Article
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Modeling and Prediction of COVID-19 Outbreak in India
Version 1
: Received: 18 August 2020 / Approved: 20 August 2020 / Online: 20 August 2020 (08:16:38 CEST)
How to cite: Kumar, S.; Agiwal, V.; Kumar, A.; Kumar, J. Modeling and Prediction of COVID-19 Outbreak in India. Preprints 2020, 2020080446. https://doi.org/10.20944/preprints202008.0446.v1 Kumar, S.; Agiwal, V.; Kumar, A.; Kumar, J. Modeling and Prediction of COVID-19 Outbreak in India. Preprints 2020, 2020080446. https://doi.org/10.20944/preprints202008.0446.v1
Abstract
As the outbreak of coronavirus disease 2019 (COVID-19) is continuously increasing in India, so epidemiological modeling of COVID-19 data is urgently required for administrative strategies. Time series and is capable to predict future observations by modeling the data based on past and present data. Here, we have modeled the epidemiological COVID-19 Indian data using various models. Based on the collected COVID-19 outbreak data, we try to find the propagation rule of this outbreak disease and predict the outbreak situations in India. For India data, the time series model gives the best results in the form of predication as compared to other models for all variables of COVID-19. For new cases, new deaths, total cases and total deaths, the best fitted ARIMA models are as follows: ARIMA(0,2,3), ARIMA(0,1,1), ARIMA(0,2,0) and ARIMA(0,2,1). Based on time series analysis, we predict all variables for next month and conclude that the predictive value of Indian COVID-19 data of total cases is more than 20 lakhs with more than 43 thousand total deaths. The present chapter recommended that a comparison between various predictive models provide the accurate and better forecast value of the COVID-19 outbreak for all study variables.
Keywords
Saurabh Kumar , Varun Agiwal , Ashok Kumar and Jitendra Kumar
Subject
Computer Science and Mathematics, Probability and Statistics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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