Mining Social Media and Web Searches For Disease Detection

Yang, Y. Tony; Horneffer, Michael; DiLisio, Nicole

which it was conducted (four years), as well as false positives associated with some searches, i.e. searches conducted due to events unrelated to the actual occurrence of flu in a population. These limitations may be corrected by extending the duration of future studies and restricting data sources to websites dedicated solely to medical information, such as WebMD and MD Consult. 7 While future studies may refine existing techniques, such refinements do not address the fact that data collected on searches might not have always represented an actual geographical location as per a specific census region. 7 In order to address this issue, search data must be analysed with a greater degree of specificity. However, this analytical specificity might represent a breach in search user privacy. Therefore, additional studies might only use aggregated search volumes representing larger geographical regions for surveillance purposes. 7 Ginsberg et al. 8 conducted such a study on aggregated search volumes towards the early detection of disease activity. Millions of Google search queries were monitored for health-seeking behaviour. These query trends were then correlated to the percentage of physician visits in which a patient displays ILI. 8 Similar to previously mentioned studies, Ginsberg et al. asserted that search engine trends on flu could be used to predict the occurrence of actual flu events. Their system built upon previous attempts by using an automated method of discovering flu-related search queries. 8 Billions of individual searches over five years of Google search logs were used to inform more comprehensive models for use in flu surveillance. 8 Instead of correlating search data to the occurrence of positive flu cultures, Ginsberg et al. examined the probability that a random physician visit in a particular region would be related to a random search query submitted from the same region. 8 The investigators examined a larger aggregated search query database due to the fact that such correlations are only meaningful across large populations. 8 The model was able to obtain a good association with Centres for Disease Control and Prevention (CDC)-reported ILI percentages with a mean correlation of 0.97. 8 The mean indicated a strong relationship between the occurrence of a physician visit and an ILI-related search query.