Neural Network Augmented Compartmental Pandemic Models
This work addresses the need for more accurate and computationally efficient pandemic modeling tools for epidemiologists and policymakers, though it is incremental as it builds on existing SIR models.
The paper tackles the limitations of traditional SIR models in epidemiology by introducing a neural network augmented SIR model that incorporates non-pharmaceutical interventions and weather effects, demonstrating improved predictive power for COVID-19 in Austria from March 2020 to March 2021.
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.