JMIR FORMATIVE RESEARCH

Lim & Johannesson

Original Paper

An Ontology to Bridge the Clinical Management of Patients andPublic Health Responses for Strengthening Infectious Disease
Surveillance: Design Science Study
Sachiko Lim, MSc; Paul Johannesson, PhD
Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden

Corresponding Author:
Sachiko Lim, MSc
Department of Computer and Systems Sciences
Stockholm University
Nodhuset, Borgarfjordsgatan 12
Kista, SE-164 07
SwedenPhone: 46 0760968462
Email: sachiko@dsv.su.se

Abstract
Background: Novel surveillance approaches using digital technologies, including the Internet of Things (IoT), have evolved,
enhancing traditional infectious disease surveillance systems by enabling real-time detection of outbreaks and reaching a wider
population. However, disparate, heterogenous infectious disease surveillance systems often operate in silos due to a lack ofinteroperability. As a life-changing clinical use case, the COVID-19 pandemic has manifested that a lack of interoperability canseverely inhibit public health responses to emerging infectious diseases. Interoperability is thus critical for building a robust
ecosystem of infectious disease surveillance and enhancing preparedness for future outbreaks. The primary enabler for semanticinteroperability is ontology.
Objective: This study aims to design the IoT-based management of infectious disease ontology (IoT-MIDO) to enhance data
sharing and integration of data collected from IoT-driven patient health monitoring, clinical management of individual patients,
and disparate heterogeneous infectious disease surveillance.
Methods: The ontology modeling approach was chosen for its semantic richness in knowledge representation, flexibility, easeof extensibility, and capability for knowledge inference and reasoning. The IoT-MIDO was developed using the basic formal
ontology (BFO) as the top-level ontology. We reused the classes from existing BFO-based ontologies as much as possible tomaximize the interoperability with other BFO-based ontologies and databases that rely on them. We formulated the competency
questions as requirements for the ontology to achieve the intended goals.
Results: We designed an ontology to integrate data from heterogeneous sources, including IoT-driven patient monitoring,clinical management of individual patients, and infectious disease surveillance systems. This integration aims to facilitate thecollaboration between clinical care and public health domains. We also demonstrate five use cases using the simplified ontologicalmodels to show the potential applications of IoT-MIDO: (1) IoT-driven patient monitoring, risk assessment, early warning, andrisk management; (2) clinical management of patients with infectious diseases; (3) epidemic risk analysis for timely response at
the public health level; (4) infectious disease surveillance; and (5) transforming patient information into surveillance information.
Conclusions: The development of the IoT-MIDO was driven by competency questions. Being able to answer all the formulatedcompetency questions, we successfully demonstrated that our ontology has the potential to facilitate data sharing and integrationfor orchestrating IoT-driven patient health monitoring in the context of an infectious disease epidemic, clinical patient management,
infectious disease surveillance, and epidemic risk analysis. The novelty and uniqueness of the ontology lie in building a bridge
to link IoT-based individual patient monitoring and early warning based on patient risk assessment to infectious disease epidemic
surveillance at the public health level. The ontology can also serve as a starting point to enable potential decision support systems,
providing actionable insights to support public health organizations and practitioners in making informed decisions in a timely
manner.
(JMIR Form Res 2024;8:e53711) doi: 10.2196/53711

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KEYWORDS
infectious disease; ontology; IoT; infectious disease surveillance; patient monitoring; infectious disease management; risk analysis;
early warning; data integration; semantic interoperability; public health

