BACKGROUND AND SIGNIFICANCE
Decisions in global population health can affect the lives of millions of people and can change the future of entire communities. For example, the decision to declare an influenza pandemic and stockpile vaccines can save millions of lives if a pandemic of highly pathogenic influenza actually occurred, or could waste millions of dollars if the decision was based on false alarm.1 Decision making in global health is often made under a high degree of uncertainty and with incomplete information.New data are rapidly emerging from mobile technology, electronic health records, and remote sensing.2 These new data can expand opportunities for data-driven decision making in global health. In reality, multiple layers of challenges, ranging from technical to ethical barriers, can limit the effective (re)use of data in global health.3,4 For example, composing an epidemic model to inform decisions about vaccine stockpiling requires the integration of existing data from a wide range of data sources, such as a population census, disease surveillance, environmental monitoring, and research studies.5
Integrating data can be a daunting task, especially since global health data are often stored in domain-specific data siloes that can each use different formats and content standards, ie, they can be syntactically and semantically heterogeneous. The heterogeneity of data in global health can slow down scientific progress, as researchers have to spend much time on data discovery and curation.6
To improve access to standardized data in global health, the Project Tycho data repository in 2013.7 The first version of Project Tycho (v1) comprised over a century of infectious disease surveillance data for the United States that had been published in weekly reports between 1888 and 2014.7
Read More at:
Journal of the American Medical Informatics Association, ocy123, https://doi.org/10.1093/jamia/ocy123
Published:
15 October 2018
Posted by: Jayne Merdith, Tendron Systems Ltd, London, UK
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