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 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 (

SUBMITTER: Hu B 

PROVIDER: S-EPMC5711876 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature