Using Baidu Search Index to Predict Dengue Outbreak in China

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.

Global dynamic spatiotemporal pattern of seasonal influenza since 2009 influenza pandemic

“…Regarding the influenza situation in Asia-Pacific region, some countries such as Australia and China had a high influenza positive rate (finding of the present study) but low/moderate mortality rate [1], implying a relatively good healthcare system in these countries but also suggesting a strong need to identify the national, regional, and local optimal vaccination timing for cost-effective influenza prevention (especially for China as it has wide latitude spans) [10], and to build up influenza early warning system which gives warning in a timely manner (e.g., internet-based early warning tools incorporating information collected through traditional surveillance system) [20]. Our prior works have suggested that early warning of infectious diseases using data from search engine (e.g., Google and Baidu) may shed some new light on infectious disease control [21,22]. The constrains and barriers for influenza control and prevention in Asia-Pacific region are multifaceted (e.g., logistic and resourcing issues) [23], and preventing people in this region from influenza attacks requires concerted efforts from policy makers, public health officials, healthcare workers, and scientists.…”

WeChat, a Chinese social media, may early detect the SARS-CoV-2 outbreak in 2019

“…This may have an advantage especially for detecting outbreaks in China considering the population of WeChat users. Previous studies used search query data, including Baidu Index, to monitor the activity of emerging infectious diseases [15][16][17]. As far as we know, this is the first time to use WeChat data in this field.…”

Association of sociodemographic factors and internet query data with pertussis infections in Shandong, China

“…Regression tree models were developed to segment the identified sociodemographic factors into subsets that were most likely to be associated with a stronger relationship between pertussis infections and internet query data [48]. We used the city-specific Spearman's correlation coefficient of temporal risk indices as the dependent variables and sociodemographic data as the independent variables.…”