Ling Chen1, Huilan Xu. 1. School of Public Health, Central South University, Changsha, China.
Abstract
OBJECTIVE: To determine the application of autoregressive integrated moving average (ARIMA) model in food poisoning prediction in Hunan Province, and to provide scientific basis for the prevention and control of food poisoning. METHODS: We collected the number of food poisoning from January 2003 to December 2009 in Hunan Province for ARIMA model fitting, and used food poisoning data of 2010 to verify the effect of model prediction. We predicted the number of food poisoning in 2011. RESULTS: ARIMA (0,1,1) (0,1,1)12 better fit the trends of the poisoning number in previous time periods and series, with prediction fitting error of 9.59%. The number of food poisoning in Hunan Province in 2011 was predicted to be 834. CONCLUSION: ARIMA model can better fit the number of food poisoning in the short term trends and series. If used for long-term forecasts.
OBJECTIVE: To determine the application of autoregressive integrated moving average (ARIMA) model in food poisoning prediction in Hunan Province, and to provide scientific basis for the prevention and control of food poisoning. METHODS: We collected the number of food poisoning from January 2003 to December 2009 in Hunan Province for ARIMA model fitting, and used food poisoning data of 2010 to verify the effect of model prediction. We predicted the number of food poisoning in 2011. RESULTS: ARIMA (0,1,1) (0,1,1)12 better fit the trends of the poisoning number in previous time periods and series, with prediction fitting error of 9.59%. The number of food poisoning in Hunan Province in 2011 was predicted to be 834. CONCLUSION: ARIMA model can better fit the number of food poisoning in the short term trends and series. If used for long-term forecasts.
Authors: Peng Sun; Ludi Zhang; Lei Han; Hengdong Zhang; Han Shen; Baoli Zhu; Boshen Wang Journal: Environ Sci Pollut Res Int Date: 2022-01-09 Impact factor: 5.190