Approximate Bayesian computation for spatial SEIR(S) epidemic models

Grant D Brown; Aaron T Porter; Jacob J Oleson; Jessica A Hinman

doi:10.1016/j.sste.2017.11.001

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Approximate Bayesian computation for spatial SEIR(S) epidemic models

Journal article

Open access

Peer reviewed

Approximate Bayesian computation for spatial SEIR(S) epidemic models

Grant D Brown, Aaron T Porter, Jacob J Oleson and Jessica A Hinman

Spatial and spatio-temporal epidemiology, Vol.24, pp.27-37

02/2018

DOI: 10.1016/j.sste.2017.11.001

PMCID: PMC5806152

PMID: 29413712

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https://www.ncbi.nlm.nih.gov/pmc/articles/5806152View

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Abstract

Approximate Bayesia n Computation (ABC) provides an attractive approach to estimation in complex Bayesian inferential problems for which evaluation of the kernel of the posterior distribution is impossible or computationally expensive. These highly parallelizable techniques have been successfully applied to many fields, particularly in cases where more traditional approaches such as Markov chain Monte Carlo (MCMC) are impractical. In this work, we demonstrate the application of approximate Bayesian inference to spatially heterogeneous Susceptible-Exposed-Infectious-Removed (SEIR) stochastic epidemic models. These models have a tractable posterior distribution, however MCMC techniques nevertheless become computationally infeasible for moderately sized problems. We discuss the practical implementation of these techniques via the open source ABSEIR package for R. The performance of ABC relative to traditional MCMC methods in a small problem is explored under simulation, as well as in the spatially heterogeneous context of the 2014 epidemic of Chikungunya in the Americas.

Colombia - epidemiology

Chikungunya Fever - epidemiology

Computer Simulation

Humans

Bayes Theorem

Chikungunya Fever - prevention & control

Dominican Republic - epidemiology

Details

Title: Subtitle: Approximate Bayesian computation for spatial SEIR(S) epidemic models
Creators: Grant D Brown - Department of Biostatistics, University of Iowa, Iowa City, Iowa 52242 USA. Electronic address: grant-brown@uiowa.edu
Aaron T Porter - Department of Applied Mathematics and Statistics, Colorado School of Mines, Golden, Colorado 80401 USA
Jacob J Oleson - Department of Biostatistics, University of Iowa, Iowa City, Iowa 52242 USA
Jessica A Hinman - Department of Epidemiology, University of Iowa, Iowa City, Iowa 52242 USA
Resource Type: Journal article
Publication Details: Spatial and spatio-temporal epidemiology, Vol.24, pp.27-37
DOI: 10.1016/j.sste.2017.11.001
PMID: 29413712
PMCID: PMC5806152
NLM abbreviation: Spat Spatiotemporal Epidemiol
ISSN: 1877-5845
eISSN: 1877-5853
Publisher: Netherlands
Grant note: R01 TW010500 / FIC NIH HHS
Language: English
Date published: 02/2018
Academic Unit: Epidemiology; Biostatistics
Record Identifier: 9983997366002771

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