Applied Ontologies for Global Health Surveillance and Pandemic Intelligence

AbstractGlobal health surveillance and pandemic intelligence rely on the systematic collection and integration of data from diverse distributed and heterogeneous sources at various levels of granularity. These sources include data from multiple disciplines represented in different formats, languages, and structures posing significant integration challenges This article provides an overview of challenges in data driven surveillance. Using Malaria surveillance as a use case we highlight the contribution made by emerging semantic data federation technologies that offer enhanced interoperability, interpretability and explainability through the adoption of ontologies. The paper concludes with a focus on the relevance of these technologies for ongoing pandemic preparedness initiatives.

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Support for global health and pandemic preparedness in medical education in Germany: Students as change agents

The International Journal of Health Planning and Management ◽

10.1002/hpm.3143 ◽

2021 ◽

Author(s):

Annika Kreitlow ◽

Sandra Steffens ◽

Alexandra Jablonka ◽

Ellen Kuhlmann

Keyword(s):

Medical Education ◽

Global Health ◽

Change Agents ◽

Pandemic Preparedness

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Robust goal programming for multi-objective optimization of data-driven problems: A use case for the United States transportation command's liner rate setting problem

Omega ◽

10.1016/j.omega.2018.10.013 ◽

2020 ◽

Vol 90 ◽

pp. 101983 ◽

Cited By ~ 4

Author(s):

Robert W. Hanks ◽

Brian J. Lunday ◽

Jeffery D. Weir

Keyword(s):

United States ◽

Goal Programming ◽

The United States ◽

Data Driven ◽

Use Case ◽

Multi Objective Optimization ◽

Multi Objective

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Big Data-Driven Health Surveillance

The Big Data Agenda ◽

10.2307/j.ctv5vddsw.7 ◽

2018 ◽

pp. 71-90

Keyword(s):

Big Data ◽

Health Surveillance ◽

Data Driven

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Global Health Surveillance Approaches: New Opportunities

Entrepreneurship in Technology for ASEAN - Managing the Asian Century ◽

10.1007/978-981-10-2281-4_5 ◽

2016 ◽

pp. 59-71

Author(s):

Insu Song ◽

Dominic Hayes ◽

Purnendu Mandal ◽

John Vong

Keyword(s):

Global Health ◽

Health Surveillance

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Ethical Applications of Big Data-Driven AI on Social Systems: Literature Analysis and Example Deployment Use Case

Information ◽

10.3390/info11050235 ◽

2020 ◽

Vol 11 (5) ◽

pp. 235

Author(s):

Paulo Garcia ◽

Francine Darroch ◽

Leah West ◽

Lauren BrooksCleator

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Social Issues ◽

Social Computing ◽

Social Systems ◽

Human Interaction ◽

Data Driven ◽

Vulnerable Groups ◽

Use Case ◽

Ongoing Research

The use of technological solutions to address the production of goods and offering of services is ubiquitous. Health and social issues, however, have only slowly been permeated by technological solutions. Whilst several advances have been made in health in recent years, the adoption of technology to combat social problems has lagged behind. In this paper, we explore Big Data-driven Artificial Intelligence (AI) applied to social systems; i.e., social computing, the concept of artificial intelligence as an enabler of novel social solutions. Through a critical analysis of the literature, we elaborate on the social and human interaction aspects of technology that must be in place to achieve such enabling and address the limitations of the current state of the art in this regard. We review cultural, political, and other societal impacts of social computing, impact on vulnerable groups, and ethically-aligned design of social computing systems. We show that this is not merely an engineering problem, but rather the intersection of engineering with health sciences, social sciences, psychology, policy, and law. We then illustrate the concept of ethically-designed social computing with a use case of our ongoing research, where social computing is used to support safety and security in home-sharing settings, in an attempt to simultaneously combat youth homelessness and address loneliness in seniors, identifying the risks and potential rewards of such a social computing application.

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Data-driven Digital Twin approach for process optimization: an industry use case

2018 IEEE International Conference on Big Data (Big Data) ◽

10.1109/bigdata.2018.8622412 ◽

2018 ◽

Cited By ~ 3

Author(s):

Nenad Stojanovic ◽

Dejan Milenovic

Keyword(s):

Process Optimization ◽

Data Driven ◽

Use Case ◽

Digital Twin

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Using Use Case Points Method In a Health Surveillance Application Estimation

2019 Fourth International Conference on Informatics and Computing (ICIC) ◽

10.1109/icic47613.2019.8985704 ◽

2019 ◽

Author(s):

Benrahman ◽

Ford Lumban Gaol ◽

Harco Leslie Hendric Spits Warners ◽

Edi Abdurahman ◽

Benfano Soewito

Keyword(s):

Health Surveillance ◽

Use Case ◽

Use Case Points ◽

Surveillance Application

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Design of a joint research data platform: A use case for severity assessment

Laboratory Animals ◽

10.1177/0023677219872228 ◽

2019 ◽

Vol 54 (1) ◽

pp. 33-39

Author(s):

Steven R Talbot ◽

Stefan Bruch ◽

Fabian Kießling ◽

Michael Marschollek ◽

Branko Jandric ◽

...

Keyword(s):

Human Factor ◽

Data Driven ◽

Use Case ◽

Severity Assessment ◽

Joint Research ◽

Web Based ◽

Specific Content ◽

Data Platform ◽

Basic Understanding ◽

Minimal Effort

Severity assessment in animal models is a data-driven process. We therefore present a use case for building a repository for interlaboratory collaboration with the potential of uploading specific content, making group announcements and internal prepublication discussions. We clearly show that it is possible to offer such a structure with minimal effort and a basic understanding of web-based services, also taking into account the human factor in individual data collection. The FOR2591 Online Repository serves as a blueprint for other groups, so that one day not only will data sharing among consortium members be improved but the transition from the private to the persistent domain will also be easier.

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