The Experts below are selected from a list of 57465 Experts worldwide ranked by ideXlab platform

Stacy Cannady - One of the best experts on this subject based on the ideXlab platform.

  • COVID-19 what have we learned? The rise of Social Machines and connected devices in pandemic management following the concepts of predictive, preventive and personalized medicine
    EPMA Journal, 2020
    Co-Authors: Petar Radanliev, David Roure, Rob Walton, Max Kleek, Rafael Mantilla Montalvo, Omar Santos, La’treall Maddox, Stacy Cannady
    Abstract:

    Objectives Review, compare and critically assess digital technology responses to the COVID-19 pandemic around the world. The specific point of interest in this research is on predictive, preventive and personalized interoperable digital healthcare solutions. This point is supported by failures from the past, where the separate design of digital health solutions has led to lack of interoperability. Hence, this review paper investigates the integration of predictive, preventive and personalized interoperable digital healthcare systems. The second point of interest is the use of new mass surveillance technologies to feed personal data from health professionals to governments, without any comprehensive studies that determine if such new technologies and data policies would address the pandemic crisis. Method This is a review paper. Two approaches were used: A comprehensive bibliographic review with R statistical methods of the COVID-19 pandemic in PubMed literature and Web of Science Core Collection, supported with Google Scholar search. In addition, a case study review of emerging new approaches in different regions, using medical literature, academic literature, news articles and other reliable data sources. Results Most countries’ digital responses involve big data analytics, integration of national health insurance databases, tracing travel history from individual’s location databases, code scanning and individual’s online reporting. Public responses of mistrust about privacy data misuse differ across countries, depending on the chosen public communication strategy. We propose predictive, preventive and personalized solutions for pandemic management, based on Social Machines and connected devices. Solutions The proposed predictive, preventive and personalized solutions are based on the integration of IoT data, wearable device data, mobile apps data and individual data inputs from registered users, operating as a Social Machine with strong security and privacy protocols. We present solutions that would enable much greater speed in future responses. These solutions are enabled by the Social aspect of human-computer interactions (Social Machines) and the increased connectivity of humans and devices (Internet of Things). Conclusion Inadequate data for risk assessment on speed and urgency of COVID-19, combined with increased globalization of human society, led to the rapid spread of COVID-19. Despite an abundance of digital methods that could be used in slowing or stopping COVID-19 and future pandemics, the world remains unprepared, and lessons have not been learned from previous cases of pandemics. We present a summary of predictive, preventive and personalized digital methods that could be deployed fast to help with the COVID-19 and future pandemics.

  • covid 19 what have we learned the rise of Social Machines and connected devices in pandemic management following the concepts of predictive preventive and personalized medicine
    The Epma Journal, 2020
    Co-Authors: Petar Radanliev, David Roure, Rafael Mantilla Montalvo, Omar Santos, Robert Walton, Max Van Kleek, La Treall Maddox, Stacy Cannady
    Abstract:

    Review, compare and critically assess digital technology responses to the COVID-19 pandemic around the world. The specific point of interest in this research is on predictive, preventive and personalized interoperable digital healthcare solutions. This point is supported by failures from the past, where the separate design of digital health solutions has led to lack of interoperability. Hence, this review paper investigates the integration of predictive, preventive and personalized interoperable digital healthcare systems. The second point of interest is the use of new mass surveillance technologies to feed personal data from health professionals to governments, without any comprehensive studies that determine if such new technologies and data policies would address the pandemic crisis. This is a review paper. Two approaches were used: A comprehensive bibliographic review with R statistical methods of the COVID-19 pandemic in PubMed literature and Web of Science Core Collection, supported with Google Scholar search. In addition, a case study review of emerging new approaches in different regions, using medical literature, academic literature, news articles and other reliable data sources. Most countries’ digital responses involve big data analytics, integration of national health insurance databases, tracing travel history from individual’s location databases, code scanning and individual’s online reporting. Public responses of mistrust about privacy data misuse differ across countries, depending on the chosen public communication strategy. We propose predictive, preventive and personalized solutions for pandemic management, based on Social Machines and connected devices. The proposed predictive, preventive and personalized solutions are based on the integration of IoT data, wearable device data, mobile apps data and individual data inputs from registered users, operating as a Social Machine with strong security and privacy protocols. We present solutions that would enable much greater speed in future responses. These solutions are enabled by the Social aspect of human-computer interactions (Social Machines) and the increased connectivity of humans and devices (Internet of Things). Inadequate data for risk assessment on speed and urgency of COVID-19, combined with increased globalization of human society, led to the rapid spread of COVID-19. Despite an abundance of digital methods that could be used in slowing or stopping COVID-19 and future pandemics, the world remains unprepared, and lessons have not been learned from previous cases of pandemics. We present a summary of predictive, preventive and personalized digital methods that could be deployed fast to help with the COVID-19 and future pandemics.

