Performance Analysis of Autoregressive Integrated Moving Average (ARIMA) and ‘earlyR’ Statistical Models for Predicting Epidemic Outbreaks: A Case Study on COVID-19 Data in India
DOI:
https://doi.org/10.48048/tis.2024.7246Keywords:
ARIMA model, Prediction model, Forecasting, Statistical model, Time series forecastingAbstract
The objective of this research article is to analyze the performance of the Autoregressive Integrated Moving Average (ARIMA) model and the ‘earlyR’ statistical model in predicting epidemic outbreaks using COVID-19 data of India. The results of this analysis can help take preventive actions and restrict the spread of the epidemic with the help of projected data. The ‘projections’ module was utilized to generate the epidemic path by aligning the available COVID-19 data from India, distribution of the time interval between successive cases and reproduction number (R0) of the corresponding regions with the ‘earlyR’ epidemic statistical model. The values of (p, d and q) were obtained utilizing the ‘auto.arima’ function, and an ARIMA time series model was created using the ‘forecast’ module to forecast future infected occurrences. The ‘earlyR’ epidemic model yielded a median projected value with an inaccuracy of 35.1 %, while the ARIMA model had a mean error of −1.9 %. A comparison of these methods indicates that the ARIMA model is a superior method compared to the ‘earlyR’ epidemic model in terms of accuracy.
HIGHLIGHTS
- The paper analyzes the performance of the Autoregressive Integrated Moving Average (ARIMA) model and the 'earlyR' statistical model in predicting epidemic outbreaks using COVID-19 data of India
- Utilized the ‘projections’ module aligning COVID-19 data, time interval between cases, and reproduction number (R0) with the ‘earlyR’ epidemic model. ARIMA model parameters (p, d, and q) were determined using the ‘auto.arima’ function
- Evaluated real-time data for both models, showing the ARIMA model's effectiveness in predicting COVID-19 cases in India
- The proposed ARIMA model outperformed existing models, achieving a mean error of −1.9 %, providing valuable insights for health policymakers
GRAPHICAL ABSTRACT
Downloads
Metrics
References
World Health Organization. Coronavirus disease 2019 (COVID-19) situation report - 90. World Health Organization, Geneva, Switzerland, 2020.
J Hellewell, S Abbott, A Gimma, NI Bosse, CI Jarvis, TW Russell, JD Munday, AJ Kucharski and WJ Edmunds. Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. Lancet Global Health 2020; 8, E488-E496.
FM Guerra, S Bolotin, G Lim, J Heffernan, SL Deeks, Y Li and NS Crowcroft. The basic reproduction number (R0) of measles: A systematic review. Lancet Infect. Dis. 2017; 17, E420-E428.
M D’Arienzo and A Coniglio. Assessment of the SARS-CoV-2 basic reproduction number, R0, based on the early phase of COVID-19 outbreak in Italy. Biosafety Health 2020; 2, 57-9.
G Chowell, H Nishiura and LMA Bettencourt. Comparative estimation of the reproduction number for pandemic influenza from daily case notification data. J. R. Soc. Interface 2007; 4, 154-66.
YC Wu, CS Chen and YJ Chan. The outbreak of COVID-19: An overview. J. Chin. Med. Assoc. 2020; 83, 217-20.
E Rezel-Potts, A Douiri, X Sun, PJ Chowienczyk, AM Shah and MC Gulliford. Cardiometabolic outcomes up to 12 months after COVID-19 infection. A matched cohort study in the UK. PloS Med. 2022; 19, e1004052.
CK Wibmer, F Ayres, T Hermanus, M Madzivhandila, P Kgagudi, B Oosthuysen, BE Lambson, TD Oliveira, M Vermeulen, KVD Berg, T Rossouw, M Boswell, V Ueckermann, S Meiring, AV Gottberg, C Cohen, L Morris, JN Bhiman and PL Moore. SARS-CoV-2 501Y.V2 escapes neutralization by South African COVID-19 donor plasma. Nat. Med. 2021; 27, 622-5.
SK Yadav and Y Akhter. Statistical modeling for the prediction of infectious disease dissemination with special reference to COVID-19 spread. Front. Publ. Health 2021; 9, 645405.
