Towards Identifying and Reducing the Bias of Disease Information Extracted from Search Engine Data.

The estimation of disease prevalence in online search engine data (e.g., Google Flu Trends (GFT)) has received a considerable amount of scholarly and public attention in recent years. While the utility of search engine data for disease surveillance has been demonstrated, the scientific community still seeks ways to identify and reduce biases that are embedded in search engine data. The primary goal of this study is to explore new ways of improving the accuracy of disease prevalence estimations by combining traditional disease data with search engine data. A novel method, Biased Sentinel Hospital-based Area Disease Estimation (B-SHADE), is introduced to reduce search engine data bias from a geographical perspective. To monitor search trends on Hand, Foot and Mouth Disease (HFMD) in Guangdong Province, China, we tested our approach by selecting 11 keywords from the Baidu index platform, a Chinese big data analyst similar to GFT. The correlation between the number of real cases and the composite index was 0.8. After decomposing the composite index at the city level, we found that only 10 cities presented a correlation of close to 0.8 or higher. These cities were found to be more stable with respect to search volume, and they were selected as sample cities in order to estimate the search volume of the entire province. After the estimation, the correlation improved from 0.8 to 0.864. After fitting the revised search volume with historical cases, the mean absolute error was 11.19% lower than it was when the original search volume and historical cases were combined. To our knowledge, this is the first study to reduce search engine data bias levels through the use of rigorous spatial sampling strategies.

Visualized Exploratory Spatiotemporal Analysis of Hand-Foot-Mouth Disease in Southern China.

OBJECTIVES: In epidemiological research, major studies have focused on theoretical models; however, few methods of visual analysis have been used to display the patterns of disease distribution. DESIGN: For this study, a method combining the space-time cube (STC) with space-time scan statistics (STSS) was used to analyze the pattern of incidence of hand-foot-mouth disease (HFMD) in Guangdong Province from May 2008 to March 2009. In this research, STC was used to display the spatiotemporal pattern of incidence of HFMD, and STSS were used to detect the local aggregations of the disease. SETTING: The hand-foot-mouth disease data were obtained from Guangdong Province from May 2008 to March 2009, with a total of 68,130 cases. RESULTS: The STC analysis revealed a differential pattern of HFMD incidence among different months and cities and also showed that the population density and average precipitation are correlated with the incidence of HFMD. The STSS analysis revealed that the most likely aggregation includes the Shenzhen, Foshan and Dongguan populations, which are the most developed regions in Guangdong Province. CONCLUSION: Both STC and STSS are efficient tools for the exploratory data analysis of disease transmission. STC clearly displays the spatiotemporal patterns of disease. Using the maximum likelihood ratio, the STSS model precisely locates the most likely aggregation.

Spatio-temporal analysis of malaria vectors in national malaria surveillance sites in China.

BACKGROUND: To reveal the spatio-temporal distribution of malaria vectors in the national malaria surveillance sites from 2005 to 2010 and provide reference for the current National Malaria Elimination Programme (NMEP) in China. METHODS: A 6-year longitudinal surveillance on density of malaria vectors was carried out in the 62 national malaria surveillance sites. The spatial and temporal analyses of the four primary vectors distribution were conducted by the methods of kernel k-means and the cluster distribution of the most widely distribution vector of An.sinensis was identified using the empirical mode decomposition (EMD). RESULTS: Totally 4 species of Anopheles mosquitoes including An.sinensis, An.lesteri, An.dirus and An.minimus were captured with significant difference of distribution as well as density. An. sinensis was the most widely distributed, accounting for 96.25% of all collections, and its distribution was divided into three different clusters with a significant increase of density observed in the second cluster which located mostly in the central parts of China. CONCLUSION: This study first described the spatio-temporal distribution of malaria vectors based on the nationwide surveillance during 2005-2010, which served as a baseline for the ongoing national malaria elimination program.