Bayesian inference of epidemiological parameters from transmission experiments

Abstract Epidemiological parameters for livestock diseases are often inferred from transmission experiments. However, there are several limitations inherent to the design of such experiments that limits the precision of parameter estimates. In particular, infection times and latent periods cannot be...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Ben Hu, Jose L. Gonzales, Simon Gubbins
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/071962ac18f7467c8b02350115e2320e
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:071962ac18f7467c8b02350115e2320e
record_format	dspace
spelling	oai:doaj.org-article:071962ac18f7467c8b02350115e2320e2021-12-02T15:06:09ZBayesian inference of epidemiological parameters from transmission experiments10.1038/s41598-017-17174-82045-2322https://doaj.org/article/071962ac18f7467c8b02350115e2320e2017-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-17174-8https://doaj.org/toc/2045-2322Abstract Epidemiological parameters for livestock diseases are often inferred from transmission experiments. However, there are several limitations inherent to the design of such experiments that limits the precision of parameter estimates. In particular, infection times and latent periods cannot be directly observed and infectious periods may also be censored. We present a Bayesian framework accounting for these features directly and employ Markov chain Monte Carlo techniques to provide robust inferences and quantify the uncertainty in our estimates. We describe the transmission dynamics using a susceptible-exposed-infectious-removed compartmental model, with gamma-distributed transition times. We then fit the model to published data from transmission experiments for foot-and-mouth disease virus (FMDV) and African swine fever virus (ASFV). Where the previous analyses of these data made various assumptions on the unobserved processes in order to draw inferences, our Bayesian approach includes the unobserved infection times and latent periods and quantifies them along with all other model parameters. Drawing inferences about infection times helps identify who infected whom and can also provide insights into transmission mechanisms. Furthermore, we are able to use our models to measure the difference between the latent periods of inoculated and contact-challenged animals and to quantify the effect vaccination has on transmission.Ben HuJose L. GonzalesSimon GubbinsNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-13 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ben Hu Jose L. Gonzales Simon Gubbins Bayesian inference of epidemiological parameters from transmission experiments
description	Abstract Epidemiological parameters for livestock diseases are often inferred from transmission experiments. However, there are several limitations inherent to the design of such experiments that limits the precision of parameter estimates. In particular, infection times and latent periods cannot be directly observed and infectious periods may also be censored. We present a Bayesian framework accounting for these features directly and employ Markov chain Monte Carlo techniques to provide robust inferences and quantify the uncertainty in our estimates. We describe the transmission dynamics using a susceptible-exposed-infectious-removed compartmental model, with gamma-distributed transition times. We then fit the model to published data from transmission experiments for foot-and-mouth disease virus (FMDV) and African swine fever virus (ASFV). Where the previous analyses of these data made various assumptions on the unobserved processes in order to draw inferences, our Bayesian approach includes the unobserved infection times and latent periods and quantifies them along with all other model parameters. Drawing inferences about infection times helps identify who infected whom and can also provide insights into transmission mechanisms. Furthermore, we are able to use our models to measure the difference between the latent periods of inoculated and contact-challenged animals and to quantify the effect vaccination has on transmission.
format	article
author	Ben Hu Jose L. Gonzales Simon Gubbins
author_facet	Ben Hu Jose L. Gonzales Simon Gubbins
author_sort	Ben Hu
title	Bayesian inference of epidemiological parameters from transmission experiments
title_short	Bayesian inference of epidemiological parameters from transmission experiments
title_full	Bayesian inference of epidemiological parameters from transmission experiments
title_fullStr	Bayesian inference of epidemiological parameters from transmission experiments
title_full_unstemmed	Bayesian inference of epidemiological parameters from transmission experiments
title_sort	bayesian inference of epidemiological parameters from transmission experiments
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/071962ac18f7467c8b02350115e2320e
work_keys_str_mv	AT benhu bayesianinferenceofepidemiologicalparametersfromtransmissionexperiments AT joselgonzales bayesianinferenceofepidemiologicalparametersfromtransmissionexperiments AT simongubbins bayesianinferenceofepidemiologicalparametersfromtransmissionexperiments
_version_	1718388559005614080

Bayesian inference of epidemiological parameters from transmission experiments

Ejemplares similares