Resolving outbreak dynamics using approximate Bayesian computation for stochastic birth–death models [version 2; peer review: 2 approved]
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wellcome
2019-08-01
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| Series: | Wellcome Open Research |
| Online Access: | https://wellcomeopenresearch.org/articles/4-14/v2 |
| _version_ | 1818038639467167744 |
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| author | Jarno Lintusaari Paul Blomstedt Brittany Rose Tuomas Sivula Michael U. Gutmann Samuel Kaski Jukka Corander |
| author_facet | Jarno Lintusaari Paul Blomstedt Brittany Rose Tuomas Sivula Michael U. Gutmann Samuel Kaski Jukka Corander |
| author_sort | Jarno Lintusaari |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T07:45:56Z |
| format | Article |
| id | doaj.art-60ae912e26c54448ad8a18c4be90a9cd |
| institution | Directory Open Access Journal |
| issn | 2398-502X |
| language | English |
| last_indexed | 2024-12-10T07:45:56Z |
| publishDate | 2019-08-01 |
| publisher | Wellcome |
| record_format | Article |
| series | Wellcome Open Research |
| spelling | doaj.art-60ae912e26c54448ad8a18c4be90a9cd2022-12-22T01:57:12ZengWellcomeWellcome Open Research2398-502X2019-08-01410.12688/wellcomeopenres.15048.216856Resolving outbreak dynamics using approximate Bayesian computation for stochastic birth–death models [version 2; peer review: 2 approved]Jarno Lintusaari0Paul Blomstedt1Brittany Rose2Tuomas Sivula3Michael U. Gutmann4Samuel Kaski5Jukka Corander6Helsinki Institute for Information Technology (HIIT), Department of Computer Science, Aalto University, Espoo, FinlandHelsinki Institute for Information Technology (HIIT), Department of Computer Science, Aalto University, Espoo, FinlandDepartment of Infectious Diseases Epidemiology and Modelling, Norwegian Institute of Public Health, Oslo, NorwayHelsinki Institute for Information Technology (HIIT), Department of Computer Science, Aalto University, Espoo, FinlandSchool of Informatics, The University of Edinburgh, Edinburgh, UKHelsinki Institute for Information Technology (HIIT), Department of Computer Science, Aalto University, Espoo, FinlandDepartment of Biostatistics, University of Oslo, Oslo, NorwayEarlier 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.https://wellcomeopenresearch.org/articles/4-14/v2 |
| spellingShingle | Jarno Lintusaari Paul Blomstedt Brittany Rose Tuomas Sivula Michael U. Gutmann Samuel Kaski Jukka Corander Resolving outbreak dynamics using approximate Bayesian computation for stochastic birth–death models [version 2; peer review: 2 approved] Wellcome Open Research |
| title | Resolving outbreak dynamics using approximate Bayesian computation for stochastic birth–death models [version 2; peer review: 2 approved] |
| title_full | Resolving outbreak dynamics using approximate Bayesian computation for stochastic birth–death models [version 2; peer review: 2 approved] |
| title_fullStr | Resolving outbreak dynamics using approximate Bayesian computation for stochastic birth–death models [version 2; peer review: 2 approved] |
| title_full_unstemmed | Resolving outbreak dynamics using approximate Bayesian computation for stochastic birth–death models [version 2; peer review: 2 approved] |
| title_short | Resolving outbreak dynamics using approximate Bayesian computation for stochastic birth–death models [version 2; peer review: 2 approved] |
| title_sort | resolving outbreak dynamics using approximate bayesian computation for stochastic birth death models version 2 peer review 2 approved |
| url | https://wellcomeopenresearch.org/articles/4-14/v2 |
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