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Neural Posterior Estimation for Stochastic Epidemic Modeling

P Chatha, F Bu, J Regier, E Snitkin, J Zelner - arXiv preprint arXiv …, 2024 - arxiv.org
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… NPE algorithm for Bayesian simulation based inference. In … for a simulation-based inference workflow in Section 5 … simulation-based inference is a effective methodology …

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@article{2412.12967v1,
Author        = {Prayag Chatha and Fan Bu and Jeffrey Regier and Evan Snitkin and Jon Zelner},
Title         = {Neural Posterior Estimation for Stochastic Epidemic Modeling},
Eprint        = {2412.12967v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ME},
Abstract      = {Stochastic infectious disease models capture uncertainty in public health
outcomes and have become increasingly popular in epidemiological practice.
However, calibrating these models to observed data is challenging with existing
methods for parameter estimation. Stochastic epidemic models are nonlinear
dynamical systems with potentially large latent state spaces, resulting in
computationally intractable likelihood densities. We develop an approach to
calibrating complex epidemiological models to high-dimensional data using
Neural Posterior Estimation, a novel technique for simulation-based inference.
In NPE, a neural conditional density estimator trained on simulated data learns
to "invert" a stochastic simulator, returning a parametric approximation to the
posterior distribution. We introduce a stochastic, discrete-time Susceptible
Infected (SI) model with heterogeneous transmission for healthcare-associated
infections (HAIs). HAIs are a major burden on healthcare systems. They exhibit
high rates of asymptotic carriage, making it difficult to estimate infection
rates. Through extensive simulation experiments, we show that NPE produces
accurate posterior estimates of infection rates with greater sample efficiency
compared to Approximate Bayesian Computation (ABC). We then use NPE to fit our
SI model to an outbreak of carbapenem-resistant Klebsiella pneumoniae in a
long-term acute care facility, finding evidence of location-based heterogeneity
in patient-to-patient transmission risk. We argue that our methodology can be
fruitfully applied to a wide range of mechanistic transmission models and
problems in the epidemiology of infectious disease.},
Year          = {2024},
Month         = {Dec},
Url           = {http://arxiv.org/abs/2412.12967v1},
File          = {2412.12967v1.pdf}
}

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