Open AccessForecasting Development of COVID-19 Epidemic in European Union Using Entropy-Randomized Approach
Author(s) -
Yuri S. Popkov,
Yuri A. Dubnov,
A. Yu. Popkov
Publication year - 2021
Publication title -
informatika i avtomatizaciâ/informatika i avtomatizaciâ (print)
Language(s) - English
Resource type - Journals
eISSN - 2713-3206
pISSN - 2713-3192
DOI - 10.15622/20.5.1
Subject(s) - hyperparameter , principle of maximum entropy , computer science , entropy (arrow of time) , covid-19 , european union , statistics , artificial intelligence , mathematics , machine learning , medicine , physics , disease , pathology , quantum mechanics , infectious disease (medical specialty) , business , economic policy
The paper is devoted to the forecasting of the COVID-19 epidemic by the novel method of randomized machine learning. This method is based on the idea of estimation of probability distributions of model parameters and noises on real data. Entropy-optimal distributions correspond to the state of maximum uncertainty which allows the resulting forecasts to be used as forecasts of the most "negative" scenario of the process under study. The resulting estimates of parameters and noises, which are probability distributions, must be generated, thus obtaining an ensemble of trajectories that considered to be analyzed by statistical methods. In this work, for the purposes of such an analysis, the mean and median trajectories over the ensemble are calculated, as well as the trajectory corresponding to the mean over distribution values of the model parameters. The proposed approach is used to predict the total number of infected people using a three-parameter logistic growth model. The conducted experiment is based on real COVID-19 epidemic data in several countries of the European Union. The main goal of the experiment is to demonstrate an entropy-randomized approach for predicting the epidemic process based on real data near the peak. The significant uncertainty contained in the available real data is modeled by an additive noise within 30%, which is used both at the training and predicting stages. To tune the hyperparameters of the model, the scheme is used to configure them according to a testing dataset with subsequent retraining of the model. It is shown that with the same datasets, the proposed approach makes it possible to predict the development of the epidemic more efficiently in comparison with the standard approach based on the least-squares method.