• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology

Article

Indian Journal of Science and Technology

Year: 2020, Volume: 13, Issue: 32, Pages: 3248-3274

Original Article

Prediction of COVID-19 trend in India using time series forecasting

Received Date:22 July 2020, Accepted Date:16 August 2020, Published Date:01 September 2020

Abstract

Objectives: COVID-19 pandemic is one of the prevalent challenges mankind has ever faced and there is a lot of uncertainty prevailing over the future with respect to COVID-19. In this situation machine learning algorithms can be useful for real-time analysis and prediction of trends of the infections. The objective of the research study is to analyze the COVID-19 trend in India and forecast the trend of outbreak in near future. This model can provisionally guide the government and healthcare organizations in making preparations for the upcoming situation arising out of COVID-19 transmission. Methods: The COVID-19 data from 30-Dec 2019 to 27-July 2020 was used for prediction of COVID-19 trend in next 30 days i.e. from 28 July to 26 August. The time series forecasting approaches with ARIMA Model and PROPHET were used for forecasting. The performance of these models was evaluated using validation metrics and good performance was indicated. Findings: The prediction results indicate an increasing trend of COVID-19 positive, active and deceased cases in India for next 30 days i.e. up to 26 August 2020. Novelty: COVID-19 pandemic is a new problem. The novelty and originality of this research lies in the fact that time series forecasting is used for real time analysis and prediction of COVID-19 pandemic.

Keywords: COVID-19; time series analysis; ARIMA model; time series forecasting; PROPHET

References

  1. Advice for the public. (accessed ) Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/advice-for-public
  2. Cheng CCV, Lau KPS, Woo CYP, Yuen KY. Severe Acute Respiratory Syndrome Coronavirus as an Agent of Emerging and Reemerging Infection. Clinical Microbiology Reviews. 2007;20:660–694. Available from: https://dx.doi.org/10.1128/cmr.00023-07
  3. Worldometer. Coronavirus Cases. (accessed ) Available from: https://www.worldometers.info/coronavirus
  4. Fan H, Tang X, Song Y, Liu P, Chen Y. Influence of COVID-19 on Cerebrovascular Disease and its Possible Mechanism. Neuropsychiatric Disease and Treatment. 2020;16:1359–1367. Available from: https://doi.org/ 10.2147/NDT.S251173
  5. Garfin DR, Silver RC, Holman EA. The novel coronavirus (COVID-2019) outbreak: Amplification of public health consequences by media exposure. Health Psychology. 2020;39(5):355–357. Available from: https://dx.doi.org/10.1037/hea0000875
  6. Gao M, Yang L, Chen X, Deng Y, Yang S, Xu H. 2020.
  7. Rajendran K, Krishnasamy N, Rangarajan J, Rathinam J, Natarajan M, Ramachandran A. Convalescent plasma transfusion for the treatment of COVID‐19: Systematic review. Journal of Medical Virology. 2020;92(9):1475–1483. Available from: https://dx.doi.org/10.1002/jmv.25961
  8. Krishnakumar B, Rana S. COVID 19 in INDIA: Strategies to combat from combination threat of life and livelihood. Journal of Microbiology, Immunology and Infection. 2020;53(3):389–391. Available from: https://dx.doi.org/10.1016/j.jmii.2020.03.024
  9. Gupta R, Pal SK, Pandey G. 2020.
  10. Lurie N, Saville M, Hatchett R, Halton J. Developing Covid-19 Vaccines at Pandemic Speed. New England Journal of Medicine. 2020;382(21):1969–1973. Available from: https://dx.doi.org/10.1056/nejmp2005630
  11. Fong SJ, Li G, Dey N, Gonzalez-Crespo R, Herrera-Viedma E. Finding an Accurate Early Forecasting Model from Small Dataset: A Case of 2019-nCoV Novel Coronavirus Outbreak. International Journal of Interactive Multimedia and Artificial Intelligence. 2020;6:132. Available from: https://dx.doi.org/10.9781/ijimai.2020.02.002
  12. Tandon H, Ranjan P, Chakraborty T, Suhag V. Coronavirus ( COVID-19 ): ARIMA based time-series analysis to forecast near future. 2020.
  13. Shinde RG, Kalamkar BA, Mahalle NP, Dey N, Chaki J, Hassanien AE. Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the State-of-the-Art. SN Computer Science. 2020;1(4):1–15. Available from: https://dx.doi.org/10.1007/s42979-020-00209-9
  14. Benvenuto D, Giovanetti M, Vassallo L, Angeletti S, Ciccozzi M. Application of the ARIMA model on the COVID-2019 epidemic dataset. Data in Brief. 2020;29. Available from: https://dx.doi.org/10.1016/j.dib.2020.105340
  15. Khan FM, Gupta R. ARIMA and NAR based prediction model for time series analysis of COVID-19 cases in India. Journal of Safety Science and Resilience. 2020;1:12–18. Available from: https://dx.doi.org/10.1016/j.jnlssr.2020.06.007
  16. JCS, Fong J, Dey N. Artificial Intelligence for Coronavirus Outbreak. Singapore. Springer..
  17. Chauhan B, Kumar S, Tripathi A, Malik RP. Modified 2D Proca Theory: Revisited under BRST and (Anti-)chiral Superfield Formalisms. Advances in High Energy Physics. 2020;2020:1–38. Available from: https://dx.doi.org/10.1155/2020/3495168

Copyright

© 2020 Mirza et al.This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee).

DON'T MISS OUT!

Subscribe now for latest articles and news.