Abstract:
:Simple mechanistic epidemic models are widely used for forecasting and parameter estimation of infectious diseases based on noisy case reporting data. Despite the widespread application of models to emerging infectious diseases, we know little about the comparative performance of standard computational-statistical frameworks in these contexts. Here we build a simple stochastic, discrete-time, discrete-state epidemic model with both process and observation error and use it to characterize the effectiveness of different flavours of Bayesian Markov chain Monte Carlo (MCMC) techniques. We use fits to simulated data, where parameters (and future behaviour) are known, to explore the limitations of different platforms and quantify parameter estimation accuracy, forecasting accuracy, and computational efficiency across combinations of modeling decisions (e.g. discrete vs. continuous latent states, levels of stochasticity) and computational platforms (JAGS, NIMBLE, Stan).
journal_name
Stat Methods Med Resjournal_title
Statistical methods in medical researchauthors
Li M,Dushoff J,Bolker BMdoi
10.1177/0962280217747054subject
Has Abstractpub_date
2018-07-01 00:00:00pages
1956-1967issue
7eissn
0962-2802issn
1477-0334journal_volume
27pub_type
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