Why Point of Care Detection Matters

While rapidly diagnosing tuberculosis is incredibly valuable at the patient level as it enables faster and more effective treatment, it is just one part of the overall solution. In India, the information gulf between retrospective and real time data for spatial distribution of disease has serious implications for public health surveillance. Little attention has been given to spatial epidemiology in national preparedness planning, disease treatments, and management practices. How can the risk posed by new, infectious disease outbreaks be assessed if we have only the crudest understanding of the geographical range and the factors impacting decision-making or disease progression? How can the useful intelligence in the growing space of Big Data be prioritized if contemporary geographical distribution of these infectious diseases is unknown?

Data Mural, our key partner in this project, will solve this problem by providing real-time mapping, indexing, and data analytics tools to health care providers and decision makers to improve their ability to triage spatially, manage infectious disease outbreak alerts, and more effectively deliver treatments to patients. Data Mural integrates secondary use of local and regional passive search query and micro-blogging data as well as actively collected crowd-sourced data for disease surveillance into the mapping and data mining user interface. The success of these methods in addressing influenza and dengue epidemics is well-documented but has never been applied to TB.

The high volume of health outcome related searches and personal accounts present new opportunities to monitor population health and correlate epidemiological information in real time. Data Mural will use this information to build holistic databases on the occurrence of many diseases, beginning with tuberculosis in India. The volume, velocity, and variety of occurrence information from these sources will increase rapidly and transform the ability to create geographical baselines for a range of diseases.

Including these large, real-time, crowd sourced data will allow Data Mural to better understand the interconnections between socio-cultural factors, demographics, weather, and other factors that make it easier to implement best practices and improve patient outcomes.

Data Mural’s proprietary platform will use the latest in mapping and spatial visualization tools, Big Data analytics, data mining methods and processes that have been developed at the University of Utah’s School of Computing. Data Mural will also leverage the resources available from our India partnerships to support local programs, deployment, and support for the data platform as it grows in users and data complexity. By carefully integrating individual diagnostic information from the tuberculosis diagnostic sensors with real-time data, we will help decision makers and healthcare providers develop solutions that revolutionize the way we respond to tuberculosis.