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A differential equations model-fitting analysis of COVID-19 epidemiological data to explain multi-wave dynamics
Maria Jardim Beira1, Pedro José Sebastião2
1Center of Physics and Engineering of Advanced Materials, Departamento de Física, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001, Lisbon, Portugal. maria.beira@tecnico.ulisboa.pt.
Scientific Reports
|August 12, 2021
Summary
Compartmental epidemiological models were used to analyze COVID-19 dynamics in Portugal. This data-driven approach validated the model and projected future scenarios, including vaccination impacts.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Infectious Disease Dynamics
Background:
- Compartmental epidemiological models are widely used for studying infectious disease dynamics.
- These models are crucial for understanding and managing pandemics like COVID-19.
Purpose of the Study:
- To perform a compartmental model fitting analysis of COVID-19 in Portugal using real-time data.
- To validate the epidemiological model through data-driven analysis.
- To generate robust projections for various future scenarios.
Main Methods:
- Utilized an online open-access platform for differential equation solving.
- Applied compartmental modeling to COVID-19 data from Portugal.
- Performed real-time data analysis and model fitting.
Main Results:
- Successfully validated the compartmental epidemiological model using Portuguese COVID-19 data.
- Generated projections for scenarios including increased detection rates, school reopenings, and vaccination.
- Demonstrated the model's capability to fit differential equation solutions to diverse epidemiological datasets.
Conclusions:
- The presented method offers a robust framework for fitting epidemiological models to real-time data.
- The analysis provides valuable insights for public health policy and pandemic management.
- The approach is adaptable for different models and geographical contexts.
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