Tensor product algorithms for inference of contact network from epidemiological data

We consider a problem of inferring contact network from nodal states observed during an epidemiological process. In a black-box Bayesian optimisation framework this problem reduces to a discrete likelihood optimisation over the set of possible networks. The cardinality of this set grows combinatoria...

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Bibliographic Details
Published in:BMC bioinformatics Vol. 25; no. 1; pp. 285 - 26
Main Authors: Dolgov, Sergey, Savostyanov, Dmitry
Format: Journal Article
Language:English
Published: London BioMed Central 02.09.2024
Springer Nature B.V
BMC
Subjects:
Algorithms
Approximation
Bayes Theorem
Bayesian analysis
Bayesian inference
Bioinformatics
Biomedical and Life Sciences
Black boxes
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Computer Simulation
Datasets
Epidemics
Epidemiological modelling
Epidemiology
Humans
Inverse problems
Life Sciences
Markov analysis
Markov chain Monte Carlo
Mathematical models
Microarrays
Networks
Optimization
Ordinary differential equations
Probability
Probability distribution
Probability theory
Statistical inference
Stochastic simulation algorithm
Tensor train
Tensors
Networks
37N25
15A69
Tensor train
65F55
90B15
Markov chain Monte Carlo
Epidemiological modelling
60J28
95C42
34A30
Stochastic simulation algorithm
Bayesian inference
62F15
ISSN:1471-2105, 1471-2105
Online Access:Get full text
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Summary:We consider a problem of inferring contact network from nodal states observed during an epidemiological process. In a black-box Bayesian optimisation framework this problem reduces to a discrete likelihood optimisation over the set of possible networks. The cardinality of this set grows combinatorially with the number of network nodes, which makes this optimisation computationally challenging. For each network, its likelihood is the probability for the observed data to appear during the evolution of the epidemiological process on this network. This probability can be very small, particularly if the network is significantly different from the ground truth network, from which the observed data actually appear. A commonly used stochastic simulation algorithm struggles to recover rare events and hence to estimate small probabilities and likelihoods. In this paper we replace the stochastic simulation with solving the chemical master equation for the probabilities of all network states. Since this equation also suffers from the curse of dimensionality, we apply tensor train approximations to overcome it and enable fast and accurate computations. Numerical simulations demonstrate efficient black-box Bayesian inference of the network.
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-024-05910-7