Analysis and Prediction of Epidemic Prevention and Control by Police Stations Based on Time Series

Abstract

It has been over two years since the outburst of the COVID-19 pandemic. Currently, China has entered into a normalization stage and police stations are still in the endeavor of improving their epidemic prevention and control measures. However, grassroots police stations are still backward in epidemic prevention and control, and lack of response measures for each period of the epidemic. This paper uses time series models to predict the epidemic trend and analyze the measures undertaken by the police stations. In the process of data pretreatment, this paper focuses on the data processing of the epidemic control period. Then the epidemic trend is predicted based on five different time series models and two different time intervals. The results indicate that the tertiary exponential smoothing prediction model with day as the interval is the best and accurate prediction method. According to the prediction model, it can be determined the current stage of the epidemic development by time points, so as to give targeted reference for the police stations. The basic idea in using various time series models is to predict the accumulated number of confirmed cases based on the existing data not only to help, guide and refine the existing epidemic measures but also offer suggestions for epidemic prevention and control by police stations in response to each period of the epidemic. Based on the findings exhaustive recommendations are proposed for real-time and targeted epidemic prevention and control by police administration.