A stochastic, individual-based model for the evaluation of the impact of non-pharmacological interventions on COVID-19 transmission in Slovakia

  1. Miroslav Gasparek
  2. Michal Racko
  3. Michal Dubovsky

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Abstract

The COVID-19 pandemic represents one of the most significant healthcare challenges that humanity faces. We developed a stochastic, individual-based model of transmission of COVID-19 in Slovakia. The proposed model is based on current clinical knowledge of the disease and takes into account the age structure of the population, distribution of the population into the households, interactions within the municipalities, and interaction among the individuals travelling between municipalities. Furthermore, the model incorporates the effect of age-dependent severity of COVID-19 and realistic trajectories of patients through the healthcare system. We assess the impact of the governmental non-pharmacological interventions, such as population-wide social distancing, social distancing within specific subsets of population, reduction of travel between the municipalities, and self-quarantining of the infected individuals. We also evaluate the impact of relaxing of strict restrictions, efficacy of the simple state feedback-based restrictions in controlling the outbreak, and the effect of superspreaders on the disease dynamics. Our simulations show that non-pharmacological interventions reduce the number of infected individuals and the number of fatalities, especially when the social distancing of particularly susceptible subgroups of the population is employed along with case isolation.

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    SciScore for 10.1101/2020.05.11.20096362: (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 model was implemented in Python programming language and was optimized for running on GPU by using the CuPy package.
    Python
    suggested: (IPython, RRID:SCR_001658)
    CuPy
    suggested: None

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

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our model has several limitations which can be addressed in future work. First, we need to conduct a detailed sensitivity analysis with respect to the selected probability distributions and their parameters to ensure the robustness of the results against the small changes in their values. Second, more data is needed to inform the selection of several key parameters, such as the number of random contacts per day before and after NPI are imposed. We hope that this lack of data will be addressed by public polls in the near future. Third, while we have done preliminary analysis of the effect of superspreading events, we have not considered the impact of fast local spread in the marginalised ethnic communities living in the eastern part of Slovakia, which can easily become critical sites, accelerating the spread of the disease. Testing and contact tracing in these locations is in progress, and we hope that it will elucidate the potential impact of these communities on the COVID-19 spread. Nevertheless, more data and discussions with experts are needed to fully capture the complexity of this issue. Fourth, currently, we do not have enough data available to describe the interaction among the individuals within the fine social network structure, which prevents us from describing the fine-grained details of the transmission of the disease through the networks of the social interactions among individuals [32] [63]. Instead, we are relying on the random mixing of the individuals within ...

    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 10.1101/2020.05.11.20096362v1 on medRxiv