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|storemode=property
|title=A Novel Representation of Terms Related to Infectious Disease Epidemiology for Epidemic Modeling: The Apollo Structured Vocabulary and Pre-existing Representations
|pdfUrl=https://ceur-ws.org/Vol-1327/icbo2014_paper_30.pdf
|volume=Vol-1327
|dblpUrl=https://dblp.org/rec/conf/icbo/BrochhausenHLBM14
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==A Novel Representation of Terms Related to Infectious Disease Epidemiology for Epidemic Modeling: The Apollo Structured Vocabulary and Pre-existing Representations==
https://ceur-ws.org/Vol-1327/icbo2014_paper_30.pdf
ICBO 2014 Proceedings
A novel representation of terms related to infectious
disease epidemiology for epidemic modeling
The Apollo Structured Vocabulary and pre-existing representations
Mathias Brochhausen, Josh Hanna Shawn T. Brown
Pittsburgh Supercomputing Center
Division of Biomedical Informatics
Carnegie Mellon University
University of Arkansas for Medical Sciences Pittsburgh, USA
Little Rock, USA
mbrochhausen@uams.edu
Michael M. Wagner, John D. Levander, Nicholas E.
William R. Hogan Millett
Department of Biomedical Informatics
Department of Health Outcomes and Policy
University of Pittsburgh
University of Florida
Pittsburgh, USA
Gainesville, USA
Abstract—The Apollo Structured Vocabulary (Apollo-SV) is a infection transmission, populations of hosts, interventions, and
Web Ontology Language 2 (OWL 2) representation of terms the outcomes of infections. Using this input information—
related to epidemic simulation. We are developing Apollo-SV by which we refer to as an infectious disease scenario—a
ontological analysis of the information used and created by simulator’s algorithm computes the progression of one or more
epidemic simulators and the entities this information is about. infections in one or more populations over time, under zero or
A key finding of our analysis is that the input of an epidemic more interventions. The result of this computation—the output
simulator is properly understood as (1) a representation of an of the simulator—is information on which decision makers can
ecosystem at simulator time zero, (2) information about base policy or decisions about disease control.
infectious diseases of interest in the ecosystem, and (3)
information about plans to control the diseases. This insight is At present, each simulator uses its own representation of
reflected in the scope of Apollo-SV, which includes terms from its input and output information. For example, the FRED
the domains of both infectious disease epidemiology and simulator, version 2.0.1 [1] refers to the duration of school
population biology. closure 2 as ‘school_closure_period’, whereas FluTE version
We also found that some definitions in the Infectious Disease 1.15 [2] refers to it as ‘schoolclosuredays’. The differences
Ontology (IDO), including ‘infection’, ‘infection acquisition’, make it difficult to compare simulators and re-use machine
‘infectious disease’, ‘pathogen’, and ‘host’, were not compatible readable information. For example, Halloran et al. spent 6
with the meanings of the terms as used in epidemic simulation;
months creating a comparative study of three simulators [3].
thus, we created new definitions of these terms.
Our analysis of epidemic simulators—which are To address this problem, we are developing machine-
mathematical models of phenomena studied by infectious disease interpretable representations for the input and outputs of DTMs
epidemiology—afforded several advantages that likely explain and promulgating their adoption as de facto standards. The key
why we discovered limitations of IDO. As a result, we goal of the standards is to enable an analyst to specify the same
recommend that development of biomedical ontologies intended infectious disease scenario exactly once, and run the scenario
for reuse consider the perspective of the overlapping biological on multiple simulators with no additional effort.
science(s) involved.
Apollo-SV is freely available at: In this paper, we describe one element of our proposed
http://purl.obolibrary.org/obo/apollo_sv.owl. standards—the Apollo Structured Vocabulary (Apollo-SV).
Apollo-SV is an OWL 2 representation of terms related to
Keywords—disease transmission models, epidemic simulators, epidemic simulation. The other two elements are an XML
biomedical ontology, infectious disease epidemiology Schema Document (XSD), which defines the syntax for
simulator input, and a database schema that defines the
I. INTRODUCTION
representation of simulator output. Apollo-SV defines the
The science and practice of infectious disease epidemiology, terminology used in the XSD and database schema. These
like climate science, is increasingly reliant on computational elements are described in Wagner et al. [4]
simulation. The simulators—known as epidemic simulators or
more generally disease transmission models (DTMs)1—require
machine-interpretable information about pathogens, rates of 2
Closing schools is one infectious disease control strategy that
simulators study for the control of influenza epidemics. The duration
1
DTMs also model endemic infections such as malaria. of the closure is the length of time during which schools are closed.
