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Tensor product algorithms for inference of contact network from epidemiological data.

Sergey Dolgov1, Dmitry Savostyanov2

  • 1University of Bath, Claverton Down, Bath, BA2 7AY, UK.

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|September 2, 2024
PubMed
Summary

This study introduces a new method for inferring epidemiological contact networks using Bayesian optimization. By solving the chemical master equation with tensor train approximations, it efficiently estimates network structures from observed disease data.

Keywords:
Bayesian inferenceEpidemiological modellingMarkov chain Monte CarloNetworksStochastic simulation algorithmTensor train

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Area of Science:

  • Epidemiology
  • Network Science
  • Computational Biology

Background:

  • Inferring contact networks is crucial for understanding disease spread.
  • Traditional methods struggle with the computational complexity of large networks.
  • Estimating likelihoods for epidemiological models is challenging due to rare events.

Purpose of the Study:

  • To develop a computationally efficient method for inferring contact networks from epidemiological data.
  • To overcome the limitations of stochastic simulation in estimating small probabilities.
  • To enable accurate black-box Bayesian inference of network structures.

Main Methods:

  • Utilizing a black-box Bayesian optimization framework.
  • Replacing stochastic simulations with solving the chemical master equation.
  • Applying tensor train approximations to manage the curse of dimensionality.

Main Results:

  • Demonstrated efficient and accurate computation of network state probabilities.
  • Successfully inferred contact networks even when probabilities are very small.
  • Numerical simulations confirmed the effectiveness of the proposed approach.

Conclusions:

  • The chemical master equation combined with tensor train approximations offers a powerful solution for network inference.
  • This approach significantly enhances the efficiency and accuracy of epidemiological modeling.
  • Enables robust inference of contact networks from observed nodal states.