Abstract
Purpose As of July 30, 2020, more than 17 million novel coronavirus disease 2019 (COVID-19) cases were registered including 671,500 deaths. Yet, there is no immediate medicine or vaccination for control this dangerous pandemic and researchers are trying to implement mathematical or time series epidemic models to predict the disease severity with national wide data. Design/methodology/approach In this study, the authors considered COVID-19 daily infection data four most COVID-19 affected nations (such as the USA, Brazil, India and Russia) to conduct 60-day forecasting of total infections. To do that, the authors adopted a machine learning (ML) model called Fb-Prophet and the results confirmed that the total number of confirmed cases in four countries till the end of July were collected and projections were made by employing Prophet logistic growth model. Findings Results highlighted that by late September, the estimated outbreak can reach 7.56, 4.65, 3.01 and 1.22 million cases in the USA, Brazil, India and Russia, respectively. The authors found some underestimation and overestimation of daily cases, and the linear model of actual vs predicted cases found a p-value (<2.2e-16) lower than the R2 value of 0.995. Originality/value In this paper, the authors adopted the Fb-Prophet ML model because it can predict the epidemic trend and derive an epidemic curve.
Highlights
The latest epidemic caused by novel coronavirus disease 2019 (COVID-19) is already spread all over the world [1]
The present study is in line with the research associated with the calculation of COVID-19 cases in China by time series and panel data models have successfully presented the control of endogeneity, dependence and unobserved heterogeneity [6]
The present analysis was conducted by considering live COVID-19 epidemic data of the USA, Brazil, India and Russia which retrieved from the John Hopkins University dashboard
Summary
The latest epidemic caused by novel coronavirus disease 2019 (COVID-19) is already spread all over the world [1]. The world has reached the brink of stagnation and struggled by daily registered new infections [2] and researchers confirmed that the present pandemic has been caused by the severe accurate respiratory syndrome coronavirus 2 (SARS-CoV-2) [3]. Forecasting by time series models can successfully analyze the COVID-19 disease characteristics and a cumulative number of infections [9]. The present study is in line with the research associated with the calculation of COVID-19 cases in China by time series and panel data models have successfully presented the control of endogeneity, dependence and unobserved heterogeneity [6]. The recent spreading characteristics of COVID-19 were compared by previous coronavirus families (i.e. SARS and Middle East respiratory syndrome (MERS)) by adopting the propagation growth model is presented in ref [10]. Results mentioned that the COVID-19 transmission rate is almost double than of SARS and MERS and infected cases increased twice every two–three days without having human intervention
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