Introduction
Overall global health has improved over the past 30 years withthe steady decline of age-standardized disability-adjusted
life-year rates [1]. Although the burden of infectious diseases
remains high among children younger than 10 years, the global
disease trend has seen a shift from communicable tononcommunicable diseases [1]. Nevertheless, (re)emerging
infectious diseases remain global health threats due to climatechange, increased wildlife-livestock-human interface associatedwith urbanization, globalization of transport, and human
movement [2]. As we all have witnessed during the COVID-19
pandemic, the rapid and transnational spread of (re)emerging
infectious diseases can have catastrophic consequences,including a significant loss of human lives, social disruption,and economic instability.
The COVID-19 pandemic has manifested a global vulnerability
to newly emerging infectious diseases. Insufficient epidemic
preparedness and delayed responses have exacerbated the spreadof SARS-CoV-2 and triggered a significant increase in incidence
and mortality. In Italy, for example, it has been claimed that thedelayed implementation of the lockdown accounted for a
substantial proportion of hospital admissions and deaths [3].
According to a study, if lockdown had been implemented 1
week earlier, Italy could have averted 60% of cases, 48% ofintensive care unit admissions, and 44% of deaths at the timeof the study [3]. Similarly, another study indicates that if control
measures had been implemented just 1-2 weeks earlier, theUnited States could have avoided 56.5% of reported cases and
54.0% of reported deaths at the time of the study [4].
Moreover, many countries implemented response measureswhen the local health systems were already becoming
overstretched [5]. The prolonged period of health system
disruption potentially caused increases in the incidence andmortality of other diseases due to core health service disruptions[6,7]. Thus, early detection is crucial for preventing andresponding to emerging infectious disease outbreaks [8]. It
demands robust public health surveillance systems to inform
effective outbreak management [8]. The main goals of thesurveillance are (1) understanding the disease burden andepidemiology; (2) monitoring disease trends; (3) identifying
and early warning of public health threats (eg, epidemics of
emerging infectious diseases); (4) assessing risks and prioritizing
diseases; (5) disseminating surveillance data to stakeholders;and (6) planning, implementing, monitoring, and evaluating
public health response measures for disease control, elimination,and eradication.
Traditional infectious disease surveillance is often divided into
active and passive surveillance. In active surveillance systems,
health department staff proactively contact physicians,
laboratories, health care providers, or the general population tocollect information about diseases [9]. In passive surveillancesystems, which are the most common type of surveillance,
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medical professionals report cases and deaths to the publichealth agency according to a list of reportable diseases [9].
While active surveillance is likely to provide complete and moreaccurate data than passive surveillance, the method is more
expensive and labor-intensive. On the other hand, passive
surveillance is incomplete and subject to underreporting anddelays between event occurrences and notifications [9]. Anothermajor limitation of traditional surveillance systems is that theycannot detect an outbreak in real time.
With advancements in information technologies and the digital
revolution, novel surveillance approaches driven by Internet ofThings (IoT) have evolved, enhancing traditional infectious
disease surveillance systems by enabling real-time detection ofoutbreaks and reaching a wider population [10]. The IoT creates
an ecosystem that connects people and objects through theinternet, allowing them to collect and transmit data over a highly
distributed network via embedded sensors. The IoT has opened
new opportunities to improve public health through enhancing
disease surveillance and assisting health care in transitioning
to a proactive P4 (predictive, preventive, personalized, andparticipatory) medicine [10,11]. Innovative IoT-based methods,
such as participatory surveillance and digital surveillance, havebeen used in disease surveillance.
Participatory surveillance that leverages digital connectivity(eg, mobile phone–based apps) requires the direct involvementof system users who voluntarily provide the information needed
for informing public health actions. The data collected fromeach user are aggregated and analyzed for public healthpurposes. Although there are event-based participatory
surveillance systems, many have been used to perform
syndromic surveillance. The approach aims to monitor disease
indicators in (near) real time for earlier detection of and response
to outbreaks to reduce morbidity and mortality [12,13].
The primary advantages of participatory surveillance systems
are fourfold [14]: (1) enable large-scale and population-based
monitoring at a low cost; (2) enable engagement withpopulations that are hard to reach by traditional surveillancesystems due to geographical constraints or social and economic
situations; (3) allow for the rapid 2-way communication between
health authorities and system users for public health messaging
and education to promote disease prevention and control
activities; and (4) provide flexible data systems and userinterfaces, which allows health authorities to modify data
elements to be collected (eg, adding new symptoms of an
emerging infectious disease) and disseminates information innear real time. The app-based technology reduced delays incontact tracing and demonstrated the potential for preventing
up to 80% of all transmissions [15]. On the other hand, asignificant challenge is recruiting and retaining a representative
sample of an at-risk population [14]. The approach also lacks
the specificity of a laboratory test to confirm a pathogen, while
it can achieve high sensitivity if the surveillance coverage issufficiently high [14].
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Digital public health surveillance uses publicly availableuser-contributed data collected outside conventional publichealth surveillance channels. Thus, such data are not generated
primarily for infectious disease surveillance [16,17].
As mentioned in the descriptions of various infectious disease
surveillance systems, each system has its strengths andlimitations, and they complement each other. A hybrid system
integrating traditional and novel surveillance approaches islikely to be the most promising option in the big data era [18].
Informing and coordinating effective and timely outbreakmanagement require seamless information flows between
disparate heterogeneous surveillance systems. However, theyoften operate in silos due to a lack of interoperability.
Interoperability is defined as “the ability of disparate computer
systems or software to exchange data in an efficient andmeaningful way [19].” As a life-changing clinical use case, the
COVID-19 pandemic has manifested that a lack ofinteroperability can severely inhibit public health responses toemerging infectious diseases [19]. Interoperability is thus criticalfor building a robust ecosystem of infectious disease surveillanceand enhancing preparedness for future outbreaks.
According to the Healthcare Information and Management
Systems Society, interoperability consists of 4 levels: functional(level 1), structural (level 2), semantic (level 3), andorganizational (level 4) [20]. This study focuses on the semantic
level of interoperability (aka semantic interoperability) to aim
for seamless data sharing and integration in an IoT-enhanced
surveillance ecosystem that includes various heterogeneous data
sources. To achieve semantic interoperability, both data andtheir unambiguous and shared meaning need to be conveyed to
the receiving systems such that they interpret and process thedata correctly [21].
In health care settings, reference terminologies or terminology
standards have played a significant role in facilitating data
standardization and providing semantic interoperability. ICD(International Classification of Diseases) is the global
terminology standard designed to promote international
comparability in classifying diseases, injuries, and causes ofdeath. ICD is also used to standardize the reporting andmonitoring of health conditions [22]. The Systematized
Nomenclature of Medicine Clinical Terms (SNOMED CT) is
an international clinical reference terminology for facilitating
the electronic exchange of clinical health informationconsistently. SNOMED CT provides the ability to createcompositional concepts that combine multiple concepts to forma more detailed representation of a clinical problem statement[23]. It can also be mapped to external coding systems such as
ICD-10 (International Statistical Classification of Diseases,
Tenth Revision) to promote semantic interoperability. In nursing,
NANDA International, the Nursing Interventions Classification,and the Nursing Outcomes Classification are 3 major
terminologies that have been used to describe nursing judgments,
treatments, and nursing-sensitive patient outcomes [21]. The
International Classification for Nursing Practice has also beendeveloped to represent the dynamic nature of nursing practices
and their cultural variations [24].

Lim & Johannesson
Moreover, various technical interoperability standards havebeen developed. For example, the HL7/FHIR (Health Level 7
Fast Healthcare Interoperability Resources) is a medical
information standard created by HL7 to enable RESTful dataexchange [25]. The Observational Medical Outcomes
Partnership Common Data Model (OMOP CDM) has been
designed to standardize the structure and content ofobservational data [26]. Clinical Data Interchange Standards
Consortium Foundational Standards have been developed tosupport nonclinical and the life cycle of the clinical research
process from planning, data collection, exchange, management,
and analysis to reporting of the findings derived from clinical
trials [27]. Although various robust reference terminologies andstandards are available in clinical medicine, further coordination
across standards is necessary to avoid creating standard-specificsilos [28].
Another enabler for semantic interoperability is ontology, whichrefers to “a formal, explicit specification of a sharedconceptualization [29].” Ontologies are machine-interpretableand provide the ability to reconcile the meaning of data held
across heterogeneous data sources. Data senders and receivers
share a common understanding of the meanings of the dataexchanged [30,31]. The main strengths of using an ontology
are that it provides flexible and technology-agnostic methods
for data sharing and integration, expresses relationships between
concepts, and enables reasoning [30]. Ontologies are similar toreference terminologies in that they both systematically represent
a domain of interest. However, ontologies are more expressive
than terminologies, providing richer semantic relationships byrepresenting concepts, their relationships, and axioms [32].
They, thus, serve as a basis for knowledge graphs and also
support semantic reasonings.
Enormous efforts have been devoted to developing ontologies
to enable data sharing, integration, and analysis for infectious
disease surveillance and response. Infectious Disease Ontology(IDO) Core, which was released in 2010, is based on basic
formal ontology (BFO) and covers entities and relations relevant
to infectious diseases in general. It also includes terms for
population-level processes (eg, infection incidence, epidemic,
and pandemic) [33]. IDO Core serves as a hub from which
extensions based on pathogen type (ie, virus, bacteria, fungi,
and parasite) are developed: IDO Virus (VIDO), IDO Bacteria,
IDO Fungus, and IDO Parasite [33]. Each of those extensions
is further partitioned into pathogen-specific ontologies such as
IDO-Dengue Fever (IDODEN) [34], IDO-HIV, andIDO-influenza. To facilitate sharing, integrating, and analyzing
COVID-19 data, 3 new IDO extensions have recently beendeveloped [33], namely, VIDO [35], the Coronavirus Infectious
Disease Ontology (CIDO) [36] and IDO-COVID-19, which isan extension of CIDO [33]. The Apollo Structured Vocabulary,which is also based on BFO, provides a standardized
representation for configurations and output of epidemic
simulators [37]. It aims to aid in locating and accessing asimulator, understanding its characteristics, performing analyses,
and analyzing outputs to inform policy or decisions about
disease control.
To our knowledge, however, no ontology exists that supports
IoT-enhanced infectious disease surveillance, risk analysis, andhttps://formative.jmir.org/2024/1/e53711