Petar Radanliev - One of the best experts on this subject based on the ideXlab platform.

  • COVID-19 what have we learned? The rise of Social Machines and connected devices in pandemic management following the concepts of predictive, preventive and personalized medicine
    EPMA Journal, 2020
    Co-Authors: Petar Radanliev, David Roure, Rob Walton, Max Kleek, Rafael Mantilla Montalvo, Omar Santos, La’treall Maddox, Stacy Cannady
    Abstract:

    Objectives Review, compare and critically assess digital technology responses to the COVID-19 pandemic around the world. The specific point of interest in this research is on predictive, preventive and personalized interoperable digital healthcare solutions. This point is supported by failures from the past, where the separate design of digital health solutions has led to lack of interoperability. Hence, this review paper investigates the integration of predictive, preventive and personalized interoperable digital healthcare systems. The second point of interest is the use of new mass surveillance technologies to feed personal data from health professionals to governments, without any comprehensive studies that determine if such new technologies and data policies would address the pandemic crisis. Method This is a review paper. Two approaches were used: A comprehensive bibliographic review with R statistical methods of the COVID-19 pandemic in PubMed literature and Web of Science Core Collection, supported with Google Scholar search. In addition, a case study review of emerging new approaches in different regions, using medical literature, academic literature, news articles and other reliable data sources. Results Most countries’ digital responses involve big data analytics, integration of national health insurance databases, tracing travel history from individual’s location databases, code scanning and individual’s online reporting. Public responses of mistrust about privacy data misuse differ across countries, depending on the chosen public communication strategy. We propose predictive, preventive and personalized solutions for pandemic management, based on Social Machines and connected devices. Solutions The proposed predictive, preventive and personalized solutions are based on the integration of IoT data, wearable device data, mobile apps data and individual data inputs from registered users, operating as a Social Machine with strong security and privacy protocols. We present solutions that would enable much greater speed in future responses. These solutions are enabled by the Social aspect of human-computer interactions (Social Machines) and the increased connectivity of humans and devices (Internet of Things). Conclusion Inadequate data for risk assessment on speed and urgency of COVID-19, combined with increased globalization of human society, led to the rapid spread of COVID-19. Despite an abundance of digital methods that could be used in slowing or stopping COVID-19 and future pandemics, the world remains unprepared, and lessons have not been learned from previous cases of pandemics. We present a summary of predictive, preventive and personalized digital methods that could be deployed fast to help with the COVID-19 and future pandemics.

  • covid 19 what have we learned the rise of Social Machines and connected devices in pandemic management following the concepts of predictive preventive and personalized medicine
    The Epma Journal, 2020
    Co-Authors: Petar Radanliev, David Roure, Rafael Mantilla Montalvo, Omar Santos, Robert Walton, Max Van Kleek, La Treall Maddox, Stacy Cannady
    Abstract:

