Energy efficient contact tracing and social interaction based patient prediction system for COVID-19 pandemic

Moremada, Charuka; Sandeepa, Chamara; Dissanayaka, Nadeeka; Gamage, Tharindu D.; Liyanage, Madhusanka

doi:10.23919/jcn.2021.000037

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…Moremada et al built a tracking system for the users and their social interactions using an energy-efficient method (i.e., Bluetooth). After that, the data collected from the tracking system was used to predict the occurrence of the epidemic with the incidence rates [ 46 ].…”

Section: Related Workmentioning

confidence: 99%

A Large-Scale IoT-Based Scheme for Real-Time Prediction of Infectious Disease Symptoms

Said

2023

Mobile Netw Appl

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In recent decades, many infectious diseases have appeared that have negatively affected life in general and people in particular, causing many economic and human losses. Recently, many attempts have emerged to confront these diseases using computer-based technology for diagnosis, prediction, and data analysis using various techniques, the most important of which is deep learning. Previous research relied primarily on a set of images taken from the patient’s body while he was in a healthcare facility, and this is the main weakness of these studies. Not all people go to a doctor or hospital when they feel the symptoms of a disease. Hence, people moving in crowded places without knowing their health status can contribute to spreading infectious diseases quickly, and this is the issue that should be confronted. Therefore, this paper presents a people-monitoring scheme, which is based on the internet of things (IoT) technology, to predict infectious disease symptoms through people’s behavior as well as through a wireless body area network (WBAN). This scheme can predict the spread of disease by tracking the movements of infected persons. Additionally, a simple methodology for processing the data extracted from the monitoring process across a range of different computing centers is introduced. Moreover, to ensure the monitoring scheme operates in real-time, it was necessary to provide a powerful coverage model for its objects. Also, a simple COVID-19 case study is presented. Finally, the performance of the prediction model is measured using images, sounds and videos files. Furthermore, the performance of the data computing and coverage methodologies is measured using an intensive simulation environment for the IoT that was constructed using NS3 package. The results showed that the proposed scheme is able to predict the symptoms of disease and its spread with accepted level of accuracy. In addition, using a mixture of coverage tools and computing techniques is recommended.

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Section: Related Workmentioning

confidence: 99%

A Large-Scale IoT-Based Scheme for Real-Time Prediction of Infectious Disease Symptoms

Said

2023

Mobile Netw Appl

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“…An energy‐efficient social collaborative trailing system based on Bluetooth Low Energy was developed by Moremada et al Based on the collected statistics, the algorithm is then anticipated to forecast the possibility that COVID‐19 would be achieved 27 . Liu et al used the NAR energetic neural network model for forecasting.…”

Section: Literature Reviewmentioning

confidence: 99%

“…26 An energy-efficient social collaborative trailing system based on Bluetooth Low Energy was developed by Moremada et al Based on the collected statistics, the algorithm is then anticipated to forecast the possibility that COVID-19 would be achieved. 27 Liu et al used the NAR energetic neural network model for forecasting. By incorporating the number of errors from logistic regression, ARIMA prediction, and SEIR standard, the NAR energetic neural network exceeds the evaluation standard in the time forecast of the new crown prevalent.…”

Section: Literature Reviewmentioning

confidence: 99%

Multifactor data analysis to forecast an individual's severity over novel COVID‐19 pandemic using extreme gradient boosting and random forest classifier algorithms

Yenurkar,

Mal,

Nyangaresi

et al. 2023

Engineering Reports

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AI and machine learning are increasingly often applied in the medical industry. The COVID‐19 epidemic will start to spread quickly over the planet around the start of 2020. At hospitals, there were more patients than there were beds. It was challenging for medical personnel to identify the patient who needed treatment right away. A machine learning approach is used to predict COVID‐19 pandemic patients at high risk. To provide input data and output results that execute the machine learning model on the backend, a straightforward Python Flask web application is employed. Here, the XGBoost algorithm, a supervised machine learning method, is applied. In order to predict high‐risk patients based on their current underlying health issues, the model uses patient characteristics as well as criteria like age, sex, health issues including diabetes, asthma, hypertension, and smoking, among others. The XGBoost model predicts the patient's severity with an accuracy of about 98% after data pre‐processing and training. The most important factors to the models are chosen to be age, diabetes, sex, and obesity. Patients and hospital personnel will benefit from this project's assistance in making timely choices and taking appropriate action. This will let medical personnel decide how much time and space to devote to the COVID‐19 high‐risk patients. providing a treatment that is both efficient and ideal. With this programme and the necessary patient data, hospitals may decide whether a patient need immediate care or not.

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Smart COVIDNet: designing an IoT-based COVID-19 disease prediction framework using attentive and adaptive-derived ensemble deep learning

Karthikeyan,

Baskaran,

Somasundaram

et al. 2023

Knowl Inf Syst

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Energy efficient contact tracing and social interaction based patient prediction system for COVID-19 pandemic

Cited by 7 publications

References 10 publications

A Large-Scale IoT-Based Scheme for Real-Time Prediction of Infectious Disease Symptoms

A Large-Scale IoT-Based Scheme for Real-Time Prediction of Infectious Disease Symptoms

Multifactor data analysis to forecast an individual's severity over novel COVID‐19 pandemic using extreme gradient boosting and random forest classifier algorithms

Smart COVIDNet: designing an IoT-based COVID-19 disease prediction framework using attentive and adaptive-derived ensemble deep learning

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