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early warning of infectious diseases at individual and publichealth levels. The aim of this paper is to design an infectious
disease ontology that can support data sharing and integrationof data collected from IoT-driven patient health monitoring,clinical management of individual patients, and disparate
heterogeneous infectious disease surveillance.
The envisaged ontology, called “IoT-based management of
infectious disease ontology” (IoT-MIDO), may aid prompt,
timely, and concerted responses to infectious disease outbreaks
with the effective allocation of limited resources. The novelty
and uniqueness of the ontology lies in incorporating IoT-related
concepts and concepts relevant to infectious disease surveillance
and management. This will facilitate semantic interoperability
between IoT-based individual patient monitoring and infectious
disease management at the public health level. It could thus
ease barriers to bringing benefits to individual as well as
population health, which are often seen in isolation due to thehistorical dichotomization of clinical medicine and public health.

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Lim & Johannesson

Methods
Objectives of the Ontology
We designed an ontology to enhance the collaboration between
IoT-driven patient health monitoring, clinical management of
individual patients, and infectious disease surveillance. The
overall goals of the ontology are to enable the sharing andintegration of data collected from disparate heterogeneous
surveillance systems and to support risk analysis and early
warning for better patient management and triage as well as forearly response to infectious disease epidemics. As requirements
for the ontology to achieve the intended goals, we formulated
the competency questions (CQs) described in Table 1. CQs
specify functional requirements for an ontology and are used
to evaluate whether the ontology fulfills the elicited
requirements [38]. The use of CQs has been proposed in several
ontology engineering methodologies such as the Tropos
methodology [39] and the NeOn Methodology framework [40].

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Table 1. Competency questions to elicit and evaluate requirements for IoTa-based management of infectious disease ontology.
CQsb
IoT-driven patient monitoring, risk assessment, early warning, and patient management
CQ1

What information is gathered that can be used for early warning of patients’ health risks?

CQ2

Which measurements are monitored using IoT devices?

CQ3

What are the main types of recommendations for patient management that can be used for patient triage
according to the detected events (ie, anomalies in early warning score)?

CQ4

How is it possible to perform contact-tracing activity based on patient information?

Clinical management of infectious diseases
CQ5

What is the outcome of an infectious disease process?

CQ6

Which treatment provides prophylaxis for the patient?

CQ7

Which treatment is used for the disease process?

CQ8

Which vaccine is used to mitigate the disease process?

CQ9Which symptoms does a person playing the role of a symptomatic infectious agent carrier develop?

CQ10

Which laboratory tests are used for diagnosing a patient?

CQ11

Which risk factors can increase the risk of contracting an infectious disease?

Epidemic risk analysis
CQ12

What is the risk score for infectious disease epidemic (ie, a score to predict epidemic severity) and theassociated risk level?

CQ13

What infectious disease control strategies has a country implemented?

CQ14

What is the strictness of a country’s response to an infectious disease epidemic?

Infectious disease surveillance
CQ15

On which infectious disease does a country conduct surveillance?

CQ16

What is an epidemic threshold to determine an infectious disease epidemic?

CQ17

What testing strategy does a country have?

CQ18

What types of disease surveillance data are collected for infectious disease surveillance?

CQ19

What kind of population-based statistics are computed?

CQ20

For which infectious diseases is the contact-tracing activity performed?

CQ21

To which surveillance system are disease surveillance data reported?

CQ22

What is a case definition (ie, a set of standard criteria for identifying cases to monitor the trend of theinfectious disease under investigation) of an infectious disease?

Transforming individual patient information into surveillance information
CQ23
aWhat patient information is integrated into case-based surveillance data?

IoT: Internet of Things.

b

CQ: competency question.

Design of the Ontology
The IoT-MIDO was developed using the BFO as the top-level
ontology. BFO is a domain-independent upper-level ontology
created to provide a common top-level structure for enhancing
semantic interoperability across different domain ontologies[41]. This facilitates information sharing with multiple
ontologies, built upon the BFO by making data smarter byadding both computer and human-interpretable semantics tothe raw data. Moreover, when developing the IoT-MIDO
ontology, we reused the classes from existing BFO-based
ontologies as much as possible to maximize interoperability
with other BFO-based ontologies and databases that rely on
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them. Instead of introducing all of the classes and theirdefinitions and properties, we demonstrate 5 use cases in the
results section to show the potential usage of IoT-MIDO, using
the simplified ontological models in a similar way to that doneby the authors of IDODEN [34]. The complete ontology modeland the definitions of all the classes are shown in Multimedia
Appendices 1 and 2.
Table 2 shows the lists of ontology prefix classes that we reusedin the use cases. When describing the following use cases,classes are written in italics starting with an upper case (eg,
IoTStream), and associations are written in italics starting witha lower case (eg, generatedBy).
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Table 2. List of ontology prefixes in which classes are reused and imported into use cases.
Ontology prefix

