  Epidemiology models are central in understanding and controlling large scale
pandemics. Several epidemiology models require simulation-based inference such
as Approximate Bayesian Computation (ABC) to fit their parameters to
observations. ABC inference is highly amenable to efficient hardware
acceleration. In this work, we develop parallel ABC inference of a stochastic
epidemiology model for COVID-19. The statistical inference framework is
implemented and compared on Intel Xeon CPU, NVIDIA Tesla V100 GPU and the
Graphcore Mk1 IPU, and the results are discussed in the context of their
computational architectures. Results show that GPUs are 4x and IPUs are 30x
faster than Xeon CPUs. Extensive performance analysis indicates that the
difference between IPU and GPU can be attributed to higher communication
bandwidth, closeness of memory to compute, and higher compute power in the IPU.
The proposed framework scales across 16 IPUs, with scaling overhead not
exceeding 8% for the experiments performed. We present an example of our
framework in practice, performing inference on the epidemiology model across
three countries, and giving a brief overview of the results.
