Impact of Statistical Approach on Time-Series Models for Forecasting COVID-19

Authors

  • Shobhana Kashyap * Department of Computer Science and Engineering, Dr. B.R. Ambedkar NIT Jalandhar, Punjab, India, 144008
  • Avtar Singh Department of Computer Science and Engineering, Dr. B.R. Ambedkar NIT Jalandhar, Punjab, India, 144008

https://doi.org/10.22105/thi.v1i1.18

Abstract

For several decades, time-series forecasting has been an engaging research area. It is an essential domain of machine learning (ML) that is mainly ignored. It is necessary because prediction problems have a time feature, which makes time series problems more difficult to tackle. Forecasting of many applications such as weather, sales, ECG patterns and even COVID-19 spreads are possible with time series techniques. Inspired by these applications, many scholars have worked on effective forecasting techniques. This paper presents a comparative study of the time series models implemented on India’s real-time data of COVID-19. The study aims to estimate the mortality rate of coming 10 days by   the interpretation of actual data. Two predictive algorithms, Holt’s Linear Exponential Smoothing (HLES) and Autoregressive Integrated Moving Average (ARIMA) have been applied. To accomplish the objective and check the model accuracy, two selection criterion methods, Root Mean Square Error (RMSE) and Akaike Information Criterion (AIC), have been used to calculate the lowest values. The results depict that the HLES model has generally outperformed ARIMA. Adding to this, HLES model has good accuracy in forecasting the mortality rate compared to ARIMA. Moreover, if we face similar circumstances again in the future, then the proposed algorithm can be used to prevent the earlier phase of the outbreak.

Keywords:

Time series model, COVID-19, HLES, ARIMA, AIC, RMSE

References

  1. [1] Singh, S., Parmar, K. S., Kumar, J., & Makkhan, S. J. S. (2020). Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of covid-19. Chaos, solitons & fractals, 135, 109866. DOI:10.1016/J.CHAOS.2020.109866

  2. [2] Poonia, N., & Azad, S. (2020). Short-term forecasts of covid-19 spread across indian states until 1 may 2020. DOI:10.20944/preprints202004.0491.v1

  3. [3] Rahimi, I., Chen, F., & Gandomi, A. H. (2023). A review on COVID-19 forecasting models. Neural computing and applications, 35(33), 23671-23681. DOI:10.1007/s00521-020-05626-8

  4. [4] Chakraborty, I., & Maity, P. (2020). COVID-19 outbreak: Migration, effects on society, global environment and prevention. Science of the total environment, 728, 138882. DOI:10.1016/J.SCITOTENV.2020.138882

  5. [5] Kitajima, M., Ahmed, W., Bibby, K., Carducci, A., Gerba, C. P., Hamilton, K. A., ... & Rose, J. B. (2020). SARS-CoV-2 in wastewater: State of the knowledge and research needs. Science of the total environment, 739, 139076. DOI:10.1016/J.SCITOTENV.2020.139076

  6. [6] Fagherazzi, G., Goetzinger, C., Rashid, M. A., Aguayo, G. A., & Huiart, L. (2020). Digital health strategies to fight COVID-19 worldwide: challenges, recommendations, and a call for papers. Journal of medical internet research, 22(6), e19284. https://www.jmir.org/2020/6/e19284

  7. [7] Choudhari, R. (2020). COVID 19 pandemic: mental health challenges of internal migrant workers of india. Asian journal of psychiatry, 54, 102254. DOI:10.1016/J.AJP.2020.102254

  8. [8] Sharma, H. B., Vanapalli, K. R., Cheela, V. S., Ranjan, V. P., Jaglan, A. K., Dubey, B., … & Bhattacharya, J. (2020). Challenges, opportunities, and innovations for effective solid waste management during and post covid-19 pandemic. Resources, conservation and recycling, 162, 105052. DOI:10.1016/J.RESCONREC.2020.105052

  9. [9] Dhamodharavadhani, S., Rathipriya, R., & Chatterjee, J. M. (2020). COVID-19 mortality rate prediction

  10. [10] for india using statistical neural network models. Frontiers in public health, 8(8), 1–12. DOI:10.3389/fpubh.2020.00441

  11. [11] Kumar, S. L., Sarobin, M, V. R., & Anbarasi, L, J. (2021). Predictive analytics of covid-19 pandemic: statistical modelling perspective. Walailak journal of science and technology (WJST), 18(16), 15583.

