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Author: search.filters.author.Chamola, VinaySubject: search.filters.subject.COVID-19

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    Enhancing Infectious Disease Outbreak Surveillance via Bidirectional Contact Tracing
    (IEEE, 2024-05) Chamola, Vinay
    Contact tracing (CT) remains essential in mitigating the spread of pandemics (including COVID-19). Specifically, backward CT helps find superspreaders and hidden chains of transmission from asymptomatically infected users. However, most literature proposing CT frameworks and apps deployed by various countries do not attempt backward CT. In this work, we present a novel approach for bidirectional CT. The proposed approach works using Bluetooth low-energy sensors that detect the presence of users in a vicinity and inform a central BS of user presence. By fixing Bluetooth low-energy sensor (BLE-S) in buildings, the proposed framework can trace the contacts resulting from contamination of a location (indirect contacts). We present two algorithms using which the proposed framework can trace forward and backward contacts. Using a simulation, we also track the spread of infection among different “generations” of the infected and the impact of backward tracing on preventing the spread across generations. We observe the effect of critical epidemiological parameters, such as the reproduction number (R) and the overdispersion parameter (k), specifically on backward CT efficiency.
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    Opportunities and challenges of drones and internet of drones in healthcare supply chains under disruption
    (Taylor & Francis, 2024-12) Chamola, Vinay
    This study investigates the use of drones and the Internet of Drones (IoDs) in healthcare supply chains (HSCs), highlighting the opportunities and challenges of integrating them to respond to HSCs disruptions, an area that is currently under-examined in supply chain literature. A mixed methods approach is employed in this research. The article establishes important contributions. Firstly, it evaluates and integrates current research to provide new insights for dealing with pandemics and other similar emergencies. Secondly, the article framework reveals relevant political, economic, social, technological, environmental, and legal influencing factors to respond to disruptions. Thirdly, the analysis through the SCOR provided a hierarchical process model for HSCs enabled by drones and IoDs. Fourthly, the paper provides a framework for a shift from people-dependent HSCs to technology-dependent HSCs. Promising future research directions are indicated to advance the supply chain research on this topic.
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    COVID-19-associated mucormycosis: A review of an emergent epidemic fungal infection in the era of COVID-19 pandemic
    (Journal of Research in Medical Sciences, 2022) Chamola, Vinay
    At a time when the COVID-19's second wave is still picking up in countries like India, a number of reports describe the potential association with a rise in the number of cases of mucormycosis, commonly known as the black fungus. This fungal infection has been around for centuries and affects those people whose immunity has been compromised due to severe health conditions. In this article, we provide a detailed overview of mucormycosis and discuss how COVID-19 could have caused a sudden spike in an otherwise rare disease in countries like India. The article discusses the various symptoms of the disease, class of people most vulnerable to this infection, preventive measures to avoid the disease, and various treatments that exist in clinical practice and research to manage the disease.
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    Enabling Cost-Effective and Secure Minor Medical Teleconsultation Using Artificial Intelligence and Blockchain
    (IEEE, 2022-03) Chamola, Vinay
    While the onset of the COVID-19 pandemic has increased the popularity of home-based consultations, worries over privacy, high consultations costs, slow response times, and the burden on doctors due to the overwhelming number of COVID-19 cases have made current in-person and online models ineffective. In this study, we present an advanced, privacy-protected, artificial intelligence and blockchain-based consultation framework for minor medical conditions. Patients can post their medical queries anonymously on the blockchain network, which may be answered by any available medical professionals. The queries are sorted into their respective domains using naive Bayes and logistic regression. The consultations provided by medical specialists are evaluated based on their reputation, expertise, detail orientation, and the use of supporting documents, and rewards are given in accordance with the evaluation scheme. This fair and incentivized system provides cheaper and more accessible healthcare to patients, which is the need of the hour.
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    Privacy-Preserving and Incentivized Contact Tracing for COVID-19 Using Blockchain
    (IEEE, 2021-09) Chamola, Vinay
    Many countries across the world have not been entirely successful in their attempts to contain the spread of the COVID-19 virus, which has caused, quite possibly, one of the greatest disasters of the 21st century. A rich variety of contact tracing applications, each offering its own unique set of features, are currently being used to track the spread of the virus from person to person, and isolate/quarantine suspected individuals. However, existing approaches have not been very successful since they lack incentives to motivate users to use them actively. Although each solution provides some form of privacy protection and data security, loopholes do exist, and there is scope for improvement. In this context, we explore the use of blockchain to help in advancing contact tracing applications. In this article, we describe how blockchain could offer enhanced data security and functionality without compromising users' privacy. The effectiveness of contact tracing applications improves with the user base. In this regard, we explore how blockchain-based smart contracts can be used to offer incentives to users at various stages of the contact tracing process. Next, we lay down a plan for future contact tracing frameworks that provide the advantages of blockchain and user incentives, and all the best features available in current solutions.
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    A Comprehensive Review of the COVID-19 Pandemic and the Role of IoT, Drones, AI, Blockchain, and 5G in Managing its Impact
    (IEEE, 2020-05) Chamola, Vinay
    The unprecedented outbreak of the 2019 novel coronavirus, termed as COVID-19 by the World Health Organization (WHO), has placed numerous governments around the world in a precarious position. The impact of the COVID-19 outbreak, earlier witnessed by the citizens of China alone, has now become a matter of grave concern for virtually every country in the world. The scarcity of resources to endure the COVID-19 outbreak combined with the fear of overburdened healthcare systems has forced a majority of these countries into a state of partial or complete lockdown. The number of laboratory-confirmed coronavirus cases has been increasing at an alarming rate throughout the world, with reportedly more than 3 million confirmed cases as of 30 April 2020. Adding to these woes, numerous false reports, misinformation, and unsolicited fears in regards to coronavirus, are being circulated regularly since the outbreak of the COVID-19. In response to such acts, we draw on various reliable sources to present a detailed review of all the major aspects associated with the COVID-19 pandemic. In addition to the direct health implications associated with the outbreak of COVID-19, this study highlights its impact on the global economy. In drawing things to a close, we explore the use of technologies such as the Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), blockchain, Artificial Intelligence (AI), and 5G, among others, to help mitigate the impact of COVID-19 outbreak.
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    IoMT and DNN-Enabled Drone-Assisted Covid-19 Screening and Detection Framework for Rural Areas
    (IEEE, 2021-06) Bitragunta, Sainath; Chamola, Vinay; Mishra, Puneet; Yenuganti, Sujan
    Providing rapid testing and proper treatment has become highly challenging due to the rapid and highly unpredictable spread of the coronavirus disease (COVID-19). In most developing countries, rural areas lack adequate medical facilities and medical staff for effective diagnosis and treatment. Recently, there have been several technological advancements across various engineering disciplines such as the Internet of Things, unmanned aerial vehicles (UAVs) or drones, deep neural networks (DNNs), and intelligent robots. This work proposes a prototype that integrates these technologies to develop a payload deployable in a drone to help in providing rapid testing and healthcare. The proposed UAV prototype combines secure patient authentication, an automated disinfection system, and medical sensors as part of the UAV payload. It uses a DNN model for real-time COVID-19 detection. It uses intelligent flight path planning, operational management, battery recharge planning, disinfectant refilling, and strategic location planning to quickly disseminate testing kits and essential medical services to remote locations without direct human involvement.
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    AI-enabled remote monitoring of vital signs for COVID-19: methods, prospects and challenges
    (Springer, 2021-03) Narang, Pratik; Narang, Pratik; Chamola, Vinay
    The COVID-19 pandemic has overwhelmed the existing healthcare infrastructure in many parts of the world. Healthcare professionals are not only over-burdened but also at a high risk of nosocomial transmission from COVID-19 patients. Screening and monitoring the health of a large number of susceptible or infected individuals is a challenging task. Although professional medical attention and hospitalization are necessary for high-risk COVID-19 patients, home isolation is an effective strategy for low and medium risk patients as well as for those who are at risk of infection and have been quarantined. However, this necessitates effective techniques for remotely monitoring the patients’ symptoms. Recent advances in Machine Learning (ML) and Deep Learning (DL) have strengthened the power of imaging techniques and can be used to remotely perform several tasks that previously required the physical presence of a medical professional. In this work, we study the prospects of vital signs monitoring for COVID-19 infected as well as quarantined individuals by using DL and image/signal-processing techniques, many of which can be deployed using simple cameras and sensors available on a smartphone or a personal computer, without the need of specialized equipment. We demonstrate the potential of ML-enabled workflows for several vital signs such as heart and respiratory rates, cough, blood pressure, and oxygen saturation. We also discuss the challenges involved in implementing ML-enabled techniques.