University of Rijeka, Faculty of Engineering, Rijeka, Croatia.
Health Informatics J. 2021 Jan-Mar;27(1):1460458220976728. doi: 10.1177/1460458220976728.
This paper investigates the possibility of the implementation of Genetic Programming (GP) algorithm on a publicly available COVID-19 data set, in order to obtain mathematical models which could be used for estimation of confirmed, deceased, and recovered cases and the estimation of epidemiology curve for specific countries, with a high number of cases, such as China, Italy, Spain, and USA and as well as on the global scale. The conducted investigation shows that the best mathematical models produced for estimating confirmed and deceased cases achieved scores of 0.999, while the models developed for estimation of recovered cases achieved the score of 0.998. The equations generated for confirmed, deceased, and recovered cases were combined in order to estimate the epidemiology curve of specific countries and on the global scale. The estimated epidemiology curve for each country obtained from these equations is almost identical to the real data contained within the data set.
本文研究了在公开的 COVID-19 数据集上实现遗传编程（GP）算法的可能性，以便获得数学模型，用于估计确诊、死亡和康复病例，并估计中国、意大利、西班牙、美国等病例数较多的特定国家以及全球的流行病学曲线。研究表明，用于估计确诊和死亡病例的最佳数学模型的得分达到了 0.999，而用于估计康复病例的模型的得分达到了 0.998。为了估计特定国家和全球的流行病学曲线，将生成的用于确诊、死亡和康复病例的方程进行了组合。从这些方程中获得的每个国家的估计流行病学曲线与数据集中包含的实际数据几乎完全相同。
