Analysis on the effect of multi-source data in early warning of respiratory infectious diseases
Objective To explore the role of multi-source data in respiratory infectious disease early warning,so as to provide reference for respiratory infectious disease early warning.Methods The multi-source data of early warning of respiratory infectious diseases in Weifang city,Shandong province from January 1,2022 to May 31,2023 were select-ed.Based on the data of respiratory infectious diseases in Weifang city,the cross-correlation function was used to analyze the lag correlation with the data of outpatient complaints,preliminary diagnosis,120 first aid,Baidu drugs,Baidu fever,Baidu symptoms,and the EARS-3Cs model was used to construct an early warning model.Results The symptom re-trieval data of Baidu was 2 days ahead of the data of respiratory infectious diseases in Weifang city,and the lag correlation coefficient(ACF)was 0.749.The lag days of Baidu drug,Baidu fever and preliminary diagnosis data were 0,and the ACF of Baidu drug was the largest(0.885).The outpatient complaints lagged by 1 day,and the data of 120 lagged by 10 days.According to the principle that the lag days of multi-source data were＜0 and the lag correlation coefficient was the largest,Baidu symptom data was selected to construct the EARS-3Cs model,and the days when the C1,C2,and C3 values exceeded the early warning threshold were 3,10,and 24 days,respectively,and were mainly concentrated in December 2022 and March 2023.Conclusion Lag correlation analysis effectively identified the optimal data source among multi-source data,and the EARS-3Cs model exhibited reasonable reliability and validity for early warning of respiratory infectious dis-eases.
万方数据
维普
