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Empirical Modeling of COVID-19 Evolution with High/Direct Impact on Public Health and Risk Assessment

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  • Noureddine Ouerfelli

    (Institut Supérieur des Technologies Médicales de Tunis, Laboratoire de Biophysique et Technologies Médicales, Université de Tunis El Manar, Tunis 1006, Tunisia)

  • Narcisa Vrinceanu

    (Department of Industrial Machinery and Equipment, Lucian Blaga University of Sibiu, 550024 Sibiu, Romania)

  • Diana Coman

    (Department of Industrial Machinery and Equipment, Lucian Blaga University of Sibiu, 550024 Sibiu, Romania)

  • Adriana Lavinia Cioca

    (Independent Researcher, 557260 Sibiu, Romania)

Abstract

This report develops a conceivable mathematical model for the transmission and spread of COVID-19 in Romania. Understanding the early spread dynamics of the infection and evaluating the effectiveness of control measures in the first wave of infection is crucial for assessing and evaluating the potential for sustained transmission occurring in the second wave. The main aim of the study was to emphasize the impact of control measures and the rate of case detection in slowing the spread of the disease. Non pharmaceutical control interventions include government actions, public reactions, and other measures. The methodology consists of an empirical model, taking into consideration the generic framework of the Stockholm Environment Institute (SEI) Epidemic–Macroeconomic Model, and incorporates the effect of interventions through a multivalued parameter, a stepwise constant varying during different phases of the interventions designed to capture their impact on the model. The model is mathematically consistent and presents various simulation results using best-estimated parameter values. The model can be easily updated later in response to real-world alterations, for example, the easing of restrictions. We hope that our simulation results may guide local authorities to make timely, correct decisions for public health and risk assessment.

Suggested Citation

  • Noureddine Ouerfelli & Narcisa Vrinceanu & Diana Coman & Adriana Lavinia Cioca, 2022. "Empirical Modeling of COVID-19 Evolution with High/Direct Impact on Public Health and Risk Assessment," IJERPH, MDPI, vol. 19(6), pages 1-13, March.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:6:p:3707-:d:775506
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    References listed on IDEAS

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    1. Phenyo E. Lekone & Bärbel F. Finkenstädt, 2006. "Statistical Inference in a Stochastic Epidemic SEIR Model with Control Intervention: Ebola as a Case Study," Biometrics, The International Biometric Society, vol. 62(4), pages 1170-1177, December.
    2. Fotios Petropoulos & Spyros Makridakis, 2020. "Forecasting the novel coronavirus COVID-19," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-8, March.
    3. Cleo Anastassopoulou & Lucia Russo & Athanasios Tsakris & Constantinos Siettos, 2020. "Data-based analysis, modelling and forecasting of the COVID-19 outbreak," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-21, March.
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