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Artificial Intelligence and Technologies in Pandemic Management
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Artificial Intelligence and Technologies in Pandemic Management

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Digital Health".

Deadline for manuscript submissions: closed (30 October 2021) | Viewed by 34607

Special Issue Editors

Dr. Kum Fai Yuen

E-Mail Website
Guest Editor
School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639789, Singapore
Interests: renewable energy and sustainability; industrial engineering and design; machine-person interoperability; supply chain management; system resilience
Special Issues, Collections and Topics in MDPI journals
Dr. Xueqin Wang

E-Mail Website
Guest Editor
Department of International Logistics, Chung-Ang University, Seoul 06974, Republic of Korea
Interests: logistics management; sustainable shipping practices
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

As a result of the COVID-19 pandemic, a renewed emphasis has been placed on pandemic management. As an important measure, artificial intelligence (AI) and related technologies have been introduced to reduce the risk of spreading the virus, help businesses to remain successful and comply with health regulations, and improve the resilience of communities. For example, AI has been employed to produce dynamic, predictive heatmaps, ease strain on healthcare systems, enforce social distancing, and predict traffic and crowd movement. Similarly, some contactless technologies that have been employed for public health management purposes including remote tracing, online shopping and contactless deliveries, digital or contactless payments, remote working, distance learning, telehealth, online entertainment, robots, and drones. 

Undoubtedly, AI and related technologies are critical to public health management and are increasingly being integrated into various aspects of society. This trend is likely to continue into the post-COVID-19 era. 

This Special Issue aims to bring together recent theoretical, applied, or methodological studies concerning the intersections between AI and related technologies and public health or pandemic management. All topics addressing the interface between AI and related technologies and pandemic management, such as public policy, economic, societal, business, environmental, legal, and security concerns and issues, are welcome. 

Possible topics for this Special Issue include, but are not limited to, the following: 

  • Deployment of AI and related technologies
  • Efficacy of AI and related technologies
  • Societal or consumer acceptance of AI and related technologies
  • Contactless technologies for daily services
  • Interaction patterns between human and AI and related technologies
  • Design and optimisation of AI algorithm
  • Policies, practices, and business strategies to manage AI and related technologies
  • Resilience management of societies, businesses, and supply chains using health-related AI and related technologies
  • Technology-readiness and industry and workforce transformation

Prof. Dr. Kum Fai Yuen
Dr. Xueqin Wang
Guest Editors

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. International Journal of Environmental Research and Public Health 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 2500 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 management 
  • Public acceptance 
  • Public health 
  • Artificial intelligence 
  • Contactless technologies 
  • Service consumption 
  • Resilient society 
  • Human–computer interaction

