PLEASE NOTE THE TIME OF 2PM ET

Two main approaches exist in modeling diseases spread. First, the interactive dynamics of all variables that are assumed to be influential in the disease spread are specified explicitly, resulting in mechanistic models, such as the well-known susceptible-infected-removed (SIR). These models have proven to be successful in predicting the short-term future and providing insight into the disease dynamics. However, they are based on our prior understanding of the world, and hence, are only as "good" as that prior understanding, and do not extend to situations where the underlying mechanisms are unknown. Second, simple to advanced machine-learning models are developed fully from data and without incorporating prior human expert knowledge. Some of these models have shown an exceptional forecasting power; however, they often provide no intuition about the dynamics — the reason why they are often questioned and even avoided by mathematicians. A natural bridging between the two approaches would be to take a mechanistic modelling approach for those compartments of the disease spread whose governing dynamics are well-understood and a machine-learning approach for those other yet not-well understood compartments, and this is what I will be discussing in this talk.