Title: Estimation of infection from COVID-19 in India using auto regressive integrated moving average model

Authors: Sunil Gupta; Durgansh Sharma

Addresses: Department of Cybernetics, School of Computer Science and Engineering, University of Petroleum and Energy, Dehradun, India ' Department of Cybernetics, School of Computer Science and Engineering, University of Petroleum and Energy, Dehradun, India

Abstract: The impact of corona virus (COVID-19) infection is gradually increasing day by day, because of its high transmission ability. The virus has deeply impacted the global economy, including India, and the number of deaths and positive infections around the world still lies unabated. India recorded its first infection on 30th January 2020, as the first patient tested positive in Kerala for COVID-19. Currently, the figure of infected people and the death rate is very high. The situation needs a forecast infected model that helps predict the exact figure. The prediction allows authorities to take a factual prevention for decision making. We have used the auto regressive integrated moving average model for predication of infection in COVID-19. In this paper, an approximate prediction of new confirmed cases and death cases is performed by using ARIMA based model. Closeness in the analytical and the available results shows the correctness of the proposed ARIMA model. By using the proposed model, an approximate estimation of case count, death count and prediction of cumulative cases in future can be done quite easily. The prediction data shows more than 98% of accuracy, when compared to available actual figures.

Keywords: COVID-19; infected cases; death rate; ARIMA; forecasting; COVIDify1.3.0; India.

DOI: 10.1504/IJEWM.2024.137523

International Journal of Environment and Waste Management, 2024 Vol.33 No.3, pp.371 - 382

Received: 12 Oct 2020
Accepted: 06 Aug 2021
Published online: 22 Mar 2024 *

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