ORBiT: Oak Ridge biosurveillance toolkit for public health dynamics
| Autor: | Tanner C Hobson, Silvia Valkova, Chakra Chennubhotla, Shannon Quinn, Laura L. Pullum, Chad A. Steed, Arvind Ramanathan |
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| Rok vydání: | 2015 |
| Předmět: |
medicine.medical_specialty
Time Factors Computer science Big data Biochemistry non-negative matrix factorization Influenza A Virus H1N1 Subtype Public health surveillance Biosurveillance Structural Biology Health care Pandemic Influenza Human medicine Flu season H1N1 2009 Pandemic Electronic Health Records Humans Social media Molecular Biology Pandemics business.industry Applied Mathematics Public health Research Incidence Data science United States Computer Science Applications electronic healthcare reimbursement Public Health Seasons business Software |
| Zdroj: | BMC Bioinformatics |
| ISSN: | 1471-2105 |
| DOI: | 10.1186/1471-2105-16-s17-s4 |
| Popis: | Background The digitization of health-related information through electronic health records (EHR) and electronic healthcare reimbursement claims and the continued growth of self-reported health information through social media provides both tremendous opportunities and challenges in developing effective biosurveillance tools. With novel emerging infectious diseases being reported across different parts of the world, there is a need to build systems that can track, monitor and report such events in a timely manner. Further, it is also important to identify susceptible geographic regions and populations where emerging diseases may have a significant impact. Methods In this paper, we present an overview of Oak Ridge Biosurveillance Toolkit (ORBiT), which we have developed specifically to address data analytic challenges in the realm of public health surveillance. In particular, ORBiT provides an extensible environment to pull together diverse, large-scale datasets and analyze them to identify spatial and temporal patterns for various biosurveillance-related tasks. Results We demonstrate the utility of ORBiT in automatically extracting a small number of spatial and temporal patterns during the 2009-2010 pandemic H1N1 flu season using claims data. These patterns provide quantitative insights into the dynamics of how the pandemic flu spread across different parts of the country. We discovered that the claims data exhibits multi-scale patterns from which we could identify a small number of states in the United States (US) that act as "bridge regions" contributing to one or more specific influenza spread patterns. Similar to previous studies, the patterns show that the south-eastern regions of the US were widely affected by the H1N1 flu pandemic. Several of these south-eastern states act as bridge regions, which connect the north-east and central US in terms of flu occurrences. Conclusions These quantitative insights show how the claims data combined with novel analytical techniques can provide important information to decision makers when an epidemic spreads throughout the country. Taken together ORBiT provides a scalable and extensible platform for public health surveillance. |
| Databáze: | OpenAIRE |
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