Implementation of the prophet model in COVID-19 cases forecast


Rodiah Rodiah(1); Eka Patriya(2*); Diana Tri Susetianingtias(3); Ety Sutanty(4);

(1) Gunadarma University
(2) Gunadarma University
(3) Gunadarma University
(4) Gunadarma University
(*) Corresponding Author

  

Abstract


One of the steps to understanding this pandemic is to look at the spread of the data by predicting an increase in cases in various countries so that prevention can be carried out as early as possible. One way to see fluctuations in COVID-19 pandemic data is to predict the rate of cases using forecasting methods so that conclusions can be drawn on the spread of COVID-19 pandemic data around the world to be processed using statistical models. This study will implement the use of the Prophet Model in seeing the rate of development of COVID-19 in the world using four features in the forecasting process such as the number of confirmed cases, the number of cases of recovered patients, the number of cases of death, and the number of active cases. The results of this study produce forecasting data on the number of cases of the COVID-19 pandemic that can be viewed daily, weekly, and even monthly. Forecasting results show the first spike at the end of March until the number of cases reached around 10,275,800 million as of June 29, 2020, where the number of cases grew exponentially until June 29, 2020. The case rate of growth in many instances experienced significant growth until the end of October, touching the number in the range of 34,507,150 million as of October 25, 2020. After June 29, 2020, a very high spike was different from the increase in cases in the previous months. Forecasting results show no point decline because historical data on the number of daily confirmed cases of the COVID-19 pandemic has not decreased. The forecasting results in this study are expected to be able to systematically predict events or events that will occur in the COVID-19 pandemic around the world with the help of valid periodic data so that some information can be obtained for preventive measures related to the COVID-19 pandemic.

Keywords


COVID-19; features, cases, forecasting; prophet model

  
  

Full Text:

PDF
  

Article Metrics

Abstract view: 457 times
PDF view: 175 times
     

Digital Object Identifier

doi  https://doi.org/10.33096/ilkom.v14i2.1219.99-111
  

Cite

References


Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. “Clinical features of patients infected with 2019 novel coronavirus in Wuhan,” China.Lancet. 2020;395(10223):497-506.

World Health Organization. “Naming the coronavirus disease (COVID-19) and the virus that causes it [Internet],” Geneva: World Health Organization; 2020 [cited 2020 March 29]. Available from: https://www.who.int/emergencies/diseases/novelcoronavirus-2019/technical-guidance/naming-the-coronavirusdisease-(covid-2019)-and-the-virus-that-causes-it.

Wu Z, McGoogan JM. “Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19),” Outbreak in China: Summary of a Report of 72314 Cases From the Chinese Center for Disease Control and Prevention. JAMA. 2020; published online February 24. DOI:10.1001/jama.2020.2648.

Kementerian Kesehatan Republik Indonesia.” Info infeksi emerging Kementerian Kesehatan RI [Internet],” 2020 [updated 2020 March 30; cited 2020 March 31]. Available from: https:// infeksiemerging.kemkes.go.id/

Roosa, K., Lee, Y., Luo, R., Kirpich, A., Rothenberg, R., Hyman, J. M., & Chowell, G. “Short-term forecasts of the COVID-19 epidemic in Guangdong and Zhejiang,” China: February 13–23, 2020. Journal of Clinical Medicine, 9(2), 596.

Singh RK, Rani M, Bhagavathula AS, Sah R, Rodriguez-Morales AJ, Kalita H, Nanda C, Sharma S, Sharma YD, Rabaan AA, Rahmani J, Kumar P. “Prediction of the COVID-19 Pandemic for the top 15 affected countries: Advanced Autoregressive Integrated Moving Average (ARIMA) model,” JMIR Public Health Surveill. 2020 May 13;6(2):e19115. doi: 10.2196/19115. PMID: 32391801; PMCID: PMC7223426.

Baloch S, Baloch MA, Zheng T, Pei X. “The Coronavirus Disease 2019 (COVID-19) Pandemic,” Tohoku J Exp Med. 2020 Apr;250(4):271-278. doi: 10.1620/tjem.250.271. PMID: 32321874.

Wang, X., Liu, S., & Huang, Y. A.” Study on the rapid parameter estimation and the grey prediction in richards model,” Journal of Systems Science and Information, 4(3), 223-234.2016

Chakraborty T, Ghosh I. “Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis,” Chaos Solitons Fractals. 2020 Jun;135:109850. doi: 10.1016/j.chaos.2020.109850. Epub 2020 Apr 30. PMID: 32355424; PMCID: PMC7190506.

Maleki M, Mahmoudi MR, Wraith D, Pho KH. “Time series modelling to forecast the confirmed and recovered cases of COVID-19,” Travel Med Infect Dis. 2020 Sep-Oct;37:101742. doi: 10.1016/j.tmaid.2020.101742. Epub 2020 May 13. PMID: 33081974.

