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Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm

Author

Listed:
  • Nikola Anđelić

    (Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia)

  • Sandi Baressi Šegota

    (Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia)

  • Ivan Lorencin

    (Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia)

  • Zdravko Jurilj

    (Clinical Hospital Centre, Rijeka, Krešimirova ul. 42, 51000 Rijeka, Croatia)

  • Tijana Šušteršič

    (Faculty of Engineering, University of Kragujevac, Sestre Janjić, 34000 Kragujevac, Serbia
    Bioengineering Research and Development Centre (BioIRC), Prvoslava Stojanovića 6, 34000 Kragujevac, Serbia)

  • Anđela Blagojević

    (Faculty of Engineering, University of Kragujevac, Sestre Janjić, 34000 Kragujevac, Serbia
    Bioengineering Research and Development Centre (BioIRC), Prvoslava Stojanovića 6, 34000 Kragujevac, Serbia)

  • Alen Protić

    (Clinical Hospital Centre, Rijeka, Krešimirova ul. 42, 51000 Rijeka, Croatia
    Faculty of Medicine, University of Rijeka, Ul. Braće Branchetta 20/1, 51000, Rijeka, Croatia)

  • Tomislav Ćabov

    (Faculty of Dental Medicine, University of Rijeka, Kresimirova 40/42, 51000 Rijeka, Croatia)

  • Nenad Filipović

    (Faculty of Engineering, University of Kragujevac, Sestre Janjić, 34000 Kragujevac, Serbia
    Bioengineering Research and Development Centre (BioIRC), Prvoslava Stojanovića 6, 34000 Kragujevac, Serbia)

  • Zlatan Car

    (Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia)

Abstract

Estimation of the epidemiology curve for the COVID-19 pandemic can be a very computationally challenging task. Thus far, there have been some implementations of artificial intelligence (AI) methods applied to develop epidemiology curve for a specific country. However, most applied AI methods generated models that are almost impossible to translate into a mathematical equation. In this paper, the AI method called genetic programming (GP) algorithm is utilized to develop a symbolic expression (mathematical equation) which can be used for the estimation of the epidemiology curve for the entire U.S. with high accuracy. The GP algorithm is utilized on the publicly available dataset that contains the number of confirmed, deceased and recovered patients for each U.S. state to obtain the symbolic expression for the estimation of the number of the aforementioned patient groups. The dataset consists of the latitude and longitude of the central location for each state and the number of patients in each of the goal groups for each day in the period of 22 January 2020–3 December 2020. The obtained symbolic expressions for each state are summed up to obtain symbolic expressions for estimation of each of the patient groups (confirmed, deceased and recovered). These symbolic expressions are combined to obtain the symbolic expression for the estimation of the epidemiology curve for the entire U.S. The obtained symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for each state achieved R 2 score in the ranges 0.9406–0.9992, 0.9404–0.9998 and 0.9797–0.99955, respectively. These equations are summed up to formulate symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for the entire U.S. with achieved R 2 score of 0.9992, 0.9997 and 0.9996, respectively. Using these symbolic expressions, the equation for the estimation of the epidemiology curve for the entire U.S. is formulated which achieved R 2 score of 0.9933. Investigation showed that GP algorithm can produce symbolic expressions for the estimation of the number of confirmed, recovered and deceased patients as well as the epidemiology curve not only for the states but for the entire U.S. with very high accuracy.

Suggested Citation

  • Nikola Anđelić & Sandi Baressi Šegota & Ivan Lorencin & Zdravko Jurilj & Tijana Šušteršič & Anđela Blagojević & Alen Protić & Tomislav Ćabov & Nenad Filipović & Zlatan Car, 2021. "Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm," IJERPH, MDPI, vol. 18(3), pages 1-26, January.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:3:p:959-:d:485231
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    References listed on IDEAS

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    Cited by:

    1. Jelena Musulin & Sandi Baressi Šegota & Daniel Štifanić & Ivan Lorencin & Nikola Anđelić & Tijana Šušteršič & Anđela Blagojević & Nenad Filipović & Tomislav Ćabov & Elitza Markova-Car, 2021. "Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review," IJERPH, MDPI, vol. 18(8), pages 1-39, April.
    2. Shaoren Wang & Yenchun Jim Wu & Ruiting Li, 2022. "An Improved Genetic Algorithm for Location Allocation Problem with Grey Theory in Public Health Emergencies," IJERPH, MDPI, vol. 19(15), pages 1-18, August.
    3. Davide Barbieri & Enrico Giuliani & Anna Del Prete & Amanda Losi & Matteo Villani & Alberto Barbieri, 2021. "How Artificial Intelligence and New Technologies Can Help the Management of the COVID-19 Pandemic," IJERPH, MDPI, vol. 18(14), pages 1-10, July.
    4. Rastko Jovanović & Miloš Davidović & Ivan Lazović & Maja Jovanović & Milena Jovašević-Stojanović, 2021. "Modelling Voluntary General Population Vaccination Strategies during COVID-19 Outbreak: Influence of Disease Prevalence," IJERPH, MDPI, vol. 18(12), pages 1-18, June.
    5. Derek Huang & Huanyu Tao & Qilong Wu & Sheng-You Huang & Yi Xiao, 2021. "Modeling of the Long-Term Epidemic Dynamics of COVID-19 in the United States," IJERPH, MDPI, vol. 18(14), pages 1-17, July.

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