Learning Interpretable Mixture of Weibull Distributions—Exploratory Analysis of How Economic Development Influences the Incidence of COVID-19 Deaths

Róbert Csalódi, Zoltán Birkner and János Abonyi
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Róbert Csalódi: MTA-PE “Lendület” Complex Systems Monitoring Research Group, Department of Process Engineering, University of Pannonia, Egyetem Street 10, H-8200 Veszprém, Hungary
Zoltán Birkner: University Center for Circular Economy Nagykanizsa, University of Pannonia, H-8800 Nagykanizsa, Hungary
János Abonyi: MTA-PE “Lendület” Complex Systems Monitoring Research Group, Department of Process Engineering, University of Pannonia, Egyetem Street 10, H-8200 Veszprém, Hungary

Data, 2021, vol. 6, issue 12, 1-11

Abstract: This paper presents an algorithm for learning local Weibull models, whose operating regions are represented by fuzzy rules. The applicability of the proposed method is demonstrated in estimating the mortality rate of the COVID-19 pandemic. The reproducible results show that there is a significant difference between mortality rates of countries due to their economic situation, urbanization, and the state of the health sector. The proposed method is compared with the semi-parametric Cox proportional hazard regression method. The distribution functions of these two methods are close to each other, so the proposed method can estimate efficiently.

Keywords: Weibull distribution; multivariate Gaussian mixture model; mortality rate; COVID-19 (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2021
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