Earlier research has suggested that Approximate Bayesian Computation (ABC) makes it possible to fit intractable simulator-based stochastic birth-death models to investigate communicable disease outbreak dynamics and that the accuracy of ABC inference can be comparable to that of exact Bayesian inference based on for example particle-filtering Markov Chain Monte Carlo. However, recent findings have indicated that key parameters such as the reproductive number, R, may remain poorly identifiable from data generated under an infinite alleles model. Here we show that the identifiability issue can be resolved by taking into account disease-specific characteristics of the transmission process in closer detail in the birth-death model. Using tuberculosis (TB) in the San Francisco Bay area as a case-study, we consider the situation where the genotype data are generated as a mixture of two stochastic processes, each with their distinct dynamics and clear epidemiological interpretation. ABC inference based on the ELFI software yields stable and accurate posterior inferences about outbreak dynamics from aggregated annual case data with genotype information. We also show that under the proposed model the infectious population size can be reliably inferred and that it is approximately two orders of magnitude smaller than considered in the previous ABC studies focusing on the same data, which is much better aligned with epidemiological knowledge about active TB prevalence. Similarly, the reproductive number R related to the primary underlying transmission process is estimated to be nearly three-fold compared with the previous estimates, which has a substantial impact on the interpretation of the fitted outbreak model. Our Python codes implementing the simulator model and the inference algorithm are freely available for further research and use at GitHub.
- Outbreak dynamics
- Stochastic birth death process
- Approximate Bayesian computation