Forecasting and classification of new cases of COVID 19 before vaccination using decision trees and Gaussian mixture model
Alexandria Engineering Journal
; 62:327-333, 2023.
Article
in English
| Scopus | ID: covidwho-2014736
ABSTRACT
Regarding the pandemic taking place in the world from the spread of the Coronavirus pandemic and viral mutations, the need has arisen to analyze the epidemic data in terms of numbers of infected and deaths, different geographical regions, and the dynamics of the spread of the virus. In China, the total number of reported infections is 224,659 on June 11, 2022. In this paper, the Gaussian Mixture Model and the decision tree method were used to classify and predict new cases of the virus. Although we focus mainly on the Chinese case, the model is general and adapted to any context without loss of validity of the qualitative results. The Chi-Squared (χ2) Automatic Interaction Detection (CHAID) was applied in creating the decision tree structure, the data has been classified into five classes, according to the BIC criterion. The best mixture model is the E (Equal variance) with five components. The considered data sets of the world health organization (WHO) were used from January 5, 2020, to 12, November 2021. We provide numerical results based on the Chinese case. © 2022 THE AUTHORS
Chi-Squared Automatic Interaction Detection (CHAID); Decision Tree (DT); Gaussian Mixture Model (GMM); Machine Learning (ML); Gaussian distribution; Geographical regions; Machine learning; Viruses; Automatic interaction detection; Chi-Squared; Chi-squared automatic interaction detection; Coronaviruses; Decision tree; Gaussian mixture model; Machine-learning; New case; Decision trees
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Topics:
Vaccines
Language:
English
Journal:
Alexandria Engineering Journal
Year:
2023
Document Type:
Article
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