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

Forskningsoutput: TidskriftsbidragArtikelVetenskapligPeer review

Sammanfattning

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.
Originalspråkengelska
Artikelnummer14
TidskriftWellcome open research
Volym4
Antal sidor26
ISSN2398-502X
DOI
StatusPublicerad - 2019
MoE-publikationstypA1 Tidskriftsartikel-refererad

Bibliografisk information

First published: 25 Jan 2019, 4:14 (https://doi.org/10.12688/wellcomeopenres.15048.1)

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  • 112 Statistik
  • 113 Data- och informationsvetenskap

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