Uncertain growth model for the cumulative number of COVID-19 infections in China

Zhe Liu ()
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Zhe Liu: Beihang University

Fuzzy Optimization and Decision Making, 2021, vol. 20, issue 2, No 5, 229-242

Abstract: Abstract As a type of coronavirus, COVID-19 has quickly spread around the majority of countries worldwide, and seriously threatens human health and security. This paper aims to depict cumulative numbers of COVID-19 infections in China using the growth model chosen by cross validation. The residual plot does not look like a null plot, so we can not find a distribution function for the disturbance term that is close enough to the true frequency. Therefore, the disturbance term can not be characterized as random variables, and stochastic regression analysis is invalid in this case. To better describe this pandemic automatically, this paper first employs uncertain growth models with the help of uncertain hypothesis tests to detect and modify outliers in data. The forecast value and confidence interval for the cumulative number of COVID-19 infections in China are provided.

Keywords: Uncertainty theory; Uncertain statistics; Uncertain regression analysis; Uncertain hypothesis test; COVID-19 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10700-020-09340-x

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