J Pang, MX Wang, IYH Ang, SHX Tan, RF Lewis, JIP Chen, RA Gutierrez, SXW Gwee, PEY Chua, Q Yang, XY Ng, RKS Yap, HY Tan, YY Teo, CC Tan, AR Cook, JCH Yap and LY Hsu. Potential rapid diagnostics, vaccine and therapeutics for 2019 novel coronavirus (2019-nCoV): A systematic review. J. Clin. Med. 2020; 9, 623.
S Xu and Y Li. Beware of the second wave of COVID-19. Lancet 2020; 395, 1321-2.
L Zhong, L Mu, J Li, J Wang, Z Yin and D Liu. Early prediction of the 2019 novel coronavirus outbreak in the mainland China based on simple mathematical model. IEEE Access 2020; 8, 51761-9.
K Mizumoto and G Chowel. Transmission potential of the novel coronavirus (COVID-19) onboard the diamond Princess Cruises Ship, 2020. Infect. Dis. Model. 2020; 5, 264-70.
C Quick, R Dey and X Lin. Regression models for understanding COVID-19 epidemic dynamics with incomplete data. J. Am. Stat. Assoc. 2021; 116, 1561-77.
M Ritter, DVM Ott, F Paul, JD Haynes and K Ritter. COVID-19: A simple statistical model for predicting intensive care unit load in exponential phases of the disease. Sci. Rep. 2021; 11, 5018.
GR Shinde, AB Kalamkar, PN Mahalle, N Dey, J Chaki and AE Hassanien. Forecasting models for coronavirus disease (COVID-19): A survey of the state-of-the-art. SN Comput. Sci. 2020; 1, 197.
D Haritha, N Swaroop and M Mounika. Prediction of COVID-19 cases using CNN with x-rays. In: Proceedings of the 5th International Conference on Computing, Communication and Security, Patna, India. 2020.
Q Guo and Z He. Prediction of the confirmed cases and deaths of global COVID-19 using artificial intelligence. Environ. Sci. Pollut. Res. 2021; 28, 11672-82.
P He. Study on epidemic prevention and control strategy of COVID-19 based on personnel flow prediction. In: Proceedings of the International Conference on Urban Engineering and Management Science, Zhuhai, China. 2020, p. 688-91.
MH Alsharif, MK Younes and J Kim. Time series ARIMA model for prediction of daily and monthly average global solar radiation: The case study of Seoul, South Korea. Symmetry 2019; 11, 240.
Ministry of Health and Family Welfare, Available at: https://www.mohfw.gov.in, accessed January 2023.
A Cori, NM Ferguson, C Fraser and S Cauchemez. A new framework and software to estimate time-varying reproduction numbers during epidemics. Am. J. Epidemiol. 2013; 178, 1505-12.
Q Li, X Guan, P Wu, X Wang, L Zhou, Y Tong, R Ren, KSM Leung, EHY Lau, JY Wong, X Xing, N Xiang, Y Wu, C Li, Q Chen, D Li, T Liu, J Zhao, M Liu, ..., Z Feng. Early transmission dynamics in Wuhan, China, of novel coronavirus - infected pneumonia. New Engl. J. Med. 2020; 382, 1199-207.
H Nishiura, NM Linton and AR Akhmetzhanov. Serial interval of novel coronavirus (COVID-19) infections. Int. J. Infect. Dis. 2020; 93, 284-6.
T Jombart, P Nouvellet, S Bhatia and ZN Kamvarprojections. Project future case incidence, Available at: https://CRAN.R-project.org/package=projections, accessed January 2023.
World Health Organization. Advice for the public: Coronavirus disease (COVID-19), Available at: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/advice-for-public, accessed January 2023.
M Wieczorek, J Siłka and M Woźniak. Neural network powered COVID-19 spread forecasting model. Chaos Solitons Fractals 2020; 140, 110203.
A Kallel, M Rekik and M Khemakhem. Hybrid-based framework for COVID-19 prediction via federated machine learning models. J. Supercomput. 2022; 78, 7078-10.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Walailak University
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
