Article Sidebar

Download
PDF
Statistic
Read Counter : 644 Download : 596

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Main Article Content

Abstract

COVID-19 has spread throughout the world, including in Southeast Asia. Many studies have made predictions using various models. However, very few are data-driven based. Meanwhile for the COVID-19 case, which is still ongoing, it is very suitable to use data-driven approach with phenomenological models. This paper aimed to obtain effective forecasting models and then predict when COVID-19 in Southeast Asia will peak and end using daily cumulative case data. The research applied the Richards curve and the logistic growth model, combining the two models to make prediction of the COVID-19 cases in Southeast Asia, both the countries with one pandemic wave or those with more than one pandemic wave. The best prediction results were obtained using the Richards curve with the logistic growth model parameters used as the initial values. In the best scenario, the Southeast Asia region is expected to be free from the COVID-19 pandemic at the end of 2021. These modeling results are expected to provide information about the provision of health facilities and how to handle infectious disease outbreaks in the future.

Keywords

COVID-19 data-driven prediction Richards curve logistic growth model Southeast Asia

Article Details

Creative Commons License

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

Authors who publish with this journal agree to the following License

CC BY: This license allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.

 

How to Cite
Zuhairoh, F., & Rosadi, D. (2022). Data-driven analysis and prediction of COVID-19 infection in Southeast Asia by using a phenomenological model. Pakistan Journal of Statistics and Operation Research, 18(1), 59-69. https://doi.org/10.18187/pjsor.v18i1.3714
Crossref
Scopus
Google Scholar
Europe PMC

