Dataset on the COVID-19 Pandemic Situation in Tunisia with Application to SIR Model

  1. Abir Lassoued
  2. Afef Ben Saad
  3. Hela Lassoued
  4. Raouf Ketata
  5. Olfa Boubaker

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Abstract

April 9, 2020 marks 100 days since the first cases of coronavirus disease 2019 in China. In this crucial day with 1 436 198 confirmed infected cases in the world and 85 521 deaths, the Global Level of the Covid-19 pandemic was evaluated at very high according to the World Health Organization (WHO) situation report. For most people, COVID-19 infection will cause mild illness (fever and at least one symptom of respiratory disease); however, for more than 3.4% of people, it can be fatal. Older people and those with preexisting medical conditions (such as cardiovascular disease, chronic respiratory disease, or diabetes) are at risk of severe disease and mortality. The incubation period of the virus is estimated to be between 2 and 14 days, but longer incubation had been reported. Furthermore, data published by world authorities show that statistics are different for different geographical regions and depend on many social and environmental factors. The sad reality of the COVID-19 is that there are currently no medications or vaccines proven to be effective for the treatment or prevention of the disease. The pandemic spread is consequenctly followed by a worldwide panic. Facing this dramatic uncertain situation, implementing a country-wide strategy for social distancing and a general logistic policy for critical and life-saving supplies is an urgent for government and sanitary authorities. Several mathematical models have been proposed to predict epidemic spread. However, models should be adapted to specific situations in countries where geographic, societal, economic, and political strategies are different. Here, we propose the application of the well-known SIR model to the case study of Tunisia for which data are collected from three databases in order to have rapidly predict the situation. Such results can be useful in the future to design a more reliable model to help in monitoring infection control.

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    SciScore for 10.1101/2020.04.23.20076802: (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 SIR differential system is finally solved using the MATLAB function ode45.
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)

    Results from OddPub: Thank you for sharing your code and data.

    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 10.1101/2020.04.23.20076802v2 on medRxiv
  3. Version published to 10.1101/2020.04.23.20076802v1 on medRxiv