    Review, compare and critically assess digital technology responses to the COVID-19 pandemic around the world. The specific point of interest in this research is on predictive, preventive and personalized interoperable digital healthcare solutions. This point is supported by failures from the past, where the separate design of digital health solutions has led to lack of interoperability. Hence, this review paper investigates the integration of predictive, preventive and personalized interoperable digital healthcare systems. The second point of interest is the use of new mass surveillance technologies to feed personal data from health professionals to governments, without any comprehensive studies that determine if such new technologies and data policies would address the pandemic crisis. This is a review paper. Two approaches were used: A comprehensive bibliographic review with R statistical methods of the COVID-19 pandemic in PubMed literature and Web of Science Core Collection, supported with Google Scholar search. In addition, a case study review of emerging new approaches in different regions, using medical literature, academic literature, news articles and other reliable data sources. Most countries’ digital responses involve big data analytics, integration of national health insurance databases, tracing travel history from individual’s location databases, code scanning and individual’s online reporting. Public responses of mistrust about privacy data misuse differ across countries, depending on the chosen public communication strategy. We propose predictive, preventive and personalized solutions for pandemic management, based on Social Machines and connected devices. The proposed predictive, preventive and personalized solutions are based on the integration of IoT data, wearable device data, mobile apps data and individual data inputs from registered users, operating as a Social Machine with strong security and privacy protocols. We present solutions that would enable much greater speed in future responses. These solutions are enabled by the Social aspect of human-computer interactions (Social Machines) and the increased connectivity of humans and devices (Internet of Things). Inadequate data for risk assessment on speed and urgency of COVID-19, combined with increased globalization of human society, led to the rapid spread of COVID-19. Despite an abundance of digital methods that could be used in slowing or stopping COVID-19 and future pandemics, the world remains unprepared, and lessons have not been learned from previous cases of pandemics. We present a summary of predictive, preventive and personalized digital methods that could be deployed fast to help with the COVID-19 and future pandemics.

Rafael Mantilla Montalvo - One of the best experts on this subject based on the ideXlab platform.

  • COVID-19 what have we learned? The rise of Social Machines and connected devices in pandemic management following the concepts of predictive, preventive and personalized medicine
    EPMA Journal, 2020
    Co-Authors: Petar Radanliev, David Roure, Rob Walton, Max Kleek, Rafael Mantilla Montalvo, Omar Santos, La’treall Maddox, Stacy Cannady
    Abstract:

    Objectives Review, compare and critically assess digital technology responses to the COVID-19 pandemic around the world. The specific point of interest in this research is on predictive, preventive and personalized interoperable digital healthcare solutions. This point is supported by failures from the past, where the separate design of digital health solutions has led to lack of interoperability. Hence, this review paper investigates the integration of predictive, preventive and personalized interoperable digital healthcare systems. The second point of interest is the use of new mass surveillance technologies to feed personal data from health professionals to governments, without any comprehensive studies that determine if such new technologies and data policies would address the pandemic crisis. Method This is a review paper. Two approaches were used: A comprehensive bibliographic review with R statistical methods of the COVID-19 pandemic in PubMed literature and Web of Science Core Collection, supported with Google Scholar search. In addition, a case study review of emerging new approaches in different regions, using medical literature, academic literature, news articles and other reliable data sources. Results Most countries’ digital responses involve big data analytics, integration of national health insurance databases, tracing travel history from individual’s location databases, code scanning and individual’s online reporting. Public responses of mistrust about privacy data misuse differ across countries, depending on the chosen public communication strategy. We propose predictive, preventive and personalized solutions for pandemic management, based on Social Machines and connected devices. Solutions The proposed predictive, preventive and personalized solutions are based on the integration of IoT data, wearable device data, mobile apps data and individual data inputs from registered users, operating as a Social Machine with strong security and privacy protocols. We present solutions that would enable much greater speed in future responses. These solutions are enabled by the Social aspect of human-computer interactions (Social Machines) and the increased connectivity of humans and devices (Internet of Things). Conclusion Inadequate data for risk assessment on speed and urgency of COVID-19, combined with increased globalization of human society, led to the rapid spread of COVID-19. Despite an abundance of digital methods that could be used in slowing or stopping COVID-19 and future pandemics, the world remains unprepared, and lessons have not been learned from previous cases of pandemics. We present a summary of predictive, preventive and personalized digital methods that could be deployed fast to help with the COVID-19 and future pandemics.

