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Estimation of the Basic Reproduction Number for the COVID-19 Pandemic in Minnesota
H Movahedi1, A Zemouche2, R Rajamani1
1University of Minnesota, Twin Cities, Minneapolis, MN 55455, USA.
Ifac-Papersonline
|April 15, 2024
Summary
This study presents a new model for COVID-19 dynamics, accurately estimating real-time spread variables like the infection rate and basic reproduction number (R0) for pandemic control.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Public Health
Background:
- COVID-19 pandemic dynamics require accurate real-time monitoring.
- Traditional epidemic models like SEIR need enhancements to include critical health metrics.
Purpose of the Study:
- To develop a nonlinear dynamic model for COVID-19 spread.
- To estimate real-time disease spread variables, including hospitalizations, ICU admissions, and deaths.
- To enable timely estimation of the basic reproduction number (R0).
Main Methods:
- A nonlinear dynamic model was developed, extending the SEIR model.
- A 6-month Minnesota dataset (infections, hospitalizations, ICU admissions, deaths) was used for parameter estimation via least-squares.
- A cascaded observer was designed for real-time variable estimation.
Main Results:
- The developed model accurately fits the observed COVID-19 data.
- The cascaded observer provides reliable real-time estimates for infected population, infection rate, and R0.
- Real-time estimates align well with parameters derived from the complete dataset.
Conclusions:
- The enhanced model and observer system effectively capture COVID-19 pandemic dynamics.
- Real-time estimation of the basic reproduction number is feasible and crucial for effective disease control strategies.
