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Statistical modelling of COVID-19 pandemic development applying branching processes.
D Atanasov1, Vessela Stoimenova2, Nikolay M Yanev3
1Department of Informatics, New Bulgarian University, Sofia, Bulgaria.
Journal of Applied Statistics
|August 2, 2023
Summary
This study introduces a statistical model using branching processes to estimate COVID-19 infection rates and predict unobserved cases. The model effectively utilizes daily infection data for accurate parameter estimation.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Statistical Analysis
Background:
- Accurate estimation of COVID-19 infection dynamics is crucial for public health.
- Existing models often require extensive data not always available.
- A need exists for models that can estimate parameters from limited observed data.
Purpose of the Study:
- To develop and present a statistical model for estimating COVID-19 infection dynamics.
- To utilize observed daily infected case data for parameter estimation.
- To predict the mean value of the unobserved infected population.
Main Methods:
- Application of two classes of branching processes (with and without immigration).
- Statistical modeling based solely on observed daily infected individuals.
- Parameter estimation for infection dynamics.
Main Results:
- The proposed model successfully estimates key infection parameters.
- Predictions for the mean of the unobserved infected population are generated.
- The model demonstrates applicability across different global regions.
Conclusions:
- The developed statistical model offers a robust method for analyzing COVID-19 spread using accessible data.
- Branching process models provide a viable alternative for epidemiological parameter estimation.
- This approach enhances the ability to understand and predict infection dynamics, even with limited official reports.
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