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Parameter estimation in behavioral epidemic models with endogenous societal risk-response.

Ann Osi1, Navid Ghaffarzadegan1

  • 1Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, Virginia, United States of America.

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Summary

Estimating parameters in behavioral epidemic models is challenging due to delayed societal risk response. Integrating public behavior data improves accuracy, especially early in a pandemic, but challenges remain for accurate pandemic projections.

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Public Health

Background:

  • Behavioral epidemic models are essential for predicting pandemic dynamics by incorporating societal risk perception and contact rate adjustments.
  • Accurate parameter estimation is critical for model validation and precise pandemic projections, yet joint estimation of disease and behavior parameters presents identifiability challenges.

Purpose of the Study:

  • To investigate the impact of delayed risk response, model structure (neglecting behavior), and integrated data on parameter estimation accuracy in behavioral epidemic models.
  • To assess the challenges in jointly estimating disease and behavior parameters, particularly concerning data limitations and the timing of societal reactions.

Main Methods:

  • Simulation experiments were conducted to evaluate parameter estimation accuracy under various conditions.
  • The study analyzed the effects of delayed risk response, exclusion of behavioral dynamics, and the integration of disease and public behavior data (e.g., mobility).

Main Results:

  • Systematic biases in behavior parameter estimation were observed, even with accurate disease data, when limited to early pandemic waves due to delayed risk-response dynamics.
  • Conventional SEIR models without behavioral components may fit early pandemic data but produce significant errors post-peak.
  • Integrating even small amounts of public behavior data early in a pandemic substantially improves estimation accuracy, with diminishing returns over time.

Conclusions:

  • Joint estimation of disease and behavior parameters in behavioral epidemic models is complex, significantly influenced by the delay between evolving risks and societal responses.
  • Model structure and data integration strategies are crucial for accurate pandemic forecasting, especially considering the temporal dynamics of public behavior.
  • Accurate pandemic prediction requires careful consideration of behavioral feedback loops and robust data assimilation techniques.