    Table 1: Rigor

    EthicsConsent: Only individuals with flu-like disease symptoms, aged ten years and above, and able to give consent were considered for sample collection the interview.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    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.

    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.