Neural Network Augmented Compartmental Pandemic Models

TL;DR

This paper presents a neural network augmented SIR model that enhances epidemiological predictions by incorporating NPIs and weather effects, offering improved accuracy and computational efficiency over traditional models.

Contribution

The paper introduces a novel neural network augmented SIR model that accounts for policies and weather, improving predictive power and enabling counterfactual analysis on commodity hardware.

Findings

Improved COVID-19 case predictions in Austria from 2020 to 2021.

Model runs efficiently on standard hardware.

Outlook for future pandemic modeling up to 2024.

Abstract

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.