  12. [12] DOI:10.48048/wjst.2021.15583

  13. [13] Kumar, S., Viral, R., Deep, V., Sharma, P., Kumar, M., Mahmud, M., & Stephan, T. (2021). Forecasting major impacts of covid-19 pandemic on country-driven sectors: challenges, lessons, and future roadmap. Personal and ubiquitous computing, 27, 807-830. DOI:10.1007 s00779-021-01530-7

  14. [14] Rustam, F., Reshi, A. A., Mehmood, A., Ullah, S., On, B. W., Aslam, W., & Choi, G. S. (2020). COVID-19 future forecasting using supervised machine learning models. IEEE access, 8, 101489–101499. DOI:10.1109/ACCESS.2020.2997311

  15. [15] Bećirović, E., & Ćosović, M. (2016, September). Machine learning techniques for short-term load forecasting. 2016 4th international symposium on environmental friendly energies and applications (EFEA) (pp. 1-4). IEEE. DOI:10.1109/EFEA.2016.7748789

  16. [16] Perc, M., Gorišek Miksić, N., Slavinec, M., & Stožer, A. (2020). Forecasting covid-19. Frontiers in physics, 8, 127. DOI:10.3389/fphy.2020.00127

  17. [17] Russo, L., Anastassopoulou, C., Tsakris, A., Bifulco, G. N., Campana, E. F., Toraldo, G., & Siettos, C. (2020). Tracing day-zero and forecasting the COVID-19 outbreak in Lombardy, Italy: A compartmental modelling and numerical optimization approach. Plos one, 15(10), e0240649. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0240649

  18. [18] Ali, M., Khan, D. M., Aamir, M., Khalil, U., & Khan, Z. (2020). Forecasting COVID-19 in Pakistan. Plos one, 15(11), e0242762. DOI:10.1371 journal.pone.0242762

  19. [19] Barría-Sandoval, C., Ferreira, G., Benz-Parra, K., & López-Flores, P. (2021). Prediction of confirmed cases of and deaths caused by COVID-19 in Chile through time series techniques: A comparative study. Plos one, 16(4), e0245414. DOI:10.1371/journal.pone.0245414

  20. [20] Lee, D. H., Kim, Y. S., Koh, Y. Y., Song, K. Y., & Chang, I. H. (2021). Forecasting covid-19 confirmed cases using empirical data analysis in korea. Healthcare (Switzerland), 9(3), 1–17. DOI:10.3390/healthcare9030254

  21. [21] Shastri, S., Singh, K., Kumar, S., Kour, P., & Mansotra, V. (2020). Time series forecasting of covid-19 using deep learning models: india-usa comparative case study. Chaos, solitons and fractals, 140, 110227. DOI:10.1016/j.chaos.2020.110227

  22. [22] Şahinli, M. A. (2020). Potato price forecasting with holt-winters and arima methods: a case study. American journal of potato research, 97(4), 336–346. DOI:10.1007/s12230-020-09788-y

  23. [23] Chandra, C., Fajrin, A. A., & Eko Suharyanto, C. (2021). Forecasting the items consumption in the hotel storage with the autoregressive integrated moving average method. Engineering, mathematics and computer science (EMACS) journal, 3(1), 13–19. DOI:10.21512/emacsjournal.v3i1.6979

  24. [24] Sharma, V. K., & Nigam, U. (2020). Modeling and forecasting of covid-19 growth curve in india. Transactions of the indian national academy of engineering, 5(4), 697–710. DOI:10.1007/s41403-020-00165-z

  25. [25] Harous, S., Al Harmoodi, M., & Biri, H. (2019). A comparative study of clustering algorithms for mixed datasets. 2019 amity international conference on artificial intelligence (AICAI) (pp. 484-488). IEEE.

