The recent COVID-19 pandemic has thrown the importance of accurately forecasting conta gion dynamics and learning infection parameters into sharp focus. At the same time, effec tive policy-making requires knowledge of the uncertainty on such predictions, in order, for
instance, to be able to ready hospitals and intensive care units for a worst-case scenario
without needlessly wasting resources. In this work, we apply a novel and powerful computa tional method to the problem of learning probability densities on contagion parameters and
providing uncertainty quantification for pandemic projections. Using a neural network, we
calibrate an ODE model to data of the spread of COVID-19 in Berlin in 2020, achieving both
a significantly more accurate calibration and prediction than Markov-Chain Monte Carlo
(MCMC)-based sampling schemes. The uncertainties on our predictions provide meaningful
confidence intervals e.g. on infection figures and hospitalisation rates, while training and
running the neural scheme takes minutes where MCMC takes hours. We show conver gence of our method to the true posterior on a simplified SIR model of epidemics, and also
demonstrate our method’s learning capabilities on a reduced dataset, where a complex
model is learned from a small number of compartments for which data is available