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Kernel-density estimation and approximate Bayesian computation for flexible epidemiological model fitting in Python.

Michael A Irvine1, T Déirdre Hollingsworth2

  • 1Institute of Applied Mathematics, University of British Columbia, Vancouver, Canada.

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PubMed
Summary
This summary is machine-generated.

We developed an adaptive approximate Bayesian computation (ABC) scheme to simplify fitting complex epidemiological models. This accessible method aids researchers in analyzing diverse datasets, including lymphatic filariasis simulations.

Keywords:
Approximate Bayesian computationIndividual-based modelLymphatic filariasisModel fittingPython library

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Area of Science:

  • Computational Epidemiology
  • Statistical Modeling
  • Parasitic Disease Research

Background:

  • Fitting complex models to epidemiological data presents significant challenges, including accessibility, uncertainty quantification, computational time, and data heterogeneity.
  • Existing methodologies often require specialized expertise, limiting their broad application in epidemiological research.

Purpose of the Study:

  • To develop an accessible and efficient computational scheme for fitting complex epidemiological models to diverse data.
  • To reduce the need for extensive hyper-parameter tuning and specialized theoretical knowledge.
  • To provide a practical tool for researchers to analyze epidemiological data, exemplified by a lymphatic filariasis model.

Main Methods:

  • An adaptive approximate Bayesian computation (ABC) scheme was developed, incorporating an adaptive tolerance strategy.
  • A novel kernel density estimation (KDE) scheme was implemented to handle dispersed and multi-dimensional data.
  • The adaptive ABC-KDE approach was compared against standard Bayesian fitting techniques.

Main Results:

  • The adaptive ABC scheme successfully fitted complex epidemiological models with minimal hyper-parameter tuning.
  • The novel KDE scheme effectively captured complex data structures, performing comparably to standard Bayesian methods.
  • The procedure was successfully applied to an individual-based simulation of lymphatic filariasis.

Conclusions:

  • The developed adaptive ABC scheme provides a versatile and accessible method for fitting a wide range of epidemiological data.
  • This approach democratizes complex model fitting, requiring less specialized theoretical background.
  • The open-access library and examples facilitate rapid model fitting for the broader epidemiological research community.