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Barnett NP, Sokolovsky AW, Meisel MK, Forkus SR, Jackson KM. A Bluetooth-Based Smartphone App for Detecting Peer Proximity: Protocol for Evaluating Functionality and Validity. JMIR Res Protoc 2024; 13:e50241. [PMID: 38578672 PMCID: PMC11031693 DOI: 10.2196/50241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 12/12/2023] [Accepted: 12/20/2023] [Indexed: 04/06/2024] Open
Abstract
BACKGROUND While ecological momentary assessment (EMA) is commonly used to study social contexts and social influence in the real world, EMA almost exclusively relies on participant self-report of present circumstances, including the proximity to influential peers. There is the potential for developing a proximity sensing approach that uses small Bluetooth beacons and smartphone-based detection and data collection to collect information about interactions between individuals passively in real time. OBJECTIVE This paper aims to describe the methods for evaluating the functionality and validity of a Bluetooth-based beacon and a smartphone app to identify when ≥2 individuals are physically proximal. METHODS We will recruit 20 participants aged 18 to 29 years with Android smartphones to complete a 3-week study during which beacon detection and self-report data will be collected using a smartphone app (MEI Research). Using an interviewer-administered social network interview, participants will identify up to 3 peers of the same age who are influential on health behavior (alcohol use in this study). These peers will be asked to carry a Bluetooth beacon (Kontakt asset tag) for the duration of the study; each beacon has a unique ID that, when detected, will be recorded by the app on the participant's phone. Participants will be prompted to respond to EMA surveys (signal-contingent reports) when a peer beacon encounter meets our criteria and randomly 3 times daily (random reports) and every morning (morning reports) to collect information about the presence of peers. In all reports, the individualized list of peers will be presented to participants, followed by questions about peer and participant behavior, including alcohol use. Data from multiple app data sets, including beacon encounter specifications, notification, and app logs, participant EMA self-reports and postparticipation interviews, and peer surveys, will be used to evaluate project goals. We will examine the functionality of the technology, including the stability of the app (eg, app crashes and issues opening the app), beacon-to-app detection (ie, does the app detect proximal beacons?), and beacon encounter notification when encounter criteria are met. The validity of the technology will be defined as the concordance between passive detection of peers via beacon-to-app communication and the participant's EMA report of peer presence. Disagreement between the beacon and self-report data (ie, false negatives and false positives) will be investigated in multiple ways (ie, to determine if the reason was technology-related or participant compliance-related) using encounter data and information collected from participants and peers. RESULTS Participant recruitment began in February 2023, and enrollment was completed in December 2023. Results will be reported in 2025. CONCLUSIONS This Bluetooth-based technology has important applications and clinical implications for various health behaviors, including the potential for just-in-time adaptive interventions that target high-risk behavior in real time. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/50241.
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Affiliation(s)
- Nancy P Barnett
- Department of Behavioral and Social Sciences, Brown University, Providence, RI, United States
| | - Alexander W Sokolovsky
- Department of Behavioral and Social Sciences, Brown University, Providence, RI, United States
| | - Matthew K Meisel
- Department of Behavioral and Social Sciences, Brown University, Providence, RI, United States
| | - Shannon R Forkus
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, United States
| | - Kristina M Jackson
- Department of Behavioral and Social Sciences, Brown University, Providence, RI, United States
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Singh K, Kaur N, Prabhu A. Combating COVID-19 Crisis using Artificial Intelligence (AI) Based Approach: Systematic Review. Curr Top Med Chem 2024; 24:737-753. [PMID: 38318824 DOI: 10.2174/0115680266282179240124072121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 12/19/2023] [Accepted: 12/27/2023] [Indexed: 02/07/2024]
Abstract
BACKGROUND SARS-CoV-2, the unique coronavirus that causes COVID-19, has wreaked damage around the globe, with victims displaying a wide range of difficulties that have encouraged medical professionals to look for innovative technical solutions and therapeutic approaches. Artificial intelligence-based methods have contributed a significant part in tackling complicated issues, and some institutions have been quick to embrace and tailor these solutions in response to the COVID-19 pandemic's obstacles. Here, in this review article, we have covered a few DL techniques for COVID-19 detection and diagnosis, as well as ML techniques for COVID-19 identification, severity classification, vaccine and drug development, mortality rate prediction, contact tracing, risk assessment, and public distancing. This review illustrates the overall impact of AI/ML tools on tackling and managing the outbreak. PURPOSE The focus of this research was to undertake a thorough evaluation of the literature on the part of Artificial Intelligence (AI) as a complete and efficient solution in the battle against the COVID-19 epidemic in the domains of detection and diagnostics of disease, mortality prediction and vaccine as well as drug development. METHODS A comprehensive exploration of PubMed, Web of Science, and Science Direct was conducted using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) regulations to find all possibly suitable papers conducted and made publicly available between December 1, 2019, and August 2023. COVID-19, along with AI-specific words, was used to create the query syntax. RESULTS During the period covered by the search strategy, 961 articles were published and released online. Out of these, a total of 135 papers were chosen for additional investigation. Mortality rate prediction, early detection and diagnosis, vaccine as well as drug development, and lastly, incorporation of AI for supervising and controlling the COVID-19 pandemic were the four main topics focused entirely on AI applications used to tackle the COVID-19 crisis. Out of 135, 60 research papers focused on the detection and diagnosis of the COVID-19 pandemic. Next, 19 of the 135 studies applied a machine-learning approach for mortality rate prediction. Another 22 research publications emphasized the vaccine as well as drug development. Finally, the remaining studies were concentrated on controlling the COVID-19 pandemic by applying AI AI-based approach to it. CONCLUSION We compiled papers from the available COVID-19 literature that used AI-based methodologies to impart insights into various COVID-19 topics in this comprehensive study. Our results suggest crucial characteristics, data types, and COVID-19 tools that can aid in medical and translational research facilitation.
