New Data Sources Fuel Understanding of Public Health Emergencies

Remember when Google search results were first used to predict the flu? Now, data from mobile phones, social media and even grocery scanners has been shown to be effective at identifying patterns in epidemics. “Human mobility contributes significantly to epidemic transmission into new regions,” Frédéric Pivetta, of Real Impact Analytics, wrote in a blog post.  Standard travel data collection methods, however, are limited and often provide outdated data. Mobile phones, on the other hand, are nearly ubiquitous, and can serve as a rich data resource. Call data, which automatically provides time and location details, can help in understanding human mobility. Information on social networks (who communicates with whom) is also valuable, as is data on mobile spending, which can be used as an indicator of socioeconomic status.

“Aggregated and anonymized, mobile telecom data fills the public data gap without ... privacy issues,” Pivetta said. “Mixing it with other public data sources results in a very precise and reliable view on human mobility patterns, which is key for preventing epidemic spreads.” Using this aggregate data, researchers can identify mobility patterns for each disease, determine epidemic incidence, build an epidemiological model, map epidemic risk flows and flag at-risk areas as well as prioritize and monitor public health measures.

In a similar initiative this summer, IBM announced a project to help track the spread of the Zika virus, using, among other things, sentiment analysis.  The company’s researchers in California planned to train scientists from Brazil's Oswaldo Cruz Foundation (Fiocruz), an institution affiliated with the Brazilian Ministry of Health, to use the Spatiotemporal Epidemiological Modeler, an open source disease modeling application that visualizes the spread of infectious diseases. The application, called STEM, helps public health officials and epidemiologists analyze the effects of factors such as geography, weather, time and human travel patterns. It has been used to study and help predict the spread of infectious diseases like influenza and Ebola and mosquito-borne diseases such as malaria and dengue fever...