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 of interoperability. As a life-changing clinical use case, the COVID-19 pandemic has manifested that a lack of interoperability can severely 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 semantic interoperability 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, ease of 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 to maximize 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 the collaboration between clinical care and public health domains. We also demonstrate five use cases using the simplified ontological models to show the potential applications of IoT-MIDO: (1) IoT-driven patient monitoring, risk assessment, early warning, and risk 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 formulated competency questions, we successfully demonstrated that our ontology has the potential to facilitate data sharing and integration for 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.

Requirements engineering has traditionally been stakeholder-driven. In addition to domain knowledge, widespread digitalization has led to the generation of vast amounts of data (Big Data) from heterogeneous digital sources such as the Internet of Things (IoT), mobile devices, and social networks. The digital transformation has spawned new opportunities to consider such data as potentially valuable sources of requirements, although they are not intentionally created for requirements elicitation. A challenge to data-driven requirements engineering concerns the lack of methods to facilitate seamless and autonomous requirements elicitation from such dynamic and unintended digital sources. There are numerous challenges in processing the data effectively to be fully exploited in organizations. This article, thus, reviews the current state-of-the-art approaches to data-driven requirements elicitation from dynamic data sources and identifies research gaps. We obtained 1848 hits when searching six electronic databases. Through a two-level screening and a complementary forward and backward reference search, 68 papers were selected for final analysis. The results reveal that the existing automated requirements elicitation primarily focuses on utilizing human-sourced data, especially online reviews, as requirements sources, and supervised machine learning for data processing. The outcomes of automated requirements elicitation often result in mere identification and classification of requirements-related information or identification of features, without eliciting requirements in a ready-to-use form. This article highlights the need for developing methods to leverage process-mediated and machine-generated data for requirements elicitation and addressing the issues related to variety, velocity, and volume of Big Data for the efficient and effective software development and evolution.

The Internet of Things (IoT) creates an ecosystem that connects people and objects through the internet. IoTenabled healthcare has revolutionized healthcare delivery by moving toward a more pervasive, patientcentered, and preventive care model. In the ongoing COVID-19 pandemic, it has also shown a great potential for effective remote patient health monitoring and management, which leads to preventing straining the healthcare system. Nevertheless, due to the heterogeneity of data sources and technologies, IoT-enabled healthcare systems often operate in vertical silos, hampering interoperability across different systems. Consequently, such sensory data are rarely shared nor integrated, which can undermine the full potential of IoT-enabled healthcare. Applying semantic technologies to IoT is a promising approach for fulfilling heterogeneity, contextualization, and situation-awareness requirements for real-time healthcare solutions. However, the enrichment of sensor streams has been under-explored in the existing literature. There is also a need for an ontology that enables effective patient health monitoring and management during infectious disease outbreaks. This study, therefore, aims to extend the existing ontology to allow patient health monitoring for the prevention, early detection, and mitigation of patient deterioration. We evaluated the extended ontology using competency questions and illustrated a proof-of-concept of ontology-based semantic representation of vital sign streams.

The health of individuals and populations requires concerted and collaborative efforts by healthcare, public health, social care, and personal health management. The inter-sectoral collaborations are more crucial than ever, especially when facing public health crises, including the ongoing pandemic of coronavirus disease-2019 (COVID-19). Although the capabilities of healthcare and public health systems have increased with a dramatic boost in the use of the Internet of Things (IoT), such IoT-enabled systems are often operating in silos. A pressing need, thus, is the seamless integration of those currently incompatible systems. A promising solution is to leverage semantic technologies to increase interoperability among such systems. Therefore, this article aims to: conduct a systematic review on the current state-of-the-art semantic IoT solutions used in health domain; identify the associated challenges; propose a federated edge-cloud semantic IoT architecture to facilitate the healthcare and public health (HC-PH) collaborations for the health and well-being of the individuals and populations.