Deep learning is also suitable because it provides the capability of ingesting data from multiple sources, which better informs the model of what is happening on the ground. Our work aims to close this gap in the literature. Existing approaches for uncertainty quantification can be categorized into three lines. The second line tries to combine the stochastic processes and DNNs. The third line is based on model ensembling [24] which trains multiple DNNs with different initializations and use their predictions for uncertainty quantification.

Accurate and trustworthy epidemic forecasting is an important problem for public health planning and disease mitigation. Recent works in deep neural models for uncertainty-aware time-series forecasting also have several limitations; e.g., it is difficult to specify proper priors in Bayesian NNs, while methods like deep ensembling can be computationally expensive. In this paper, we propose to use neural functional processes to fill this gap. We model epidemic time-series with a probabilistic generative process and propose a functional neural process model called EpiFNP, which directly models the probability distribution of the forecast value in a non-parametric way. In EpiFNP, we use a dynamic stochastic correlation graph to model the correlations between sequences, and design different stochastic latent variables to capture functional uncertainty from different perspectives.