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II. METHODS In accordance with the Foundry principle of orthogonality,
We developed Apollo-SV for use in a set of Web services which stipulates that a given term is defined only once across
designed to improve access to epidemic simulators. We begin all ontologies, we search for and import pre-existing
this section with an overview of these services, then detail our ontological representations into Apollo-SV. Besides importing
methods to develop Apollo-SV, including conformance to entire ontologies, we import selected classes, individuals, and
OBO Foundry Principles, methods to ensure validity for its properties using the Minimum Information to Reference an
intended use, and multi-disciplinary development. External Ontology Term (MIREOT) [9] Protégé plugin that we
developed [10].
The Apollo Web Services: Briefly, the Apollo Web Services are
a set of Web services designed to allow a publicly available, We also adhere to Foundry naming conventions [11]. We
Web-based, end-user application to access multiple epidemic edit our terms to (1) avoid connectives ('and', 'or'), (2) prefer
simulators through requests to a single Broker service (Fig. 1). singular nouns, (3) avoid the use of negations, and (4) avoid
catch-all terms such as "Unknown x".
In Figure 1, the Simple End User Application (SEUA)
creates an infectious disease scenario for simulation, encoded To help link the OWL file with the XSD, we create a
in an XML document that conforms to the Apollo XSD, which Unique Apollo Label (UAL) for classes in Apollo-SV. The
uses terminology defined by Apollo-SV. The SEUA invokes UAL is the exact XSD type or attribute name to which the
the runSimulation() method of the Broker service with the class in Apollo-SV corresponds, for example,
infectious disease scenario. The Broker service invokes the InfectiousDisease and basicReproductionNumber.
Translator service, which translates the infectious disease Analysis of simulators’ input and output files, and
scenario into the native terminology and syntax of the documentation: We analyzed the input and output files of four
requested simulator(s). epidemic simulators. We also analyzed documentation, such as
user guides and published papers. We reviewed terms that we
extracted from these resources with the developers of the
simulators to identify relevant but missing terms, to discover
synonymy among terms, and to detect and resolve ambiguity.
Validation by representation in XSD message syntax: We
further refine our OWL DL representations by using the terms
in the XSD representation as it progressively expands to be
able to represent the input of four simulators.
Validation by automatic translation: The process of
developing the mappings from the XSD and Apollo-SV terms
to the native language of the simulators identifies additional
issues with Apollo-SV that we feed back into our analysis.
Validation by implementation in a user application: The SEUA
exposes Apollo-SV definitions and elucidations in tool tips that
appear when the mouse hovers over a term. This view
Fig. 1. The relationships of Apollo components and epidemic simulators.
Apollo-SV defines the terminology used in Apollo XSD, which specifies the identifies problems with elucidations by placing them into the
message syntax for the Web services [1]. The SEUA calls the Broker service context of an end-user configuring a simulator, and wanting to
to configure simulators (messages passed along blue arrows) and to access understand what is meant by a term.
simulator output (messages passed along red arrows). The Translator service
translates Apollo messages to/from native simulator input/output. The SEUA Public release: To encourage adoption of Apollo-SV and to
is available at http://research.rods.pitt.edu; the XSD is available at: allow external scientific review, comments, and requests for
http://research.rods.pitt.edu/apollo-types_2.0.2.xsd. Purple ovals represent additions, we make Apollo-SV publicly available at
Apollo standards; blue ovals represent Apollo-developed software that use the http://purl.obolibrary.org/obo/apollo_sv.owl, a permanent URL
Apollo Web services; and red ovals represent entities interacting with Apollo.
(PURL). We also ensure that Apollo-SV is easily accessible for
Upper ontology: We import Basic Formal Ontology browsing and download at the Web-based Ontobee portal:
(http://www.ifomis.org/bfo/1.1) into Apollo-SV as its upper http://www.ontobee.org/browser/index.php?o=APOLLO_SV.
ontology [5]. The issue tracker and under-development version of Apollo-
SV are located at our Google Code site. The PURL to the
Conformance with OBO Foundry principles: We followed the development version of Apollo-SV is
principles of the OBO Foundry in implementing Apollo-SV [6, http://purl.obolibrary.org/obo/apollo_sv/dev/apollo_sv.owl.
7]. Thus we release it in a common format, OWL 2 [8].