Ontology full name

FOAF

The Friend Of A Friend ontology [42]IDOMAL

Malaria Ontology [43]IoT-Stream

A Lightweight Ontology for IoT (Internet of Things) Data Streams [44]NCIT

NCI Thesaurus OBO Edition [45]SOSA

The Sensor, Observation, Sample, and Actuator ontology [46]

UO

Units of Measurement Ontology [47]geo

GeoSPARQL Ontology [48]CODO

The COviD-19 Ontology for cases and patient information [49]EFO

Experimental Factor Ontology [50]IDO

Infectious Disease Ontology [51]SYMP

Symptom Ontology [52]LABO

clinical LABoratory Ontology [53]OGMS

Ontology for General Medical Science [54]VO

Vaccine Ontology [55]ICDOInternational Classification of Disease Ontology [56]GENEPIO

Genomic Epidemiology Ontology [57]

APOLLO_SVApollo Structured Vocabulary [37]TRANS

Pathogen Transmission Ontology [58]OBI

Ontology for Biomedical Investigations [59]IDOBRU

Brucellosis Ontology [60]IoT4PHM

IoT for Patient Health Monitoring Ontology [61]Ethical Considerations
This study did not collect any primary or secondary data.
Therefore, it was not necessary to obtain approval from theinstitutional review board. The authors, however, recognize theimportance of responsible data handling. Furthermore, informed
consent was not applicable, as the study did not directly involve
human subjects.

Disclosure of Generative Artificial Intelligence Usage
We used generative artificial intelligence only for checking andimproving formulations. We did not use it for creating thecontent of the study, including the ontology and use cases.

Results
Use Case 1: IoT-Driven Patient Monitoring, Risk
Assessment, Early Warning, and Risk Management
The first use case is remote monitoring of patients’ vital signs
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health risks proactively (Figure 1). We reused our IoT for patient
health monitoring (IoT4PHM) ontology with modifications tocreate IoT-MIDO [61]. The IoT4PHM was built on the
IoT-Stream ontology, consisting of 4 original concepts:
IoTStream, StreamObservation, Analytics, and Event. In
addition, the ontology is linked with 6 concepts imported fromexternal ontologies: qoi:Quality, iot-lite:Service, sosa:Sensor,
qu:QuanityKind, qu:Unit, and geo:Point. We developed the
IoT4PHM by adding 3 classes to the IoT-Stream ontology:
Patient, UnderlyingHealthcondition, and PatientManagement.
The goal of the ontology was to facilitate data integration andsharing, knowledge representation, reasoning, andcomputer-assisted data analysis to enable IoT-based patient
health monitoring and management for the prevention, early
detection, and mitigation of patient deterioration. We firstmodified the IoT4PHM ontology to better conform to BFO byimporting classes from BFO-based ontologies (eg, replacing
the qu:QuantityKind class with the Measurement class from
Experimental Factor Ontology).

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Figure 1. An ontology model for Internet of Things–powered remote monitoring of a patient’s vital signs, health risk assessment, early warning, andproactive health risk management. CODO: The COviD-19 Ontology for cases and patient information; EFO: Experimental Factor Ontology; FOAF:
The Friend Of A Friend ontology; IDOMAL: Malaria Ontology; NCIT: NCI Thesaurus OBO Edition; UO: Units of Measurement Ontology.

Use Case 2: Clinical Patient Management of Infectious
Diseases
The second use case is clinical patient management of infectious
diseases (Figure 2). When a Person who has acquired an
infection seeks health care services, they start to play the roleof a Patient. The Patient receives a Diagnosis of
InfectiousDisease and is classified into CaseClassification
imported from the Ontology for General Medical Science [54].
The subclasses of CaseClassification are ConfirmedCase,
ProbableCase, SuspectedCase, and Negative. The

ConfirmedCase
furtherhasasubclassofLaboratoryConfirmedCase, a case confirmed by 1 or more of
the laboratory methods that conform to the laboratory criteria
included in the case definition. The Diagnosis includes
information on CaseClassification. The latter only specifies
whether a person has a particular infectious disease, while
Diagnosis is a more comprehensive summary of patients’
medical conditions. The Diagnosis is based on the
LaboratoryTest imported from the clinical LABoratory Ontology[53]. The class includes information on the type of laboratory
test the Patient has undergone.

Figure 2. An ontology model for clinical management of infectious diseases in health care settings, supporting the seamless integration of data on
vaccination status, laboratory test results, diagnosis, treatment, and underlying health conditions that may affect the course and outcome of an infectious
disease. EFO: Experimental Factor Ontology; FOAF: The Friend Of A Friend ontology; GENEPIO: Genomic Epidemiology Ontology; ICDO: International
Classification of Disease Ontology; IDO: Infectious Disease Ontology; IDOMAL: Malaria Ontology; LABO: clinical LABoratory Ontology; NCIT:
NCI Thesaurus OBO Edition; OGMS: Ontology for General Medical Science; VO: Vaccine Ontology.

An example of a laboratory test is a nucleic acid amplification
test, such as a reverse transcription-polymerase chain reaction,an antigen test, and an antibody test in the case of testing for
SARS-CoV-2. When a Person is identified as a case of a
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particular infectious disease, they start to play the role ofInfectiousAgentHost, which has 2 child classes:
AsymptomaticHostRoleOfInfectiousAgentandSymptomaticHostRoleOfInfectiousAgent. In some infections
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such as SARS-CoV-2, a patient who never has symptoms
associated with an infection (ie, an asymptomatic patient) canstill transmit an infectious agent to others. The Person whoplays the role of SymptomaticHostRoleOfInfectiousAgent has
the developes association with the Symptom.
The Patient experiences the DiseaseProcess, which realizes theInfectiousDisease. The Vaccine administered to the Person may
mitigate the DiseaseProcess. The Treatment may either
provideProphylaxis to the Person or is usedForCuring the
DiseaseProcess that the Person experiences. The
DiseaseProcess has the resultsIn association with the
DiseaseOutcome such as death, convalescence, or long-termsequelae. The Person receives Vaccination using the Vaccine,which results in having VaccinationStatus.

