Indian COVID-19 dynamics: prediction using autoregressive integrated moving average modelling
Keywords:
Autoregressive integrated moving average, COVID-19, Epidemic curve, Forecast, Mathematical modelling, Prediction
Abstract
Background: The forecasting of Coronavirus Disease-19 (COVID-19) dynamics is a centrepiece in evidence-based disease management. Numerous approaches that use mathematical modelling have been used to predict the outcome of the pandemic, including data-driven models, empirical and hybrid models. This study was aimed at prediction of COVID-19 evolution in India using a model based on autoregressive integrated moving average (ARIMA).
Material and Methods: Real-time Indian data of cumulative cases and deaths of COVID-19 was retrieved from the Johns Hopkins dashboard. The dataset from 11 March 2020 to 25 June 2020 (n = 107 time points) was used to fit the autoregressive integrated moving average model. The model with minimum Akaike Information Criteria was used for forecasting. The predicted root mean square error (PreRMSE) and base root mean square error (BaseRMSE) were used to validate the model.
Results: The ARIMA (1,3,2) and ARIMA (3,3,1) model fit best for cumulative cases and deaths, respectively, with minimum Akaike Information Criteria. The prediction of cumulative cases and deaths for next 10 days from 26 June 2020 to 5 July 2020 showed a trend toward continuous increment. The PredRMSE and BaseRMSE of ARIMA (1,3,2) model were 21,137 and 166,330, respectively. Similarly, PredRMSE and BaseRMSE of ARIMA (3,3,1) model were 668.7 and 5,431, respectively.
Conclusion: It is proposed that data on COVID-19 be collected continuously, and that forecasting continue in real time. The COVID-19 forecast assist government in resource optimisation and evidence-based decision making for a subsequent state of affairs.
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[4] Bhandari S, Shaktawat AS, Tak A, Patel B, Shukla J, Singhal S, et al. Logistic regression analysis to predict mortality risk in COVID-19 patients from routine hematologic parameters. Ibnosina J Med Biomed Sci [serial online] 2020 [cited 2020 Jul 1];12:123-9. Available from: http://www.ijmbs.org/text.asp?2020/12/2/123/288204
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[6] Johns Hopkins University Center for Systems Science and Engineering, 2019. (Accessed : 25th Jun 2020). Available from: https://github.com/CSSEGISandData/COVID-19
[7] Cryer JD, Chan KS. Models for Non-stationary time series in Time series analysis: with applications in R (2nd edition ) 98-99 (Springer Science & Business Media, 2008 ).
[8] Indrayan, Abhaya, Rajeev Kumar Malhotra Relationships: Quantitative Outcome in Medical biostatistics (4th edition) 456 (CRC Press, 2018).
[9] Metcalfe AV, Cowpertwait PS. Non-stationary models in Introductory time series with R 137-140 (Springer-Verlag New York, 2009).
[10] MATLAB Team, Statistics and Machine Learning Toolbox 10.2. version 9.0.0.341360 (R 2016a). Natick, Massachusetts : The Mathworks Inc
[11] Shinde GR, Kalamkar AB, Mahalle PN, Dey N, Chaki J, Hassanien AE. Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the State-of-the-Art. SN Computer Science [Internet]. 2020 Jun 11;1(4). Available from: http://dx.doi.org/10.1007/s42979-020-00209-9
[12] National Portal of India (cited on: 27 April 2020). Available from: https://www.india.gov.in/india-glance/profile
[13] Yang C, Wang J. A mathematical model for the novel coronavirus epidemic in Wuhan, China Math Biosci Eng [Internet]. 2020;17(3):2708–24. Available from: http://dx.doi.org/10.3934/mbe.2020148
[14] Chatterjee A, Gerdes MW, Martinez SG. Statistical Explorations and Univariate Timeseries Analysis on COVID-19 Datasets to Understand the Trend of Disease Spreading and Death. Sensors [Internet]. 2020 May 29;20(11):3089. Available from: http://dx.doi.org/10.3390/s20113089
[15] Tiwari S, Kumar S, Guleria K. Outbreak Trends of Coronavirus Disease–2019 in India: A Prediction. Disaster Med Public Health Prep [Internet]. 2020 Apr 22;1–6. Available from: http://dx.doi.org/10.1017/dmp.2020.115
[16] Tomar A, Gupta N. Prediction for the spread of COVID-19 in India and effectiveness of preventive measures. Sci. Total Environ [Internet]. 2020 Aug;728:138762. Available from: http://dx.doi.org/10.1016/j.scitotenv.2020.138762
[17] Tuli S, Tuli S, Tuli R, Gill SS. Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing. Internet of Things [Internet]. 2020 Sep;11:100222. Available from: http://dx.doi.org/10.1016/j.iot.2020.100222
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[20] Pai C, Bhaskar A, Rawoot V. Investigating the dynamics of COVID-19 pandemic in India under lockdown. Chaos Soliton Fract [Internet]. 2020 Sep;138:109988. Available from: http://dx.doi.org/10.1016/j.chaos.2020.109988
[21] Arora P, Kumar H, Panigrahi BK. Prediction and analysis of COVID-19 positive cases using deep learning models: A descriptive case study of India. Chaos Soliton Fract [Internet]. 2020 Oct;139:110017. Available from: http://dx.doi.org/10.1016/j.chaos.2020.110017
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[24] Singhal A, Singh P, Lall B, Joshi SD. Modeling and prediction of COVID-19 pandemic using Gaussian mixture model. Chaos Soliton Fract [Internet]. 2020 Sep;138:110023. Available from: http://dx.doi.org/10.1016/j.chaos.2020.110023
[25] Tabish SA. The COVID-19 pandemic: Emerging perspectives and future trends. J Public Health Res [Internet]. 2020 Jun 4;9(1). Available from: http://dx.doi.org/10.4081/jphr.2020.1786
[26] Benvenuto D, Giovanetti M, Vassallo L, Angeletti S, Ciccozzi M. Application of the ARIMA model on the COVID-2019 epidemic dataset. Data Brief [Internet]. 2020 Apr;29:105340. Available from: http://dx.doi.org/10.1016/j.dib.2020.105340
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[29] Bhandari S, Sharma R, Singh Shaktawat A, Banerjee S, Patel B, Tak A, et al. COVID-19 related mortality profile at a tertiary care centre: a descriptive study. Scr Med 2020;51(2):69-73. DOI:10.5937/scriptamed51-27126
[30] Salgotra R, Gandomi M, Gandomi AH. Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming Chaos Soliton Fract [Internet]. 2020 Sep;138:109945. Available from: http://dx.doi.org/10.1016/j.chaos.2020.109945
[31] Sujath R, Chatterjee JM, Hassanien AE. A machine learning forecasting model for COVID-19 pandemic in India. Stoch Env Res Risk A [Internet]. 2020 May 30;34(7):959–72. Available from: http://dx.doi.org/10.1007/s00477-020-01827-8
[32] Yadav RS. Data analysis of COVID-2019 epidemic using machine learning methods: a case study of India. International Journal of Information Technology [Internet]. 2020 May 26; Available from: http://dx.doi.org/10.1007/s41870-020-00484-y
Published
2021/03/26
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