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“COVID-19” Forecast Using Time Series Methods
[Full Text]
AUTHOR(S)
Milind Talele, Dr. Rajashree Jain
KEYWORDS
Coronavirus, “COVID-19”, Day Level Forecasting, time series algorithms, Exponential Smoothing.
ABSTRACT
The coronavirus “COVID-19” pandemic spreading over the world. This paper presents three time series models, exponential smoothing, Prophet additive forecast and Holts forecast method on understanding predictive patterns from published data on the number of “COVID-19” infected with coronavirus in India. This paper objective to introduce a different effective time series method to predict “COVID-19” forecast. The paper presented “COVID-19” confirmed cases in India till 30 June 2020. The data set used was from the Ministry of Health & Family Welfare and COVID 19india published through kaggle. The simple exponential smoothing model was applied using the Tableau tool. Prophet additive forecast method applied using R language and Holt method used in SPSS tool.
REFERENCES
[1] World Health Organization, 2020; https://www.who.int/emergencies/diseases/novel-coronavirus-2019w.who.int/emergencies/diseases/novel-coronavirus-2019Who.int. 2020. Coronavirus. [online] Available at: [Accessed 13 May 2020]
[2] Penn State. (2020, May 7). Which COVID-19 models should we use to make policy decisions?. ScienceDaily. Retrieved May 11, 2020 from www.sciencedaily.com/releases/2020/05/200507143011.htm
[3] G. Wainer, C. R. Martin, and H. Khalil, “Coronavirus: How new simulations can predict the spread of future pandemics,” The Conversation, 07-May-2020. [Online]. Available: https://theconversation.com/coronavirus-how-new-simulations-can-predict-the-spread-of-future-pandemics-134217. [Accessed: 15-May-2020].
[4] “Modelers Struggle to Predict the Future of the COVID-19 Pandemic,” the-scientist.com, 13-Mar-2020.
[5] Benzekry, S., Lamont, C., Beheshti, A., Tracz, A., Ebos, J. M., Hlatky, L., & Hahnfeldt, P. (2014). Classical mathematical models for description and prediction of experimental tumor growth. PLoS computational biology, 10(8), e1003800. https://doi.org/10.1371/journal.pcbi.10038000
[6] “Which COVID-19 models should we use to make policy decisions?,” ScienceDaily, 07-May-2020. [Online]. Available: http://www.sciencedaily.com/releases/2020/05/200507143011.htm. [Accessed: 15-May-2020].
[7] L. Li, Z. Yang, Z. Dang, C. Meng, J. Huang, H. Meng, D. Wang, G. Chen, J. Zhang, H. Peng, and Y. Shao, “Propagation analysis and prediction of the COVID-19,” Infectious Disease Modelling, vol. 5, pp. 282–292, Mar. 2020
[8] W. C. Roda, M. B. Varughese, D. Han, and M. Y. Li, “Why is it difficult to accurately predict the COVID-19 epidemic?,” Infectious Disease Modelling, vol. 5, pp. 271–281, 2020.
[9] Pourhomayoun, Mohammad & Shakibi, Mahdi. (2020). Predicting Mortality Risk in Patients with COVID-19 Using Artificial Intelligence to Help Medical Decision-Making. 10.1101/2020.03.30.20047308.
[10] G. Miksić, Nina, Mitja, Andraž, and Perc, “Forecasting COVID-19,” Frontiers, 01-Apr-2020. [Online]. Available: https://www.frontiersin.org/articles/10.3389/fphy.2020.00127/full. [Accessed: 15-May-2020].
[11] Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis. (2020). doi: 10.1016/S1473-3099(20)30120-1. [Epub ahead of print].
[12] FCSSEGISandData, “CSSEGISandData/COVID-19,” GitHub, 16-May-2020. [Online]. Available: https://github.com/CSSEGISandData/COVID-19. [Accessed: 17-May-2020].
[13] R. J. Hyndman, A. B. Koehler, R. D. Snyder, and S. Grose, “A state space framework for automatic forecasting using exponential smoothing methods,” International Journal of Forecasting, vol. 18, no. 3, pp. 439–454, 2002.
[14] J. W. Taylor, “Exponential smoothing with a damped multiplicative trend,” International Journal of Forecasting, vol. 19, no. 4, pp. 715–725, 2003.
[15] Srk, “COVID-19 in India,” Kaggle, 14-May-2020. [Online]. Available: https://www.kaggle.com/sudalairajkumar/covid19-in-india. [Accessed: 15-May-2020].
[16] Petropoulos F, Makridakis S (2020) Forecasting the novel coronavirus COVID-19. PLoS ONE 15(3): e0231236. https://doi.org/10.1371/journal.pone.0231236
[17] “Exponential smoothing,” Wikipedia, 02-May-2020. [Online]. Available: https://en.wikipedia.org/wiki/Exponential_smoothing. [Accessed: 17-May-2020].
[18] ”NCSS Statistical Software," NCSS Statistical Software, 08 05 2020. [Online]. Available: https://ncss-wpengine.netdna-ssl.com/wp-content/themes/ncss/pdf/Procedures/NCSS/Exponential_Smoothing-Horizontal.pdf. [Accessed 08-May-2020].
[19] “Tableau: Business Intelligence and Analytics Software,” Tableau Software. [Online]. Available: https://www.tableau.com/. [Accessed: 15-May-2020].
[20] Tableau, "How forcasting work in tableau," tabeau, 08 05 2020. [Online]. Available: https://help.tableau.com/current/pro/desktop/en-us/forecast_how_it_works.htm accessed. [Accessed 05 2020].
[21] “Exponential smoothing,” Wikipedia, 02-May-2020. [Online]. Available: https://en.wikipedia.org/wiki/Exponential_smoothing. [Accessed: 17-May-2020].
[22] Circulars for Covid-19: Ministry of Home Affairs,” Ministry of Home Affairs | GoI. [Online]. Available: https://www.mha.gov.in/notifications/circulars-covid-19. [Accessed: 15-May-2020].
[23] S. J. Taylor and B. J. Letham, “Forecasting at scale,” the Journal of Life and Environmental Sciences, pp. 1–25, Sep. 2017.
[24] “Prophet is a forecasting procedure implemented in R and Python. It is fast and provides completely automated forecasts that can be tuned by hand by data scientists and analysts.,” Prophet. [Online]. Available: https://facebook.github.io/prophet/. [Accessed: 27-May-2020].
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