Prediction of COVID-19 Risk in Public Areas Using IoT and Machine Learning

• Published in: Electronics (Basel) Vol. 10; no. 14; p. 1677
• Main Authors: Elbasi, Ersin, Topcu, Ahmet E., Mathew, Shinu
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 14.07.2021
• Subjects: Algorithms; Artificial intelligence; Classifiers; Coronaviruses; COVID-19; Cyber-physical systems; Decision trees; Disease control; Feature extraction; Internet of Things; Machine learning; Masks; Neural networks; Risk analysis; Signs and symptoms; Support vector machines; Viral diseases; Wireless networks
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics10141677

COVID-19 is a community-acquired infection with symptoms that resemble those of influenza and bacterial pneumonia. Creating an infection control policy involving isolation, disinfection of surfaces, and identification of contagions is crucial in eradicating such pandemics. Incorporating social distancing could also help stop the spread of community-acquired infections like COVID-19. Social distancing entails maintaining certain distances between people and reducing the frequency of contact between people. Meanwhile, a significant increase in the development of different Internet of Things (IoT) devices has been seen together with cyber-physical systems that connect with physical environments. Machine learning is strengthening current technologies by adding new approaches to quickly and correctly solve problems utilizing this surge of available IoT devices. We propose a new approach using machine learning algorithms for monitoring the risk of COVID-19 in public areas. Extracted features from IoT sensors are used as input for several machine learning algorithms such as decision tree, neural network, naïve Bayes classifier, support vector machine, and random forest to predict the risks of the COVID-19 pandemic and calculate the risk probability of public places. This research aims to find vulnerable populations and reduce the impact of the disease on certain groups using machine learning models. We build a model to calculate and predict the risk factors of populated areas. This model generates automated alerts for security authorities in the case of any abnormal detection. Experimental results show that we have high accuracy with random forest of 97.32%, with decision tree of 94.50%, and with the naïve Bayes classifier of 99.37%. These algorithms indicate great potential for crowd risk prediction in public areas.