Use Case 3: Epidemic Risk Analysis for Timely
Response at the Public Health Level

Lim & Johannesson
informing evidence-based public health decision-making about
preparedness for and response to an infectious disease epidemic[62]. We thus added the RiskAssessment to handle information
on risk assessment for the Epidemic regarding theInfectiousDisease. The information may include geographicalscope (ie, national, subnational, and local or community level),the time of the risk assessment performed, population group(eg, general population and vulnerable population), and theperson or the organization that has performed the risk
assessment. Various risk-scoring criteria can be used for risk
assessment. It is informative to understand which risk-scoring
criteria are implemented for risk assessment. The
RiskScoringCriteria handles information on the criteria’s source,
version, and developer. The RiskScore represents the overallrisk score for an outbreak of an infectious disease in a specificgeographical area. Since risk scores may change over time, we
also add the RiskScoreChange class to track the risk trajectories.

The third use case is epidemic risk assessment at the publichealth level (Figure 3). Risk assessment is essential for
Figure 3. An ontology model for autonomously assessing infectious disease epidemic risk at the national level, with the goal of improving preparedness
and promoting timely response. IDO: Infectious Disease Ontology; NCIT: NCI Thesaurus OBO Edition.

In addition, we included the RiskLevel class because risk level(eg, high, moderate, low) is often determined based on the risk
score and is frequently used for risk communications (eg, risk
assessment reports). Because risk scores can change over time,
the risk level also changes accordingly. The RiskLevelChange
is thus also included. An infectious disease epidemic’s risk
depends on the infectious agent’s transmission mode. In our
ontology, the InfectiousAgent is transmitted by theTransmissionProcess. For vector-borne infectious diseases, theInfectiousAgent has the InfectiousVector. Based on the risk
assessment outputs, the Country may implement
InfectiousDiseaseControlStrategy, which has a response levelaccordingtothe
ResponseMeasureIndex.
The
ResponseMeasureIndexChange is included to assess the changes
in implemented infectious control strategies over time.
There are various ways of operationalizing risk assessment. For
example, one possible way is to use risk score criteria that areproposed by the World Health Organization or the European
Centre for Disease Prevention and Control [62,63] (Figure 4

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RenderX[63]). Both scoring criteria output the overall risk score basedon impact score and likelihood or probability scores. For theWorld Health Organization criteria, the impact is determinedby 3 factors: vulnerability assessment, severity assessment, andcoping capacity assessment. Another possible way tooperationalize is to use the criteria suggested by Lesmanawati
et al [64] (Figure 5 [64]). The scoring criteria of their risk
analysis framework, called “EpiRisk,” provide rapid risk
prediction based on country-specific risk scores computed bysumming disease and country risk scores. The disease risk scorehas 7 parameters (ie, the type of pathogen, basic reproductive
number, mode of transmission, the occurrence of asymptomatictransmission, case fatality rate, therapy or drug availability, andvaccine availability). On the other hand, the country risk scorehas 7 parameters, including the World Bank’s income
classification, the proportion of health expenditure to thecountry’s GDP, the state of peace (assessed by the peace index
proposed by the Peace Institute), the type of country border,
population density, physician density, and hospital beds per1000 individuals.

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Figure 4. Implementation example using European Centre for Disease Prevention and Control rapid risk assessment methodology [63].

Figure 5. Implementation example of risk assessment based on the proposed framework by Lesmanawati et al [64].

Use Case 4: Infectious Disease Surveillance
The fourth use case is modeling infectious disease surveillance(Figure 6). The InfectiousDiseaseSurveillance is a subclass of
DiseaseSurveillance, which is a subclass of HealthSurveillance.
The
HealthSurveillanceisperformed
foranInfectiousDiseaseAgent. The InfectiousDiseaseSurveillance,
performed
inaparticularCountry,
collects

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DiseaseSurveillanceData that are reported to the
DiseaseSurveillanceSystem and are used for computing the
PopulationBasedStatistic based on the Population data of theCountry. The DiseaseSurveillanceData has 2 subclasses:
CaseBasedDiseaseSurveillanceData
andAggregatedDiseaseSurveillanceData. We also included
DiseaseSurveillanceDataChange for handling longitudinalsurveillance data.

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Figure 6. An ontology model for integrating dynamic data on infectious disease case definition, testing strategy, epidemic threshold, and statistics suchas morbidity and mortality to track the spread of the disease at the national level. IDO: Infectious Disease Ontology; IDOBRU: Brucellosis Ontology;
NCIT: NCI Thesaurus OBO Edition; OBI: Ontology for Biomedical Investigations.

HealthSurveillance refers to the EpidemicThreshold, which
defines the Epidemic regarding InfectiousDisease. The
CaseDefinition is a set of standard criteria for identifying cases
to monitor the trend of the InfectiousDisease under investigation[65]. Using uniform case definitions is essential for public healthsurveillance. By ensuring that every case is equivalent, thenumber of cases and disease incidence across different time
points and geographical areas can be meaningfully compared[66]. Case definitions can vary across countries, especially at
an early stage of newly emerged infectious disease outbreaks[67]. Furthermore, the definitions can be modified as new
evidence on infectious diseases becomes available. When
analyzing and interpreting surveillance data, it is essential toknow on which case definition the diagnosis of an infectious
disease is based. The number of cases needs to be interpretedwith caution when a new version becomes available. The
CaseDefinition may include information on the source of thecase definition and its version and has 4 subclasses: laboratory
criteria, clinical criteria, epidemiological criteria, and diagnostic
imaging criteria.
HealthSurveillance can use the ProxyDiseaseActivityDataItem,
which can include data items that may provide an earlier
indication of an epidemic spread than traditional epidemiologicalmetrics such as confirmed cases or deaths [68]. Kogan et al [68]evaluated 6 digital data sources as proxies of COVID-19 activity
to detect COVID-19 outbreaks as early as possible [68]:1.
2.

Google Trends patterns for a suite of COVID-19–related
terms
COVID-19–related Twitter activity

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3.
4.
5.
6.

COVID-19–related clinician searches from UpToDate
Predictions by the global epidemic and mobility model, astate-of-the-art metapopulation mechanistic model
Anonymized and aggregated human mobility data fromsmartphones
Kinsa smart thermometer measurements

They found that increased digital data stream activity anticipates
the increase in confirmed cases and deaths 2-3 weeks earlier
than traditional surveillance methods. Although all metrics
discussed in their study have limitations, the authors proposedusing the combination of disparate health and behavioral dataor early warning of increased COVID-19 activity [68]. We
believe that those digital proxy data become an important asset
for future infectious disease surveillance; thus, they are included
in our ontology.

