Literature DB >> 33499219

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

Nikola Anđelić1, Sandi Baressi Šegota1, Ivan Lorencin1, Zdravko Jurilj2, Tijana Šušteršič3,4, Anđela Blagojević3,4, Alen Protić2,5, Tomislav Ćabov6, Nenad Filipović3,4, Zlatan Car1.   

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 22nd January 2020-3rd 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.

Entities:  

Keywords:  COVID-19; artificial intelligence; epidemiology curve; genetic programming algorithm; regression modeling

Mesh:

Year:  2021        PMID: 33499219     DOI: 10.3390/ijerph18030959

Source DB:  PubMed          Journal:  Int J Environ Res Public Health        ISSN: 1660-4601            Impact factor:   3.390


  2 in total

Review 1.  Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review.

Authors:  Jelena Musulin; Sandi Baressi Šegota; Daniel Štifanić; Ivan Lorencin; Nikola Anđelić; Tijana Šušteršič; Anđela Blagojević; Nenad Filipović; Tomislav Ćabov; Elitza Markova-Car
Journal:  Int J Environ Res Public Health       Date:  2021-04-18       Impact factor: 3.390

2.  An Improved Genetic Algorithm for Location Allocation Problem with Grey Theory in Public Health Emergencies.

Authors:  Shaoren Wang; Yenchun Jim Wu; Ruiting Li
Journal:  Int J Environ Res Public Health       Date:  2022-08-08       Impact factor: 4.614

  2 in total

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