Mathematical Modelling the Impact Evaluation of Lockdown on Infection Dynamics of COVID-19 in Italy

  1. S. Kadyrov
  2. A. Orynbassar
  3. H.B Saydaliyev
  4. D. Yergesh

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Abstract

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the cause of the coronavirus disease-2019 (COVID-19), within months of emergence from Wuhan, China, has rapidly spread, exacting a devastating human toll across around the world reaching the pandemic stage at the the beginning of March 2020. Thus, COVID-19’s daily increasing cases and deaths have led to worldwide lockdown, quarantine and some restrictions. Covid-19 epidemic in Italy started as a small wave of 2 infected cases on January 31. It was followed by a bigger wave mainly from local transmissions reported in 6387 cases on March 8. It caused the government to impose a lockdown on 8 March to the whole country as a way to suppress the pandemic. This study aims to evaluate the impact of the lockdown and awareness dynamics on infection in Italy over the period of January 31 to July 17 and how the impact varies across different lockdown scenarios in both periods before and after implementation of the lockdown policy. The findings SEIR reveal that implementation lockdown has minimised the social distancing flattening the curve. The infections associated with COVID-19 decreases with quarantine initially then easing lockdown will not cause further increasing transmission until a certain period which is explained by public high awareness. Completely removing lockdown may lead to sharp transmission second wave. Policy implementation and limitation of the study were evaluated at the end of the paper.

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    SciScore for 10.1101/2020.07.27.20162537: (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
    More specifically, we use ‘scipy.integrate.ode function’ of python programming language to simulate S(t), I(t), E(t), and R(t) where we let initial values as S(0)=country population, I(0)=the first observed number of cases in the country, E(0)=0, and R(0)=0.
    python
    suggested: (IPython, RRID:SCR_001658)
    Then, we ‘call scipy.optimize.curve_fit’ function for least-square fitting of the theoretical model solution to the observed daily number of confirmed cases and recovered individuals.
    scipy
    suggested: (SciPy, RRID:SCR_008058)

    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: We detected the following sentences addressing limitations in the study:
    One of the limitations of the study was to estimate the death rate as a constant parameter using only first 50 days of the dataset. However, Figure 4a suggests the death rate is not a constant number but piecewise constant. It could be an interesting research problem to find out why there is a negative slope in the scatter plot. As can be readily seen from Figure 6 and Figure 7 our lockdown function β(t) in (1) nicely approximated the observed active cases with exponent = 0.0243. In Italy, the start of imposing the lockdown measures started on March 8 which corresponds to t = 38 day since the first confirmed case. However, our estimates for the start of lockdown is t1 = 67.5. It maybe interpreted as the real effect of lockdown started appearing after 29 days since the start of the lockdown. This is consistent with the early reports that state that consequences of lockdown were apparent after three weeks. Our analysis reveal that the awareness parameter is α = 3230.4. There is no upper bound for α and hence it is not clear how it should be interpreted, other than noting that awareness is high. We interpret αI term as awareness with increase of infectious the term increases and hence the incidence rate β(t)SI/(N + αI) decreases. On the other hand, some studies interpret it as “the measure of inhibition” taken by infectious individuals [6]. Based on the results and findings of this study, a few policy implications were determined. Firstly, with respect to the objective of this ...

    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

    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.

  2. ScreenIT

    SciScore for 10.1101/2020.07.27.20162537: (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
    More specifically, we use ‘scipy.integrate.ode function’ of python programming language to simulate S(t), I(t), E(t), and R(t) where we let initial values as S(0)=country population, I(0)=the first observed number of cases in the country, E(0)=0, and R(0)=0.
    python
    suggested: (IPython, SCR_001658)
    Then, we ‘call scipy.
    scipy
    suggested: (SciPy, SCR_008058)

    Data from additional tools added to each annotation on a weekly basis.

    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 is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.07.27.20162537 on medRxiv