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A Simulation of a COVID-19 Epidemic Based on a Deterministic SEIR Model
José M Carcione1, Juan E Santos2,3,4, Claudio Bagaini5
1National Institute of Oceanography and Applied Geophysics - OGS, Trieste, Italy.
Frontiers in Public Health
|June 24, 2020
Summary
This study models the COVID-19 epidemic in Lombardy, Italy, using an SEIR model. It estimates 15,600 deaths and 2.7 million infections, highlighting the impact of isolation and social distancing on epidemic dynamics.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Public Health
Background:
- A novel coronavirus caused an epidemic in Northern Italy with high contagion.
- The Italian Region of Lombardy experienced a significant outbreak starting February 24.
Purpose of the Study:
- To implement an SEIR model to compute the infected population and casualties.
- To analyze the impact of varying parameters and initial conditions on epidemic spread.
- To calibrate the model for the Lombardy region using available data.
Main Methods:
- Utilized a compartmental SEIR (Susceptible-Exposed-Infectious-Recovered) model.
- Varied parameters such as incubation period, infectious period, and fatality rate.
- Calibrated the model using reported death tolls and literature-based parameter ranges for Lombardy.
Main Results:
- The epidemic peak in Lombardy was estimated around March 31 (day 37).
- Initial reproduction ratio (R0) was 3, decreasing to 0.8 after 35 days due to lockdown measures.
- Predicted total deaths: ~15,600; total infected: ~2.7 million.
- Optimal incubation period: 4.25 days; infectious period: 4 days; fatality rate: 0.00144/day.
- Infection Fatality Rate (IFR) estimated at 0.57% to 2.37%, dependent on initial exposed individuals and reported deaths.
Conclusions:
- Isolation and social distancing measures significantly impact epidemic dynamics.
- Accurate parameter estimation is crucial for reliable epidemic forecasting.
- Quantifying epidemic spread is essential for evaluating the effectiveness of public health interventions like lockdowns.
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