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Affiliation(s)
- Kavya Singh
- Department of Biotechnology, Banasthali University, Banasthali Vidyapith, Banasthali, 304022, Rajasthan, India
| | - Navjeet Kaur
- Department of Chemistry & Division of Research and Development, Lovely Professional University, Phagwara, 144411, Punjab, India
| | - Ashish Prabhu
- Biotechnology Department, NIT Warangal, Warangal, 506004, Telangana, India
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Ali Y, Khan HU. A Survey on harnessing the Applications of Mobile Computing in Healthcare during the COVID-19 Pandemic: Challenges and Solutions. COMPUTER NETWORKS 2023; 224:109605. [PMID: 36776582 PMCID: PMC9894776 DOI: 10.1016/j.comnet.2023.109605] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 11/17/2022] [Accepted: 01/31/2023] [Indexed: 06/18/2023]
Abstract
The COVID-19 pandemic ravaged almost every walk of life but it triggered many challenges for the healthcare system, globally. Different cutting-edge technologies such as Internet of things (IoT), machine learning, Virtual Reality (VR), Big data, Blockchain etc. have been adopted to cope with this menace. In this regard, various surveys have been conducted to highlight the importance of these technologies. However, among these technologies, the role of mobile computing is of paramount importance which is not found in the existing literature. Hence, this survey in mainly targeted to highlight the significant role of mobile computing in alleviating the impacts of COVID-19 in healthcare sector. The major applications of mobile computing such as software-based solutions, hardware-based solutions and wireless communication-based support for diagnosis, prevention, self-symptom reporting, contact tracing, social distancing, telemedicine and treatment related to coronavirus are discussed in detailed and comprehensive fashion. A state-of-the-art work is presented to identify the challenges along with possible solutions in adoption of mobile computing with respect to COVID-19 pandemic. Hopefully, this research will help the researchers, policymakers and healthcare professionals to understand the current research gaps and future research directions in this domain. To the best level of our knowledge, this is the first survey of its type to address the COVID-19 pandemic by exploring the holistic contribution of mobile computing technologies in healthcare area.
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Affiliation(s)
- Yasir Ali
- Higher Education Department, Khyber Pakhtunkhwa, Government Degree College Kotha Swabi, KP, Pakistan
- Higher Education Department, Shahzeb Shaheed Government Degree College Razzar, Swabi, KP, Pakistan
| | - Habib Ullah Khan
- Accounting and Information, College of Business and Economics, Qatar University, Doha Qatar
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Yang L, Iwami M, Chen Y, Wu M, van Dam KH. Computational decision-support tools for urban design to improve resilience against COVID-19 and other infectious diseases: A systematic review. PROGRESS IN PLANNING 2023; 168:100657. [PMID: 35280114 PMCID: PMC8904142 DOI: 10.1016/j.progress.2022.100657] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
The COVID-19 pandemic highlighted the need for decision-support tools to help cities become more resilient to infectious diseases. Through urban design and planning, non-pharmaceutical interventions can be enabled, impelling behaviour change and facilitating the construction of lower risk buildings and public spaces. Computational tools, including computer simulation, statistical models, and artificial intelligence, have been used to support responses to the current pandemic as well as to the spread of previous infectious diseases. Our multidisciplinary research group systematically reviewed state-of-the-art literature to propose a toolkit that employs computational modelling for various interventions and urban design processes. We selected 109 out of 8,737 studies retrieved from databases and analysed them based on the pathogen type, transmission mode and phase, design intervention and process, as well as modelling methodology (method, goal, motivation, focus, and indication to urban design). We also explored the relationship between infectious disease and urban design, as well as computational modelling support, including specific models and parameters. The proposed toolkit will help designers, planners, and computer modellers to select relevant approaches for evaluating design decisions depending on the target disease, geographic context, design stages, and spatial and temporal scales. The findings herein can be regarded as stand-alone tools, particularly for fighting against COVID-19, or be incorporated into broader frameworks to help cities become more resilient to future disasters.
