SARS-CoV-2 forecasting using regression and ARIMA

Chaman Verma (), Purushottam Sharma (), Sanjay Singla (), Abhishek Srivastava () and Ruchi Sharma ()
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Chaman Verma: Eotvos Lorand University
Purushottam Sharma: ASET, Amity University Uttar Pradesh
Sanjay Singla: UIE, Chandigarh University
Abhishek Srivastava: ASET, Amity University Uttar Pradesh
Ruchi Sharma: International Management Institute

International Journal of System Assurance Engineering and Management, 2023, vol. 14, issue 6, No 45, 2626-2641

Abstract: Abstract Respiratory syndrome coronavirus 2 (SARS-CoV-2) is a pandemic coronavirus that is spreading quickly throughout the world. Every person experiences fear due to the unexpected pandemic (Covid-19), which is spreading quickly and affecting people. The affected patients' daily growth rate is accelerating. A predictive analysis was done to determine the potential number of deaths brought on by this pandemic. An official dataset of 35 states/UTs of India was investigated with predictive analysis (regression modeling). To predict the patient's deceased, both recovered and active cases have been impacted. This paper estimated future deceased counts based on active and recovered cases individually and jointly. The high positive linear correlation proved that the active and recovered case affected the patient's deceased rate. Regression models explored high aspects of the deceased ahead. Multiple linear regression has predicted a deceased patient based on active and recovered patients with significant R2 = 0.89. Further, temporal dynamics of Covid-19 timing analyzed with Auto-Regressive Integrated Moving Average forecasted confirmed, active, recovered, and deceased cases for the next 40 days. According to the results, a high cure is still required for active and recovered patients, and the government should follow in obligatory footsteps to avoid more deceased predicted with the models.

Keywords: Covid-19; Correlation; Deceased prediction; Regression; ARIMA (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s13198-023-02127-4

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