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

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Research

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17 pages, 737 KiB  
Article
Impact of the First Phase of the COVID-19 Pandemic on the Acquisition of Goods and Services in the Italian Health System
by Martina Capuzzo, Gian Luca Viganò, Cinzia Boniotti, Lucia Maria Ignoti, Claudia Duri and Veronica Cimolin
Int. J. Environ. Res. Public Health 2022, 19(4), 2000; https://doi.org/10.3390/ijerph19042000 - 11 Feb 2022
Cited by 6 | Viewed by 1957
Abstract
The emergency caused by the escalation in the COVID-19 pandemic, which became widespread starting on 31 January 2020, put a strain on the Italian National Health System and forced purchasing centres to deviate from the ordinary general principles dictated by current legislation. The [...] Read more.
The emergency caused by the escalation in the COVID-19 pandemic, which became widespread starting on 31 January 2020, put a strain on the Italian National Health System and forced purchasing centres to deviate from the ordinary general principles dictated by current legislation. The aim of this paper is to describe how Spedali Civili Hospital in Brescia challenged the crisis, structured itself optimally, followed simplified procedures, launched new processes, and opened up more Intensive Care Unit beds to accommodate the high number of COVID cases. From an analysis of the equipment variation in terms of increased purchases, subsequent installations, and tests carried out compared with the pre-pandemic period, we report the difficulties that hospitals had to face in the first phase of the pandemic and how they were able to respond to their needs. Our data clearly displayed how the pandemic situation led to a deep internal reorganisation and that the drafting of simpler, effective, and adaptable procedures represents a first key element to ensure receptivity and responsiveness in the management of ordinary and non-ordinary events such as this pandemic condition. Full article
(This article belongs to the Special Issue Artificial Intelligence and Technologies in Pandemic Management)
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20 pages, 5821 KiB  
Article
A Reliable and Efficient Tracking System Based on Deep Learning for Monitoring the Spread of COVID-19 in Closed Areas
by Radwa Ahmed Osman, Sherine Nagy Saleh, Yasmine N. M. Saleh and Mazen Nabil Elagamy
Int. J. Environ. Res. Public Health 2021, 18(24), 12941; https://doi.org/10.3390/ijerph182412941 - 8 Dec 2021
Cited by 3 | Viewed by 2003
Abstract
Since 2020, the world is still facing a global economic and health crisis due to the COVID-19 pandemic. One approach to fighting this global crisis is to track COVID-19 cases by wireless technologies, which requires receiving reliable, efficient, and accurate data. Consequently, this [...] Read more.
Since 2020, the world is still facing a global economic and health crisis due to the COVID-19 pandemic. One approach to fighting this global crisis is to track COVID-19 cases by wireless technologies, which requires receiving reliable, efficient, and accurate data. Consequently, this article proposes a model based on Lagrange optimization and a distributed deep learning model to assure that all required data for tracking any suspected COVID-19 patient is received efficiently and reliably. Finding the optimum location of the Radio Frequency Identifier (RFID) reader relevant to the base station results in the reliable transmission of data. The proposed deep learning model, developed using the one-dimensional convolutional neural network and a fully connected network, resulted in lower mean absolute squared errors when compared to state-of-the-art regression benchmarks. The proposed model based on Lagrange optimization and deep learning algorithms is evaluated when changing different network parameters, such as requiring signal-to-interference-plus-noise-ratio, reader transmission power, and the required system quality-of-service. The analysis of the obtained results, which indicates the appropriate transmission distance between an RFID reader and a base station, shows the effectiveness and the accuracy of the proposed approach, which leads to an easy and efficient tracking system. Full article
(This article belongs to the Special Issue Artificial Intelligence and Technologies in Pandemic Management)
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22 pages, 1133 KiB  
Article
Does COVID-19 Promote Self-Service Usage among Modern Shoppers? An Exploration of Pandemic-Driven Behavioural Changes in Self-Collection Users
by Xueqin Wang, Yiik Diew Wong and Kum Fai Yuen
Int. J. Environ. Res. Public Health 2021, 18(16), 8574; https://doi.org/10.3390/ijerph18168574 - 13 Aug 2021
Cited by 21 | Viewed by 3222
Abstract
Due to health concerns related to COVID-19, shoppers have learned to minimise social contact by adopting various contactless self-service technologies to fulfil their consumption needs. This study explores shoppers’ behavioural changes in relation to self-service, using the special research context of e-commerce self-collection [...] Read more.
Due to health concerns related to COVID-19, shoppers have learned to minimise social contact by adopting various contactless self-service technologies to fulfil their consumption needs. This study explores shoppers’ behavioural changes in relation to self-service, using the special research context of e-commerce self-collection services. By synthesising insights from the health psychology literature, this study proposes an affective-cognitive-social perspective to explain the pandemic-driven behavioural changes of self-collection users. The survey instrument is used for online data collection (n = 500), and a combined (descriptive and quantitative) method is adopted for data analysis. Our results suggest that, although with a relatively weak predictive power, the affective and cognitive appraisals of health risks lead to the reinforced usage of self-collection service. This also applies to the factors of action/coping planning and subjective norm. This study theoretically contributes to the self-service literature and creates managerial implications for retailers and logistics operators. Full article
(This article belongs to the Special Issue Artificial Intelligence and Technologies in Pandemic Management)
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32 pages, 17745 KiB  
Article
Using Mobile Phone Data to Estimate the Relationship between Population Flow and Influenza Infection Pathways
by Qiushi Chen, Michiko Tsubaki, Yasuhiro Minami, Kazutoshi Fujibayashi, Tetsuro Yumoto, Junzo Kamei, Yuka Yamada, Hidenori Kominato, Hideki Oono and Toshio Naito
Int. J. Environ. Res. Public Health 2021, 18(14), 7439; https://doi.org/10.3390/ijerph18147439 - 12 Jul 2021
Cited by 3 | Viewed by 2790
Abstract
This study aimed to analyze population flow using global positioning system (GPS) location data and evaluate influenza infection pathways by determining the relationship between population flow and the number of drugs sold at pharmacies. Neural collective graphical models (NCGMs; Iwata and Shimizu 2019) [...] Read more.
This study aimed to analyze population flow using global positioning system (GPS) location data and evaluate influenza infection pathways by determining the relationship between population flow and the number of drugs sold at pharmacies. Neural collective graphical models (NCGMs; Iwata and Shimizu 2019) were applied for 25 cell areas, each measuring 10 × 10 km2, in Osaka, Kyoto, Nara, and Hyogo prefectures to estimate population flow. An NCGM uses a neural network to incorporate the spatiotemporal dependency issue and reduce the estimated parameters. The prescription peaks between several cells with high population flow showed a high correlation with a delay of one to two days or with a seven-day time-lag. It was observed that not much population flows from one cell to the outside area on weekdays. This observation may have been due to geographical features and undeveloped transportation networks. The number of prescriptions for anti-influenza drugs in that cell remained low during the observation period. The present results indicate that influenza did not spread to areas with undeveloped traffic networks, and the peak number of drug prescriptions arrived with a time lag of several days in areas with a high amount of area-to-area movement due to commuting. Full article
(This article belongs to the Special Issue Artificial Intelligence and Technologies in Pandemic Management)
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22 pages, 993 KiB  
Article
How Does the Pandemic Facilitate Mobile Payment? An Investigation on Users’ Perspective under the COVID-19 Pandemic
by Yuyang Zhao and Fernando Bacao
Int. J. Environ. Res. Public Health 2021, 18(3), 1016; https://doi.org/10.3390/ijerph18031016 - 24 Jan 2021
Cited by 122 | Viewed by 13983
Abstract
Owing to the convenience, reliability and contact-free feature of Mobile payment (M-payment), it has been diffusely adopted in China during the COVID-19 pandemic to reduce the direct and indirect contacts in transactions, allowing social distancing to be maintained and facilitating stabilization of the [...] Read more.
Owing to the convenience, reliability and contact-free feature of Mobile payment (M-payment), it has been diffusely adopted in China during the COVID-19 pandemic to reduce the direct and indirect contacts in transactions, allowing social distancing to be maintained and facilitating stabilization of the social economy. This paper aims to comprehensively investigate the technological and mental factors affecting users’ adoption intentions of M-payment under the COVID-19 pandemic, to expand the domain of technology adoption under the emergency situation. This study integrated Unified Theory of Acceptance and Use of Technology (UTAUT) with perceived benefits from Mental Accounting Theory (MAT), and two additional variables (perceived security and trust) to investigate 739 smartphone users’ adoption intentions of M-payment during the COVID-19 pandemic in China. The empirical results showed that users’ technological and mental perceptions conjointly influence their adoption intentions of M-payment during the COVID-19 pandemic, wherein perceived benefits are significantly determined by social influence and trust, corresponding with the situation of pandemic. This study initially integrated UTAUT with MAT to develop the theoretical framework for investigating users’ adoption intentions. Meanwhile, this study originally investigated the antecedents of M-payment adoption under the pandemic situation and indicated that users’ perceptions will be positively influenced when technology’s specific characteristics can benefit a particular situation. Full article
(This article belongs to the Special Issue Artificial Intelligence and Technologies in Pandemic Management)
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Review

