Modified exponential time series model with prediction of total COVID-19 cases in Belgium, Czech Republic, Poland and Switzerland

Wachirapond Permpoonsinsup; Rapin Sunthornwat

doi:10.2478/stattrans-2022-0035

Modified exponential time series model with prediction of total COVID-19 cases in Belgium, Czech Republic, Poland and Switzerland

Wachirapond Permpoonsinsup Industrial Technology and Innovation Management Program, Faculty of Science and Technology, Pathumwan Institute of Technology, Bangkok, Thailand ORCID:https://orcid.org/0000-0003-4033-493X , Rapin Sunthornwat Industrial Technology and Innovation Management Program, Faculty of Science and Technology, Pathumwan Institute of Technology, Bangkok, Thailand ORCID:https://orcid.org/0000-0001-8981-5107 Statistics in Transition new series, vol. 23, 2022, 3, pages: 147-165 Published online: 14 September 2022 DOI 10.2478/stattrans-2022-0035

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ABSTRACT

The coronavirus (COVID-19) pandemic affected every country worldwide. In particular, outbreaks in Belgium, the Czech Republic, Poland and Switzerland entered the second wave and was exponentially increasing between July and November, 2020. The aims of the study are: to estimate the compound growth rate, to develop a modified exponential time-series model compared with the hyperbolic time-series model, and to estimate the optimal parameters for the models based on the exponential least-squares, three selected points, partial-sums methods, and the hyperbolic least-squares for the daily COVID-19 cases in Belgium, the Czech Republic, Poland and Switzerland. The speed and spreading power of COVID-19 infections were obtained by using derivative and root-mean-squared methods, respectively. The results show that the exponential least-squares method was the most suitable for the parameter estimation. The compound growth rate of COVID-19 infection was the highest in Switzerland, and the speed and spreading power of COVID-19 infection were the highest in Poland between July and November, 2020.

KEYWORDS

COVID-19, modified exponential time-series model, method of parameter estimation, compound growth rate.

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