论文标题
神经网络增强室大流行模型
Neural Network Augmented Compartmental Pandemic Models
论文作者
论文摘要
隔室模型是一种在流行病学中常用的工具，用于传染病传播的数学建模，其最受欢迎的代表性是易感感染的被感染的（SIR）模型及其衍生物。但是，当前的SIR模型在其能力上以非药物干预措施（NPI）的形式建模政府政策，并提供有限的预测能力。基于代理的模型（ABM）等功能强大的替代方案在计算上是昂贵的，并且需要专门的硬件。我们引入了一个神经网络增强的SIR模型，该模型可以在商品硬件上运行，考虑NPI和天气效果，并提供改进的预测能力以及反事实分析功能。我们展示了我们在03.2020至03.2021时期的奥地利最先进的模型Covid-19的模型改进，并为未来提供了最高01.2024的前景。
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.