  • covid 19 what have we learned the rise of Social Machines and connected devices in pandemic management following the concepts of predictive preventive and personalized medicine
    The Epma Journal, 2020
    Co-Authors: Petar Radanliev, David Roure, Rafael Mantilla Montalvo, Omar Santos, Robert Walton, Max Van Kleek, La Treall Maddox, Stacy Cannady
    Abstract:

    Review, compare and critically assess digital technology responses to the COVID-19 pandemic around the world. The specific point of interest in this research is on predictive, preventive and personalized interoperable digital healthcare solutions. This point is supported by failures from the past, where the separate design of digital health solutions has led to lack of interoperability. Hence, this review paper investigates the integration of predictive, preventive and personalized interoperable digital healthcare systems. The second point of interest is the use of new mass surveillance technologies to feed personal data from health professionals to governments, without any comprehensive studies that determine if such new technologies and data policies would address the pandemic crisis. This is a review paper. Two approaches were used: A comprehensive bibliographic review with R statistical methods of the COVID-19 pandemic in PubMed literature and Web of Science Core Collection, supported with Google Scholar search. In addition, a case study review of emerging new approaches in different regions, using medical literature, academic literature, news articles and other reliable data sources. Most countries’ digital responses involve big data analytics, integration of national health insurance databases, tracing travel history from individual’s location databases, code scanning and individual’s online reporting. Public responses of mistrust about privacy data misuse differ across countries, depending on the chosen public communication strategy. We propose predictive, preventive and personalized solutions for pandemic management, based on Social Machines and connected devices. The proposed predictive, preventive and personalized solutions are based on the integration of IoT data, wearable device data, mobile apps data and individual data inputs from registered users, operating as a Social Machine with strong security and privacy protocols. We present solutions that would enable much greater speed in future responses. These solutions are enabled by the Social aspect of human-computer interactions (Social Machines) and the increased connectivity of humans and devices (Internet of Things). Inadequate data for risk assessment on speed and urgency of COVID-19, combined with increased globalization of human society, led to the rapid spread of COVID-19. Despite an abundance of digital methods that could be used in slowing or stopping COVID-19 and future pandemics, the world remains unprepared, and lessons have not been learned from previous cases of pandemics. We present a summary of predictive, preventive and personalized digital methods that could be deployed fast to help with the COVID-19 and future pandemics.

Omar Santos - One of the best experts on this subject based on the ideXlab platform.

  • COVID-19 what have we learned? The rise of Social Machines and connected devices in pandemic management following the concepts of predictive, preventive and personalized medicine
    EPMA Journal, 2020
    Co-Authors: Petar Radanliev, David Roure, Rob Walton, Max Kleek, Rafael Mantilla Montalvo, Omar Santos, La’treall Maddox, Stacy Cannady
    Abstract:

    Objectives Review, compare and critically assess digital technology responses to the COVID-19 pandemic around the world. The specific point of interest in this research is on predictive, preventive and personalized interoperable digital healthcare solutions. This point is supported by failures from the past, where the separate design of digital health solutions has led to lack of interoperability. Hence, this review paper investigates the integration of predictive, preventive and personalized interoperable digital healthcare systems. The second point of interest is the use of new mass surveillance technologies to feed personal data from health professionals to governments, without any comprehensive studies that determine if such new technologies and data policies would address the pandemic crisis. Method This is a review paper. Two approaches were used: A comprehensive bibliographic review with R statistical methods of the COVID-19 pandemic in PubMed literature and Web of Science Core Collection, supported with Google Scholar search. In addition, a case study review of emerging new approaches in different regions, using medical literature, academic literature, news articles and other reliable data sources. Results Most countries’ digital responses involve big data analytics, integration of national health insurance databases, tracing travel history from individual’s location databases, code scanning and individual’s online reporting. Public responses of mistrust about privacy data misuse differ across countries, depending on the chosen public communication strategy. We propose predictive, preventive and personalized solutions for pandemic management, based on Social Machines and connected devices. Solutions The proposed predictive, preventive and personalized solutions are based on the integration of IoT data, wearable device data, mobile apps data and individual data inputs from registered users, operating as a Social Machine with strong security and privacy protocols. We present solutions that would enable much greater speed in future responses. These solutions are enabled by the Social aspect of human-computer interactions (Social Machines) and the increased connectivity of humans and devices (Internet of Things). Conclusion Inadequate data for risk assessment on speed and urgency of COVID-19, combined with increased globalization of human society, led to the rapid spread of COVID-19. Despite an abundance of digital methods that could be used in slowing or stopping COVID-19 and future pandemics, the world remains unprepared, and lessons have not been learned from previous cases of pandemics. We present a summary of predictive, preventive and personalized digital methods that could be deployed fast to help with the COVID-19 and future pandemics.