References

  1. Al-Qaness, M. A., Ewees, A. A., Fan, H., and Aziz, M. A. E. (2020). Optimization method for forecasting confirmed cases of COVID-19 in China. Journal of Clinical Medicine, 9(3):674-688.
  2. Allen, L. J. S. (1994). Some discrete-time SI, SIR, and SIS epidemic models. Mathematical Biosciences.
  3. Anastassopoulou, C., Russo, L., Tsakris, A., and Siettos, C. (2020). Data-based analysis, modelling and forecasting of the COVID-19 outbreak. PLoS ONE.
  4. Azlan, A. A., Hamzah, M. R., Sern, T. J., Ayub, S. H., and Mohamad, E. (2020). Public knowledge, attitudes and practices towards COVID-19: A cross-sectional study in Malaysia. PLoS ONE.
  5. Bozdogan, H. (2000). Akaike's information criterion and recent developments in information complexity. Journal of Mathematical Psychology, 44(1):62-91.
  6. de Leon, U. A.-P., Perez, A. G., and Avila-Vales, E. (2020). A data driven analysis and forecast of an SEIARD epidemic model for COVID-19 in Mexico. Big Data and Information Analytics, 5(1):14-28.
  7. ECDC (2020). Download today ' s data on the geographic distribution of COVID-19 cases worldwide.
  8. Edrada, E. M., Lopez, E. B., Villarama, J. B., Salva Villarama, E. P., Dagoc, B. F., Smith, C., Sayo, A. R., Verona, J. A., Trifalgar-Arches, J., Lazaro, J., Balinas, E. G. M., Telan, E. F. O., Roy, L., Galon, M., Florida, C. H. N., Ukawa, T., Villaneuva, A. M. G., Saito, N., Nepomuceno, J. R., Ariyoshi, K., Carlos, C., Nicolasora, A. D., and Solante, R. M. (2020). First COVID-19 infections in the Philippines: A case report. Tropical Medicine and Health, 48(1).
  9. Hsieh, Y.-H. (2009). Richards Model: A Simple Procedure for Real-time Prediction of Outbreak Severity.
  10. Hsieh, Y. H. (2010). Pandemic influenza A (H1N1) during winter influenza season in the southern hemisphere. Influenza and other Respiratory Viruses.
  11. Maleki, M., Mahmoudi, M. R., Wraith, D., and Pho, K. H. (2020). Time series modelling to forecast the confirmed and recovered cases of COVID-19. Travel Medicine and Infectious Disease, 37:101742.
  12. Manning, J. E., Bohl, J. A., Lay, S., Chea, S., Sovann, L., Sengdoeurn, Y., Heng, S., Vuthy, C., Kalantar, K., Ahyong, V., Tan, M., Sheu, J., Tato, C. M., DeRisi, J. L., Baril, L., Duong, V., Dussart, P., and Karlsson, E. A. (2020). Rapid metagenomic characterization of a case of imported COVID-19 in Cambodia.
  13. Nishiura, H. (2011). Real-time forecasting of an epidemic using a discrete time stochastic model: A case study of pandemic influenza (H1N1-2009). BioMedical Engineering Online, 10(15):1-16.
  14. Okada, P., Buathong, R., Phuygun, S., Thanadachakul, T., Parnmen, S., Wongboot, W., Waicharoen, S., Wacharapluesadee, S., Uttayamakul, S., Vachiraphan, A., Chittaganpitch, M., Mekha, N., Janejai, N., Iamsirithaworn, S., Lee, R. T., and Maurer-Stroh, S. (2020). Early transmission patterns of coronavirus disease 2019 (COVID-19) in travellers from Wuhan to Thailand, January 2020.
  15. Pung, R., Chiew, C. J., Young, B. E., Chin, S., Chen, M. I., Clapham, H. E., Cook, A. R., Maurer-Stroh, S., Toh, M. P., Poh, C., Low, M., Lum, J., Koh, V. T., Mak, T. M., Cui, L., Lin, R. V., Heng, D., Leo, Y. S., Lye, D. C., Lee, V. J., qian Kam, K., Kalimuddin, S., Tan, S. Y., Loh, J., Thoon, K. C., Vasoo, S., Khong, W. X., Suhaimi, N. A., Chan, S. J., Zhang, E., Oh, O., Ty, A., Tow, C., Chua, Y. X., Chaw, W. L., Ng, Y., Abdul-Rahman, F., Sahib, S., Zhao, Z., Tang, C., Low, C., Goh, E. H., Lim, G., Hou, Y., Roshan, I., Tan, J., Foo, K., Nandar, K., Kurupatham, L., Chan, P. P., Raj, P., Lin, Y., Said, Z., Lee, A., See, C., Markose, J., Tan, J., Chan, G., See, W., Peh, X., Cai, V., Chen, W. K., Li, Z., Soo, R., Chow, A. L., Wei, W., Farwin, A., and Ang, L. W. (2020). Investigation of three clusters of COVID-19 in Singapore: implications for surveillance and response measures. The Lancet.
  16. Quach, H. L. and Hoang, N. A. (2020). COVID-19 in Vietnam: A lesson of pre-preparation. Journal of Clinical Virology.
  17. Richards, F. J. (1959). A flexible growth function for empirical use. Journal of Experimental Botany.
  18. Roosa, K., Lee, Y., Luo, R., Kirpich, A., Rothenberg, R., Hyman, J. M., Yan, P., and Chowell, G. (2020). Short-term Forecasts of the COVID-19 Epidemic in Guangdong and Zhejiang, China: February 13-23, 2020. Journal of Clinical Medicine.
  19. Santosh, K. C. (2020). COVID-19 Prediction Models and Unexploited Data. Journal of Medical Systems, 44(170):1-4.
  20. Valitutto, M. T., Aung, O., Tun, K. Y. N., Vodzak, M. E., Zimmerman, D., Yu, J. H., Win, Y. T., Maw, M. T., Thein, W. Z., Win, H. H., Dhanota, J., Ontiveros, V., Smith, B., Tremeau-Brevard, A., Goldstein, T., Johnson, C. K., Murray, S., and Mazet, J. (2020). Detection of novel coronaviruses in bats in Myanmar. PLoS ONE.
  21. Wang, X., Liu, S., and Huang, Y. (2016). A Study on the Rapid Parameter Estimation and the Grey Prediction in Richards Model. Journal of Systems Science and Information, 4(3):223-234.
  22. Wong, J., Abdul Aziz, A. B., Chaw, L., Mahamud, A., Griffith, M. M., Ying-Ru, L. O., and Naing, L. (2020). High proportion of asymptomatic and presymptomatic COVID-19 infections in travelers and returning residents to Brunei. Journal of travel medicine.
  23. Worldometer (2021). Coronavirus Update (Live): Cases and Deaths from COVID-19 Virus Pandemic.