Multi-disciplinary development: The team developing Apollo-
In accordance with Foundry principles, we write a textual SV comprises personnel with backgrounds in simulator
definition for every term that we create. Because formal development, disease surveillance, medicine, biomedical
ontological textual definitions often use the technical language informatics, medical terminologies, ontological engineering,
of ontologists, we created an elucidation annotation for classes artificial intelligence, and formal logic. All these individuals,
in Apollo-SV. The elucidation restates the definition in including a simulator developer (author SB), have been
language more familiar to subject matter experts. We also
axiomatize Apollo-SV terms wherever possible (e.g., Fig. 2-5).
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ICBO 2014 Proceedings
actively engaged in review of Apollo-SV, and their feedback TABLE II. CLASSES IN APOLLO-SV BY SUBDOMAIN
guides design decisions.
Domain Classes in Apollo-SV
III. RESULTS Infectious disease Infection Infection acquisition
epidemiology Pathogen Host
Overall, Apollo-SV has 594 classes: 287 that we created
Latent period Infectious period
new in Apollo-SV and 307 that we imported: 57 via MIREOT Contaminated thing Contamination acquisition
(Table I) and 250 from entire ontologies. The number of Contamination
imported classes is artificially high because the import of entire Infectious disease Basic reproduction
ontologies brings classes into Apollo-SV we do not require. scenario number
Transmission Transmission probability
TABLE I. RE-USE OF CLASSES AND OBJECT PROPERTIES FROM PRE- coefficient
EXISTING ONTOLOGIES IN APOLLO-SV VIA MIREOT.
Disease Infectious disease control
Ontology (by Classes Object Total transmission model strategy
OBO Foundry Properties Susceptible Exposed population
namespace) population
Infectious Resistant population
UBERON 7 1 8 population
OMRSE 26 7 33
Population biology Ecosystem Biotic ecosystem
GO 1 0 1
OGMS 11 0 11 Abiotic ecosystem Community
OBI 9 5 14 Population Population census
IDO 3 7 10 Population Abiotic ecosystem census
Totals 57 20 77 infection and
immunity census
The core classes in Apollo-SV represent key entities of
interest to infectious disease epidemiology and population abnormality to immunocompetent organisms of the same
biology (Table II). Throughout the course of developing the Species as the host (the organism corresponding to the
Apollo standard, we reached the conclusion that the input to an extended organism) through transmission of a member or
epidemic simulator is properly understood as a representation offspring of a member of the infectious agent population.
of an ecosystem at simulator time zero, with additional However, epidemic simulators represent infection as a
information about infectious diseases and planned or ongoing process, because that is how ‘infection’ is defined in infectious
interventions to control them. This conclusion motivates the
disease epidemiology. For example, [13, 14] define ‘infection’
inclusion in Apollo-SV of terms from population biology. In
as the invasion of a host organism's tissue by pathogens, the
turn, the ecosystem viewpoint heavily influenced our
definitions of key terms in infectious disease epidemiology. multiplication of those pathogens, and the reaction of the
host’s tissue(s) to the pathogens and the toxins they produce.
At present, Apollo-SV and the XSD enable configuration
of three epidemic simulators with the same infectious disease Also, the IDO definition requires that an infection cause
scenario in the SEUA. We are piloting a fourth simulator. They clinical abnormality in an individual of a particular species.
are (1) a compartmental model developed by authors MMW, However, infectious disease epidemiology recognizes the
NEM, and JDL (disease agnostic); (2) the FRED model existence of species that do not experience clinical
developed by the University of Pittsburgh Public Health abnormalities when infected with a particular pathogen. The
Dynamics Laboratory in collaboration with the Pittsburgh importance in epidemic simulation is that members of species
Supercomputing Center and the School of Computer Science at that can experience clinical abnormalities when infected can
Carnegie Mellon University, University of Pittsburgh and acquire infection with the pathogen from a ‘carrier’ species.
Imperial College (influenza A in humans); and (3) the FluTE
model developed by the University of Washington and Fred Apollo-SV defines ‘infection’ as: A reproduction of a
Hutchinson Cancer Research Center in Seattle and the Los pathogen in (a part of) the tissue of an organism from another
Alamos National Laboratories (influenza A in humans). species (Fig. 2).
With respect to Foundry orthogonality, we attempted to This biologically-grounded definition recognizes that two
reuse IDO’s definitions of ‘infection’, ‘pathogen’, ‘host’, but species are interacting and—from the pathogen species point of
had to create new definitions (and thus new representations) for view—infection is a process of reproduction. The definition
them in Apollo-SV as discussed in the following sections. only requires reproduction of one species within the tissues of
an individual (organism) from another species.
