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

Abstract

BackgroundWe 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.ResultsApollo-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.ConclusionOur 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.

A realism-based approach to an ontological representation of symbiotic interactions

“…Our motivation for representing symbiotic interactions arose out of our work with the Apollo Structured Vocabulary (Apollo-SV)-an application ontology that covers population biology and infectious disease epidemiology [12]-and the need to represent interactions among pathogens, hosts, and vector organisms. Apollo-SV is licensed under a Creative Commons Attribution 4.0 International License and can be found at https:// github.com/ApolloDev/apollo-sv.…”

A survey of epidemic management data models

“…22 Apollo Structured Vocabulary (Apollo-SV) provides a vocabulary for specifying input and output parameters for infectious disease simulators. 23 It helps to judge whether simulators are simulating same scenario and increases the availability of epidemiological simulators in public health events. BioCaster Ontology (BCO) is a multilingual ontology constructed by Collier et al for a textbased system that automatically monitors network information of different languages and detects and track outbreaks and distribution of epidemics.…”

Use of artificial intelligence in infectious diseases

“…This will enable hospitals, which are often the first site where patients are in contact with professionals, to become experts in forensic science, where the culprit is a microorganism. Recent advances were proposed such as building on simulation experiences (Hogan et al, 2016). Preparedness for epidemic is mostly tested in hospital regarding number of beds, and activation of specific measures to avoid transmission of infection.…”