Use Case 5: Ontological Model of Transforming Patient
Information Into Surveillance Information
The last use case is transforming patient information into
surveillance information (Figure 7). Individual patient
information such as VaccinationStatus, CaseClassification, andDiseaseOutcome
isincluded
inthe
CaseBasedSurveillanceDataChange. The information on
ContactTracing may also be included in the
DiseaseSurveillanceDataChange. This contributes totransforming individual patient information into data sets thatcan be used for infectious disease surveillance. The rest of theparts in Figure 5 [64] have already been described in the use
cases 2 and 4.

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Figure 7. An ontology model for aggregating individual patient data into surveillance information to provide actionable insights for informed
decision-making and timely public health interventions. CODO: The COviD-19 Ontology for cases and patient information; FOAF: The Friend Of A
Friend ontology; GENEPIO: Genomic Epidemiology Ontology; ICDO: International Classification of Disease Ontology; IDO: Infectious Disease
Ontology; IDOMAL: Malaria Ontology; NCIT: NCI Thesaurus OBO Edition; OBI: Ontology for Biomedical Investigations; VO: Vaccine Ontology.

Answers to CQs
IoT-Driven Patient Monitoring, Risk Assessment, Early
Warning, and Patient Management
The answer to CQ1 is that the EarlyWarningScore class handles
the information on the absolute value of the early warning scorethat is computed based on a particular ClinicalClassification
scheme. An anomaly in the early warning score is detected as
an Event that warns patients of health risks. A type ofmeasurement that an IoT device is monitoring can be found in
the Measurement class, which has the subclasses
PhysiologicalMeasurement and EnvironmentalMeasurement.
This answers CQ2. The answer to CQ3 is that thePatientManagementRecommendation has 3 subclasses:
HealthEducation, ReferralToHealthcare, and EmergencyAlertdepending on the severity of the detected Event (eg, an early
warning score). The classification can serve as triage whichhelps ensure the effective allocation of limited resources to those
who need it most and prevent overburdening health caresystems. The answer to CQ4 is that an IoTStream generated by
the Sensor has a hasGeocoordinate relationship with the Point
class. The information on the geolocation handled in the Point
class can support the ContactTracing activity by using
positioning systems.

Clinical Management of Infectious Diseases
The answer to CQ5 is that the outcome of an infectious disease
process that a Patient experiences is handled in the
DiseaseOutcome, which can have instances of convalescence,
death, intensive care unit admission, and hospitalization. The
answer to CQ7 and CQ8 is that the Treatment class has aprovideProphylaxis association with the Patient class and has
the usedForCuring association with the DiseaseProcess. Thus,
the information on medications used for treating an infectious
disease and prophylaxis can be handled using our ontology. The
answer to CQ8 is that the Vaccine having a mitigated association
with the DiseaseProcess may include information such as theidentifier of the vaccine, the status code of the vaccine, theadministered vaccine product, the vaccine manufacturer, theexpiration date, the administered date, a performer who
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administered the vaccine to a person, and possible side effects
that can be caused by the vaccine. The answer to CQ9 is that
the Symptom class represents symptoms developed by thesymptomatic patients, which is especially important forcharacterizing newly emerging infectious diseases. The answer
to CQ10 is that a Person playing the role of the Patient has a
hasLabTest association with the LaboratoryTest class, which
has a basedOn association with the Diagnosis. The LaboratyTest
represents the information on the laboratory test used for thepatient. The Diagnosis includes the CaseClassification of the
Person, which includes instances of confirmed case,laboratory-confirmed case, probable case, suspected case, andnegative. The answer to CQ11 is that theUnderlyingHealthCondition class handles the information on
the risk factors that pose an increased risk of an infectious
disease. This information is critical to identifying high-risk
groups who need to be prioritized for treatment and public healthinterventions.

Epidemic Risk Analysis
The answer to CQ12 is that the RiskAssessment class, which is
performed for the InfectiousAgent, uses the RiskScoringCriteria
to output the RiskScore that can be classified as a RiskLevel
such as very high, high, medium, low, or very low. The answer
to CQ13 is that the Country class has the implements association
with the InfectiousDiseaseControlStrategy so that it allows theretrieval of information on how a country responds to an
infectious disease epidemic at a given time. This information
is important for evaluating the effectiveness of the control
strategies in reducing the transmission of an infectious disease.
Theanswer
to
CQ14
is
that
the
InfectiousDiseaseControlStrategy has the hasResponseLevelassociation with the ResponseMeasureIndex class, which handles
the information on the score showing the strictness ofgovernment responses. The ResponseMeasureIndex has asubclass of OxfordCOVID-19GovenmentSringencyIndex, which
systematically collects data related to closure and containmentand health and economic policy from more than 180 countries
and territories, which allows comparisons of policy responses
within and across countries over time [69]. If another stringency
index is preferred, a user can add it as a subclass of the
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ResponseMeasureIndex on demand. To track the change instringency
index
scores,we
introducedtheResponseMeasureIndexChange class in the model.

Infectious Disease Surveillance
The answer to CQ15 is that HealthSurveillance, which is theupper class of InfectiousDiseaseSurveillance, has a performedIn
association with the Country and has a performedFor association
with the InfectiousAgent. Thus, it is possible to model multiple
infectious disease surveillances for different infectious agentsthat have been or are currently underway in a particular country.
The answer to CQ16 is that HealthSurveillance has a refersTo
association
withtheEpidemicThreshold.
The
EpidemicThreshold has a determine association with the
Epidemic class and has a thresholdFor association with the
InfectiousDisease. For example, crossing the epidemic thresholdof 10% positive influenza laboratory tests indicates increased
influenza activity and thus the start of the seasonal epidemic[70]. The answer to CQ17 is that Country has an association
with the SamplingStrategySpecification, which has a subclassof TestingStrategy. Knowing that case counts are reported undereach testing strategy is vital since the changes in testing
strategies significantly influence case counts, and thus the
epidemic dynamics need to be interpreted with caution when
there are changes in testing strategies [71,72]. The answer to
CQ18 is that DiseaseSurveillanceData has 2 subclasses:
CaseBasedInfectiousDiseaseSurveillanceData
andAggregatedInfectiousDiseaseData. Thus, both types ofsurveillance data can be represented in each subclass since somecountries report case-based data while others submit only
aggregated data. The answer to CQ19 is that the
PopulationBasedStatistic can have subclasses as necessary,
such
as
CaseNotificationRate,
DeathRate,
HospitalAdmissionRate,
HospitalBedOccupancyRate,
TestPositivityRate,
TestingRate,
VaccinationUptake,
EffectiveReproductiveNumber,
Seroprevalence,and
CaseFatalityRate. The Country has a Population, which has an
useForComputing association with PopulationBasedStatistic
since the (sub)population data are necessary for computing
population-based statistics. The answer to CQ20 is that
ContactTracing has a performedFor association with the
InfectiousDisease class. Thus, contact-tracing activities for
multiple infectious diseases can be modeled simultaneously.
The answer to CQ21 is that the DiseaseSurveillanceData has
a reportedTo association with DiseaseSurveillanceSystems since
several infectious disease surveillance systems collect different
types of disease surveillance data that may be combined foranalysis, and knowledge of the provenance of data is thus needed
when referencing to the raw data. The answer to CQ22 is that
the CaseDefinition has a definedFor association with the
InfectiousDisease and has a basedOn association with the
CaseClassification. The case definition is a set of criteria forsystematically counting cases and thus is indispensable for
infectious disease surveillance. The CaseDefinition has 4
subclasses:
LaboratoryCriteria,
ClinicalCriteria,
EpidemiologicalCriteria, and DiagnosticImagingCriteria.

