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Computers
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Future Systems Based on Healthcare 5.0 for Pandemic Preparedness 2024
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Future Systems Based on Healthcare 5.0 for Pandemic Preparedness 2024

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "Internet of Things (IoT) and Industrial IoT".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 5503

Special Issue Editor

Dr. Xuhui Chen

E-Mail Website
Guest Editor
College of Aeronautics & Engineering, Kent State University, Kent, OH 44240, USA
Interests: security and privacy in big data analytics (machine learning, cloud computing); system design; internet of things (IoT); smart health; cyber–physical systems; wireless networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Coronavirus will not be the last pandemic in our lifetime considering factors such as increased population density, growing capacity to travel across the globe, environmental changes, infectious diseases that jump from animals to humans, etc. Therefore, the risk of a new pandemic is higher now than ever before. COVID-19 has shown us the lack of smart systems in healthcare organizations for pandemic response (e.g., early reporting of the risk of the pandemic outbreak). The better data drive smarter and earlier decisions. The combination of advanced technologies of Healthcare 5.0, including nanotechnology, 5G technologies, drone technology, blockchain, digital twins, robotics, big data, IoT, AI, and cloud computing, has significant advantages in designing smart systems for pandemic preparedness. Although there has been significant progress towards smart and connected healthcare systems during the pandemic, more research innovation, dissemination and technologies are needed to unbundle new opportunities and move towards adopting Healthcare 5.0 for pandemic preparedness to save human lives.

This Special Issue of Computers presents cutting-edge research and commentary to explore the future of adopting Healthcare 5.0 technologies for pandemic preparedness. Researchers, developers, and industry practitioners working in this area are invited to present their views and research work on the current pandemic preparedness trends.

Therefore, the suggested topics of interest for this Special Issue include, but are not limited to, the following:

  • Pandemic alerting frameworks and systems;
  • Applications for pandemic preparedness;
  • IoT solutions for pandemic preparedness;
  • Blockchain solutions for pandemic preparedness;
  • Collaborative solutions based on Healthcare 5.0 for pandemic preparedness;
  • Digital twins/ Digital twin collaboration solutions for pandemic preparedness;
  • Federated learning, machine learning, and deep learning for pandemic preparedness;
  • Personalised COVID-19 medicine;
  • COVID-19 survivor follow-up care;
  • Robot collaboration for contact-less systems to combat the pandemic outbreak;
  • Drone collaboration to combat the pandemic outbreak;
  • Cloud computing;
  • Big data/healthcare data;
  • 5/6G roles for pandemic response;
  • Human-in-the-loop AI for pandemic response;
  • Explainable AI for diagnosis;
  • Smart decision-making for pandemic response;
  • Remote patient care for combating the pandemic outbreak.

Dr. Xuhui Chen
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computers is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • pandemic alerting
  • IoT
  • blockchain
  • digital twins
  • digital twin collaboration
  • personal digital twins
  • human digital twins
  • federated learning
  • machine learning
  • deep learning
  • personalised COVID-19 medicine
  • COVID-19 survivor follow-up care
  • robot collaboration
  • drone collaboration
  • cloud computing
  • big data
  • 5/6G
  • human-in-the-loop AI for healthcare
  • explainable AI
  • smart decision making
  • remote patient care

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Published Papers (3 papers)

