The Recent Technologies to Curb the Second-Wave of COVID-19 Pandemic

Poongodi, M.; Malviya, Mohit; Hamdi, Mounir; Rauf, Hafiz Tayyab; Kadry, Seifedine; Thinnukool, Orawit

doi:10.1109/access.2021.3094400

Cited by 20 publications

(4 citation statements)

References 59 publications

(58 reference statements)

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“…More research is expected to quantify the resurgence conditions, control potential resurgences after lifting certain restrictions and reactivating businesses and activities [163, 206], distinguish the characteristics and containment measures between waves and mutations [83, 99], predict future resurgences and mutations [101, 134], and prepare for their responsive countermeasures [12]. Various modeling methods are applied, including mathematical and statistical models such as regression models, ordinary differential equation and Bayesian inference [163], compartmental models [36, 206], simulation models, and various AI techniques including NLP and machine learning models [218].…”

Section: Covid-19 Modeling Landscapementioning

confidence: 99%

COVID-19 Modeling: A Review

Cao

Liu

2022

Preprint

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The unprecedented and overwhelming SARS-CoV-2 virus and COVID-19 disease significantly challenge our life and the society and economy. Many questions emerge, a critical one is to quantify the challenges, realities, intervention effect and influence of the pandemic. With the massive effort in modeling COVID-19, what COVID-19 issues have been modeled? what and how well have epidemiology, AI, data science, machine learning, deep learning, mathematics and social science characterized the COVID-19 epidemic? what are the gaps and opportunities of quantifying the pandemic? Such questions involve a wide body of knowledge and literature, unclear but important for the present and future health crisis quantification. Here, we provide a comprehensive review of the challenges, tasks, methods, progress, gaps and opportunities in relation to modeling COVID-19 processes, data, mitigation and impact. With a research landscape of COVID-19 modeling, we further categorize, summarize, compare and discuss the related methods and progress of modeling COVID-19 epidemic transmission processes and dynamics, case identification and tracing, infection diagnosis and medical treatments, nonpharmaceutical interventions and their effects, drug and vaccine development, psychological, economic and social influence and impact, and misinformation, etc. The review shows how modeling methods such as mathematical and statistical models, domain-driven modeling by epidemiological compartmental models, medical and biomedical analysis, AI and data science in particular shallow and deep machine learning, simulation modeling, social science methods and hybrid modeling have addressed the COVID-19 challenges and what gaps and directions exist for better futures.

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Section: Covid-19 Modeling Landscapementioning

confidence: 99%

COVID-19 Modeling: A Review

Cao

Liu

2022

Preprint

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“…From research perspective, COVID-19 is the most searched term on Internet in 2020. A lot of research related to COVID-19 is currently going on throughout the globe [ 6 ]. Medical professionals are trying to come up with an antidote that can prevent corona infection.…”

Section: Introductionmentioning

confidence: 99%

IoT-Enabled Framework for Early Detection and Prediction of COVID-19 Suspects by Leveraging Machine Learning in Cloud

Mir

Jamwal

Mehbodniya

et al. 2022

Journal of Healthcare Engineering

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COVID-19 is the repugnant but the most searched word since its outbreak in November 2019 across the globe. The world has to battle with it until an effective solution is developed. Due to the advancement in mobile and sensor technology, it is possible to come up with Internet of things-based healthcare systems. These novel healthcare systems can be proactive and preventive rather than traditional reactive healthcare systems. This article proposes a real-time IoT-enabled framework for the detection and prediction of COVID-19 suspects in early stages, by collecting symptomatic data and analyzing the nature of the virus in a better manner. The framework computes the presence of COVID-19 virus by mining the health parameters collected in real time from sensors and other IoT devices. The framework is comprised of four main components: user system or data collection center, data analytic center, diagnostic system, and cloud system. To point out and detect the COVID-19 suspected in real time, this work proposes the five machine learning techniques, namely support vector machine (SVM), decision tree, naïve Bayes, logistic regression, and neural network. In our proposed framework, the real and primary dataset collected from SKIMS, Srinagar, is used to validate our work. The experiment on the primary dataset was conducted using different machine learning techniques on selected symptoms. The efficiency of algorithms is calculated by computing the results of performance metrics such as accuracy, precision, recall, F1 score, root-mean-square error, and area under the curve score. The employed machine learning techniques have shown the accuracy of above 95% on the primary symptomatic data. Based on the experiment conducted, the proposed framework would be effective in the early identification and prediction of COVID-19 suspect realizing the nature of the disease in better way.

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“…( Poongodi et al., 2021a ) This research assessed upcoming and prospective innovations that will enhance COVID-19 impacts for the general population. This can be done by applying artificial intelligence techniques such as Natural Language Processing (NLP), Machine Learning (ML), and Computer vision to a variety of processing files.…”

Section: Related Workmentioning

confidence: 99%

A smart IoMT based architecture for E-healthcare patient monitoring system using artificial intelligence algorithms

Dahan

Alroobaea

et al. 2023

Front. Physiol.

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Generally, cloud computing is integrated with wireless sensor network to enable the monitoring systems and it improves the quality of service. The sensed patient data are monitored with biosensors without considering the patient datatype and this minimizes the work of hospitals and physicians. Wearable sensor devices and the Internet of Medical Things (IoMT) have changed the health service, resulting in faster monitoring, prediction, diagnosis, and treatment. Nevertheless, there have been difficulties that need to be resolved by the use of AI methods. The primary goal of this study is to introduce an AI-powered, IoMT telemedicine infrastructure for E-healthcare. In this paper, initially the data collection from the patient body is made using the sensed devices and the information are transmitted through the gateway/Wi-Fi and is stored in IoMT cloud repository. The stored information is then acquired, preprocessed to refine the collected data. The features from preprocessed data are extracted by means of high dimensional Linear Discriminant analysis (LDA) and the best optimal features are selected using reconfigured multi-objective cuckoo search algorithm (CSA). The prediction of abnormal/normal data is made by using Hybrid ResNet 18 and GoogleNet classifier (HRGC). The decision is then made whether to send alert to hospitals/healthcare personnel or not. If the expected results are satisfactory, the participant information is saved in the internet for later use. At last, the performance analysis is carried so as to validate the efficiency of proposed mechanism.

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The Recent Technologies to Curb the Second-Wave of COVID-19 Pandemic

Cited by 20 publications

References 59 publications

COVID-19 Modeling: A Review

COVID-19 Modeling: A Review

IoT-Enabled Framework for Early Detection and Prediction of COVID-19 Suspects by Leveraging Machine Learning in Cloud

A smart IoMT based architecture for E-healthcare patient monitoring system using artificial intelligence algorithms

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