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Explaining COVID-19 outbreaks with reactive SEIRD models.

Kunal Menda1, Lucas Laird2, Mykel J Kochenderfer3

  • 1Department of Aeronautics & Astronautics, Stanford University, Stanford, CA, USA. kmenda@alumni.stanford.edu.

Scientific Reports
|September 10, 2021
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel SEIRD model extension using neural networks to predict COVID-19 spread, explaining diverse epidemic patterns. The enhanced model significantly reduces prediction errors compared to standard models.

Area of Science:

  • Epidemiology
  • Computational Biology
  • Data Science

Background:

  • COVID-19 epidemic trajectories in the U.S. exhibit significant heterogeneity, with some regions showing distinct peaks and others displaying multiple, unpredictable surges.
  • This variability complicates accurate disease spread prediction and necessitates improved epidemiological models.

Purpose of the Study:

  • To develop and validate an extended SEIRD (Susceptible-Exposed-Infectious-Recovered-Deceased) model capable of capturing diverse epidemic progression patterns.
  • To improve the accuracy of predicting infectious disease dynamics by incorporating time- and prevalence-dependent infection rates.

Main Methods:

  • An extended SEIRD model was developed, utilizing a neural network to dynamically predict the infection rate based on time and disease prevalence.

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  • A novel fitting methodology employing Expectation-Maximization was implemented to handle partially observable county-level case and death data.
  • The model was trained on U.S. county data exhibiting varied epidemic behaviors.

    • Main Results:

    • Simulations from the proposed model successfully reproduced both single-peak and multi-peak epidemic behaviors observed in training and unseen data.
    • The enhanced model demonstrated a substantial reduction in prediction errors compared to the standard SEIRD model.
    • The Expectation-Maximization approach for partial observability significantly outperformed standard methods in estimating unobserved epidemiological states.

    Conclusions:

    • The neural network-enhanced SEIRD model provides a more accurate and flexible framework for understanding and predicting heterogeneous epidemic progressions.
    • The developed methodology effectively addresses data limitations in real-world epidemiological surveillance.
    • This approach offers a promising tool for public health policy and resource allocation during pandemics.