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Statistical Analysis of the Dynamics of Coronavirus Cases using Stepwise Switching Regression
1V. M. Glushkov Institute of Cybernetics, National Academy of Sciences of Ukraine, Kyiv, Ukraine.
Cybernetics and Systems Analysis
|November 30, 2020
Summary
This study models coronavirus case dynamics using unknown switching regression points. The analysis details a stepwise time-based regression construction for Ukraine
Area of Science:
- Epidemiology
- Biostatistics
- Time Series Analysis
Background:
- Understanding the transmission dynamics of infectious diseases like coronavirus is crucial for public health.
- Traditional epidemiological models may not fully capture abrupt changes in disease spread.
- Accurate modeling aids in predicting future trends and implementing effective control measures.
Purpose of the Study:
- To propose and describe a novel modeling approach for coronavirus dynamics using switching regression.
- To detail the methodology for constructing time-varying regression models with unknown change points.
- To analyze the specific dynamics of coronavirus cases in Ukraine.
Main Methods:
- Application of switching regression models to epidemiological data.
- Stepwise construction of regression models over time.
- Identification of unknown switching points in the time series data.
- Analysis of coronavirus case numbers in Ukraine.
Main Results:
- The study successfully applies switching regression to model coronavirus dynamics.
- The stepwise construction method effectively identifies changes in disease trends.
- Specific patterns in coronavirus case numbers in Ukraine are elucidated.
Conclusions:
- Switching regression offers a robust framework for modeling infectious disease dynamics with unknown change points.
- The stepwise approach provides a practical method for analyzing time-series epidemiological data.
- The findings offer insights into the specific trajectory of coronavirus in Ukraine.
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