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Modified SIQR model for the COVID-19 outbreak in several countries
Carla M A Pinto1,2, J A Tenreiro Machado1, Clara Burgos-Simón3
1School of Engineering Polytechnic of Porto Rua Dr António Bernardino de Almeida, 431 Porto 4249-015 Portugal.
Mathematical Methods in the Applied Sciences
|April 25, 2022
Summary
This study introduces a modified Susceptible-Infected-Quarantine-Recovered (mSIQR) model to analyze the COVID-19 pandemic. The model provides insights into disease dynamics and future patterns based on real-world data from multiple countries.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Infectious Disease Dynamics
Background:
- The COVID-19 pandemic necessitates robust epidemiological models for understanding transmission and informing public health strategies.
- Existing models may not fully capture the complexities of disease spread, including asymptomatic or unreported cases.
Purpose of the Study:
- To introduce and validate a modified Susceptible-Infected-Quarantine-Recovered (mSIQR) model for COVID-19.
- To analyze the sensitivity of the model to key epidemiological parameters.
- To project future pandemic trends using real-world data.
Main Methods:
- Development and mathematical analysis of the mSIQR model, including well-posedness and reproduction number calculation.
- Sensitivity analysis of epidemiological parameters such as contact rate and recovery rate.
- Simulation of the mSIQR model and fitting to COVID-19 data from France, US, UK, and Portugal.
Main Results:
- The mSIQR model demonstrates well-posedness and provides calculable reproduction numbers and sensitivity indices.
- Analysis reveals the impact of contact rates, unknown infections, and recovery rates on disease spread.
- Model simulations show good agreement with COVID-19 data from selected countries.
Conclusions:
- The mSIQR model is a viable tool for understanding COVID-19 dynamics.
- Parameter sensitivity analysis offers crucial insights for targeted public health interventions.
- The model can inform future pandemic preparedness and policy decisions.
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