A. Infection
B. Infection Acquisition (reformulation of Transmission
IDO defines infection as a material entity that is: Process)
A part of an extended organism that itself has as part a IDO imports two definitions of ‘transmission process’ from
population of one or more infectious agents and that is (1) the Transmission Ontology:
clinically abnormal in virtue of the presence of this infectious
agent population, or (2) has a disposition to bring clinical 1. A process that is the means during which the pathogen is
transmitted directly or indirectly from its natural
reservoir, a susceptible host or source to a new host.
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2. Suggested definition: A process by which a pathogen Apollo-SV defines ‘infection acquisition’ as: The
passes from one host organism to a second host organism biological process of pathogen organism(s) entering (the body
of the same Species. of) a host organism from a contagious host or a contaminated
thing and reproducing using host resources.
As with our definition of ‘infection’, this definition is
biologically grounded and recognizes that from the pathogen
species’ point of view, infection acquisition is the entry into a
host and the beginning of reproduction there. Note that Apollo-
SV’s definition of ‘contaminated thing’ is general and includes
natural reservoirs, vector organisms that are not infected (a.k.a.
mechanical vectors), and fomites like contaminated pencils.
C. Host
IDO defines ‘host’ as: An organism bearing a host role
This definition is not sufficient in and of itself to
understand what IDO refers to by ‘host’. It is also necessary to
review its definitions of ‘host role’ and ‘extended organism’:
1. ‘Host role’: A role borne by an organism in virtue of the
fact that its extended organism contains a material
entity other than the organism.
2. ‘Extended organism’: An object aggregate consisting of
an organism and all material entities located within the
Fig. 2. Representation of the equivalent class axiom for ‘infection’ in organism, overlapping the organism, or occupying sites
Apollo-SV. Boxes represent named classes, boxes with curved bases represent
anonymous classes, arrows represent object properties. In the boxes is the
formed in part by the organism.
rdfs:label and the namespace of the source ontology, if different from Apollo- Under these definitions, any organism that has an artificial
SV. Each arrow is labeled with the rdfs:label of the property it represents.
joint, a penny in its gut, or an arrow through its chest is a host.
The second, “suggested definition” erroneously restricts The fact that a person with a prosthetic knee is a “host” is
transmission to occur only between two hosts of the same counterintuitive. This definition is too admissive for our use
species. It is thus not usable in infectious disease epidemiology cases (and for clinical medicine, too): any foreign material
or any other science that deals with cross-species transmission. entity inside the organism’s body renders the organism a host.
The first definition of ‘transmission process’ has two major In addition, from the ontological perspective, we doubt
problems. The first problem is that it is circular, defining there is any such entity as host role. First, according to BFO, a
‘transmission process’ in terms of a pathogen being role is manifested or realized in one or more processes.
transmitted, with no definition of ‘transmitted.’ However, because there is no representation of the infection
process in IDO, infection cannot be the realization. No other
The second problem is an ontological one. It attributes to process in IDO suffices, either. If there is no process that
one process the property of being the means by which realizes a role, then by definition of ‘role’, there is no role.
something else happens. For example, assume droplet spread
of infection from one host to another by a sneeze. This Apollo-SV defines host as: An organism that has as part
definition equates the sneeze with the transmission process. some tissue that is the location of an infection (Fig. 3).
That is, it says that only the sneeze exists, but it also has the We therefore distinguish pathogen and host based on which
property of “having transmitted the pathogen”. However, one is reproducing inside tissue (pathogen) and which one is
equating the sneeze to the transmission process is nonsensical the location of the reproduction (host).
because for transmission to be complete, the second host must
have an infection. But this infection will not begin for minutes D. Pathogen
to hours after the sneeze is over. The sneeze cannot somehow
IDO defines ‘pathogen’ as: A material entity with a
extend itself in time until an infection is established, but
pathogenic disposition.
conversely not extend in time when no infection results. There
exist two distinct processes: the sneeze and the transmission. Again, this definition requires the definitions of other terms to
understand its meaning:
We also had the insight that it is only the second host who
acquires the infection that undergoes a change during the 1. ‘Pathogenic disposition’: A disposition to initiate
process. Therefore, we chose to rename it ‘infection processes that result in a disorder.
acquisition’. We recognize that we are diverging from
standard terminology in the field, but anyone wishing to add an 2. ‘Disorder’: A material entity which is clinically
alternative label to the infection acquisition class in Apollo-SV abnormal and part of an extended organism. Disorders
could do so without changing the meaning of the class. are the physical basis of disease.
