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Random resampling numerical simulations applied to a SEIR compartmental model
Francisco Morillas1, José Valero2
1Departament d'Economia Aplicada, Facultat d'Economia, Universitat de València, Campus dels Tarongers s/n, 46022 València, Spain.
European Physical Journal Plus
|November 1, 2021
Summary
This study enhances epidemic modeling by incorporating undetected cases into the SEIR model. Resampling techniques improved COVID-19 pandemic estimates in Spain, reducing prediction errors.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Computational Statistics
Background:
- Compartmental models like SEIR are crucial for understanding epidemic dynamics.
- Undetected infections significantly impact disease transmission and control.
- Accurate estimation of epidemic parameters is vital for public health interventions.
Purpose of the Study:
- To refine the SEIR model by accounting for undetected infected individuals.
- To assess the effectiveness of resampling techniques in improving epidemic parameter estimation.
- To apply the enhanced model to the first wave of the COVID-19 pandemic in Spain.
Main Methods:
- Modification of a standard SEIR model to include a compartment for undetected infections.
- Application of bootstrap resampling techniques to estimate model parameters.
- Numerical simulations to model the COVID-19 first wave in Spain (2020).
- Evaluation of estimate accuracy using goodness-of-fit measures.
Main Results:
- Bootstrap resampling significantly improved the accuracy of point estimates compared to original data.
- The relative error for detected currently infected individuals decreased from 0.061 to 0.0538.
- The enhanced model provides more reliable estimates of epidemic evolution.
Conclusions:
- Resampling techniques offer a robust method for enhancing compartmental epidemic models.
- Accounting for undetected cases and using bootstrap methods leads to more precise COVID-19 pandemic estimations.
- This approach can improve epidemiological forecasting and inform public health strategies.
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