Rath S, Tripathy A, Tripathy AR. “Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model,” Diabetes Metab Syndr. 2020 Sep-Oct;14(5):1467-1474. doi: 10.1016/j.dsx.2020.07.045. Epub 2020 Aug 1. PMID: 32771920; PMCID: PMC7395225.

Chatterjee, A.; Gerdes, M.W.; Martinez, S.G. “Statistical explorations and Univariate Timeseries Analysis on COVID-19 datasets to understand the trend of disease spreading and death,” Sensors 2020, 20, 3089. https://doi.org/10.3390/s20113089

Devakumar K. P. “COVID-19 - analysis, visualization & comparisons, ” Kaggle COVID-19 dataset, available from: https://www.kaggle.com/imdevskp/covid-19-analysis-visualization-comparisons/data, 2020

V. Tulshyan, D. Sharma, and M. Mittal. “An eye on the future of COVID-19: prediction of likely positive cases and fatality in India over a 30-day horizon using the Prophet model,” Disaster Medicine and Public Health Preparedness, pp. 1–7, 2020.

P. Mishra, A. Mohammad, G. Al Khatib et al., “Modelling and forecasting of COVID-19 in India,” Journal of Infectious Diseases and Epidemiology, vol. 6, no. 5, 2020.

D. Benvenuto, M. Giovanetti, L. Vassallo, S. Angeletti, and M. Ciccozzi, “Application of the ARIMA model on the COVID-2019 epidemic dataset,” Data in Brief, vol. 29, p. 105340, 2020.

M. Indhuja and P. P. Sindhuja, “Prediction of covid-19 cases in India using prophet,” International Journal of Statistics and Applied Mathematics, vol. 5, no. 4, pp. 103–106, 2020.

Duccio Fanelli and Francesco Piazza, “Analysis and forecast of Covid-19 spreading in China, Italy and France,” Chaos, Solitons & Fractals, 134:109761, 2020.

Milad Haghani, Michiel CJ Bliemer, Floris Goerlandt, and Jie Li, “The scientific literature on coronaviruses, covid-19 and its associated safety-related research dimensions: A scientometric analysis and scoping review,” Safety Science, page 104806, 2020

Github Inc. Covid-19 cases. https://github.com/cssegisanddata/covid-19 (accessed in 21 may, 2021).

Lin Jia, Kewen Li, Yu Jiang, Xin Guo, et al, “Prediction and analysis of coronavirus disease 2019.” arXiv preprint arXiv:2003.05447, 2020.

Parikshit N Mahalle, Nilesh P Sable, Namita P Mahalle, and Gitanjali R Shinde, “Data analytics: Covid-19 prediction using multimodal data,” Preprints, 2020

Manotosh Mandal, Soovoojeet Jana, Swapan Kumar Nandi, Anupam Khatua, Sayani Adak, and TK Kar, “A model based study on the dynamics of covid-19: Prediction and control,” Chaos, Solitons & Fractals, page 109889, (accessed in 21 may, 2021).

[Prophet: forecasting at scale - Facebook Research. https://research.fb.com/blog/2017/02/prophet-forecasting-at-scale, (accessed in 21 may, 2021).

9] Al-Rousan, N., Al-Najjar, H, “Data analysis of coronavirus COVID-19 epidemic in South Korea based on recovered and death cases,” Journal of Medical Virology, 92(9): 1603-1608. https://doi.org/10.1002/jmv.25850, 2020

Gambhir, E., Jain, R., Gupta, A. & Tomer, U, “Regression analysis of COVID-19 using machine learning algorithms, “ Proceedings of the International Conference on Smart Electronics and Communication, pp. 65–71. https://doi.org/10.1201/9781351073974, 2020

Shinde G.R., Kalamkar A.B., Mahalle P.N., Dey N., Chaki J., Hassanien A.E, “Forecasting models for coronavirus disease (COVID-19): a survey of the state-of-the-art”, SN Comp Sci. 2020;1:197. doi: 10.1007/s42979-020-00209-9.

8. Wang C., Horby P.W., Hayden F.G., Gao G.F, “A novel coronavirus outbreak of global health concern,” Lancet North Am Ed. 2020;395(10223):470–473. 10.1016%2FS0140-6736(20)30185-9.

McKinney W, “Data structures for statistical computing in python,”, Pp. 51–56, Proceedings of the 9th Python in Science Conference. Austin, TX. Vol. 445. 2010.

Oliphant T.E, “A guide to NumPy, “ Vol. 1. Trelgol Publishing USA, 2006.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 Rodiah, Eka Patriya, Diana Tri Susetianingtias, Ety Sutanty

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.