  The COVID-19 pandemic has highlighted the importance of in-silico
epidemiological modelling in predicting the dynamics of infectious diseases to
inform health policy and decision makers about suitable prevention and
containment strategies. Work in this setting involves solving challenging
inference and control problems in individual-based models of ever increasing
complexity. Here we discuss recent breakthroughs in machine learning,
specifically in simulation-based inference, and explore its potential as a
novel venue for model calibration to support the design and evaluation of
public health interventions. To further stimulate research, we are developing
software interfaces that turn two cornerstone COVID-19 and malaria epidemiology
models COVID-sim, (https://github.com/mrc-ide/covid-sim/) and OpenMalaria
(https://github.com/SwissTPH/openmalaria) into probabilistic programs, enabling
efficient interpretable Bayesian inference within those simulators.
