Itís Monday, and our medical theme continues.
IBM announced earlier today that some of its research scientists are now collaborating with Johns Hopkins University and the University of California, San Francisco, to combat illness and infectious diseases in real-time with smarter data tools for public health.
The focus is to help contain global outbreaks of dengue fever and malaria by applying the latest analytic models, computing technology, and mathematical skills on an open-source framework.
Vector-borne diseases, for example, malaria and dengue fever, are infections transmitted to humans and other animals by blood-feeding insects, such as mosquitoes, ticks, and fleas.
Once thought to be limited geographically to the tropics or developing countries, they continue to show up all over the world and are among the most complex and dangerous infectious diseases to prevent and control.
The rise of global transportation, trade, and climate change allows insects to carry disease organisms across borders easily, infecting animals as well as humans. Dengue fever, for example, has spread to over 100 countries, including the United States, and malaria is responsible for over one million annual deaths.
The data of disease modeling
Finding and implementing new, innovative methods 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 inefficient data collection and lack of computing power.
This approach to model development makes it too slow to respond on a timescale relevant to unexpected pandemics as large populations can be crippled by never before seen viruses or illnesses in a matter of days or weeks. Scientists need 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.
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.
Previous models of dengue fever treated the mosquito vector indirectly, approximating transmission as a human-to-human contact process.
IBM Research and UCSF used STEMís ability to build models on top of 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.
Available through the Eclipse Foundation, STEM is free and open to any scientist or researcher who chooses to build on and contribute to its library of models, computer code, and denominator data.
This openness facilitates the development of advanced mathematical models, the creation of flexible models involving multiple populations (species), interactions between diseases, and a better understanding of epidemiology.
These achievements were published in the peer-reviewed journals Malaria and Theoretical Biology.