Bayesian analysis of experimental epidemics of foot–and–mouth disease

Streftaris, George; Gibson, Gavin J.

doi:10.1098/rspb.2004.2715

Cited by 44 publications

(39 citation statements)

References 22 publications

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“…some parameters are herd-dependent). Inference is performed from field data (described in Guatteo et al 2007) using Markov chain Monte Carlo (MCMC) methodology (Gilks et al 1996), which is being increasingly used in epidemic modelling (O'Neill & Roberts 1999;Cauchemez et al 2004;Streftaris & Gibson 2004;Höhle et al 2005;Lekone & Finkenstädt 2006). Posterior distributions of model parameters are analysed and biological interpretations are proposed.…”

Section: Introductionmentioning

confidence: 99%

Spread of Q fever within dairy cattle herds: key parameters inferred using a Bayesian approach

et al. 2010

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Q fever is a worldwide zoonosis caused by Coxiella burnetii. Although ruminants are recognized as the most important source of human infection, no previous studies have focused on assessing the characteristics of the bacterial spread within a cattle herd and no epidemic model has been proposed in this context. We assess the key epidemiological parameters from field data in a Bayesian framework that takes into account the available knowledge, missing data and the uncertainty of the observation process owing to the imperfection of diagnostic tests. We propose an original individual-based Markovian model in discrete time describing the evolution of the infection for each animal. Markov chain Monte Carlo methodology is used to estimate parameters of interest from data consisting of individual health states of 217 cows of five chronically infected dairy herds sampled every week for a four-week period. Outputs are the posterior distributions of the probabilities of transition between health states and of the environmental bacterial load. Our findings show that some herds are characterized by a very low infection risk while others have a mild infection risk and a non-negligible intermittent shedding probability. Moreover, the antibody status seems to be a key point in the bacterial spread (shedders with antibodies shed for a longer period of time than shedders without antibodies). In addition to the biological insights, these estimates also provide information for calibrating simulation models to assess control strategies for C. burnetii infection.

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Section: Introductionmentioning

confidence: 99%

Spread of Q fever within dairy cattle herds: key parameters inferred using a Bayesian approach

et al. 2010

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“…The assessment of fit of a model to a given dataset is then achieved in the Bayesian framework by imputing these residuals, and testing them for compliance with their (known) sampling model using classical tests. The approach has its roots in the framework proposed in [19], and extended in [20,22]. The key innovation in this paper is to design the residual processes so that the resulting tests are sensitive to mis-specification of specific aspects of the model under consideration.…”

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confidence: 99%

New model diagnostics for spatio-temporal systems in epidemiology and ecology

Lau

Marion

Streftaris³

et al. 2014

J. R. Soc. Interface.

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A cardinal challenge in epidemiological and ecological modelling is to develop effective and easily deployed tools for model assessment. The availability of such methods would greatly improve understanding, prediction and management of disease and ecosystems. Conventional Bayesian model assessment tools such as Bayes factors and the deviance information criterion (DIC) are natural candidates but suffer from important limitations because of their sensitivity and complexity. Posterior predictive checks, which use summary statistics of the observed process simulated from competing models, can provide a measure of model fit but appropriate statistics can be difficult to identify. Here, we develop a novel approach for diagnosing mis-specifications of a general spatio-temporal transmission model by embedding classical ideas within a Bayesian analysis. Specifically, by proposing suitably designed non-centred parametrization schemes, we construct latent residuals whose sampling properties are known given the model specification and which can be used to measure overall fit and to elicit evidence of the nature of misspecifications of spatial and temporal processes included in the model. This model assessment approach can readily be implemented as an addendum to standard estimation algorithms for sampling from the posterior distributions, for example Markov chain Monte Carlo. The proposed methodology is first tested using simulated data and subsequently applied to data describing the spread of Heracleum mantegazzianum (giant hogweed) across Great Britain over a 30-year period. The proposed methods are compared with alternative techniques including posterior predictive checking and the DIC. Results show that the proposed diagnostic tools are effective in assessing competing stochastic spatio-temporal transmission models and may offer improvements in power to detect model mis-specifications. Moreover, the latent-residual framework introduced here extends readily to a broad range of ecological and epidemiological models.

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“…One could take a Bayesian approach using methodology based on the likelihood of the data under the SEIR model (22). The Bayesian approach allows one to include prior information in the estimation procedure.…”

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confidence: 99%

Quantification of the effect of vaccination on transmission of avian influenza (H7N7) in chickens

Goot

Koch

Jong

et al. 2005

Proc. Natl. Acad. Sci. U.S.A.

217

154

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Recent outbreaks of highly pathogenic avian influenza (HPAI) viruses in poultry and their threatening zoonotic consequences emphasize the need for effective control measures. Although vaccination of poultry against avian influenza provides a potentially attractive control measure, little is known about the effect of vaccination on epidemiologically relevant parameters, such as transmissibility and the infectious period. We used transmission experiments to study the effect of vaccination on the transmission characteristics of HPAI A͞Chicken͞Netherlands͞03 H7N7 in chickens. In the experiments, a number of infected and uninfected chickens is housed together and the infection chain is monitored by virus isolation and serology. Analysis is based on a stochastic susceptible, latently infected, infectious, recovered (SEIR) epidemic model. We found that vaccination is able to reduce the transmission level to such an extent that a major outbreak is prevented, important variables being the type of vaccine (H7N1 or H7N3) and the moment of challenge after vaccination. Two weeks after vaccination, both vaccines completely block transmission. One week after vaccination, the H7N1 vaccine is better than the H7N3 vaccine at reducing the spread of the H7N7 virus. We discuss the implications of these findings for the use of vaccination programs in poultry and the value of transmission experiments in the process of choosing vaccine.SEIR model ͉ highly pathogenic avian influenza ͉ final size ͉ generalized linear model ͉ reproduction ratio H ighly pathogenic avian influenza (HPAI) is a disease of poultry caused by H5 or H7 AI A strains, with mortality that ranges up to 100%. The number of outbreaks in the last few years has been

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Bayesian analysis of experimental epidemics of foot–and–mouth disease

Cited by 44 publications

References 22 publications

Spread of Q fever within dairy cattle herds: key parameters inferred using a Bayesian approach

Spread of Q fever within dairy cattle herds: key parameters inferred using a Bayesian approach

New model diagnostics for spatio-temporal systems in epidemiology and ecology

Quantification of the effect of vaccination on transmission of avian influenza (H7N7) in chickens

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