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Re-visiting the COVID-19 analysis using the class of high ordered integer-valued time series models with harmonic
Naushad Mamode Khan1, Ashwinee Devi Soobhug2, Noha Youssef3
1Faculty of Social Sciences and Humanities, University of Mauritius, Réduit, Mauritius.
Healthcare Analytics (New York, N.Y.)
|July 31, 2023
Summary
This study introduces advanced integer-valued auto-regressive (INAR) models to better capture COVID-19 time series volatility. These novel harmonic INAR models offer improved analysis for infectious disease data.
Area of Science:
- Statistics
- Epidemiology
- Time Series Analysis
Background:
- COVID-19 time series exhibit high volatility, including spikes, oscillations, over-dispersion, and excess zeros.
- Conventional integer-valued auto-regressive (INAR) models struggle to accurately represent these complex features.
Purpose of the Study:
- To propose novel formulations of INAR models capable of handling COVID-19 time series characteristics.
- To explore high-ordered INAR models with harmonic innovation distributions and their bivariate extensions.
Main Methods:
- Development of high-ordered INAR models with harmonic innovation distributions.
- Extension to bivariate INAR processes.
- Application to COVID-19 time series data from South Africa and Mauritius.
- Simulation experiments to validate model performance and estimation.
Main Results:
- The proposed harmonic INAR models demonstrate improved ability to capture volatility and specific features of COVID-19 data.
- Bivariate extensions provide a framework for analyzing multiple related infectious disease time series.
- Simulation studies confirm the efficacy of the new models and estimation techniques.
Conclusions:
- Advanced INAR models with harmonic innovations offer a more robust approach for analyzing volatile infectious disease time series.
- The developed models and methods are valuable tools for epidemiological forecasting and understanding disease dynamics.
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