A Mixture Integer GARCH Model with Application to Modeling and Forecasting COVID-19 Counts

Wooi Chen Khoo (), Seng Huat Ong, Victor Jian Ming Low and Hari M. Srivastava ()
Additional contact information
Wooi Chen Khoo: Institute of Actuarial Science and Data Analytics, UCSI University, Kuala Lumpur 56000, Malaysia
Seng Huat Ong: Institute of Actuarial Science and Data Analytics, UCSI University, Kuala Lumpur 56000, Malaysia
Victor Jian Ming Low: Asia School of Business, Kuala Lumpur 50480, Malaysia
Hari M. Srivastava: Department of Mathematics and Statistics, University of Victoria, Victoria, BC V8W 3R4, Canada

Stats, 2025, vol. 8, issue 3, 1-13

Abstract: This article introduces a flexible time series regression model known as the Mixture of Integer-Valued Generalized Autoregressive Conditional Heteroscedasticity (MINGARCH). Mixture models provide versatile frameworks for capturing heterogeneity in count data, including features such as multiple peaks, seasonality, and intervention effects. The proposed model is applied to regional COVID-19 data from Malaysia. To account for geographical variability, five regions—Selangor, Kuala Lumpur, Penang, Johor, and Sarawak—were selected for analysis, covering a total of 86 weeks of data. Comparative analysis with existing time series regression models demonstrates that MINGARCH outperforms alternative approaches. Further investigation into forecasting reveals that MINGARCH yields superior performance in regions with high population density, and significant influencing factors have been identified. In low-density regions, confirmed cases peaked within three weeks, whereas high-density regions exhibited a monthly seasonal pattern. Forecasting metrics—including MAPE, MAE, and RMSE—are significantly lower for the MINGARCH model compared to other models. These results suggest that MINGARCH is well-suited for forecasting disease spread in urban and densely populated areas, offering valuable insights for policymaking.

Keywords: mixture; time series of count; regression; coronavirus; infectious disease; Pegram’s operator; thinning (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2571-905X/8/3/73/pdf (application/pdf)
https://www.mdpi.com/2571-905X/8/3/73/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jstats:v:8:y:2025:i:3:p:73-:d:1723882

Access Statistics for this article

Stats is currently edited by Mrs. Minnie Li

More articles in Stats from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().