  Compartmental models are a tool commonly used in epidemiology for the
mathematical modelling of the spread of infectious diseases, with their most
popular representative being the Susceptible-Infected-Removed (SIR) model and
its derivatives. However, current SIR models are bounded in their capabilities
to model government policies in the form of non-pharmaceutical interventions
(NPIs) and weather effects and offer limited predictive power. More capable
alternatives such as agent based models (ABMs) are computationally expensive
and require specialized hardware. We introduce a neural network augmented SIR
model that can be run on commodity hardware, takes NPIs and weather effects
into account and offers improved predictive power as well as counterfactual
analysis capabilities. We demonstrate our models improvement of the
state-of-the-art modeling COVID-19 in Austria during the 03.2020 to 03.2021
period and provide an outlook for the future up to 01.2024.