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Fig. 3. Representation of the equivalent class axiom for "host" in Apollo-SV. Fig. 4. Representation of the equivalent class axiom for "pathogen" in
The graphical representation is analogous to Fig. 1. Apollo-SV. The graphical representation is analogous to Fig. 1.
Thus per IDO any material that causes injury is a pathogen, We set a high priority on implementing Apollo-SV in a
including the endotoxin of Clostridium difficile or an overdose Web service for three reasons. First, we wanted to demonstrate
of acetaminophen. IDO does have an infectious agent class as a the capability to initialize multiple simulators with one
subtype to pathogen that refers specifically to organisms that infectious disease scenario to motivate the adoption of Apollo.
enter into a host cause injury. But this definition is not how In addition, we wanted to lower barriers to adoption by making
infectious disease epidemiology uses the term ‘pathogen’. available a reference implementation. Lastly, implementation
is the basis of our iterative development and refinement
IDO also asserts pathogens must typically cause disease. process that ensures Apollo is production ready and flexible,
However, attenuated poliovirus used in oral polio vaccines which also lowers the barriers to adoption.
infects the gut mucosa of humans and thus is a pathogen (or
infectious agent per IDO), but it causes disease in only one per A key insight from our iterative development of Apollo-SV
2.7 million first doses of vaccine. and XSD is that a simulator configuration is properly
understood as a representation of an ecosystem at a particular
Apollo-SV defines ‘pathogen’ as: A material entity that is time. This insight led us to include in Apollo-SV key terms
the bearer of a disposition that, when realized, is realized as an from population biology, such as ‘ecosystem’ and ‘census’.
infection (Fig. 4). Furthermore, it led us to our redefinition of ‘infection’, which
E. Infectious disease was central to redefining other IDO terms.
IDO defines ‘infectious disease’ as:
A disease whose physical basis is an infectious disorder.
Per IDO, infectious disorder is a subytpe of infection.
However, we require a representation of infectious disease that
is consistent with our definition of ‘infection’ as a process. But
because IDO defines ‘infection’ and thus by inheritance
‘infectious disorder’ as a material entity, we could not reuse
this definition of ‘infectious disease’.
Apollo-SV defines ‘infectious disease’ as: A disease that
inheres in a host and, when realized, is realized as a disease
course that is causally preceded by an infection (Fig. 5). This
definition is compatible with the OBO Foundry definition of
disease, which is in the Ontology of General Medical Science
(OGMS) [15]. We thus were able to reuse the OGMS
definition of disease, in keeping with the Foundry principle of
orthogonality.
Fig. 5. Representation of the equivalent class axiom for "infectious disease"
Note that the disease inheres only in the host. From the in Apollo-SV. The graphical representation is analogous to Fig. 1.
pathogen’s perspective, there is no clinical abnormality (which
is a necessary condition to meet the definition of disease in A key result of our development of Apollo-SV was that we
OGMS). For the pathogen, infection is perfectly normal. could not reuse IDO definitions of ‘infection’, ‘host’,
‘pathogen’, and ‘infectious disease’, and thus we created the
IV. DISCUSSION definitions presented here. This result was surprising because
Apollo-SV version 2.0.1 is an ontology for use in representing we had anticipated reuse of IDO at the outset of Apollo-SV
DTM input and output. It includes core terms from infectious development. Given that we did not expect this result, it is
disease epidemiology and population biology. Apollo-SV worth considering the possible reasons behind it.
currently supports the representation of infectious disease A key reason is that our concentration on how terms are
scenarios that can be run on three epidemic simulators and the used in biological sciences—especially population biology—
results of the simulations. exposed many issues. This focus differed fundamentally from
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ICBO 2014 Proceedings
IDO’s concentration on how the terms are used in clinical potential to generate the XSD from the ontology, a successful
medicine. In particular, our focus led us to a requirement to strategy in other projects [18].
represent the process of infection as opposed to the steady-
state, material-entity view of IDO. ACKNOWLEDGMENTS
So what then led us to the perspective of population This work was funded by award R01GM101151 from the
biology? We believe the reason we reached this perspective, as National Institute for General Medical Sciences (NIGMS).
well as ontological clarity elsewhere in Apollo-SV, is that This paper does not represent the view of NIGMS. This work
working with epidemic simulators quickly brought into view used the Protégé resource, which is supported by grant
the key phenomena studied (that are also of relevance to GM10331601 from NIGMS.
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3
The link provided at http://surveillance.mcgill.ca/trac/star/
wiki/StarComponents/Ontology is broken as of this writing.
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