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Transforming Individual Patient Information Into
Surveillance Information
The answer to CQ23 is that the CaseClassification, theVaccinationStatus, and the DiseaseOutcome classes have an
includedIn
association
with
the
CaseBasedDiseaseSurveillanceData. Thus, individual patients’
data on case classification, vaccination status, and disease
outcomes can be integrated seamlessly into surveillance data
through the CaseBasedDiseaseSurveillanceData class.
Therefore, the IoT-MIDO successfully addresses all of the CQs
and can potentially be used for (1) IoT-driven remote patient
health monitoring, risk assessment, early warning, and patientmanagement in the context of infectious disease outbreaks, (2)clinical management of infectious diseases, (3) epidemic risk
analysis, (4) infectious disease surveillance, and (5) transforming
individual patient information into surveillance information.

Discussion
Principal Findings
The aim of our work was to design an infectious disease
ontology that can support semantic data integration fromdisparate heterogeneous sources, including IoT-driven patientmonitoring, clinical management, and infectious disease
surveillance systems, and interlinking them. For this end, wedeveloped the IoT-MIDO ontology, and by addressing CQs andpresenting 5 use cases, we demonstrated the potential of thisontology to enhance data interoperability, integration, andanalysis across health care and public health domains, and toassist health care practitioners and public health decision makers
in interpreting data available and their semantic relationships.
The IoT-MIDO ontology can also serve as a starting point for
enabling automated reasoning to drive actionable insights andinformed decision-making to improve the health of individual
patients as well as populations at large.

Comparing IoT-MIDO and OMOP CDM
Similar to our work, the OMOP CDM can be used to support
data integration from disparate sources in the context of
infectious diseases. The model was developed by theObservational Health Data Sciences and Informatics as an
open-source common data standard to store observational healthdata [73]. The OMOP CDM effectively leverages a relational
database schema to represent structured tabular data. Throughintegration with standardized vocabularies, it facilitates data
analysis across multiple data sources and meaningfully comparesand reproduces results from various observational studies [73].
However, in contrast to OMOP CDM, we have chosen an
ontology modeling approach for knowledge representation
because of the following capabilities. Ontologies offer morecomprehensive and explicit knowledge representation, enrichingdata with richer semantic contexts and meanings, and modeling
relationships between concepts in both human- andmachine-interpretable formats [74]. Their expressivenessprovides capabilities for automated reasoning, inferencing, andmore precise querying over data.

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Furthermore, ontologies provide greater flexibility in modelingcomplex relationships beyond mere hierarchies, accommodating
any data formats, including structured, unstructured, andsemistructured data [75]. While their flexibility enables moreseamless data integration, their ease of extensibility allows foradaptation to the dynamic growth of data.
Ontologies are compatible with the World Wide Web
Consortium standards for the Semantic Web. The World Wide
Web Consortium endorses the use of International Resource
Identifiers [76] and Uniform Resource Identifiers [77] andUniform Resource Identifiers allows data from distributed andheterogeneous systems and databases to be linked together on
the Web of Linked Data. This enables the establishment ofsemantic links between IoT-MIDO and existing specificinfectious disease ontologies such as IDODEN [34] andIDO-COVID-19 [33], addressing the major issue of siloed
information across disconnected systems, which prevents acomprehensive understanding of public health data.

Compatibility With Other BFO-Based Ontologies
There are many well-established BFO-based ontologies existing,such as VIDO [35], CIDO [36] and IDO-COVID-19 [33], which
are all ultimately extended from IDO [51]. IoT-MIDO adopts
terms from those established ontologies, such as Infectious
disease and Infectious agent, and infectious disease surveillancefrom IDO. This provides the possibility of easily extending
IoT-MIDO further, according to the needs of ontology users tocreate semantic relationships with other BFO-based ontology
concepts, for example, to characterize virus species and tooverlapping or concurrently occurring multiple infectious
disease epidemics.
The novelty and uniqueness characterizing our ontology lie in
adopting concepts from IoT-Stream [44], such as IoT stream
and stream observation. Incorporating these concepts, together
with concepts related to infectious disease surveillance, control
strategy, and response measure, which are both newly created
and adopted from existing ontologies, builds a bridge to link
IoT-based individual patient monitoring and early warning basedon patient risk assessment to infectious disease epidemic
surveillance at the public health level.