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Affiliation(s)
- Liu Yang
- School of Architecture, Southeast University, Nanjing, China
- Research Center of Urban Design, Southeast University, Nanjing, China
| | - Michiyo Iwami
- Department of Infectious Disease, Faculty of Medicine, Imperial College London, UK
| | - Yishan Chen
- Architecture and Urban Design Research Center, China IPPR International Engineering CO., LTD, Beijing, China
| | - Mingbo Wu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Koen H van Dam
- Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, UK
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Shen J, Ghatti S, Levkov NR, Shen H, Sen T, Rheuban K, Enfield K, Facteau NR, Engel G, Dowdell K. A survey of COVID-19 detection and prediction approaches using mobile devices, AI, and telemedicine. Front Artif Intell 2022; 5:1034732. [PMID: 36530356 PMCID: PMC9755752 DOI: 10.3389/frai.2022.1034732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 11/02/2022] [Indexed: 09/19/2023] Open
Abstract
Since 2019, the COVID-19 pandemic has had an extremely high impact on all facets of the society and will potentially have an everlasting impact for years to come. In response to this, over the past years, there have been a significant number of research efforts on exploring approaches to combat COVID-19. In this paper, we present a survey of the current research efforts on using mobile Internet of Thing (IoT) devices, Artificial Intelligence (AI), and telemedicine for COVID-19 detection and prediction. We first present the background and then present current research in this field. Specifically, we present the research on COVID-19 monitoring and detection, contact tracing, machine learning based approaches, telemedicine, and security. We finally discuss the challenges and the future work that lay ahead in this field before concluding this paper.
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Affiliation(s)
- John Shen
- Department of Computer Science, University of Virginia, Charlottesville, VA, United States
| | - Siddharth Ghatti
- Department of Computer Science, University of Virginia, Charlottesville, VA, United States
| | - Nate Ryan Levkov
- Department of Computer Science, University of Virginia, Charlottesville, VA, United States
| | - Haiying Shen
- Department of Computer Science, University of Virginia, Charlottesville, VA, United States
| | - Tanmoy Sen
- Department of Computer Science, University of Virginia, Charlottesville, VA, United States
| | - Karen Rheuban
- School of Medicine, University of Virginia, Charlottesville, VA, United States
| | - Kyle Enfield
- School of Medicine, University of Virginia, Charlottesville, VA, United States
| | - Nikki Reyer Facteau
- University of Virginia (UVA) Health System, University of Virginia, Charlottesville, VA, United States
| | - Gina Engel
- School of Medicine, University of Virginia, Charlottesville, VA, United States
| | - Kim Dowdell
- School of Medicine, University of Virginia, Charlottesville, VA, United States
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Vahedi A, Moghaddasi H, Asadi F, Hosseini A, Nazemi E. Applications, features and key indicators for the development of Covid-19 dashboards: A systematic review study. INFORMATICS IN MEDICINE UNLOCKED 2022; 30:100910. [PMID: 35342788 PMCID: PMC8933049 DOI: 10.1016/j.imu.2022.100910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 03/05/2022] [Accepted: 03/06/2022] [Indexed: 11/25/2022] Open
Abstract
Introduction Interactive dashboards can collect data from various information sources and be used nationally and internationally. These information systems have played an important role in managing and controlling epidemic diseases, especially Covid-19. This study aimed to identify the applications, features, and key indicators of advanced dashboards in Covid-19. Method The present article is a systematic review study that searched the PubMed, Scopus, and ISI web of sciences databases in 2021 by combining the relevant keywords. After applying the inclusion and exclusion criteria and selecting articles, data collection was prepared using a data collection form. Data analysis was performed using the content analysis method. Results Out of 171 articles retrieved, 19 were included in the study for review by applying inclusion and exclusion criteria in the first stage. The most important data sources for the studied dashboards included general online, national, and hospital databases. Monitoring and tracking in the target community and resource management (hospital and public) are the most important issues in Covid-19 dashboards. The study showed that KPIs in 5 main categories of indicators related to hospital beds, clinical data in the hospital, diagnostic and therapeutic measures of hospitals, epidemiological data at the level community, and follow-up indicators of Covid-19 studies were worldwide. Conclusion Considering the technological advances at the world level and the large amount of data produced, one of the effective solutions for managing and controlling epidemic and pandemic conditions and diseases is the rapid development of interactive dashboards; Therefore, it is suggested that health officials and policymakers, in addition to developing and updating the existing dashboards in the field of Covid-19, developing the dashboard immediately in case of similar conditions.
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Shah H, Shah S, Tanwar S, Gupta R, Kumar N. Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends. MULTIMEDIA SYSTEMS 2022; 28:1189-1222. [PMID: 34276140 PMCID: PMC8275905 DOI: 10.1007/s00530-021-00818-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 05/29/2021] [Indexed: 05/05/2023]
Abstract
The COVID-19 pandemic is rapidly spreading across the globe and infected millions of people that take hundreds of thousands of lives. Over the years, the role of Artificial intelligence (AI) has been on the rise as its algorithms are getting more and more accurate and it is thought that its role in strengthening the existing healthcare system will be the most profound. Moreover, the pandemic brought an opportunity to showcase AI and healthcare integration potentials as the current infrastructure worldwide is overwhelmed and crumbling. Due to AI's flexibility and adaptability, it can be used as a tool to tackle COVID-19. Motivated by these facts, in this paper, we surveyed how the AI techniques can handle the COVID-19 pandemic situation and present the merits and demerits of these techniques. This paper presents a comprehensive end-to-end review of all the AI-techniques that can be used to tackle all areas of the pandemic. Further, we systematically discuss the issues of the COVID-19, and based on the literature review, we suggest their potential countermeasures using AI techniques. In the end, we analyze various open research issues and challenges associated with integrating the AI techniques in the COVID-19.
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Affiliation(s)
- Het Shah
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India
| | - Saiyam Shah
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India
| | - Sudeep Tanwar
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India
| | - Rajesh Gupta
- Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India
| | - Neeraj Kumar
- Department of Computer Science Engineering, Thapar Institute of Engineering and Technology, Deemed to be University, Patiala, India
- School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand India
- King Abdul Aziz University, Jeddah, Saudi Arabia
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Kaur A, Chopra M, Bhushan M, Gupta S, Kumari P H, Sivagurunathan N, Shukla N, Rajagopal S, Bhalothia P, Sharma P, Naravula J, Suravajhala R, Gupta A, Abbasi BA, Goswami P, Singh H, Narang R, Polavarapu R, Medicherla KM, Valadi J, Kumar S A, Chaubey G, Singh KK, Bandapalli OR, Kavi Kishor PB, Suravajhala P. The Omic Insights on Unfolding Saga of COVID-19. Front Immunol 2021; 12:724914. [PMID: 34745097 PMCID: PMC8564481 DOI: 10.3389/fimmu.2021.724914] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 09/27/2021] [Indexed: 12/15/2022] Open
Abstract
The year 2019 has seen an emergence of the novel coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing coronavirus disease of 2019 (COVID-19). Since the onset of the pandemic, biological and interdisciplinary research is being carried out across the world at a rapid pace to beat the pandemic. There is an increased need to comprehensively understand various aspects of the virus from detection to treatment options including drugs and vaccines for effective global management of the disease. In this review, we summarize the salient findings pertaining to SARS-CoV-2 biology, including symptoms, hosts, epidemiology, SARS-CoV-2 genome, and its emerging variants, viral diagnostics, host-pathogen interactions, alternative antiviral strategies and application of machine learning heuristics and artificial intelligence for effective management of COVID-19 and future pandemics.
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Affiliation(s)
- Arvinpreet Kaur
- Department of Bioinformatics, Hans Raj Mahila Maha Vidyalaya, Punjab, India
- Bioclues.org, Hyderabad, India
| | - Mehak Chopra
- Centre for Bioinformatics, School of Life Sciences, Pondicherry University, Puducherry, India
| | - Mahak Bhushan
- Department of Biological Sciences, Indian Institute of Science Education and Research, Kolkata, India
| | - Sonal Gupta
- Bioclues.org, Hyderabad, India
- Department of Biotechnology and Bioinformatics, Birla Institute of Scientific Research, Jaipur, India
| | | | - Narmadhaa Sivagurunathan
- Department of Biotechnology and Bioinformatics, Birla Institute of Scientific Research, Jaipur, India
| | - Nidhi Shukla
- Bioclues.org, Hyderabad, India
- Department of Biotechnology and Bioinformatics, Birla Institute of Scientific Research, Jaipur, India
| | - Shalini Rajagopal
- Vignan’s Foundation for Science, Technology & Research (Deemed to be University), Guntur, India
| | - Purva Bhalothia
- Department of Biotechnology and Bioinformatics, Birla Institute of Scientific Research, Jaipur, India
| | - Purnima Sharma
- Department of Bioinformatics, Hans Raj Mahila Maha Vidyalaya, Punjab, India
| | - Jalaja Naravula
- Vignan’s Foundation for Science, Technology & Research (Deemed to be University), Guntur, India
| | - Renuka Suravajhala
- Bioclues.org, Hyderabad, India
- Department of Chemistry, School of Basic Sciences, Manipal University Jaipur, Jaipur, India
| | - Ayam Gupta
- Vignan’s Foundation for Science, Technology & Research (Deemed to be University), Guntur, India
| | - Bilal Ahmed Abbasi
- Functional Genomics Unit, Council of Scientific and Industrial Research- Institute of Genomics & Integrative Biology (CSIR-IGIB), Delhi, India
| | - Prittam Goswami
- Department of Biotechnology, Haldia Institute of Technology, West Bengal, India
| | - Harpreet Singh
- Department of Bioinformatics, Hans Raj Mahila Maha Vidyalaya, Punjab, India
- Bioclues.org, Hyderabad, India
| | - Rahul Narang
- Department of Microbiology, All India Institute of Medical Sciences, Bibinagar, Hyderabad, India
| | | | - Krishna Mohan Medicherla
- Department of Biotechnology and Bioinformatics, Birla Institute of Scientific Research, Jaipur, India
| | - Jayaraman Valadi
- Bioclues.org, Hyderabad, India
- Department of Computer Science, Flame University, Pune, India
| | - Anil Kumar S
- Vignan’s Foundation for Science, Technology & Research (Deemed to be University), Guntur, India
| | - Gyaneshwer Chaubey
- Cytogenetics Laboratory, Department of Zoology, Benaras Hindu University, Varanasi, India
| | - Keshav K. Singh
- Department of Genetics, University of Alabama, Birmingham, AL, United States
| | - Obul Reddy Bandapalli
- Bioclues.org, Hyderabad, India
- German Cancer Research Centre (DKFZ), Heidelberg, Germany
- Department of Applied Biology, Council of Scientific and Industrial Research-Indian Institute of Chemical Technology (CSIR-IICT), Hyderabad, India
| | - Polavarapu Bilhan Kavi Kishor
- Bioclues.org, Hyderabad, India
- Vignan’s Foundation for Science, Technology & Research (Deemed to be University), Guntur, India
| | - Prashanth Suravajhala
- Bioclues.org, Hyderabad, India
- Department of Biotechnology and Bioinformatics, Birla Institute of Scientific Research, Jaipur, India
- Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Kerala, India
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Dayaramani C, De Leon J, Reiss AB. Cardiovascular Disease Complicating COVID-19 in the Elderly. MEDICINA (KAUNAS, LITHUANIA) 2021; 57:833. [PMID: 34441038 PMCID: PMC8399122 DOI: 10.3390/medicina57080833] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 08/11/2021] [Accepted: 08/12/2021] [Indexed: 12/20/2022]
Abstract
SARS-CoV-2, a single-stranded RNA coronavirus, causes an illness known as coronavirus disease 2019 (COVID-19). The highly transmissible virus gains entry into human cells primarily by the binding of its spike protein to the angiotensin-converting enzyme 2 receptor, which is expressed not only in lung tissue but also in cardiac myocytes and the vascular endothelium. Cardiovascular complications are frequent in patients with COVID-19 and may be a result of viral-associated systemic and cardiac inflammation or may arise from a virus-induced hypercoagulable state. This prothrombotic state is marked by endothelial dysfunction and platelet activation in both macrovasculature and microvasculature. In patients with subclinical atherosclerosis, COVID-19 may incite atherosclerotic plaque disruption and coronary thrombosis. Hypertension and obesity are common comorbidities in COVID-19 patients that may significantly raise the risk of mortality. Sedentary behaviors, poor diet, and increased use of tobacco and alcohol, associated with prolonged stay-at-home restrictions, may promote thrombosis, while depressed mood due to social isolation can exacerbate poor self-care. Telehealth interventions via smartphone applications and other technologies that document nutrition and offer exercise programs and social connections can be used to mitigate some of the potential damage to heart health.
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Affiliation(s)
| | | | - Allison B. Reiss
- Department of Medicine and Biomedical Research Institute, NYU Long Island School of Medicine, Mineola, NY 11501, USA; (C.D.); (J.D.L.)