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Research

40 pages, 4555 KiB  
Article
A Novel Data Analytics Methodology for Discovering Behavioral Risk Profiles: The Case of Diners During a Pandemic
by Thouraya Gherissi Labben and Gurdal Ertek
Computers 2024, 13(10), 272; https://doi.org/10.3390/computers13100272 - 19 Oct 2024
Viewed by 1444
Abstract
Understanding tourist profiles and behaviors during health pandemics is key to better preparedness for unforeseen future outbreaks, particularly for tourism and hospitality businesses. This study develops and applies a novel data analytics methodology to gain insights into the health risk reduction behavior of [...] Read more.
Understanding tourist profiles and behaviors during health pandemics is key to better preparedness for unforeseen future outbreaks, particularly for tourism and hospitality businesses. This study develops and applies a novel data analytics methodology to gain insights into the health risk reduction behavior of restaurant diners/patrons during their dining out experiences in a pandemic. The methodology builds on data relating to four constructs (question categories) and measurements (questions and attributes), with the constructs being worry, health risk prevention behavior, health risk reduction behavior, and demographic characteristics. As a unique contribution, the methodology generates a behavioral typology by identifying risk profiles, which are expressed as one- and two-level decision rules. For example, the results highlighted the significance of restaurants’ adherence to cautionary measures and diners’ perception of seclusion. These and other factors enable a multifaceted analysis, typology, and understanding of diners’ risk profiles, offering valuable guidance for developing managerial strategies and skill development programs to promote safer dining experiences during pandemics. Besides yielding novel types of insights through rules, another practical contribution of the research is the development of a public web-based analytics dashboard for interactive insight discovery and decision support. Full article
(This article belongs to the Special Issue Future Systems Based on Healthcare 5.0 for Pandemic Preparedness 2024)
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16 pages, 1081 KiB  
Article
Optimized Machine Learning Classifiers for Symptom-Based Disease Screening
by Auba Fuster-Palà, Francisco Luna-Perejón, Lourdes Miró-Amarante and Manuel Domínguez-Morales
Computers 2024, 13(9), 233; https://doi.org/10.3390/computers13090233 - 14 Sep 2024
Viewed by 1808
Abstract
This work presents a disease detection classifier based on symptoms encoded by their severity. This model is presented as part of the solution to the saturation of the healthcare system, aiding in the initial screening stage. An open-source dataset is used, which undergoes [...] Read more.
This work presents a disease detection classifier based on symptoms encoded by their severity. This model is presented as part of the solution to the saturation of the healthcare system, aiding in the initial screening stage. An open-source dataset is used, which undergoes pre-processing and serves as the data source to train and test various machine learning models, including SVM, RFs, KNN, and ANNs. A three-phase optimization process is developed to obtain the best classifier: first, the dataset is pre-processed; secondly, a grid search is performed with several hyperparameter variations to each classifier; and, finally, the best models obtained are subjected to additional filtering processes. The best-results model, selected based on the performance and the execution time, is a KNN with 2 neighbors, which achieves an accuracy and F1 score of over 98%. These results demonstrate the effectiveness and improvement of the evaluated models compared to previous studies, particularly in terms of accuracy. Although the ANN model has a longer execution time compared to KNN, it is retained in this work due to its potential to handle more complex datasets in a real clinical context. Full article
(This article belongs to the Special Issue Future Systems Based on Healthcare 5.0 for Pandemic Preparedness 2024)
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14 pages, 751 KiB  
Article
Passenger Routing Algorithm for COVID-19 Spread Prevention by Minimising Overcrowding
by Dimitrios Tolikas, Evangelos D. Spyrou and Vassilios Kappatos
Computers 2024, 13(2), 47; https://doi.org/10.3390/computers13020047 - 5 Feb 2024
Cited by 1 | Viewed by 1711
Abstract
COVID-19 has become a pandemic which has resulted in measures being taken for the health and safety of people. The spreading of this disease is particularly evident in indoor spaces, which tend to get overcrowded with people. One such place is the airport [...] Read more.
COVID-19 has become a pandemic which has resulted in measures being taken for the health and safety of people. The spreading of this disease is particularly evident in indoor spaces, which tend to get overcrowded with people. One such place is the airport where a plethora of passengers gather in common places, such as coffee shops and duty-free shops as well as toilets and gates. Guiding the passengers to less overcrowded places within the airport may be a solution to reduce disease spread. In this paper, we suggest a passenger routing algorithm whereby the passengers are guided to less crowded places by using a weighting factor, which is minimised to accomplish the desired goal. We modeled a number of shops in an airport using the AnyLogic software and we tested the algorithm showing that the exposure time is less with routing and that people are appropriately spread out across the common spaces, thus preventing overcrowding. Finally, we added a real airport in Kavala, Greece to show the efficiency of our approach. Full article
(This article belongs to the Special Issue Future Systems Based on Healthcare 5.0 for Pandemic Preparedness 2024)
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