Outbreak Prediction of COVID-19 in India Using ARIMA and Prophet Model with Lockdown and Unlock

Abstract:

Coronavirus becomes cerebral pain every day throughout the world. Many cases of coronavirus continue to grow, directly irritating human daily exercises and devastating the economy of nations. The Indian Government announced a one day Janta curfew on March 22, 2020. After three days on March 25 2020, 19 days of lockdown were declared in the country for mitigation of the COVID-19 pandemic. Four lockdowns and six unlock periods were implemented to control the pandemic, but lockdown is the major obstacle to the economy. In unlocking period government open the economic activity stepwise to boost the economy. Coronavirus infection is under control during a lockdown time, but the infection becomes pandemic unlock 1.0, 2.0 and 3.0 period. In Unlock 4.0 and unlock 5.0 coronavirus cases growth goes down but in unlock period 6.0, a sudden spike in confirmed cases. It is due to the festival session and relaxation provided by the Government in the unlock 6.0. The research aimed to forecast the trend towards the COVID-19 pandemic in India with data from June 01, 2020, by applying the ARIMA and Prophet model. Based on several presumptions, the findings of the analysis have shown that, after the unlock-up period is completed, it has been predicted that India's pandemic is expected to decrease by approximately about December 2020 and that it will crest around within the initial weeks of March 2021.

• Periodical: Advances in Science and Technology (Volume 105)
• Pages: 318-330
• DOI: https://doi.org/10.4028/www.scientific.net/AST.105.318
• Online since: April 2021
• Authors: Avinash Sharma*, Dharminder Yadav, Umesh Chandra, Himani Maheswari
• Keywords: ARIMA, Coronavirus, COVID-19, Linear Regression, Prophet

References

[1] Adebiyi, A.A.; Adewumi, A.O.; Ayo, C.K., Comparison of ARIMA and artificial neural networks models for stock price prediction. J. Appl. Math., Article ID 614342 (2014).

DOI: 10.1155/2014/614342

[2] Alsharif, M.H.; Younes, M.K.; Kim, J., Time series ARIMA model for prediction of daily and monthly average global solar radiation: The case study of Seoul, South Korea. Symmetry, 11(2019) 240-258.

DOI: 10.3390/sym11020240

[3] Bianco, V.; Manca*, O.; Nardini, S., Electricity consumption forecasting in Italy using linear regression models, Energy. 34 (2009) 1413–1421.

DOI: 10.1016/j.energy.2009.06.034

[4] Box, G.E.P.; Jenkins, G.M., Time Series Analysis: Forecasting and Control. Revised Edition, Holden Day, San Francisco. (1976).

[5] Covid-19.in, (2020). https://www.mygov.in/covid-19/?cbps=1.

[6] Covid-19 in India, (2020). https://www.kaggle.com/sudalairajkumar/covid19-in-india.

[7] Covid-19 India, (2020). https://www.covid19india.org/.

[8] Dehesh, T.; Mardani-Fard, H.A.; Dehesh, P., Forecasting of COVID-19 Confirmed Cases in Different Countries with ARIMA Models. MedRxiv. (2019) 1-12.

DOI: 10.1101/2020.03.13.20035345

[9] Hassan, S.; Sheikh, F.N.; Jamal, S.; Ezeh, J.K.; Akhtar, A., Coronavirus (COVID-19): A review of clinical features, diagnosis, and treatment. Cureus. 12(2020) 1-7.

DOI: 10.7759/cureus.7355

[10] Jakhar, M.; Ahluwalia, P.K.; Kumar, A. COVID-19 Epidemic Forecast in Different States of India using SIR Model. medRXiv The Preprint server for health sciences. (2020) 1-19.

DOI: 10.1101/2020.05.14.20101725

[11] Khashei, M.; Bijari, M.; Ardali, G.A.R., Improvement of auto-regressive integrated moving average models using fuzzy logic and artificial neural networks (ANNs). Neurocomputing. 72(2009) 956-967.

DOI: 10.1016/j.neucom.2008.04.017

[12] Liu, Y.; Gayle, A.A.; Wilder-Smith, A.; Rocklöv, J., The reproductive number of COVID-19 is higher compared to SARS coronavirus. J. Travel Med., 27(2020) 1-4.

DOI: 10.1093/jtm/taaa021

[13] Lockdown, (2020), https://en.wikipedia.org/wiki/Lockdown.

[14] Maheshwari, H.; Yadav, D.; Chandra, U.; Rai, D.S., Forecasting epidemic spread of COVID-19 in India using arima model and effectiveness of lockdown. Advances in Mathematics: Scientific Journal, 9(6) (2020) 3417–3430.

DOI: 10.37418/amsj.9.6.22

[15] Meyler, A.; Kenny, G.; Quinn, T., Forecasting Irish Inflation Using ARIMA Models. Technical Paper 3/RT/1998, Central Bank of Ireland Research Department. (1998).

[16] Ranjan, R., Predictions Forcovid-19 Outbreak in India Using Epidemiological Models. medRXiv The Preprint server for health sciences. (2020) 1-11.

[17] Shereen, M.A.; Khan, S.; Kazmi, A.; Bashir, N.; Siddique, R., COVID-19 infection: origin, transmission, and characteristics of human coronaviruses. J. Adv. Res. 24(2020) 91-98.

DOI: 10.1016/j.jare.2020.03.005

[18] Sohrabi, C.; Alsafi, Z.; O'Neill, N.; Khan, M.; Kerwan, A.; Al-Jabir, A.; Iosifidis, C.; Agha, R., World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19). Int. J. Surg. 76 (2020).

DOI: 10.1016/j.ijsu.2020.02.034

[19] Tomar, A.; Gupta, N., Prediction for the spread of COVID-19 in India and effectiveness of preventive measures. Science of the Total Environment, 728(2020) 13876.

DOI: 10.1016/j.scitotenv.2020.138762

[20] Tran, T.T.; Pham, L.T.; Ngo, Q.X., Forecasting epidemic spread of SARS-CoV-2 using ARIMA model (Case study: Iran). Global J. Environ. Sci. Manage. 6(2020).

[21] WHO, (2020a). World Health Organization website. https://www.who.int/emergencies/ diseases/novel-coronavirus-2019/technical-guidance/naming-the-coronavirus-disease-(covid-2019)-and-the-virus-that-causes-it.

[22] WHO, (2020b). World Health Organization website. https://www.who.int/health-topics/coronavirus.

[23] WHO, (2020c). Coronavirus disease 2019 (COVID-19) situation Report–95. https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200424-sitrep-95-covid-19.pdf?sfvrsn=e8065831_4.

[24] Worldometers, (2020). Countries where COVID-19 has spread. https://www.worldometers.info/coronavirus/countries-where-coronavirus-has-spread/.

[25] Yadav, D.; Maheshwari, H.; Chandra, U., (2020). Outbreak prediction of covid-19 in most susceptible countries, Global J. Environ. Sci. Manage. 6(2020).

[26] Yadav, D.; Maheshwari, H.; Chandra, U., Sharma, A., COVID-19 Analysis by Using Machine and Deep Learning, Internet of Medical Things for Smart Healthcare, 2020, pp.31-63.

DOI: 10.1007/978-981-15-8097-0_2