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 coincidence, 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 policies are proposed.
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