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Identification of COVID-19 spread mechanisms based on first-wave data, simulation models, and evolutionary

Vladimir Stanovov1, Stanko Grabljevec2, Shakhnaz Akhmedova1

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Modified epidemiological models, including Bass diffusion, better predict COVID-19 spread dynamics than traditional SI, SIR, and SEIR models. These enhanced models capture external factors influencing infection rates, improving accuracy for real-world data analysis.

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Area of Science:

  • Epidemiology
  • Mathematical modeling
  • Infectious disease dynamics

Background:

  • COVID-19 pandemic highlighted limitations of standard SI, SIR, and SEIR models for predicting disease spread.
  • Novel model structures are needed to capture complex, unobserved mechanisms driving COVID-19 transmission.

Purpose of the Study:

  • To investigate COVID-19 spread mechanisms by parameterizing and comparing various epidemiological models.
  • To identify superior models for accurately fitting first-wave pandemic data.

Main Methods:

  • Analysis of COVID-19 data from Our World in Data for the first wave.
  • Comparison of SI, SIR, SEIR, SEIUR, and Bass diffusion models using differential evolution optimization (L-SHADE).
  • Calculation of reproduction rates (R0) for 61 countries based on best-fit model parameters.

Main Results:

  • Modified Bass diffusion and SEIR models demonstrated superior performance in fitting cumulative infection curves compared to classical models.
  • Modified SEIR outperformed classical SEIR in 43/61 countries; Bass diffusion outperformed SI in 57 countries.
  • Identified limitations of traditional models and highlighted the importance of external factors in transmission dynamics.

Conclusions:

  • Modified epidemiological models, particularly those incorporating external spread factors, offer improved accuracy for COVID-19 prediction.
  • A significant, non-contact-dependent mechanism influencing COVID-19 spread dynamics was identified.
  • These findings provide a basis for refining predictive models for infectious diseases.