基于非线性多项式拟合函数的意大利新冠疫情新增病例的分析
Empirical Study on New Cases of COVID-19 in Italy Based on Nonlinear Polynomial Fitting Function

基于非线性多项式拟合函数，以意大利为例，使用Python对数据进行非线性多项式拟合函数估计，对该国新冠疫情新增病例进行分析。利用2月23日至4月16日意大利的新冠疫情的历史数据，选取具有代表性的数据，通过对使用非线性多项式拟合函数进行之后十天的趋势分析，对比重合程度最高的模型，从而构建了对意大利新冠疫情新增病例预测的最佳函数，进而通过该函数对意大利未来5天的趋势进行分析。结果显示，幂函数的拟合程度最高，更加贴近于实际数值，故选其作为最终的预测结果，并结合意大利国家目前防疫工作的进度和国家情况，提出可行和科学的防疫措施和政策。
Based on the ring nonlinear polynomial fitting function, taking Italy as an example, Python is used to estimate the nonlinear polynomial fitting function of the data and analyze the new cases of the COVID-19 in this country. Using the historical data of Italy’s COVID-19 from February 23 to April 16, this paper selects representative data, analyzes the trend of the next ten days by using the non-linear polynomial fitting function, and compares the model with the highest degree of coinci-dence, so as to build the best function for predicting the new cases of Italy’s COVID-19, and then uses the function to predict the trend of Italy’s next 5 days potential analysis. The results show that the power function has the highest fitting degree and is more close to the actual value, so it is selected as the final prediction result, and combined with the progress and national situation of the current epidemic prevention work in Italy, feasible and scientific epidemic prevention measures and poli-cies are proposed.