Spatio-Temporal Model to Forecast COVID-19 Confirmed Cases in High-Density Areas of Malaysia

Authors

  • Nur Haizum Abd Rahman Centre for Mathematical Sciences, Universiti Malaysia Pahang Al-Sultan Abdullah, Lebuh Persiaran Tun Khalil Yaakob, 26300 Kuantan, Pahang, Malaysia
  • Saidatul Nurfarahin Muhammad Yusof Department of Mathematics and Statistics, Faculty of Science, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
  • Iszuanie Syafidza Che Ilias Institute for Mathematical Research, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia;
  • Kathiresan Gopal Institute for Mathematical Research, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
  • Hannuun Yaacob Department of Decision Science, Faculty of Business and Economics, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
  • Noraishah Mohammad Sham Environmental Health Research Centre, Institute for Medical Research, Shah Alam, 40170, Selangor, Malaysia

DOI:

https://doi.org/10.11113/mjfas.v20n5.3389

Keywords:

Spatio-temporal model, forecasting, Generalized STAR, COVID-19.

Abstract

The coronavirus 2019 disease has spread across the world. The number of coronaviruses 2019 (COVID-19) cases throughout Malaysia is high in the densely populated state of Selangor. In assisting the early preventive measures, this study utilises time series methods to model and forecast the number of daily positive cases in three Selangor districts: Petaling, Hulu Langat, and Klang. Specifically, the study compares the effectiveness of the Autoregressive Integrated Moving Average (ARIMA), a univariate model and the Generalized Space-Time autoregressive integrated (GSTARI), a multivariate model. For the GSTARI model, uniform and inverse distance weights represent the relationship between locations. The analysed data are from January to August 2021, and the lowest root mean square error (RMSE) is chosen as the best model. The results show GSTARI (1,1) with both spatial weights outperformed ARIMA (0,1,1) in Petaling and Klang but not in Hulu Langat. However, the average RMSE values show that the most accurate and effective for forecasting the number of daily confirmed positive cases in Selangor is using GSTARI. In conclusion, by utilising advanced time series methods such as spatial analysis, this study provides important insights into forecasting trends of infectious diseases like COVID-19 and can help in early preventive measures.

References

Gill, B., et al. (2020). Modelling the effectiveness of epidemic control measures in preventing the transmission of COVID-19 in Malaysia. International Journal of Environmental Research and Public Health, 17, 1–13. https://doi.org/10.3390/ijerph17155509

Umair, S., Waqas, U., & Faheem, M. (2020). COVID-19 pandemic: Stringent measures of Malaysia and implications for other countries. Postgraduate Medical Journal, 97, postgradmedj-2020. https://doi.org/10.1136/postgradmedj-2020-138079

Mahalle, P., Kalamkar, M., Dey, N., Chaki, J., Hassanien, A. E., & Shinde, G. (2020). Forecasting models for coronavirus disease (COVID-19): A survey of the state-of-the-art. https://doi.org/10.36227/techrxiv.12101547

Lai, D. (2022). Monitoring the SARS epidemic in China: A time series analysis. Journal of Data Science, 3(3), 279–293. https://doi.org/10.6339/JDS.2005.03(3).229

Nishiura, H., Klinkenberg, D., Roberts, M., & Heesterbeek, J. A. P. (2009). Early epidemiological assessment of the virulence of emerging infectious diseases: A case study of an influenza pandemic. PLoS ONE, 4(8), e6852–. https://doi.org/10.1371/journal.pone.0006852

Chen, D., Moulin, B., & Wu, J. (2014). Analyzing and modeling spatial and temporal dynamics of infectious diseases. Wiley. https://books.google.com.my/books?id=L-mNBQAAQBAJ

Jia, W., et al. (2020). Extended SIR prediction of the epidemics trend of COVID-19 in Italy and compared with Hunan, China. Frontiers in Medicine (Lausanne), 7, 169. https://doi.org/10.3389/fmed.2020.00169

Nasab, S., Zahiri, A.-P., & Roohi, E. (2020). Prediction of peak and termination of novel coronavirus COVID-19 epidemic in Iran. International Journal of Modern Physics C, 31. https://doi.org/10.1142/S0129183120501521

