The rise of electronic health records (EHRs) is cutting the time it takes to spot changes in incidence of disease, but without context it is hard to see what is driving the shifts. A Big Data analytics platform from IBM ($IBM), Johns Hopkins University and the University of California is aiming to facilitate these insights.
IBM and its partners built the open-source modeling platform--called Spatio Temporal Epidemiological Modeler (STEM)--to combine data from different sources. Early projects include looking at World Health Organization data on incidence of malaria alongside climate and temperature information. The Eclipse Foundation has made these capabilities freely available to researchers, and is now set to roll out STEM 2.0 later this month, MedCityNews reports.
By opening up the ability to quickly crunch disease data, the collaborators hope to improve the speed and effectiveness of responses to outbreaks. "We have to be ready at the drop of a hat to parse through disparate data from global disease surveillance systems, conduct computationally intense research and transfer our knowledge to public health officials to help them visualize population health, detect outbreaks, develop new models, and evaluate the effectiveness of policies," University of California's Simone Bianco said.
University of California is involved with a project to improve modeling of dengue fever, which, along with malaria, has been an initial area of focus for the data-centric approach. Having established the platform--which IBM is planning to provide in the cloud--the collaborators hope it sparks further projects. "One of the nice things about open-source projects like STEM is that now whoever wants to can download the model and start tweaking it, seeing if their own data or assumptions fundamentally change the results," Justin Lessler, of Johns Hopkins Bloomberg School of Public Health, said.