Estimation of COVID-19 epidemic curves using genetic programming algorithm

被引:15
作者
Andelic, Nikola [1 ]
Segota, Sandi Baressi [1 ]
Lorencin, Ivan [1 ]
Mrzljak, Vedran [1 ]
Car, Zlatan [1 ]
机构
[1] Univ Rijeka, Fac Engn, Vukovarska 58, Rijeka 51000, Croatia
关键词
COVID-19; disease spread modeling; evolutionary computing; genetic programming; machine learning; DISEASE; OPTIMIZATION; MODELS;
D O I
10.1177/1460458220976728
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
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 R-2 scores of 0.999, while the models developed for estimation of recovered cases achieved the R-2 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.
引用
收藏
页码:1 / 40
页数:40
相关论文
共 74 条
  • [1] Al-Maqtari QA., 2020, NOVEL CORONAVIRUS 20
  • [2] [Anonymous], 1994, GENETIC PROGRAMMING
  • [3] [Anonymous], 1997, GENETIC PROGRAMMING
  • [4] Banzhaf W., 2019, ARXIV PREPRINT ARXIV
  • [5] Modeling the Spread of COVID-19 Infection Using a Multilayer Perceptron
    Car, Zlatan
    Baressi Segota, Sandi
    Andelic, Nikola
    Lorencin, Ivan
    Mrzljak, Vedran
    [J]. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE, 2020, 2020 (2020)
  • [6] Self-tuning geometric semantic Genetic Programming
    Castelli, Mauro
    Manzoni, Luca
    Vanneschi, Leonardo
    Silva, Sara
    Popovic, Ales
    [J]. GENETIC PROGRAMMING AND EVOLVABLE MACHINES, 2016, 17 (01) : 55 - 74
  • [7] Chan Kai Siang, 2019, JMIR Med Educ, V5, pe13930, DOI 10.2196/13930
  • [8] Cormen T. H., 2009, INTRO ALGORITHMS
  • [9] Big data in biomedicine
    Costa, Fabricio F.
    [J]. DRUG DISCOVERY TODAY, 2014, 19 (04) : 433 - 440
  • [10] A proposal for distinguishing between bacterial and viral meningitis using genetic programming and decision trees
    D'Angelo, Gianni
    Pilla, Raffaele
    Tascini, Carlo
    Rampone, Salvatore
    [J]. SOFT COMPUTING, 2019, 23 (22) : 11775 - 11791