GMDH-Based Discovering Dynamic Regularities of the Ukraine Covid-19 Pandemic Process

Abstract

This article presents the modeling results of the Ukraine Covid-19 pandemic process using official statistical data on the confirmed cases. Main goal is discovering dynamic regularities of the process given as daily data of the time series. That is why we use four different methods to build predictive difference models of the autoregression type: ordinary autoregression; autoregression of optimal structure obtained using the combinatorial-genetic GMDH algorithm COMBI-GA; another variant of the optimal structure built by the well-known method Lasso; and we compare prediction results of these methods with independent predictions published by the World Data Center. In our study, the baseline prediction is the one produced by the ordinary autoregression which includes all lags from 1 to a given their number. Unlike it, algorithms COMBI-GA as well as Lasso construct autoregressions with in some respect optimal compositions of lags. The WDC independent predictions are made using the Backpropagation ANN as nonlinear transformation of lag variables. This comparative study we carried out in two stages: first, for the period of strong quarantine in Ukraine and second, for the period of stepwise quarantine weakening. For both stages, the optimal models built by the COMBI-GA are most interpretable and demonstrate better predictive accuracy on validation datasets. This research is useful for defining tendency of coronavirus evolvement in time and predicting its future activity in order to take some protective measures.