Markov Chain Monte Carlo (MCMC) Modelling of Diagnostics for Pandemic Planning Using JAGS Package in R
Markov Chain Monte-Carlo methods are a Bayesian approach to predictive statistics, which takes advantage of prior beliefs and conditions as well as the existing data to produce posterior distributions of relevant parameters. This approach, implementable through the JAGS packaging in R, is promising for its impact on the diagnostics space, which is a critical bottleneck for pandemic planning and rapid response. Specifically, these methods provide the means to optimize diagnostic testing, for example, by determining whether it is best to test individuals in a certain locale once or multiple times. This study compares the expected accuracy of single and double testing under two specific conditions, a general and Icelandic test case, in order to ascertain the validity of MCMC methods in this space and inform decisionmakers and future research in the space. Models based on this platform may eventually be tailored to the priors of specific locales. Additionally, the ability to test multiple regimes of real or simulated data while maintaining uncertainty widens the pool of researchers that can impact the space. In future studies, ensemble methods investigating the full range of parameters and their combinations can be studied.