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22 pages, 1158 KiB  
Review
Rise of ‘Lonely’ Consumers in the Post-COVID-19 Era: A Synthesised Review on Psychological, Commercial and Social Implications
by Xueqin Wang, Yiik Diew Wong and Kum Fai Yuen
Int. J. Environ. Res. Public Health 2021, 18(2), 404; https://doi.org/10.3390/ijerph18020404 - 6 Jan 2021
Cited by 51 | Viewed by 9317
Abstract
Loneliness is a pervasive problem recognised as a serious social issue, and the prevailing COVID-19 pandemic has exacerbated loneliness to greater prominence and concern. We expect a rise of a massive group of ‘lonely’ consumers who are deeply entrenched in the social isolation [...] Read more.
Loneliness is a pervasive problem recognised as a serious social issue, and the prevailing COVID-19 pandemic has exacerbated loneliness to greater prominence and concern. We expect a rise of a massive group of ‘lonely’ consumers who are deeply entrenched in the social isolation caused by COVID-19. There is an urgent need to revisit the phenomenon of lonely consumers to better prepare academic researchers, public policy makers and commercial managers in the post-COVID-19 era. Thus, this study conducts a synthesised review on past studies of lonely consumers. Based on an inductive analysis of 56 articles, 74 key themes are identified. These key themes are further categorised into five major clusters by way of a co-occurrence network analysis. Respectively, the five clusters address the psychological implications related to the dynamics between nonhuman attachment and consumers’ loneliness, the commercial implications related to the paradoxical motivations of affiliation and self-affirmation in product selection and the dual information processing mechanism in response to advertisement appeals, and the social implications related to consumers’ well-being in an ageing society and the anthropomorphic companionship in a virtual world. A list of research questions is proposed that concludes the review study. Full article
(This article belongs to the Special Issue Artificial Intelligence and Technologies in Pandemic Management)
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