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J Biomed Semantics. 2016 Aug 18;7:50. doi: 10.1186/s13326-016-0092-y.

The Apollo Structured Vocabulary: an OWL2 ontology of phenomena in infectious disease epidemiology and population biology for use in epidemic simulation.

Author information

1
University of Florida, P.O. Box 100219, 2004 Mowry Rd, Gainesville, FL, 32610-0219, USA. hoganwr@ufl.edu.
2
University of Pittsburgh, 5607 Baum Boulevard, Room 434, Pittsburgh, PA, 15206, USA.
3
University of Arkansas for Medical Sciences, 4301 W. Markham St. Slot #782, Little Rock, AR, 72205, USA.
4
University of Pittsburgh, 5607 Baum Boulevard, Room 434G, Pittsburgh, PA, 15206, USA.
5
Pittsburgh Supercomputing Center, 300 S. Craig St., Pittsburgh, PA, 15213, USA.
6
University of Pittsburgh, 5607 Baum Boulevard, Room 435 J, Pittsburgh, PA, 15206, USA.
7
University of Florida, P.O. Box 100212, Gainesville, FL, 32610-0212, USA.

Abstract

BACKGROUND:

We developed the Apollo Structured Vocabulary (Apollo-SV)-an OWL2 ontology of phenomena in infectious disease epidemiology and population biology-as part of a project whose goal is to increase the use of epidemic simulators in public health practice. Apollo-SV defines a terminology for use in simulator configuration. Apollo-SV is the product of an ontological analysis of the domain of infectious disease epidemiology, with particular attention to the inputs and outputs of nine simulators.

RESULTS:

Apollo-SV contains 802 classes for representing the inputs and outputs of simulators, of which approximately half are new and half are imported from existing ontologies. The most important Apollo-SV class for users of simulators is infectious disease scenario, which is a representation of an ecosystem at simulator time zero that has at least one infection process (a class) affecting at least one population (also a class). Other important classes represent ecosystem elements (e.g., households), ecosystem processes (e.g., infection acquisition and infectious disease), censuses of ecosystem elements (e.g., censuses of populations), and infectious disease control measures. In the larger project, which created an end-user application that can send the same infectious disease scenario to multiple simulators, Apollo-SV serves as the controlled terminology and strongly influences the design of the message syntax used to represent an infectious disease scenario. As we added simulators for different pathogens (e.g., malaria and dengue), the core classes of Apollo-SV have remained stable, suggesting that our conceptualization of the information required by simulators is sound. Despite adhering to the OBO Foundry principle of orthogonality, we could not reuse Infectious Disease Ontology classes as the basis for infectious disease scenarios. We thus defined new classes in Apollo-SV for host, pathogen, infection, infectious disease, colonization, and infection acquisition. Unlike IDO, our ontological analysis extended to existing mathematical models of key biological phenomena studied by infectious disease epidemiology and population biology.

CONCLUSION:

Our ontological analysis as expressed in Apollo-SV was instrumental in developing a simulator-independent representation of infectious disease scenarios that can be run on multiple epidemic simulators. Our experience suggests the importance of extending ontological analysis of a domain to include existing mathematical models of the phenomena studied by the domain. Apollo-SV is freely available at: http://purl.obolibrary.org/obo/apollo_sv.owl .

KEYWORDS:

Biomedical ontology; Disease transmission model; Epidemic simulation; Epidemic simulator; Infection; Infectious disease epidemiology; Population biology

PMID:
27538448
PMCID:
PMC4989460
DOI:
10.1186/s13326-016-0092-y
[Indexed for MEDLINE]
Free PMC Article

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