Abstract
This study identified the possible threshold to predict dengue fever (DF) outbreaks using Baidu Search Index (BSI). Time-series classification and regression tree models based on BSI were used to develop a predictive model for DF outbreak in Guangzhou and Zhongshan, China. In the regression tree models, the mean autochthonous DF incidence rate increased approximately 30-fold in Guangzhou when the weekly BSI for DF at the lagged moving average of 1–3 weeks was more than 382. When the weekly BSI for DF at the lagged moving average of 1–5 weeks was more than 91.8, there was approximately 9-fold increase of the mean autochthonous DF incidence rate in Zhongshan. In the classification tree models, the results showed that when the weekly BSI for DF at the lagged moving average of 1–3 weeks was more than 99.3, there was 89.28% chance of DF outbreak in Guangzhou, while, in Zhongshan, when the weekly BSI for DF at the lagged moving average of 1–5 weeks was more than 68.1, the chance of DF outbreak rose up to 100%. The study indicated that less cost internet-based surveillance systems can be the valuable complement to traditional DF surveillance in China.
Highlights
Dengue fever (DF) is a major public health concern, in the tropical and sub-tropical regions
This study aimed to identify the association between Baidu Search Index (BSI) and dengue fever (DF), and detect the possible threshold of DF outbreaks with machine-learning method time-series classification and regression trees (CART) model based on Baidu search query data
38,866 autochthonous DF cases and 771 imported cases were reported for Guangzhou; 1,476 autochthonous cases and 167 imported DF cases were reported in Zhongshan city
Summary
Dengue fever (DF) is a major public health concern, in the tropical and sub-tropical regions. Digital surveillance systems that are built on internet search engines data can provide health authorities with important information regarding the emergence and spread of diseases in the community which can be used to complement traditional healthcare-based surveillance systems[7]. The real-time detection and prediction using the internet-based surveillance systems have been explored in some other diseases such as Ebola, malaria, and breast cancer[11,12,13]. Some studies[12,14] have presented the benefits of internet search query data on diseases for improving real-time tracking and surveillance systems, this innovative approach requires further development. The majority of web searches originating from China are submitted through the Baidu search engine (http://www.baidu.com/). DF activity in China has not been explored and tracked with the web search behavior
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