Abstract
The development of zero-inflated time series models is well known to account for excessive number of zeros and overdispersion in discrete count time series data. By using Zero-inflated models, we analyzed the daily count of COVID-19 deaths occurrence in Kelantan with excess zeros. Considering factors such as COVID-19 deaths in neighboring state and lag of 1 to 7 days of COVID-19 death in Kelantan, the Zero-Inflated models (Zero-Inflated Poisson (ZIP) and the Zero-Inflated Negative Binomial (ZINB)) were employed to predict the COVID-19 deaths in Kelantan. The ZIP and ZINB were compared with the basic Poisson and Negative Binomial models to find the significant contributing factors from the model. The final results show that the best model was the ZINB model with lag of 1,2,5 and lag of 6 days of Kelantan COVID-19 death, lag of 1-day COVID-19 deaths in neighboring State of Terengganu and Perak significantly influenced the COVID-19 deaths occurrence in Kelantan. The model gives the smallest value of AIC and BIC compared to the basic Poisson and Negative Binomial model. This indicate that the Zero Inflated model predict the excess zeros in the COVID-19 deaths occurrence well compared to the basic count model. Hence, the fitted models for COVID-19 deaths served as a novel understanding on the disease transmission and dissemination in a particular area.
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Acknowledgement
The registration fee is funded by Pembiayaan Yuran Prosiding Berindeks (PYPB), Tabung Dana Kecemerlangan Pendidikan (DKP), Universiti Teknologi MARA (UiTM), Malaysia. Special thanks to School of Mathematical Sciences, College of Computing, Informatics and Media, Universiti Teknologi MARA for supporting this research project. We are also would like to express our gratitude to Ministry of Health for the data used in the study. We are also grateful to the Editor, the Associate Editor and anonymous referees for their insightful comments and suggestions.
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Ismail, M.H., Roslee, H.B., Wan Yaacob, W.F., Sapri, N.N.F.F. (2023). Zero-Inflated Time Series Model for Covid-19 Deaths in Kelantan Malaysia. In: Yusoff, M., Hai, T., Kassim, M., Mohamed, A., Kita, E. (eds) Soft Computing in Data Science. SCDS 2023. Communications in Computer and Information Science, vol 1771. Springer, Singapore. https://doi.org/10.1007/978-981-99-0405-1_21
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