论文标题
通过小波理论和机器学习的流行动力学，应用于Covid-19
Epidemic Dynamics via Wavelet Theory and Machine Learning, with Applications to Covid-19
论文作者
论文摘要
我们介绍了流行病的概念，尤其是特殊情况，因为特殊情况是经典的Sir Models及其衍生产品的时间$ i（t）$ i（t）$。我们提出了一种使用小波理论的模型选择方法对流行动力学进行建模的新方法，以及其应用基于机器学习的曲线拟合技术。我们的通用模型是函数，它们是流行拟合小波的有限线性组合。我们通过基于约翰·霍普金斯大学数据集建模和预测来应用我们的方法，即当前的Covid-19（SARS-COV-2）在法国，德国，意大利和捷克共和国以及美国联邦纽约和佛罗里达州的流行病的传播。
We introduce the concept of epidemic-fitted wavelets which comprise, in particular, as special cases the number $I(t)$ of infectious individuals at time $t$ in classical SIR models and their derivatives. We present a novel method for modelling epidemic dynamics by a model selection method using wavelet theory and, for its applications, machine learning based curve fitting techniques. Our universal models are functions that are finite linear combinations of epidemic-fitted wavelets. We apply our method by modelling and forecasting, based on the John Hopkins University dataset, the spread of the current Covid-19 (SARS-CoV-2) epidemic in France, Germany, Italy and the Czech Republic, as well as in the US federal states New York and Florida.
