تقرير
Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference
| العنوان: | Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference |
|---|---|
| المؤلفون: | Charles, Giovanni, Wolock, Timothy M., Winskill, Peter, Ghani, Azra, Bhatt, Samir, Flaxman, Seth |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science Mathematics Quantitative Biology Statistics |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Mathematics - Probability, Quantitative Biology - Populations and Evolution, Statistics - Machine Learning |
| الوصف: | Epidemic models are powerful tools in understanding infectious disease. However, as they increase in size and complexity, they can quickly become computationally intractable. Recent progress in modelling methodology has shown that surrogate models can be used to emulate complex epidemic models with a high-dimensional parameter space. We show that deep sequence-to-sequence (seq2seq) models can serve as accurate surrogates for complex epidemic models with sequence based model parameters, effectively replicating seasonal and long-term transmission dynamics. Once trained, our surrogate can predict scenarios a several thousand times faster than the original model, making them ideal for policy exploration. We demonstrate that replacing a traditional epidemic model with a learned simulator facilitates robust Bayesian inference. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2209.09617 |
| رقم الأكسشن: | edsarx.2209.09617 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |