ICT Intervention in the Containment of the Pandemic Spread of COVID-19: An Exploratory Study

• URL: http://arxiv.org/abs/2004.09888v1
• Date: Tue, 21 Apr 2020 10:34:14 GMT
• Title: ICT Intervention in the Containment of the Pandemic Spread of COVID-19: An Exploratory Study
• Authors: Akib Zaman, Muhammad Nazrul Islam, Tarannum Zaki, and Mohammad Sajjad Hossain
• Abstract summary: Review study revealed ICT interventions that include websites and dashboards, mobile applications, robotics and drones, artificial intelligence (AI), data analytic, wearable and sensor technology, social media and learning tools, and interactive voice response (IVR) Focus Group Discussion (FGD) was replicated with 22 participants and explored the possible strengths, weaknesses, opportunities, and threats (SWOT) of deploying such technologies to fight against the COVID-19 pandemic.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The objective of this article is to explore the Information and Communication Technology (ICT) interventions and its strengths, weaknesses, opportunities and threats for the containment of the pandemic spread of novel Coronavirus. The research adopted a qualitative research approach, while the study data were collected through online content review and Focus Group Discussion (FGD). Starting with a preliminary set of about 1200 electronic resources or contents, 56 were selected for review study, applying an inclusion and exclusion criteria. The review study revealed ICT interventions that include websites and dashboards, mobile applications, robotics and drones, artificial intelligence (AI), data analytic, wearable and sensor technology, social media and learning tools, and interactive voice response (IVR) as well as explored their respective usages to combat the pandemic spread of COVID-19. Later, the FGD was replicated with 22 participants and explored the possible strengths, weaknesses, opportunities, and threats (SWOT) of deploying such technologies to fight against the COVID-19 pandemic. This research not only explores the exiting status of ICT interventions to fight with the COVID-19 pandemic but also provides a number of implications for the government, practitioners, doctors, policymakers and researchers for the effective utilization of the existing ICT interventions and for the future potential research and technological development to the containment of the pandemic spread of COVID-19 and future pandemics.

Related papers

• COVIDx-US -- An open-access benchmark dataset of ultrasound imaging data for AI-driven COVID-19 analytics [116.6248556979572]
  COVIDx-US is an open-access benchmark dataset of COVID-19 related ultrasound imaging data. It consists of 93 lung ultrasound videos and 10,774 processed images of patients infected with SARS-CoV-2 pneumonia, non-SARS-CoV-2 pneumonia, as well as healthy control cases.
  arXiv  Detail & Related papers  (2021-03-18T03:31:33Z)
• Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions [17.01346748101477]
  This paper presents a survey of AI methods being used in various applications in the fight against the COVID-19 outbreak. We touch on areas where AI plays as an essential component, from medical image processing, data analytics, text mining and natural language processing. Research directions on exploring the potential of AI and enhancing its capability and power in the pandemic battle are thoroughly discussed.
  arXiv  Detail & Related papers  (2020-07-30T11:11:55Z)
• Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing [66.63200823918429]
  The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world. We used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research. Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues.
  arXiv  Detail & Related papers  (2020-07-22T18:02:39Z)
• A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19 [75.84689958489724]
  The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak. As a powerful tool against COVID-19, artificial intelligence (AI) technologies are widely used in combating this pandemic. This survey presents medical and AI researchers with a comprehensive view of the existing and potential applications of AI technology in combating COVID-19.
  arXiv  Detail & Related papers  (2020-07-04T22:48:15Z)
• Visualising COVID-19 Research [4.664989082015335]
  We develop a novel automated theme-based visualisation method. It combines advanced data modelling of large corpora, information mapping and trend analysis. It provides a top-down and bottom-up browsing and search interface for quick discovery of topics and research resources.
  arXiv  Detail & Related papers  (2020-05-13T15:45:14Z)
• When Wireless Communication Faces COVID-19: Combating the Pandemic and Saving the Economy [93.08344893433639]
  The year 2020 is experiencing a global health and economic crisis due to the COVID-19 pandemic. Countries across the world are using digital technologies to fight this global crisis. We show how these technologies are helping to combat this pandemic, including monitoring of the virus spread. We discuss the challenges faced by wireless technologies, including privacy, security, and misinformation.
  arXiv  Detail & Related papers  (2020-05-12T12:27:29Z)
• Computer Vision For COVID-19 Control: A Survey [10.032488704661903]
  The COVID-19 pandemic has triggered an urgent need to contribute to the fight against an immense threat to the human population. Computer Vision, as a subfield of Artificial Intelligence, has enjoyed recent success in solving various complex problems in health care. This survey paper is intended to provide a preliminary review of the available literature on the computer vision efforts against COVID-19 pandemic.
  arXiv  Detail & Related papers  (2020-04-15T05:43:52Z)
• Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19 [71.41929762209328]
  The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world. Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19. The recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists.
  arXiv  Detail & Related papers  (2020-04-06T15:21:34Z)
• Mapping the Landscape of Artificial Intelligence Applications against COVID-19 [59.30734371401316]
  COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization. We present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence to tackle many aspects of the COVID-19 crisis.
  arXiv  Detail & Related papers  (2020-03-25T12:30:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.