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      Information Technology Solutions, Challenges, and Suggestions for Tackling the COVID-19 Pandemic

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          Abstract

          Various technology innovations and applications have been developed to fight the coronavirus pandemic. The pandemic also has implications for the design, development, and use of technologies. There is an urgent need for a greater understanding of what roles information systems and technology researchers can play in this global pandemic. This paper examines emerging technologies used to mitigate the threats of COVID-19 and relevant challenges related to technology design, development, and use. It also provides insights and suggestions into how information systems and technology scholars can help fight the COVID-19 pandemic. This paper helps promote future research and technology development to produce better solutions for tackling the COVID-19 pandemic and future pandemics.

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          Using social and behavioural science to support COVID-19 pandemic response

          Jay Bavel, Katherine Baicker, Paulo S Boggio … (2020)
          The COVID-19 pandemic represents a massive global health crisis. Because the crisis requires large-scale behaviour change and places significant psychological burdens on individuals, insights from the social and behavioural sciences can be used to help align human behaviour with the recommendations of epidemiologists and public health experts. Here we discuss evidence from a selection of research topics relevant to pandemics, including work on navigating threats, social and cultural influences on behaviour, science communication, moral decision-making, leadership, and stress and coping. In each section, we note the nature and quality of prior research, including uncertainty and unsettled issues. We identify several insights for effective response to the COVID-19 pandemic and highlight important gaps researchers should move quickly to fill in the coming weeks and months.
          Article 0 comments Cited 1801 times – based on 0 reviews     Review now
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            Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing

            Luca Ferretti, Chris Wymant, Michelle L. Kendall … (2020)
            The newly emergent human virus SARS-CoV-2 is resulting in high fatality rates and incapacitated health systems. Preventing further transmission is a priority. We analyzed key parameters of epidemic spread to estimate the contribution of different transmission routes and determine requirements for case isolation and contact-tracing needed to stop the epidemic. We conclude that viral spread is too fast to be contained by manual contact tracing, but could be controlled if this process was faster, more efficient and happened at scale. A contact-tracing App which builds a memory of proximity contacts and immediately notifies contacts of positive cases can achieve epidemic control if used by enough people. By targeting recommendations to only those at risk, epidemics could be contained without need for mass quarantines (‘lock-downs’) that are harmful to society. We discuss the ethical requirements for an intervention of this kind.
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              Automated detection of COVID-19 cases using deep neural networks with X-ray images

              Tulin Oztürk, Muhammed Talo, Eylul Yildirim … (2020)
              The novel coronavirus 2019 (COVID-2019), which first appeared in Wuhan city of China in December 2019, spread rapidly around the world and became a pandemic. It has caused a devastating effect on both daily lives, public health, and the global economy. It is critical to detect the positive cases as early as possible so as to prevent the further spread of this epidemic and to quickly treat affected patients. The need for auxiliary diagnostic tools has increased as there are no accurate automated toolkits available. Recent findings obtained using radiology imaging techniques suggest that such images contain salient information about the COVID-19 virus. Application of advanced artificial intelligence (AI) techniques coupled with radiological imaging can be helpful for the accurate detection of this disease, and can also be assistive to overcome the problem of a lack of specialized physicians in remote villages. In this study, a new model for automatic COVID-19 detection using raw chest X-ray images is presented. The proposed model is developed to provide accurate diagnostics for binary classification (COVID vs. No-Findings) and multi-class classification (COVID vs. No-Findings vs. Pneumonia). Our model produced a classification accuracy of 98.08% for binary classes and 87.02% for multi-class cases. The DarkNet model was used in our study as a classifier for the you only look once (YOLO) real time object detection system. We implemented 17 convolutional layers and introduced different filtering on each layer. Our model (available at (https://github.com/muhammedtalo/COVID-19)) can be employed to assist radiologists in validating their initial screening, and can also be employed via cloud to immediately screen patients.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Int J Inf Manage
                Journal ID (iso-abbrev): Int J Inf Manage
                Title: International Journal of Information Management
                Publisher: Elsevier Ltd.
                ISSN (Print): 0268-4012
                ISSN (Electronic): 0143-6236
                Publication date PMC-release: 9 December 2020
                Publication date (Electronic): 9 December 2020
                Electronic Location Identifier: 102287
                Affiliations
                [a ]Department of Information Technology & Decision Sciences, Old Dominion University, Norfolk, VA, 23529, USA
                [b ]Journal of Global Information Management, University of North Florida, Jacksonville, FL 32224, USA
                [c ]Department of Information Technology & Decision Sciences, Old Dominion University, Norfolk, VA, 23529, USA
                Author notes
                [* ]Corresponding author.
                Article
                Publisher Item ID: S0268-4012(20)31486-9 Publisher ID: 102287
                DOI: 10.1016/j.ijinfomgt.2020.102287
                PMC ID: 7724285
                PubMed ID: 33318721
                SO-VID: f2295663-e348-41d0-9e8c-c391336ec8bf
                Copyright © © 2020 Elsevier Ltd. All rights reserved.
                License:

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                Date received : 13 June 2020
                Date revision received : 22 November 2020
                Date accepted : 23 November 2020
                Categories
                Subject: Opinion Paper

                Keywords: covid-19,artificial intelligence,big data,blockchain,information systems,human behavior,digital divide,system integration
                Data availability:
                Keywords: covid-19, artificial intelligence, big data, blockchain, information systems, human behavior, digital divide, system integration

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