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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.11836 |
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| _version_ | 1866915295445123072 |
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| author | Neill, James Lester, Rebecca Bakali, Winnie Roberts, Gareth Feasey, Nicholas Chapman, Lloyd A. C. Jewell, Chris |
| author_facet | Neill, James Lester, Rebecca Bakali, Winnie Roberts, Gareth Feasey, Nicholas Chapman, Lloyd A. C. Jewell, Chris |
| contents | Infectious disease transmission is often modelled by discrete-valued stochastic state-transition processes. Due to a lack of complete data, Bayesian inference for these models often relies on data-augmentation techniques. These techniques are often inefficient or time consuming to implement. We introduce a novel data-augmentation Markov chain Monte Carlo method for discrete-time individual-based epidemic models, which we call the Rippler algorithm. This method uses the transmission model in the proposal step of the Metropolis-Hastings algorithm, rather than in the accept-reject step. We test the Rippler algorithm on simulated data and apply it to data on extended-spectrum beta-lactamase (ESBL)-producing E. coli collected in Blantyre, Malawi. We compare the Rippler algorithm to two other commonly used Bayesian inference methods for partially observed epidemic data, and find that it has a good balance between mixing speed and computational complexity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_11836 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Non-centering for discrete-valued state transition models: an application to ESBL-producing E. coli transmission in Malawi Neill, James Lester, Rebecca Bakali, Winnie Roberts, Gareth Feasey, Nicholas Chapman, Lloyd A. C. Jewell, Chris Methodology Infectious disease transmission is often modelled by discrete-valued stochastic state-transition processes. Due to a lack of complete data, Bayesian inference for these models often relies on data-augmentation techniques. These techniques are often inefficient or time consuming to implement. We introduce a novel data-augmentation Markov chain Monte Carlo method for discrete-time individual-based epidemic models, which we call the Rippler algorithm. This method uses the transmission model in the proposal step of the Metropolis-Hastings algorithm, rather than in the accept-reject step. We test the Rippler algorithm on simulated data and apply it to data on extended-spectrum beta-lactamase (ESBL)-producing E. coli collected in Blantyre, Malawi. We compare the Rippler algorithm to two other commonly used Bayesian inference methods for partially observed epidemic data, and find that it has a good balance between mixing speed and computational complexity. |
| title | Non-centering for discrete-valued state transition models: an application to ESBL-producing E. coli transmission in Malawi |
| topic | Methodology |
| url | https://arxiv.org/abs/2504.11836 |