Identifying Potential Norovirus Epidemics in China via Internet Surveillance

Abstract

Background: Norovirus is a common virus that causes acute gastroenteritis worldwide, but a monitoring system for norovirus is unavailable in China.

Objective: We aimed to identify norovirus epidemics through Internet surveillance and construct an appropriate model to predict potential norovirus infections.

Methods: The 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.

Results: The combination of two vital keywords at a time lag of 1 day was ultimately identified as optimal (ρ=.924, P<.001). The exponential curve model was constructed to fit the trend of this epidemic, suggesting 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. In addition to Jiaxing Municipality, Hangzhou Municipality might have had some potential epidemics in the study time from the predicted model.

Conclusions: Although there are limitations with early warning and unavoidable biases, Internet surveillance may be still useful for the monitoring of norovirus epidemics when a monitoring system is unavailable.

Keywords: Internet surveillance; disease prediction; norovirus.