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Collaboration to Use Big Data to Predict Malaria Outbreaks and Dengue Fever

By BiotechDaily International staff writers
Posted on 22 Oct 2013
Scientists are collaborating to battle infectious diseases and other illnesses in real-time with better data applications for public health. The emphasis of this partnership is to help contain global outbreaks of dengue fever and malaria by applying extremely sophisticated analytic models, computing technology, and mathematical talents on an open-source framework.

Involved in the partnership are scientists from IBM (New York, NY, USA) are collaborating with Johns Hopkins University (Baltimore, MD, USA) and University of California, San Francisco (UCSF; USA). Malaria and dengue fever, which are vector-borne diseases are infections transmitted to humans and other animals by blood-feeding insects, such as mosquitoes, fleas, and ticks. Once believed to be limited geographically to the tropics or developing countries, they continue to present worldwide and are among the most complex and dangerous infectious diseases to prevent and control.

The rise of global trade, transport, and climate change allows insects to easily carry disease organisms across borders, infecting animals as well as humans. Dengue fever, for instance, has spread to over 100 countries, including the United States, and malaria is responsible for over one million per year deaths. Finding and implementing new, novel ways of predicting outbreaks is key to saving lives.

Epidemiologists rely on disease and vaccine simulations to determine the spread of global infection. Until recently, these models were hosted on closed systems and took years to produce due to ineffective data collection and lack of computing power. This approach to model development makes it too slow to respond on a timetable pertinent to unforeseen pandemics as large populations can be crippled by never before seen viruses or illnesses in a matter of days or weeks. Scientists need to better comprehend not only the dynamics of the disease itself, but also the spread of insect vectors and contributing environmental influences.

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, spatiotemporal epidemiologic modeler (STEM). The scientists identified the prospect of integrating population analytics, algorithms of disease paths, and powerful computing to build realistic and accessible models of these infectious diseases. This tool 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 alterations in local climate factors such as temperature and precipitation. Determining 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.

“There are a lot of tacit assumptions out there about how changes in climate will impact the distribution of diseases like malaria. This work suggests that things probably are not so simple, a change that has a huge effect on malaria transmission in one place might not be as important somewhere else,” said Justin Lessler, Johns Hopkins Bloomberg School of Public Health. “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.”

Earlier models of dengue fever treated the mosquito vector indirectly, approximating transmission as a human-to-human contact process. IBM Research and UCSF used STEM’s ability to construct many models and integrate them with location-specific climate data. This allowed to the inclusion of the vector population into existing models, providing a more realistic description of the disease dynamics, which can present public health officials more effective predictions of epidemics spread.

“It is important to recognize the synergistic effort of theoretical and computational scientists, disease experts and public health officials making a difference in how rapidly and effectively we fight infectious diseases,” said Simone Bianco, from UC San Francisco, bioengineering and therapeutic sciences. “We have to be ready at the drop of a hat to parse through disparate data from global disease surveillance systems Anchor, 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.”

Available through the Eclipse Foundation, STEM is free and open to any scientist or researcher who chooses to build on and add to its library of models, computer code, and denominator data. This openness help in the development of advanced mathematical models, the generation of flexible models involving multiple species, interactions between diseases, and a better determination of epidemiology.

These achievements were published October 2013 in the journals Malaria and Theoretical Biology.

Related Links:

IBM
Johns Hopkins University
University of California, San Francisco



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