The authors describe the forecasting of both confirmed and recovered cases of SARS-CoV-2 through April of 2020. Figure 6 below illustrates the recovered cases from April 21st to April 30, 2020.
Maleki et al. conclude:
”There exist many situations in the real world that the assumption of symmetric distribution of the error terms is not satisfactory. In our methodology, we considered autoregressive time series models based on the two–piece scale mixturenormal (TP–SMN) distributions. The results indicated that the proposed method performed well in forecasting confirmed and recovered COVID-19 cases in the world. Using model selection criteria, the proposed models were also more reasonable than the standard Gaussian autoregressive time series model which is the simplest member of our proposed models. For future works, we suggest that the researchers apply cyclostationary, almost cyclostationary, and simple processes based on the TP–SMN distributions, instead of stationary processes.”
AUTHORS: Mohsen Maleki, Mohammad Reza, Mahmoudi Darren Wraith, Kim-Hung Pho
AUTHOR AFFILIATIONS:
a Department of Statistics, University of Isfahan, Isfahan, Iran
b Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam
c Department of Statistics, Faculty of Science, Fasa University, Fasa, Fars, Iran
d Institute of Health and Biomedical Innovation (IHBI), Queensland University of Technology (QUT), Queensland, Australia
e Fractional Calculus, Optimization and Algebra Research Group, Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City, Vietnam
CORRESPONDING AUTHORS:
E-mail addresses:
m.maleki.stat@gmail.com (M. Maleki),
mohammadrezamahmoudi@duytan.edu or vnmahmoudi.m.r@fasau.ac.ir (M.R. Mahmoudi)
d.wraith@qut.edu.au (D. Wraith)
phokimhung@tdtu.edu.vn (K.-H. Pho)
ABSTRACT:
Coronaviruses are enveloped RNA viruses from the Coronaviridae family affecting neurological, gastrointestinal, hepatic, and respiratory systems. In late 2019 a new member of this family belonging to the Betacoronavirus genera (referred to as COVID-19) originated and spread quickly across the world calling for strict containmentplans and policies. In most countries in the world, the outbreak of the disease has been serious and the number of confirmed COVID-19 cases has increased daily, while, fortunately the recovered COVID-19 cases have also increased. Clearly, forecasting the “confirmed” and “recovered” COVID-19 cases helps planning to control the disease and plan for utilization of health care resources. Time series models based on statistical methodology are useful to model time-indexed data and for forecasting. Autoregressive time series models based on two-piece scale mixture normal distributions, called TP–SMN–AR models, is a flexible family of models involving many classical symmetric/asymmetric and light/heavy tailed autoregressive models. In this paper, we use this family of models to analyze the real world time series data of confirmed and recovered COVID-19 cases.
Ref.https://doi.org/10.1016/j.tmaid.2020.101742Received 9 March 2020; Received in revised form 7 May 2020; Accepted 9 May 2020∗
Travel Medicine and Infectious Disease xxx (xxxx) xxxx1477-8939/ © 2020 Published by Elsevier Ltd.
Please cite this article as: Mohsen Maleki, et al., Travel Medicine and Infectious Disease,
https://doi.org/10.1016/j.tmaid.2020.101742
