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