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Shah S, Mulahuwaish A, Ghafoor KZ, Maghdid HS. Prediction of global spread of COVID-19 pandemic: a review and research challenges. Artif Intell Rev 2021; 55:1607-1628. [PMID: 34305251 PMCID: PMC8285044 DOI: 10.1007/s10462-021-09988-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/13/2021] [Indexed: 11/22/2022]
Abstract
Since the initial reports of the Coronavirus surfacing in Wuhan, China, the novel virus currently without a cure has spread like wildfire across the globe, the virus spread exponentially across all inhabited continent, catching local governments by surprise in many cases and bringing the world economy to a standstill. As local authorities work on a response to deal with the virus, the scientific community has stepped in to help analyze and predict the pattern and conditions that would influence the spread of this unforgiving virus. Using existing statistical modeling tools to the latest artificial intelligence technology, the scientific community has used public and privately available data to help with predictions. A lot of this data research has enabled local authorities to plan their response—whether that is to deploy tightly available medical resources like ventilators or how and when to enforce policies to social distance, including lockdowns. On the one hand, this paper shows what accuracy of research brings to enable fighting this disease; while on the other hand, it also shows what lack of response from local authorities can do in spreading this virus. This is our attempt to compile different research methods and comparing their accuracy in predicting the spread of COVID-19.
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Affiliation(s)
- Saloni Shah
- Department of Computer Science and Information Systems, Saginaw Valley State University, 7400 Bay Rd, University Center, MI USA
| | - Aos Mulahuwaish
- Department of Computer Science and Information Systems, Saginaw Valley State University, 7400 Bay Rd, University Center, MI USA
| | - Kayhan Zrar Ghafoor
- Department of Computer Science, Knowledge University, University Park, Kirkuk Road, Erbil, 446015 Iraq.,Department of Software Engineering, Salahaddin University, Erbil, Iraq
| | - Halgurd S Maghdid
- Department of Software Engineering, Faculty of Engineering, Koya University, Koysinjaq, Kurdistan Region-F.R. Iraq
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Kolasa K, Mazzi F, Leszczuk-Czubkowska E, Zrubka Z, Péntek M. State of the Art in Adoption of Contact Tracing Apps and Recommendations Regarding Privacy Protection and Public Health: Systematic Review. JMIR Mhealth Uhealth 2021; 9:e23250. [PMID: 34033581 PMCID: PMC8195202 DOI: 10.2196/23250] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 10/30/2020] [Accepted: 02/22/2021] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND During the COVID-19 pandemic, contact tracing apps have received a lot of public attention. The ongoing debate highlights the challenges of the adoption of data-driven innovation. We reflect on how to ensure an appropriate level of protection of individual data and how to maximize public health benefits that can be derived from the collected data. OBJECTIVE The aim of the study was to analyze available COVID-19 contact tracing apps and verify to what extent public health interests and data privacy standards can be fulfilled simultaneously in the process of the adoption of digital health technologies. METHODS A systematic review of PubMed and MEDLINE databases, as well as grey literature, was performed to identify available contact tracing apps. Two checklists were developed to evaluate (1) the apps' compliance with data privacy standards and (2) their fulfillment of public health interests. Based on both checklists, a scorecard with a selected set of minimum requirements was created with the goal of estimating whether the balance between the objective of data privacy and public health interests can be achieved in order to ensure the broad adoption of digital technologies. RESULTS Overall, 21 contact tracing apps were reviewed. In total, 11 criteria were defined to assess the usefulness of each digital technology for public health interests. The most frequently installed features related to contact alerting and governmental accountability. The least frequently installed feature was the availability of a system of medical or organizational support. Only 1 app out of 21 (5%) provided a threshold for the population coverage needed for the digital solution to be effective. In total, 12 criteria were used to assess the compliance of contact tracing apps with data privacy regulations. Explicit user consent, voluntary use, and anonymization techniques were among the most frequently fulfilled criteria. The least often implemented criteria were provisions of information about personal data breaches and data gathered from children. The balance between standards of data protection and public health benefits was achieved best by the COVIDSafe app and worst by the Alipay Health Code app. CONCLUSIONS Contact tracing apps with high levels of compliance with standards of data privacy tend to fulfill public health interests to a limited extent. Simultaneously, digital technologies with a lower level of data privacy protection allow for the collection of more data. Overall, this review shows that a consistent number of apps appear to comply with standards of data privacy, while their usefulness from a public health perspective can still be maximized.
