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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

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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.

Keywords:
SARS‐CoV‐2epidemiologymSIQR modelreal data fitting

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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.