title: The Apollo Structured Vocabulary: an OWL2 ontology of phenomena in infectious disease epidemiology and population biology for use in epidemic simulation.
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.
  
  
  CONCLUSIONS
  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 .
authors.affiliation: University of Florida, P.O. Box 100219, 2004 Mowry Rd, Gainesville, FL, 32610-0219, USA. hoganwr@ufl.edu.
authors.forename: William R
authors.initials: WR
authors.lastname: Hogan
authors.affiliation: University of Pittsburgh, 5607 Baum Boulevard, Room 434, Pittsburgh, PA, 15206, USA.
authors.forename: Michael M
authors.initials: MM
authors.lastname: Wagner
authors.affiliation: University of Arkansas for Medical Sciences, 4301 W. Markham St. Slot #782, Little Rock, AR, 72205, USA.
authors.forename: Mathias
authors.initials: M
authors.lastname: Brochhausen
authors.affiliation: University of Pittsburgh, 5607 Baum Boulevard, Room 434G, Pittsburgh, PA, 15206, USA.
authors.forename: John
authors.initials: J
authors.lastname: Levander
authors.affiliation: Pittsburgh Supercomputing Center, 300 S. Craig St., Pittsburgh, PA, 15213, USA.
authors.forename: Shawn T
authors.initials: ST
authors.lastname: Brown
authors.affiliation: University of Pittsburgh, 5607 Baum Boulevard, Room 435 J, Pittsburgh, PA, 15206, USA.
authors.forename: Nicholas
authors.initials: N
authors.lastname: Millett
authors.affiliation: Pittsburgh Supercomputing Center, 300 S. Craig St., Pittsburgh, PA, 15213, USA.
authors.forename: Jay
authors.initials: J
authors.lastname: DePasse
authors.affiliation: University of Florida, P.O. Box 100212, Gainesville, FL, 32610-0212, USA.
authors.forename: Josh
authors.initials: J
authors.lastname: Hanna
mesh_terms: D064229:Biological Ontologies; D003141:Communicable Diseases / Q000453:epidemiology*; D058872:Epidemics; D006801:Humans; D015233:Models, Statistical; D012984:Software
keywords: Biomedical ontology; Disease transmission model; Epidemic simulation; Epidemic simulator; Infection; Infectious disease epidemiology; Population biology