Replacing the SIR epidemic model with a neural network and training it further to increase prediction accuracy

G. Bogacsovics, A. Hajdu, Róbert Lakatos, Marcell Beregi-Kovács, Attila Tiba, H. Tomán
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引用次数: 1

Abstract

Researchers often use theoretical models which provide a relatively sim- ple, yet concise and effective way of modelling various phenomena. However, it is a well-known fact that the more complex the model, the more complex the mathematical description is. For this reason, theoretical models generally avoid large complexity and aim for the simplest possible definition, which although makes models mathematically more manageable, in practice it also often leads to sub-optimal performance. Furthermore, the data collected during the observations usually contain confounding factors, for which a simple theoretical model can not be prepared. Overall, mathematical models are usually too rigid and sophisticated, and therefore cannot really deal with sudden changes in the environment. The application of artificial intelligence, however, provides a good opportunity to develop complex models that can combine the basic capabilities of the theoretical models with the ability to learn more complex relationships. It has been shown [16] that with neural networks, we can build such models that can approximate mathematical functions. Trained artificial neural networks are thus able to behave like theoretical models developed for different fields, while still retaining their overall flexibility, which guarantees an overall better performance in a complex realworld environment. The aim of our study is to show our notion that we can create an architecture using neural networks, which is able to approximate a given theoretical model, and then further improve it with the help of real data to suit the real world and its various aspects better. In order to validate the functionality of the architecture developed by us, we have selected a simple theoretical model, namely the Kermack-McKendrick one [4] as the base of our research. This is an SIR [2] model, which is a relatively simple compartmental epidemic model, based on differential equations that can be used well for infections that spread very similarly to influenza or COVID. However, on one hand, the SIR model relies too heavily on its parameters, with slight changes in them leading to drastic overall changes of the S, I and R curves, and on the other hand, the simplicity of the SIR model distorts its accuracy in many cases. In our paper, by using the SIR model, we will show that the architecture described above can be a valid approach to modeling the spread of a given disease (such as influenza or COVID-19). To this end, we detail the accuracy of our models with different settings and configurations and show that it performs better than both a simple mathematical model and a plain neural network with randomly initialized weights.
用神经网络代替SIR流行病模型,并对其进行进一步训练,提高预测精度
研究人员经常使用理论模型,这些模型提供了一种相对简单,但简洁有效的方法来模拟各种现象。然而,一个众所周知的事实是,模型越复杂,数学描述就越复杂。由于这个原因,理论模型通常避免大的复杂性,并以尽可能简单的定义为目标,尽管这使得模型在数学上更易于管理,但在实践中,它也经常导致次优性能。此外,观测过程中收集到的数据通常包含混淆因素,无法建立简单的理论模型。总的来说,数学模型通常过于僵化和复杂,因此不能真正处理环境中的突然变化。然而,人工智能的应用为开发复杂模型提供了很好的机会,这些模型可以将理论模型的基本能力与学习更复杂关系的能力结合起来。已经证明,使用神经网络,我们可以建立这样的模型来近似数学函数。因此,经过训练的人工神经网络能够像为不同领域开发的理论模型一样运行,同时仍然保持其整体灵活性,这保证了在复杂的现实环境中具有更好的整体性能。我们研究的目的是展示我们的概念,我们可以使用神经网络创建一个架构,它能够近似给定的理论模型,然后在实际数据的帮助下进一步改进它,以更好地适应现实世界及其各个方面。为了验证我们开发的架构的功能,我们选择了一个简单的理论模型,即Kermack-McKendrick one[4]作为我们研究的基础。这是一个SIR[2]模型,这是一个相对简单的区隔流行病模型,基于微分方程,可以很好地用于与流感或COVID传播非常相似的感染。然而,SIR模型一方面过于依赖其参数,参数的微小变化会导致S、I和R曲线的整体剧烈变化,另一方面,SIR模型的简单性在很多情况下扭曲了其准确性。在我们的论文中,通过使用SIR模型,我们将展示上述架构可以是对给定疾病(如流感或COVID-19)传播建模的有效方法。为此,我们详细介绍了不同设置和配置下我们的模型的准确性,并表明它比简单的数学模型和随机初始化权重的普通神经网络表现得更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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