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Characterising model dynamics using sparse grid interpolation: Parameter estimation of cholera.

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  • 1a Weldon School of Biomedical Engineering , Purdue University , West Lafayette , IN , USA.

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This study uses sparse grid interpolation to calibrate epidemiological models with time-series data. The computational tool efficiently identifies optimal parameter regions, aiding in understanding disease dynamics and fitting epidemic data.

Keywords:
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Area of Science:

  • Computational epidemiology
  • Numerical analysis
  • Mathematical modeling

Background:

  • High-dimensional problems require efficient numerical methods.
  • Epidemiological models need accurate calibration using time-series data.
  • Distinguishing between phenomenological and mechanistic model perspectives is crucial.

Purpose of the Study:

  • To apply sparse grid interpolation for calibrating epidemiological models.
  • To leverage time-series data for model parameter estimation.
  • To demonstrate the utility of sparse grids in fitting epidemic data and comparing hypotheses.

Main Methods:

  • Utilizing sparse grid interpolation for numerical discretization.
  • Calibrating epidemiological models using time-series data.
  • Analyzing both global and local parameter space dynamics.

Main Results:

  • Sparse grid interpolants effectively capture underlying dynamics.
  • The method identifies optimal parameter space regions.
  • The approach successfully fits epidemic data and discriminates between competing hypotheses.

Conclusions:

  • Sparse grid interpolation is a valuable tool for epidemiological model calibration.
  • This technique enhances the resolution of parameter space dynamics.
  • The method aids in understanding and explaining epidemic outbreaks, such as cholera in Yemen.