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Maíra Aguiar1, Sebastién Ballesteros2, João Pedro Boto1

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This study calibrates epidemiological models for influenza and dengue fever, revealing complex dynamics and stochasticity in disease modeling. Advanced parameter estimation techniques face computational challenges with intricate models.

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Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Computational Biology

Background:

  • Population biological dynamical systems are crucial for understanding disease spread.
  • Parameter estimation in complex epidemiological models presents significant computational challenges.
  • Existing techniques like maximum likelihood iterated filtering struggle with multi-strain dynamics.

Purpose of the Study:

  • To apply parameter estimation frameworks to calibrate epidemiological models using empirical time series data.
  • To investigate the computational limits of advanced parameter estimation techniques for complex disease models.
  • To explore the interplay between stochasticity and deterministic dynamics in dengue fever models.

Main Methods:

  • Utilized parameter estimation frameworks for population biological dynamical systems.
  • Calibrated models using empirical time series data for influenza and dengue fever.
  • Applied maximum likelihood iterated filtering to assess computational limits.

Main Results:

  • Parameter estimation was applied to influenza and dengue fever time series data.
  • Complex models, such as multi-strain dengue dynamics, pushed computational limits.
  • Initial results from dengue fever data in Thailand suggest a nuanced interaction between stochasticity and deterministic elements.

Conclusions:

  • The deterministic system underlying dengue fever exhibits complex dynamics, including chaos and multiple attractors.
  • Understanding the interplay of stochasticity and deterministic factors is key for accurate dengue fever modeling.
  • Further research is needed to overcome computational limitations in parameter estimation for complex epidemiological systems.