Strengths, Limitations, and Future Directions
This study introduced IoT-MIDO, making a crucial initial stepin addressing the long-standing issue of information silos causedby the historical segmentation between clinical medicine andpublic health, as well as the lack of interoperability acrossdisparate systems. We made efforts to minimize the creation of
new ontology concepts, opting instead to reuse existingdomain-specific ontology concepts, particularly those based on
BFO, to enhance semantic interoperability with external
ontologies. Furthermore, digital data collected throughIoT-driven systems provide information, including patients’
real-time physical and mental health statuses, lifestyles, andsociodemographics [78]. Such data are underutilized in thecontext of infectious disease surveillance, yet they can enhanceexisting surveillance systems by integrating with data obtainedusing conventional surveillance approaches. This integration

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Lim & Johannesson
can contribute to the development of more comprehensive
epidemiological profiles at public health level.
However, 2 primary limitations must be acknowledged. First,
the ontology has yet to undergo formalization using dedicatedontology development tools, such as Protégé. We recognize thisshortfall and intend to rectify it by formalizing the ontology in
the Web Ontology Language using Protégé in future iterations.
Second, our evaluation of the ontology has been primarily
focused on its ability to answer CQs. We acknowledge the needfor iterative empirical evaluation using live data sets to assess
the capabilities of the ontology to integrate individual patientand surveillance data. Such evaluations are crucial not only forvalidating its practical use but also for facilitating continuous
enhancements to its quality over time.
These limitations highlight areas for future research anddevelopment. Moreover, several studies have proposed
ontology-driven approach to ensure data compliance [79-81].
For instance, Debruyne et al [79] proposed an ontology, an
extension of the provenance ontology called “PROVO,” torepresent collected informed consent and its changes over time[79]. While many of existing studies predominantly focus on
verifying compliance with policies regulations during thedata-processing stage, their ontology model is used to assess
potential compliance issues at data set creation stage before thedata undergo processing.
Inspired by these studies, we plan to expand our ontology toinclude entities representing health data security, privacy,
informed consent, and compliance with the legal requirements
such as General Data Protection Regulation in future versions.
Incorporating these entities is essential for safeguarding patients’sensitive information and ensuring compliance with legal
requirements and contractual agreements. Enabling complianceverification at the data set creation stage saves time andresources that would otherwise be spent on post hoc complianceanalysis during data processing. In addition, it strengthens data
security and privacy measures by identifying and addressing
vulnerabilities or compliance gaps early in the data life cycle.

Conclusions
The primary aim of IoT-MIDO is to address the issue of
information silos between clinical medicine and public healthand enhance interoperability across different systems.
Demonstrating IoT-MIDO’s capability to answer diverse CQs
highlights its potential as a tool for achieving this goal. The
seamless integration and sharing of clinical and public healthdata are particularly important during the rise of emerging
infectious disease outbreaks when knowledge and evidence
about infectious disease agents, transmission routes, and diseaseprofiles are still scarce. This integration can accelerate theunderstanding of comprehensive and consistent epidemiological
profiles and facilitate the effective and timely planning andexecution of preventive and control measures.
As health data volume and complexity grow with increasing
IoT applications in health care, standardizing such disparate
heterogeneous data is essential for the effective and efficient
use and integration of such data in infectious disease
surveillance. Ontologies such as IoT-MIDO help systematically
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JMIR FORMATIVE RESEARCH
represent knowledge to make disparate heterogeneous data readyfor integration and comparable for further analyses, enabling
informed and proactive interventions at both individual andpopulation levels. IoT-MIDO’s interoperability with existing
ontologies, especially BFO-based ones like IDO, also facilitates
seamless data exchange and the creation of comprehensive
surveillance ecosystems.
Moreover, while the ontology standardizes the way of data
sharing, it also provides flexibility to meet local needs, forexample, by adding country-specific concepts and reference
data such as codes while semantically linking them to global
concepts or reference data.
Looking ahead, IoT-MIDO could underpin advanced decision
support systems and predictive analytics tools in health care.

Lim & Johannesson
These systems, leveraging computational semantic and logicalreasoning, can assist in accurate diagnoses and optimal treatment
decisions while enabling proactive surveillance and early
detection of public health threats.
IoT-MIDO contributes to using IoT technologies to enhance
health care delivery and modernize infectious disease
surveillance by providing means to bridge individual patient
data with epidemiological data. By enabling the integration ofthese data from multiple domains, the ontology enhancesinteroperability, thereby advancing the use of IoT technologies
in providing personalized preventive measures and care forindividual patients as well as improving preparedness andresponse to infectious disease epidemics.

Data Availability
The link to the IoT-based management of infectious disease ontology model and additional information, including definitions of
the entities and their attributes, is provided in Multimedia Appendices 1 and 2.

Authors' Contributions
SL conceived the idea for the study. SL developed the ontology, and PJ acted as a reviewer of successive versions of the ontology.
After SL drafted the manuscript, PJ reviewed and improved the content of the paper. PJ approved the final version of the manuscript
for submission.

Conflicts of Interest
None declared.

Multimedia Appendix 1
The entire model of the IoT-MIDO. IoT-MIDO: Internet of Things–based management of infectious disease ontology.
[PNG File , 694 KB-Multimedia Appendix 1]

Multimedia Appendix 2
IoT-MIDO definition table. IoT-MIDO: Internet of Things–based management of infectious disease ontology.
[XLSX File (Microsoft Excel File), 454 KB-Multimedia Appendix 2]

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Abbreviations
BFO: basic formal ontology
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Lim & Johannesson

CIDO: Coronavirus Infectious Disease Ontology
CQ: competency question
HL7/FHIR: Health Level 7 Fast Healthcare Interoperability Resources
ICD: International Classification of Diseases
ICD-10: International Statistical Classification of Diseases, Tenth Revision
IDO: Infectious Disease Ontology
IDODEN: Infections Disease Ontology–Dengue Fever
IoT: Internet of Things
IoT4PHM: Internet of Things for patient health monitoring
IoT-MIDO: Internet of Things–based management of infectious disease ontology
OMOP CDM: Observational Medical Outcomes Partnership Common Data Model
SNOMED CT: Systematized Nomenclature of Medicine Clinical Terms
VIDO: Virus Infectious Disease Ontology

Edited by A Mavragani; submitted 16.10.23; peer-reviewed by T Karen; comments to author 06.02.24; revised version received
31.03.24; accepted 01.07.24; published 26.09.24
Please cite as:
Lim S, Johannesson P
An Ontology to Bridge the Clinical Management of Patients and Public Health Responses for Strengthening Infectious Disease
Surveillance: Design Science StudyJMIR Form Res 2024;8:e53711
URL: https://formative.jmir.org/2024/1/e53711
doi: 10.2196/53711
PMID: 39325530©Sachiko Lim, Paul Johannesson. Originally published in JMIR Formative Research (https://formative.jmir.org), 26.09.2024.
This is an open-access article distributed under the terms of the Creative Commons Attribution License(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information,a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.

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