  26. [26] Khan, F. M., & Gupta, R. (2020). ARIMA and nar based prediction model for time series analysis of covid-19 cases in india. Journal of safety science and resilience, 1(1), 12–18. DOI:10.1016/j.jnlssr.2020.06.007

  27. [27] Lydia, M., Kumar, S. S., Selvakumar, A. I., & Kumar, G. E. P. (2016). Linear and non-linear autoregressive models for short-term wind speed forecasting. Energy conversion and management, 112, 115-124. DOI:10.1016/j.enconman.2016.01.007

  28. [28] Katoch, R., & Sidhu, A. (2021). An application of ARIMA model to forecast the dynamics of COVID-19 epidemic in India. Global business review, 0972150920988653. https://journals.sagepub.com/doi/abs/10.1177/0972150920988653

  29. [29] Chu, J. (2021). A statistical analysis of the novel coronavirus (COVID-19) in Italy and Spain. PloS one, 16(3), e0249037. https://journals.plos.org plosone/article?id=10.1371/journal.pone.0249037

  30. [30] Li, L., Yang, Z., Dang, Z., Meng, C., Huang, J., Meng, H., … & Shao, Y. (2020). Propagation analysis and prediction of the covid-19. Infectious disease modelling, 5, 282. DOI:10.1016/J.IDM.2020.03.002

  31. [31] Bayham, J., & Fenichel, E. P. (2020). Impact of school closures for COVID-19 on the US health-care workforce and net mortality: a modelling study. The lancet public health, 5(5), e271-e278. DOI:10.1101/2020.03.09.20033415

  32. [32] Hembram, K. P. S. S., & Kumar, J. (2021). Epidemiological study of novel coronavirus (COVID-19): macroscopic and microscopic analysis. International journal of community medicine and public health, 8, 1364-1369.

  33. [33] Banerjee, A., Pasea, L., Harris, S., Gonzalez-Izquierdo, A., Torralbo, A., Shallcross, L., … & Hemingway, H. (2020). Estimating excess 1-year mortality associated with the covid-19 pandemic according to underlying conditions and age: a population-based cohort study. The lancet, 395(10238), 1715–1725. DOI:10.1016/S0140-6736(20)30854-0

  34. [34] Caccavo, D. (2020). Chinese and Italian COVID-19 outbreaks can be correctly described by a modified SIRD model. https://doi.org/10.1101/2020.03.19.20039388

  35. [35] Theerthagiri, P., Jacob, I. J., Ruby, A. U., & Vamsidhar, Y. (2020). Prediction of COVID-19 possibilities using KNN classification algorithm. https://doi.org/10.21203/rs.3.rs-70985/v2

  36. [36] Arpaci, I., Huang, S., Al-Emran, M., Al-Kabi, M. N., & Peng, M. (2021). Predicting the covid-19 infection with fourteen clinical features using machine learning classification algorithms. Multimedia tools and applications, 80(8), 11943–11957. DOI:10.1007/s11042-020-10340-7

  37. [37] Chatfield, C. (2000). Multivariate forecasting methods. Time-series forecasting, 2, 28–39. DOI:10.1201/9781420036206.ch5

  38. [38] Iwok, I. A., & Okpe, A. S. (2016). A comparative study between univariate and multivariate linear stationary time series models. American journal of mathematics and statistics, 6(5), 203–212. DOI:10.5923/j.ajms.20160605.02

Published

2024-05-15

How to Cite

Impact of Statistical Approach on Time-Series Models for Forecasting COVID-19. (2024). Trends in Health Informatics, 1(1), 9-22. https://doi.org/10.22105/thi.v1i1.18