Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19. (June 2020)

• Record Type: Journal Article
• Authors: Singh, Sarbjit; Parmar, Kulwinder Singh; Kumar, Jatinder; Makkhan, Sidhu Jitendra Singh
• Is Part Of: Chaos, solitons and fractals. Volume 135(2020)
• Publication Date: 2020-06
• Subjects: COVID-19 casualties cases -- Discrete wavelet decomposition -- Hybrid model -- ARIMA model -- Prediction; Chaotic behavior in systems -- Periodicals; Solitons -- Periodicals; Fractals -- Periodicals; Chaotic behavior in systems; Fractals; Solitons; Periodicals; 003.7
• DOI: 10.1016/j.chaos.2020.109866
• Languages: English
• ISSNs: 0960-0779

Abstract

Highlights: The study is about the forecasting the deaths because of COVID-19 in major countries around the globe. Its noble study. It will help the different countries to make the decision on this coronavirus disease. Wavelet and ARIMA are coupled to develop new hybrid model, which computes accurate prediction with the least error. The model provides the 99% approximate accuracy. The manuscript will also help to all for preparing isolation wards and strategy for the newly infected patients. Abstract: Everywhere around the globe, the hot topic of discussion today is the ongoing and fast-spreading coronavirus disease (COVID-19), which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2). Earlier detected in Wuhan, Hubei province, in China in December 2019, the deadly virus engulfed China and some neighboring countries, which claimed thousands of lives in February 2020. The proposed hybrid methodology involves the application of discreet wavelet decomposition to the dataset of deaths due to COVID-19, which splits the input data into component series and then applying an appropriate econometric model to each of the component series for making predictions of death cases in future. ARIMA models are well known econometric forecasting models capable of generating accurate forecasts when applied on wavelet decomposed time series. The input dataset consists of daily death cases from most affected five countries by COVID-19, which is given to the hybrid model for validation and to make one month ahead prediction of death cases. These predictions are compared with that obtained from an ARIMA model to estimate the performance of prediction. The predictions indicate a sharp rise in death cases despite various precautionary measures taken by governments of these countries.