Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference
This work addresses computational bottlenecks in epidemic modeling for public health policy, though it is incremental as it applies existing seq2seq methods to a new domain.
The paper tackles the computational intractability of complex epidemic models by using deep sequence-to-sequence models as surrogates, achieving predictions several thousand times faster and enabling robust Bayesian inference.
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.