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

Created by W.Langdon from gp-bibliography.bib Revision:1.8110

@Article{andelic:2021:IJERPH,

• author = "Nikola Andelic and Sandi {Baressi Segota} and Ivan Lorencin and Zdravko Jurilj and Tijana Sustersic and Andela Blagojevic and Alen Protic and Tomislav Cabov and Nenad Filipovic and Zlatan Car",
• title = "Estimation of {COVID-19} Epidemiology Curve of the United States Using Genetic Programming Algorithm",
• journal = "International Journal of Environmental Research and Public Health",
• year = "2021",
• volume = "18",
• number = "3",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "1660-4601",
• URL = "https://www.mdpi.com/1660-4601/18/3/959",
• DOI = "doi:10.3390/ijerph18030959",
• 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 used 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 used 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 R2 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 R2 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 R2 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.",
• notes = "also known as \cite{ijerph18030959}",

}

Genetic Programming entries for Nikola Andelic Sandi Baressi Segota Ivan Lorencin Zdravko Jurilj Tijana Geroski Andela Blagojevic Alen Protic Tomislav Cabov Nenad Filipovic Zlatan Car

Citations