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Computationally efficient parameter estimation for spatial individual-level models of infectious disease

Madeline A Ward1, Lorna E Deeth1, Rob Deardon2

  • 1Department of Mathematics and Statistics, University of Guelph, Stone Road, Guelph, N1G 2W1, Canada.

Spatial and Spatio-Temporal Epidemiology
|June 12, 2022
PubMed
Summary

A new Cluster-Aggregate-Disaggregate (CAD) method simplifies fitting complex infectious disease models to aggregate data. This approach efficiently provides individual-level insights, outperforming approximate Bayesian computation methods in ease of use and speed.

Keywords:
Approximate Bayesian computationIndividual-level modelsInfectious disease modellingMCMC

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Area of Science:

  • Epidemiology
  • Computational Statistics
  • Mathematical Modeling

Background:

  • Individual-level models are crucial for understanding infectious disease transmission dynamics.
  • Traditional Bayesian methods for complex individual-level models become computationally intensive with large populations or intricate models.
  • Existing methods struggle with incorporating individual-specific covariates like spatial location.

Purpose of the Study:

  • To propose and evaluate a novel method for fitting spatial individual-level infectious disease models using aggregate data.
  • To compare the performance of the proposed Cluster-Aggregate-Disaggregate (CAD) method against approximate Bayesian computation (ABC) algorithms.
  • To assess the efficiency and ease of implementation of the CAD method for obtaining individual-level epidemic metrics.

Main Methods:

  • Development of the Cluster-Aggregate-Disaggregate (CAD) method, utilizing Metropolis-Hastings Markov chain Monte Carlo (MCMC) on aggregate data.
  • Comparison of the CAD method with two approximate Bayesian computation (ABC) algorithms through simulation studies.
  • Application of the CAD and ABC methods to real-world data from the 2001 UK foot and mouth disease epidemic.

Main Results:

  • Both CAD and ABC methods demonstrated reasonable performance in capturing key epidemic metrics.
  • The CAD method proved significantly easier to implement compared to the ABC algorithms.
  • The CAD method consistently offered greater reductions in computation time relative to traditional individual-level model fitting.

Conclusions:

  • The Cluster-Aggregate-Disaggregate (CAD) method provides an efficient and practical alternative for fitting spatial individual-level infectious disease models.
  • CAD facilitates the disaggregation of results from aggregate data to obtain valuable individual-level epidemic insights.
  • The CAD method offers a substantial improvement in computational efficiency and implementation simplicity over traditional and ABC approaches.