  • covid 19 what have we learned the rise of Social Machines and connected devices in pandemic management following the concepts of predictive preventive and personalized medicine
    The Epma Journal, 2020
    Co-Authors: Petar Radanliev, David Roure, Rafael Mantilla Montalvo, Omar Santos, Robert Walton, Max Van Kleek, La Treall Maddox, Stacy Cannady
    Abstract:

    Review, compare and critically assess digital technology responses to the COVID-19 pandemic around the world. The specific point of interest in this research is on predictive, preventive and personalized interoperable digital healthcare solutions. This point is supported by failures from the past, where the separate design of digital health solutions has led to lack of interoperability. Hence, this review paper investigates the integration of predictive, preventive and personalized interoperable digital healthcare systems. The second point of interest is the use of new mass surveillance technologies to feed personal data from health professionals to governments, without any comprehensive studies that determine if such new technologies and data policies would address the pandemic crisis. This is a review paper. Two approaches were used: A comprehensive bibliographic review with R statistical methods of the COVID-19 pandemic in PubMed literature and Web of Science Core Collection, supported with Google Scholar search. In addition, a case study review of emerging new approaches in different regions, using medical literature, academic literature, news articles and other reliable data sources. Most countries’ digital responses involve big data analytics, integration of national health insurance databases, tracing travel history from individual’s location databases, code scanning and individual’s online reporting. Public responses of mistrust about privacy data misuse differ across countries, depending on the chosen public communication strategy. We propose predictive, preventive and personalized solutions for pandemic management, based on Social Machines and connected devices. The proposed predictive, preventive and personalized solutions are based on the integration of IoT data, wearable device data, mobile apps data and individual data inputs from registered users, operating as a Social Machine with strong security and privacy protocols. We present solutions that would enable much greater speed in future responses. These solutions are enabled by the Social aspect of human-computer interactions (Social Machines) and the increased connectivity of humans and devices (Internet of Things). Inadequate data for risk assessment on speed and urgency of COVID-19, combined with increased globalization of human society, led to the rapid spread of COVID-19. Despite an abundance of digital methods that could be used in slowing or stopping COVID-19 and future pandemics, the world remains unprepared, and lessons have not been learned from previous cases of pandemics. We present a summary of predictive, preventive and personalized digital methods that could be deployed fast to help with the COVID-19 and future pandemics.

David Roure - One of the best experts on this subject based on the ideXlab platform.

  • COVID-19 what have we learned? The rise of Social Machines and connected devices in pandemic management following the concepts of predictive, preventive and personalized medicine
    EPMA Journal, 2020
    Co-Authors: Petar Radanliev, David Roure, Rob Walton, Max Kleek, Rafael Mantilla Montalvo, Omar Santos, La’treall Maddox, Stacy Cannady
    Abstract:

    Objectives Review, compare and critically assess digital technology responses to the COVID-19 pandemic around the world. The specific point of interest in this research is on predictive, preventive and personalized interoperable digital healthcare solutions. This point is supported by failures from the past, where the separate design of digital health solutions has led to lack of interoperability. Hence, this review paper investigates the integration of predictive, preventive and personalized interoperable digital healthcare systems. The second point of interest is the use of new mass surveillance technologies to feed personal data from health professionals to governments, without any comprehensive studies that determine if such new technologies and data policies would address the pandemic crisis. Method This is a review paper. Two approaches were used: A comprehensive bibliographic review with R statistical methods of the COVID-19 pandemic in PubMed literature and Web of Science Core Collection, supported with Google Scholar search. In addition, a case study review of emerging new approaches in different regions, using medical literature, academic literature, news articles and other reliable data sources. Results Most countries’ digital responses involve big data analytics, integration of national health insurance databases, tracing travel history from individual’s location databases, code scanning and individual’s online reporting. Public responses of mistrust about privacy data misuse differ across countries, depending on the chosen public communication strategy. We propose predictive, preventive and personalized solutions for pandemic management, based on Social Machines and connected devices. Solutions The proposed predictive, preventive and personalized solutions are based on the integration of IoT data, wearable device data, mobile apps data and individual data inputs from registered users, operating as a Social Machine with strong security and privacy protocols. We present solutions that would enable much greater speed in future responses. These solutions are enabled by the Social aspect of human-computer interactions (Social Machines) and the increased connectivity of humans and devices (Internet of Things). Conclusion Inadequate data for risk assessment on speed and urgency of COVID-19, combined with increased globalization of human society, led to the rapid spread of COVID-19. Despite an abundance of digital methods that could be used in slowing or stopping COVID-19 and future pandemics, the world remains unprepared, and lessons have not been learned from previous cases of pandemics. We present a summary of predictive, preventive and personalized digital methods that could be deployed fast to help with the COVID-19 and future pandemics.

