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

Nikola Anđelić, Sandi Baressi Šegota, Ivan Lorencin, Zdravko Jurilj, Tijana Šušteršič, Anđela Blagojević, Alen Protić, Tomislav Ćabov, Nenad Filipović and Zlatan Car
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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
Anđela Blagojević: Faculty of Engineering, University of Kragujevac, Sestre Janjić, 34000 Kragujevac, Serbia
Alen Protić: Clinical Hospital Centre, Rijeka, Krešimirova ul. 42, 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
Zlatan Car: Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia

IJERPH, 2021, vol. 18, issue 3, 1-26

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.

Keywords: artificial intelligence; COVID-19; epidemiology curve; genetic programming algorithm; regression modeling (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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