Tang, S., Wang, L., Li, D., Bragazzi, N. L., Xiao, Y., & Wu, J. (2020). Estimation of the transmission risk of the 2019-nCoV and its implication for public health interventions. Journal of Clinical Medicine, 9, 462. https://doi.org/10.3390/jcm9020462

Sun, T., & Weng, D. (2020). Estimating the effects of asymptomatic and imported patients on COVID‐19 epidemic using mathematical modeling. Journal of Medical Virology, 92. https://doi.org/10.1002/jmv.25939

Borovkova, S., Lopuhaä, H., & Ruchjana, B. (2008). Consistency and asymptotic normality of least squares estimators in generalized STAR models. Statistica Neerlandica, 62, 482–508. https://doi.org/10.1111/j.1467-9574.2008.00391.x

Wutsqa, D. U., Suhartono, & Sutijo, B. (2010). Generalized space-time autoregressive modeling. https://api.semanticscholar.org/CorpusID:12920082

Siettos, C. S., & Russo, L. (2013). Mathematical modeling of infectious disease dynamics. Virulence, 4. https://doi.org/10.4161/viru.24041

Ceylan, Z. (2020). Estimation of COVID-19 prevalence in Italy, Spain, and France. Science of The Total Environment, 729, 138817. https://doi.org/10.1016/j.scitotenv.2020.138817

Mishra, P., et al. (2020). Modelling and forecasting of COVID-19 in India. Journal of Infectious Diseases and Epidemiology, 6, 162. https://doi.org/10.23937/2474-3658/1510162

Sun, J. (2021). Forecasting COVID-19 pandemic in Alberta, Canada using modified ARIMA models. Computer Methods and Programs in Biomedicine Update, 1, 100029. https://doi.org/10.1016/j.cmpbup.2021.100029

Yamamoto, N., Jiang, B., & Wang, H. (2021). Quantifying compliance with COVID-19 mitigation policies in the US: A mathematical modeling study. Infectious Disease Modelling, 6, 503–513. https://doi.org/10.1016/j.idm.2021.02.004

Furtado, P. (2021). Epidemiology SIR with regression, ARIMA, and Prophet in forecasting COVID-19. Engineering Proceedings, 5, 52. https://doi.org/10.3390/engproc2021005052

Sukarna, S., Syahrul, N., Sanusi, W., Aswi, A., Abdy, M., & Irwan, I. (2023). Estimating and forecasting COVID-19 cases in Sulawesi Island using generalized space-time autoregressive integrated moving average model. Media Statistika, 15, 186–197. https://doi.org/10.14710/medstat.15.2.186-197

Alawiyah, M., Kusuma, D. A., & Ruchjana, B. N. (2021). Application of generalized space time autoregressive integrated (GSTARI) model in the phenomenon of COVID-19. Journal of Physics: Conference Series, 1722(1), 012035. https://doi.org/10.1088/1742-6596/1722/1/012035

Singh, S., et al. (2020). Forecasting daily confirmed COVID-19 cases in Malaysia using ARIMA models. The Journal of Infection in Developing Countries, 14(09), 971–976. https://doi.org/10.3855/jidc.13116

MA, E., ZA, M. A., & AR, J. (2020). Forecasting Malaysia COVID-19 incidence based on movement control order using ARIMA and expert modeler. IIUM Medical Journal Malaysia, 19(2). https://doi.org/10.31436/imjm.v19i2.1606

Abdullah, S. N. S., Shabri, A., Saeed, F., Samsudin, R., & Basurra, S. (2023). Modelling COVID-19 daily new cases using GSTAR-ARIMA forecasting method: Case study on five Malaysian states. In Advances in Data Science and Management (pp. 439–448). https://doi.org/10.1007/978-3-031-36258-3_39

Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control. Wiley Series in Probability and Statistics. Wiley. https://books.google.com.my/books?id=rNt5CgAAQBAJ

Downloads

Published

15-10-2024

Issue

Vol. 20 No. 5 (2024): September - October

Section

Article

License

Copyright (c) 2024 Nur Haizum Abd Rahman, Saidatul Nurfarahin Muhammad Yusof, Iszuanie Syafidza Che Ilias, Kathiresan Gopal, Hannuun Yaacob, Noraishah Mohammad Sham

Creative Commons License

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