Analysis of geo-temporal evolution and modeling of the COVID-19 epidemic in Libya

  1. Amin Bredan
  2. Hani Benamer
  3. Omran Bakoush

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Abstract

Having been experiencing years of political fragmentation and military conflict, Libya was poorly prepared to meet the challenges of the COVId-19 pandemic. Nevertheless, restriction of travel and social distancing rules were put in place days before the first case was detected. During the next two months, the number of cases grew gradually to 77 cases, followed by a rapid spread that has produced 3691 confirmed cases and 80 deaths by the end of July 2020. The turning point on 26 May 2020 was preceded three weeks earlier by the arrival of the first of a series of flights repatriating Libyans who became stranded abroad when air travel was suspended. In the first weeks of the surge, the number of cases was particularly high in the less densely populated southern region, raising questions about the implementation of social distancing and other protective measures in that region. The epidemic in Libya was modeled using the classical Susceptible-Exposed-Infected-Recovered (SEIR) mathematical model of infectious disease epidemics. Three scenarios were developed based on three estimates of the fraction of the population exposed to the disease (1.5, 2.5 and 3.5%). The modeling portrays the peak of the epidemic around early August and estimates that the number of deaths will flatten out around early November at between 250 and 600, depending on the parameter employed. More deaths than those estimated implies that it is more widespread than assumed. Greater promotion of awareness and understanding of social distancing practices and their value is needed, particularly in the south, and better protection of the elderly should decrease mortality.

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  1. ScreenIT

    SciScore for 10.1101/2020.09.19.20197822: (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.

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  2. Version published to 10.1101/2020.09.19.20197822v1 on medRxiv