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Made in IBM Labs: Scientists Turn Data into Disease Detective to Predict Dengue Fever and Malaria Outbreaks
Date:9/29/2013

ed to understand not only the dynamics of the disease itself, but also the spread of insect vectors and contributing environmental factors.

Using existing vector borne disease models from Johns Hopkins and UCSF, IBM researchers are developing new dengue fever and malaria models that are shared as part of the open-source modeling application, Spatio Temporal Epidemiological Modeler (STEM). The scientists identified the opportunity to combine population analytics, algorithms of disease paths and powerful computing to build realistic and accessible models of these infectious diseases. This capability allows for the study of disease dynamics in humans and intervention strategies such as vector control and vaccine distribution.

"Public health officials can't afford to act on speculation during an epidemic. They need accurate and timely access to data to see what the potential spread of a disease might be for a given geographic region over a period of time," said James Kaufman, Public Health Manager, IBM Research. "The scientific and research communities are collaborating to lead a new age of science-based, data-centric disease modeling to protect the health of people. By understanding how and why these diseases spread, we can identify those regions most susceptible to emerging disease, inform public health, and allow them to focus on specific interventions in locations where they can have the greatest impact."

In the case of malaria, using both the model and data from the World Health Organization, IBM and Johns Hopkins demonstrated new analytic measures for the sensitivity of malaria incidence to changes in local climate factors such as temperature and precipitation. Knowing this sensitivity makes it possible to predict where malaria incidence is most likely to increase or decrease based on predicted changes in local weather and environmental conditions in a specific region.'/>"/>

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