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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.

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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.