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Neural Network-Based Predictive Control of COVID-19 Transmission Dynamics to Support Institutional Decision-Making

Author

Listed:
  • Cristina-Maria Stăncioi

    (Automation Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania)

  • Iulia Adina Ștefan

    (Automation Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania)

  • Violeta Briciu

    (Department of Infectious Diseases and Epidemiology, Iuliu Hațieganu University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
    The Clinical Hospital of Infectious Diseases Cluj-Napoca, 400012 Cluj-Napoca, Romania)

  • Vlad Mureșan

    (Automation Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania)

  • Iulia Clitan

    (Automation Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania)

  • Mihail Abrudean

    (Automation Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania)

  • Mihaela-Ligia Ungureșan

    (Automation Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania)

  • Radu Miron

    (Automation Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania)

  • Ecaterina Stativă

    (Alessandrescu Rusescu National Institute for Mother and Child Health, 011062 Bucharest, Romania)

  • Michaela Nanu

    (Alessandrescu Rusescu National Institute for Mother and Child Health, 011062 Bucharest, Romania)

  • Adriana Topan

    (Department of Infectious Diseases and Epidemiology, Iuliu Hațieganu University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
    The Clinical Hospital of Infectious Diseases Cluj-Napoca, 400012 Cluj-Napoca, Romania)

  • Ioana Nanu

    (Alessandrescu Rusescu National Institute for Mother and Child Health, 011062 Bucharest, Romania)

Abstract

The COVID-19 pandemic was a profoundly influential global occurrence in recent history, impacting daily life, economics, and healthcare systems for an extended period. The abundance of data has been essential in creating models to simulate and forecast the dissemination of infectious illnesses, aiding governments and health organizations in making educated decisions. This research primarily focuses on designing a control technique that incorporates the five most important inputs that impact the spread of COVID-19 on the Romanian territory. Quantitative analysis and data filtering are two crucial aspects to consider when developing a mathematical model. In this study the transfer function principle was used as the most accurate method for modeling the system, based on its superior fit demonstrated in a previous study. For the control strategy, a PI (Proportional-Integral) controller was designed to meet the requirements of the intended behavior. Finally, it is showed that for such complex models, the chosen control strategy, combined with fine tuning, led to very accurate results.

Suggested Citation

  • Cristina-Maria Stăncioi & Iulia Adina Ștefan & Violeta Briciu & Vlad Mureșan & Iulia Clitan & Mihail Abrudean & Mihaela-Ligia Ungureșan & Radu Miron & Ecaterina Stativă & Michaela Nanu & Adriana Topan, 2025. "Neural Network-Based Predictive Control of COVID-19 Transmission Dynamics to Support Institutional Decision-Making," Mathematics, MDPI, vol. 13(15), pages 1-24, August.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:15:p:2528-:d:1718909
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