Analysis of COVID‐19 and comorbidity co‐infection model with optimal control

  1. Andrew Omame
  2. Ndolane Sene
  3. Ikenna Nometa
  4. Cosmas I. Nwakanma
  5. Emmanuel U. Nwafor
  6. Nneka O. Iheonu
  7. Daniel Okuonghae

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Abstract

In this work, we develop and analyze a mathematical model for the dynamics of COVID‐19 with re‐infection in order to assess the impact of prior comorbidity (specifically, diabetes mellitus ) on COVID‐19 complications. The model is simulated using data relevant to the dynamics of the diseases in Lagos, Nigeria, making predictions for the attainment of peak periods in the presence or absence of comorbidity. The model is shown to undergo the phenomenon of backward bifurcation caused by the parameter accounting for increased susceptibility to COVID‐19 infection by comorbid susceptibles as well as the rate of reinfection by those who have recovered from a previous COVID‐19 infection. Simulations of the cumulative number of active cases (including those with comorbidity), at different reinfection rates, show infection peaks reducing with decreasing reinfection of those who have recovered from a previous COVID‐19 infection. In addition, optimal control and cost‐effectiveness analysis of the model reveal that the strategy that prevents COVID‐19 infection by comorbid susceptibles is the most cost‐effective of all the control strategies for the prevention of COVID‐19.

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  1. Version published to 10.1002/oca.2748
  2. ScreenIT

    SciScore for 10.1101/2020.08.04.20168013: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot 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.

    About SciScore

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  3. Version published to 10.1101/2020.08.04.20168013 on medRxiv