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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2511.08124 |
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| _version_ | 1866912701787144192 |
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| author | Morariu, Alin Bridgen, Jess Jewell, Chris |
| author_facet | Morariu, Alin Bridgen, Jess Jewell, Chris |
| contents | gemlib is a Python library for defining, simulating, and calibrating Markov state-transition models. Stochastic models are often computationally intensive, making them impractical to use in pandemic response efforts despite their favourable interpretations compared to their deterministic counterparts. gemlib decomposes state-transition models into three key ingredients which succinctly encapsulate the model and are sufficient for executing the subsequent computational routines. Simulation is performed using implementations of Gillespie's algorithm for continuous-time models and a generic Tau-leaping algorithm for discrete time models. gemlib models integrate seamlessly with Markov Chain Monte Carlo samplers as they provide a target distribution for the inference algorithm. Algorithms are implemented using the machine learning computational frameworks JAX and TensorFlow Probability, thus taking advantage of modern hardware to accelerate computation. This abstracts away computational concerns from modellers, allowing them to focus on developing and testing different model structures or assumptions. The gemlib library enables users to rapidly implement and calibrate stochastic epidemic models with the flexibility and robustness required to support decision during an emerging outbreak. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_08124 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | gemlib: Probabilistic programming for epidemic models Morariu, Alin Bridgen, Jess Jewell, Chris Computation Applications gemlib is a Python library for defining, simulating, and calibrating Markov state-transition models. Stochastic models are often computationally intensive, making them impractical to use in pandemic response efforts despite their favourable interpretations compared to their deterministic counterparts. gemlib decomposes state-transition models into three key ingredients which succinctly encapsulate the model and are sufficient for executing the subsequent computational routines. Simulation is performed using implementations of Gillespie's algorithm for continuous-time models and a generic Tau-leaping algorithm for discrete time models. gemlib models integrate seamlessly with Markov Chain Monte Carlo samplers as they provide a target distribution for the inference algorithm. Algorithms are implemented using the machine learning computational frameworks JAX and TensorFlow Probability, thus taking advantage of modern hardware to accelerate computation. This abstracts away computational concerns from modellers, allowing them to focus on developing and testing different model structures or assumptions. The gemlib library enables users to rapidly implement and calibrate stochastic epidemic models with the flexibility and robustness required to support decision during an emerging outbreak. |
| title | gemlib: Probabilistic programming for epidemic models |
| topic | Computation Applications |
| url | https://arxiv.org/abs/2511.08124 |