Resolving outbreak dynamics using approximate bayesian computation for stochastic birth–death models [version 2; peer review: 2 approved]

Jarno Lintusaari, Paul Blomstedt, Brittany Rose, Tuomas Sivula, Michael Urs Gutmann, Samuel Kaski, Jukka Corander

Research output: Contribution to journalArticleScientificpeer-review


Earlier research has suggested that approximate Bayesian computation (ABC) makes it possible to fit simulator-based intractable birth–death models to investigate communicable disease outbreak dynamics with accuracy comparable to that of exact Bayesian methods. However, recent findings have indicated that key parameters, such as the reproductive number R, may remain poorly identifiable with these models. Here we show that this identifiability issue can be resolved by taking into account disease-specific characteristics of the transmission process in closer detail. Using tuberculosis (TB) in the San Francisco Bay area as a case study, we consider a model that generates genotype data from a mixture of three stochastic processes, each with its own distinct dynamics and clear epidemiological interpretation. We show that our model allows for accurate posterior inferences about outbreak dynamics from aggregated annual case data with genotype information. As a byproduct of the inference, the model provides an estimate of the infectious population size at the time the data were collected. The acquired estimate is approximately two orders of magnitude smaller than assumed in earlier related studies, and it 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 times larger than previous estimates, which has a substantial impact on the interpretation of the fitted outbreak model. © 2019 Lintusaari J et al.
Original languageEnglish
Article number14
JournalWellcome open research
Number of pages26
Publication statusPublished - 2019
MoE publication typeA1 Journal article-refereed

Bibliographical note

First published: 25 Jan 2019, 4:14 (

Fields of Science

  • 112 Statistics and probability
  • 113 Computer and information sciences

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