Research On Norovirus Infection Surveillance Based On Baidu Search Engine

ObjectiveNorovirus is a common virus that causes acute gastroenteritis worldwide,but a monitoring system for norovirus is unavailable in China.This study aimed to identify norovirus epidemics through Internet surveillance and construct an appropriate model to predict potential norovirus infections.MethodsThe norovirus-related data of a selected outbreak in Jiaxing Municipality,Zhejiang Province of China,in 2014 were collected from immediate epidemiological investigation,and the Internet search volume,as indicated by the Baidu Index,was acquired from the Baidu search engine.All correlated search keywords in relation to norovirus were captured,screened,and composited to establish the composite Baidu Index at different time lags by Spearman rank correlation.The optimal model was chosen and possibly predicted maps in Zhejiang Province were presented by ArcGIS software.We selecting several Norovirus epidemics to identify fluctuation of composite Baidu Index during the epidemic and test the model.Results1.This epidemic was first reported in Haiyan,followed by Haining,within the Jiaxing Municipality in Zhejiang Province of China.A total of 924 cases with a ratio of 1 male to 1.2 females involving 13 schools were detected from February 12 to 21,2014;The clinical symptoms were mild and the main symptom was vomiting accompanied by nausea,diarrhea,fever,and abdominal pain,but no death occurred.2.Related keywords in five possible time lags(0,1,2,3,and 4 days)were screened by Spearman rank correlation.Time lag 0 include keywords ” Norovirus”,” Noro” and “Vomiting and bleeding”,time lag 1 include keywords “Noro” and “Norovirus”,time lag 2 include keywords “Norovirus”,”Noro”,“Vomiting and diarrhea”,“Nausea and vomiting” and “Viral diarrhea in infants”,time lag 3 include keywords “Norovirus”,” Why feel headache and nausea”,“Noro” and “Why feel dizziness and nausea “,time lag 4 include keywords” Why feel headache and nausea”.3.The correlation coefficient peaked at the time lag of 2 days with five keywords(?=.945,P<.001).Considering the potential epidemiology significance and delicate difference at the time lag of 1 day with two keywords(?=.924,P<.001),both time lags were included to construct appropriate models.Of the five candidate models considered in our study,ECM was determined the optimal model for the time lag of 1 day,whereas the top model for the time lag of 2 days was GCM.Then,OCR values of both models in Jiaxing Municipality were calculated,demonstrating that OCR in ECM was 90.69% and in GCM was 66.00%.Consequently,the optimal model was decided as ECM with 1 day lag.In this model,which was interpreted as a one-unit increase in the mean composite Baidu Index contributed to an increase of norovirus infections by 2.15 times during the outbreak.4.Based on the optimal model,potential norovirus cases of 11 municipalities in Zhejiang Province during the study time were evaluated.And composite Baidu Index nationwide was also presented.During the outbreak,composite Baidu Index in Jiaxing and Hangzhou was higher than the rest area of Zhejiang Province.Besides,Jiangsu Province and Guangdong Province also had a high level of attention.From the displayed map,Jiaxing Municipality in 2014 showed the peak of the norovirus infection than other areas in the same period.Moreover,there might have been potential norovirus epidemics in other municipalities,such as Hangzhou,Huzhou,Quzhou and Zhoushan.During the period of four outbreak,composite Baidu Index also show the peak.Compared with the data in the Public Health Emergency Management Information System,the predicted value of the model may underestimate the epidemic in a degree.However,it still could identify three prevalence in Jinhua,Hangzhou and Huzhou,respectively.ConclusionsThe role of forecasting and warning against infectious diseases through the Internet has been identified in some available studies.In this study,we try to explore the significant keywords involving norovirus,construct an effective model,and eventually identify the potential epidemics of norovirus in Zhejiang Province using Internet surveillance.The optimal model indicated that a one-unit increase in the mean composite Baidu Index contributed to an increase of norovirus infections by 2.15 times during the outbreak.Despite existing limitations in early warning and unavoidable biases,Internet surveillance may be still useful for the monitoring of norovirus epidemics when a monitoring system is unavailable.