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Affiliation(s)
- Katarzyna Kolasa
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
| | - Francesca Mazzi
- Queen Mary University of London, London, United Kingdom
- Maastricht University, Maastricht, Netherlands
| | | | - Zsombor Zrubka
- Health Economics Research Center, Óbuda University, Budapest, Hungary
- Corvinus Institute for Advanced Studies, Corvinus University of Budapest, Budapest, Hungary
| | - Márta Péntek
- Health Economics Research Center, Óbuda University, Budapest, Hungary
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Khorram-Manesh A, Dulebenets MA, Goniewicz K. Implementing Public Health Strategies-The Need for Educational Initiatives: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:5888. [PMID: 34070882 PMCID: PMC8198884 DOI: 10.3390/ijerph18115888] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 05/23/2021] [Accepted: 05/28/2021] [Indexed: 12/11/2022]
Abstract
In the absence of a specific treatment or vaccines, public health strategies are the main measures to use in the initial stages of a pandemic to allow surveillance of infectious diseases. During the ongoing global pandemic of coronavirus disease 2019 (COVID-19), several countries initiated various public health strategies, such as contact tracing and quarantine. The present study aims to conduct a systematic literature review to identify the presence of educational initiatives that promote the implementation of public health strategies before public health emergencies, with a special focus on contact tracing applications. Using Science Direct, PubMed, Scopus, and Gothenburg University search engines, all published scientific articles were included, while conference, reports, and non-scientific papers were excluded. The outcomes of the reviewed studies indicate that the effective implementation of public health strategies depends on the peoples' willingness to participate and collaborate with local authorities. Several factors may influence such willingness, of which ethical, psychological, and practical factors seem to be the most important and frequently discussed. Moreover, individual willingness and readiness of a community may also vary based on the acquired level of knowledge about the incident and its cause and available management options. Educational initiatives, proper communication, and timely information at the community level were found to be the necessary steps to counteract misinformation and to promote a successful implementation of public health strategies and attenuate the effects of a pandemic. The systematic review conducted as a part of this study would benefit the relevant stakeholders and policy makers and assist with effective designing and implementation.
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Affiliation(s)
- Amir Khorram-Manesh
- Department of Surgery, Institute of Clinical Sciences, Sahlgrenska Academy, Gothenburg University, 413 45 Gothenburg, Sweden
- Department of Development and Research, Armed Forces Center for Defense Medicine, Gothenburg, 426 76 Västra Frölunda, Sweden
| | - Maxim A. Dulebenets
- Department of Civil & Environmental Engineering, College of Engineering, Florida A&M University-Florida State University (FAMU-FSU), 2525 Pottsdamer Street, Building A, Suite A124, Tallahassee, FL 32310-6046, USA;
| | - Krzysztof Goniewicz
- Department of Aviation Security, Military University of Aviation, 08521 Deblin, Poland;
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Mutanga SS, Ngungu M, Tshililo FP, Kaggwa M. Systems dynamics approach for modelling South Africa's response to COVID-19: A "what if" scenario. J Public Health Res 2021; 10:1897. [PMID: 33849258 PMCID: PMC7883018 DOI: 10.4081/jphr.2021.1897] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 12/26/2020] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Many countries in the world are still struggling to control COVID-19 pandemic. As of April 28, 2020, South Africa reported the highest number of COVID-19 cases in Sub-Sahara Africa. The country took aggressive steps to control the spread of the virus including setting a national command team for COVID-19 and putting the country on a complete lockdown for more than 100 days. Evidence across most countries has shown that, it is vital to monitor the progression of pandemics and assess the effects of various public health measures, such as lockdowns. Countries need to have scientific tools to assist in monitoring and assessing the effectiveness of mitigation interventions. The objective of this study was thus to assess the extent to which a systems dynamics model can forecast COVID-19 infections in South Africa and be a useful tool in evaluating government interventions to manage the epidemic through 'what if' simulations. DESIGN AND METHODS This study presents a systems dynamics model (SD) of the COVID-19 infection in South Africa, as one of such tools. The development of the SD model in this study is grounded in design science research which fundamentally builds on prior research of modelling complex systems. RESULTS The SD model satisfactorily replicates the general trend of COVID-19 infections and recovery for South Africa within the first 100 days of the pandemic. The model further confirms that the decision to lockdown the country was a right one, otherwise the country's health capacity would have been overwhelmed. Going forward, the model predicts that the level of infection in the country will peak towards the last quarter of 2020, and thereafter start to decline. Conclusions: Ultimately, the model structure and simulations suggest that a systems dynamics model can be a useful tool in monitoring, predicting and testing interventions to manage COVID-19 with an acceptable margin of error. Moreover, the model can be developed further to include more variables as more facts on the COVID-19 emerge.
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Affiliation(s)
- Shingirirai Savious Mutanga
- Council for Scientific and Industrial Research (CSIR), Smart Place Cluster, Holistic Climate Change-Climate Services Group, Pretoria.
| | - Mercy Ngungu
- Human Sciences Research Council Developmental, Capable and Ethical States, Pretoria.
| | - Fhulufhelo Phillis Tshililo
- Department of Quality and Operations Management, Faculty of Engineering and Built Environment, University of Johannesburg.
