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

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 and Ioana Nanu
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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
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
Ioana Nanu: Alessandrescu Rusescu National Institute for Mother and Child Health, 011062 Bucharest, Romania

Mathematics, 2025, vol. 13, issue 15, 1-24

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.

Keywords: COVID-19; SARS-CoV-2 virus; control design; prediction; pandemic dynamics; mathematical model; data processing; neural networks (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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