Fitting mechanistic epidemic models to data: A comparison of simple Markov chain Monte Carlo approaches

Statistical Methods in Medical Research
Michael LiBenjamin M Bolker

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).

References

Sep 1, 1973·Computers in Biology and Medicine·D Ludwig
Nov 24, 2006·Proceedings of the National Academy of Sciences of the United States of America·E L IonidesA A King
Jun 1, 1984·IEEE Transactions on Pattern Analysis and Machine Intelligence·S Geman, D Geman
Apr 4, 2015·Proceedings. Biological Sciences·Aaron A KingPejman Rohani
Aug 16, 2016·Journal of Theoretical Biology·Bradford P TaylorJoshua S Weitz

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Software Mentioned

NIMBLE
JAGS
Stan
JAGS ( Just Another Gibbs Sampler )

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