Estimates of the COVID-19 pandemic dynamics in Ukraine based on two data sets

  1. Igor Nesteruk

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Abstract

Background

To simulate how the number of COVID-19 cases increases versus time, various data sets for the number of new cases and different mathematical models can be used. Since there are some differences in statistical data, the results of simulations can be different. Complex mathematical models contain many unknown parameters, the values of which must be determined using a limited number of observations of the disease over time. Even long-term monitoring of the epidemic may not provide reliable estimates of its parameters due to the constant change of testing conditions, isolation of infected and quarantine. Therefore, simpler approaches are necessary. In particular, previous simulations of the COVID-19 epidemic dynamics in Ukraine were based on smoothing of the dependence of the number of cases on time and the generalized SIR (susceptible-infected-removed) model. These approaches allowed to detect the waves of pandemic and to make adequate predictions of the their duration and final sizes. In particular, eight waves of the COVID-19 pandemic in Ukraine were investigated.

Objective

We will compare the results simulation of a new epidemic wave in Ukraine based on national statistics and data reported by Johns Hopkins University (JHU).

Methods

In this study we use the smoothing method for the dependences of the number of cases on time, the generalized SIR model for the dynamics of any epidemic wave, the exact solution of the linear differential equations, and statistical approach developed before.

Results

Ninth epidemic wave in Ukraine was simulated. The optimal values of the SIR model parameters were calculated and compared with the use of two data sets. Both predictions are not very optimistic: new cases will not stop appearing until June-July 2021.

Conclusions

New waves of COVID-19 pandemic can be detected, calculated and predicted with the use of rather simple mathematical models. The results of calculations depend on the data sets for the number of confirmed cases. The expected long duration of the pandemic forces us to be careful and in solidarity. The government and all Ukrainians must strictly adhere to quarantine measures in order to avoid fatal consequences. Probably the presented results could be useful in order to estimate the efficiency of future vaccinations.

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  1. ScreenIT

    SciScore for 10.1101/2021.02.18.21252000: (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

    Antibodies
    SentencesResources
    For example, employees of two kindergartens and two schools in the Ukrainian city of Chmelnytskii were tested for antibodies to COVID-19, [25].
    COVID-19
    suggested: None

    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 10.1101/2021.02.18.21252000v1 on medRxiv