Trend prediction of COVID-19 based on ARIMA model in mainland of China

ABSTRACT

The ongoing pandemic of COVID-19 has aroused widespread concern around the world and poses a severe threat to public health worldwide. In this paper, the autoregressive integrated moving average (ARIMA) model was used to predict the epidemic trend of COVID-19 in mainland of China. We collected the cumulative cases, cumulative deaths, and cumulative recovery in mainland of China from January 20 to June 30, 2020, and divided the data into experimental group and test group. The ARIMA model was fitted with the experimental group data, and the optimal model was selected for prediction analysis. The predicted data were compared with the test group. The average relative errors of actual cumulative cases, deaths, recovery and predicted values in each province are between -22.32%-22.66%, -9.52%-0.08%, -8.84%-1.16, the results of the comprehensive experimental group and test group show The error of fitting and prediction is small, the degree of fitting is good, the model supports and is suitable for the prediction of the epidemic situation, which has practical guiding significance for the prevention and control of the epidemic situation. HighlightsO_LIWe predicted future COVID-19 occurrences in mainland of China based on ARIMA model. C_LIO_LIWe validated the model based on the previous outbreak data with actual data for June, 2020. C_LIO_LIThe measures taken by the government have contained spread of the epidemic C_LIO_LIThe combination of multiple models may improve the robustness of the model C_LI