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14
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Mutanga SS, Ngungu M, Tshililo FP, Kaggwa M. Systems dynamics approach for modelling South Africa's response to COVID-19: A "what if" scenario. J Public Health Res 2021. [PMID: 33634045 DOI: 10.4081/jphr.2021.11897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Background: Many countries in the world are still struggling to control COVID-19 pandemic. As of April 28, 2020, South Africa reported the highest number of COVID-19 cases in Sub- Sahara Africa. The country took aggressive steps to control the spread of the virus including setting a national command team for COVID-19 and putting the country on a complete lockdown for more than 100 days. Evidence across most countries has shown that, it is vital to monitor the progression of pandemics and assess the effects of various public health measures, such as lockdowns. Countries need to have scientific tools to assist in monitoring and assessing the effectiveness of mitigation interventions. The objective of this study was thus to assess the extent to which a systems dynamics model can forecast COVID-19 infections in South Africa and be a useful tool in evaluating government interventions to manage the epidemic through 'what if' simulations. Design and Methods: This study presents a systems dynamics model (SD) of the COVID-19 infection in South Africa, as one of such tools. The development of the SD model in this study is grounded in design science research which fundamentally builds on prior research of modelling complex systems. Results: The SD model satisfactorily replicates the general trend of COVID-19 infections and recovery for South Africa within the first 100 days of the pandemic. The model further confirms that the decision to lockdown the country was a right one, otherwise the country's health capacity would have been overwhelmed. Going forward, the model predicts that the level of infection in the country will peak towards the last quarter of 2020, and thereafter start to decline. Conclusions: Ultimately, the model structure and simulations suggest that a systems dynamics model can be a useful tool in monitoring, predicting and testing interventions to manage COVID-19 with an acceptable margin of error. Moreover, the model can be developed further to include more variables as more facts on the COVID-19 emerge.
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Affiliation(s)
- Shingirirai Savious Mutanga
- Council for Scientific and Industrial Research (CSIR), Smart Place Cluster, Holistic Climate Change-Climate Services Group, Pretoria.,Department of Quality and Operations Management, Faculty of Engineering and Built Environment, University of Johannesburg
| | - Mercy Ngungu
- Human Sciences Research Council Developmental, Capable and Ethical States, Pretoria
| | - Fhulufhelo Phillis Tshililo
- Department of Quality and Operations Management, Faculty of Engineering and Built Environment, University of Johannesburg.,Human Sciences Research Council Developmental, Capable and Ethical States, Pretoria
| | - Martin Kaggwa
- Sam Tambani Research Institute, Johannesburg, South Africa
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Hosseini E, Ghafoor KZ, Sadiq AS, Guizani M, Emrouznejad A. COVID-19 Optimizer Algorithm, Modeling and Controlling of Coronavirus Distribution Process. IEEE J Biomed Health Inform 2020; 24:2765-2775. [PMID: 32750974 PMCID: PMC8545158 DOI: 10.1109/jbhi.2020.3012487] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 06/29/2020] [Accepted: 07/18/2020] [Indexed: 11/09/2022]
Abstract
The emergence of novel COVID-19 is causing an overload on public health sector and a high fatality rate. The key priority is to contain the epidemic and reduce the infection rate. It is imperative to stress on ensuring extreme social distancing of the entire population and hence slowing down the epidemic spread. So, there is a need for an efficient optimizer algorithm that can solve NP-hard in addition to applied optimization problems. This article first proposes a novel COVID-19 optimizer Algorithm (CVA) to cover almost all feasible regions of the optimization problems. We also simulate the coronavirus distribution process in several countries around the globe. Then, we model a coronavirus distribution process as an optimization problem to minimize the number of COVID-19 infected countries and hence slow down the epidemic spread. Furthermore, we propose three scenarios to solve the optimization problem using most effective factors in the distribution process. Simulation results show one of the controlling scenarios outperforms the others. Extensive simulations using several optimization schemes show that the CVA technique performs best with up to 15%, 37%, 53% and 59% increase compared with Volcano Eruption Algorithm (VEA), Gray Wolf Optimizer (GWO), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), respectively.
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Affiliation(s)
- Eghbal Hosseini
- Mechanical and Energy Engineering Department, Erbil Technical Engineering CollegeErbil Polytechnic Univeristy, Kurdistan RegionErbil44001Iraq
| | - Kayhan Zrar Ghafoor
- Department of Software EngineeringSalahaddin University-ErbilErbil44001Iraq
- School of Mathematics and Computer ScienceUniversity of WolverhamptonWolverhamptonWV1 1LYU.K.
| | - Ali Safaa Sadiq
- Wolverhampton Cyber Research InstituteSchool of Mathematics and Computer ScienceUniversity of WolverhamptonWolverhamptonWV1 1LYU.K.
- Centre for Artificial Intelligence Research and OptimisationTorrens University AustraliaFortitude ValleyQLD4006Australia
| | - Mohsen Guizani
- Department of Computer Science and EngineeringQatar UniversityDoha00000Qatar
| | - Ali Emrouznejad
- Department of Operations and Information ManagementAston Business SchoolAston UniversityBirminghamB4 7ETU.K.
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