Liu Kui, Huang Sichao, Miao Zi-Ping, Chen Bin, Jiang Tao, Cai Gaofeng, Jiang Zhenggang, Chen Yongdi, Wang Zhengting, Gu Hua, Chai Chengliang, Jiang Jianmin
Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China.
Key Laboratory of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Hangzhou, China.
J Med Internet Res. 2017 Aug 8;19(8):e282. doi: 10.2196/jmir.7855.
Norovirus is a common virus that causes acute gastroenteritis worldwide, but a monitoring system for norovirus is unavailable in China.
We aimed to identify norovirus epidemics through Internet surveillance and construct an appropriate model to predict potential norovirus infections.
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.
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.
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.
诺如病毒是一种在全球范围内引起急性胃肠炎的常见病毒，但中国尚无诺如病毒监测系统。
我们旨在通过互联网监测识别诺如病毒疫情，并构建合适的模型来预测潜在的诺如病毒感染。
收集2014年中国浙江省嘉兴市一起选定疫情的诺如病毒相关数据，这些数据来自即时流行病学调查，同时从百度搜索引擎获取以百度指数表示的互联网搜索量。捕获、筛选并合成所有与诺如病毒相关的搜索关键词，通过Spearman等级相关性在不同时间滞后建立综合百度指数。选择最优模型，并通过ArcGIS软件呈现浙江省可能的预测地图。
最终确定在1天时间滞后时两个关键关键词的组合为最优（ρ = 0.924，P < 0.001）。构建指数曲线模型以拟合此次疫情的趋势，表明在疫情期间平均综合百度指数每增加一个单位，诺如病毒感染增加2.15倍。根据预测模型，除嘉兴市外，杭州市在研究期间可能也存在一些潜在疫情。
尽管早期预警存在局限性且不可避免存在偏差，但在没有监测系统时，互联网监测对于诺如病毒疫情监测可能仍有用。
