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Epidemic models are powerful tools in understanding infectious disease.However, as they increase in size and complexity, they can quickly becomecomputationally intractable. Recent progress in modelling methodology has shownthat surrogate models can be used to emulate complex epidemic models with ahigh-dimensional parameter space. We show that deep sequence-to-sequence(seq2seq) models can serve as accurate surrogates for complex epidemic modelswith sequence based model parameters, effectively replicating seasonal andlong-term transmission dynamics. Once trained, our surrogate can predictscenarios a several thousand times faster than the original model, making themideal for policy exploration. We demonstrate that replacing a traditionalepidemic model with a learned simulator facilitates robust Bayesian inference.

Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference