A COVID-19 Prediction Optimization Algorithm Based on Real-time Neural Network Training—Taking Italy as an Example

摘要

A reasonable COVID-19 prediction is of great significance in many areas such as epidemic prevention and control, social resource adjustment and so on in the global fight against COVID-19 epidemic. In this paper, the BP neural network algorithm is used to analyze and predict the development trend of COVID-19. This paper first collects the epidemic data of Italy released by WHO (covering multiple indicators), then analyzes several indicators through computer programs such as R and MATALB, and combines the results of multiple indicators through computer language to carry out the prediction analysis of COVID-19 in Italy. The prediction results show that the trend of Italy epidemic situation in the study period is overall good. However, it is still necessary to strengthen the epidemic prevention and control. Finally, the fitting value of the model is compared with the real value, and the fitting degree reaches 0.99. This shows that the BP neural network algorithm provides a reference for COVID-19’s prediction.