  • covid 19 what have we learned the rise of Social Machines and connected devices in pandemic management following the concepts of predictive preventive and personalized medicine
    The Epma Journal, 2020
    Co-Authors: Petar Radanliev, David Roure, Rafael Mantilla Montalvo, Omar Santos, Robert Walton, Max Van Kleek, La Treall Maddox, Stacy Cannady
    Abstract:

    Review, compare and critically assess digital technology responses to the COVID-19 pandemic around the world. The specific point of interest in this research is on predictive, preventive and personalized interoperable digital healthcare solutions. This point is supported by failures from the past, where the separate design of digital health solutions has led to lack of interoperability. Hence, this review paper investigates the integration of predictive, preventive and personalized interoperable digital healthcare systems. The second point of interest is the use of new mass surveillance technologies to feed personal data from health professionals to governments, without any comprehensive studies that determine if such new technologies and data policies would address the pandemic crisis. This is a review paper. Two approaches were used: A comprehensive bibliographic review with R statistical methods of the COVID-19 pandemic in PubMed literature and Web of Science Core Collection, supported with Google Scholar search. In addition, a case study review of emerging new approaches in different regions, using medical literature, academic literature, news articles and other reliable data sources. Most countries’ digital responses involve big data analytics, integration of national health insurance databases, tracing travel history from individual’s location databases, code scanning and individual’s online reporting. Public responses of mistrust about privacy data misuse differ across countries, depending on the chosen public communication strategy. We propose predictive, preventive and personalized solutions for pandemic management, based on Social Machines and connected devices. The proposed predictive, preventive and personalized solutions are based on the integration of IoT data, wearable device data, mobile apps data and individual data inputs from registered users, operating as a Social Machine with strong security and privacy protocols. We present solutions that would enable much greater speed in future responses. These solutions are enabled by the Social aspect of human-computer interactions (Social Machines) and the increased connectivity of humans and devices (Internet of Things). Inadequate data for risk assessment on speed and urgency of COVID-19, combined with increased globalization of human society, led to the rapid spread of COVID-19. Despite an abundance of digital methods that could be used in slowing or stopping COVID-19 and future pandemics, the world remains unprepared, and lessons have not been learned from previous cases of pandemics. We present a summary of predictive, preventive and personalized digital methods that could be deployed fast to help with the COVID-19 and future pandemics.

  • archetypal narratives in Social Machines approaching Sociality through prosopography
    Web Science, 2015
    Co-Authors: S Tarte, Pip Willcox, Hugh Glaser, David Roure
    Abstract:

    Introducing Social Machines as web-enabled entities integrating Social energies and computational powers into a socio-technical system (whether purposeful or not) where Social dynamics animate communities, this paper proposes a theoretical framework in which to observe them. Attempting to strike a balance between the roles of humans and non-humans, and aware of the difficulties that this heterogeneity presents, we propose to approach the questions of capturing the Social dynamics of a Social Machine through prosopography. Prosopography is a method, used in particular by historians, that allows to systematically study a collection of biographies, be they of persons, artefacts, infrastructures of groups thereof. Systematization is achieved through designing an appropriate questionnaire to gather homogeneous data across the biographies. Our questionnaire design relies on the identification of five archetypal elements in biographical narratives. Illustrating our method with three examples, we demonstrate how our archetypal narratives have the potential to describe at least aspects of the Social dynamics in Social Machines.