Title: Mathematical modelling for prediction of spread of corona virus and artificial intelligence/machine learning-based technique to detect COVID-19 via smartphone sensors
Authors: Sumeet Goyal; Digvijay Pandey; Harjinder Singh; Joginder Singh; Rahul Kakkar; P. Naga Srinivasu
Addresses: Department of Applied Science, Chandigarh Group of Colleges, Landran, Punjab, India ' Department of Technical Education, IET, Dr. A.P.J. Abdul Kalam Technical University, Uttar Pradesh, India ' Department of ECE, Punjabi University, Patiala, Punjab, India ' Department of Applied Science, Chandigarh Group of Colleges, Landran, Punjab, India ' Department of Applied Science, Chandigarh Group of Colleges, Landran, Punjab, India ' Department of Computer Science and Engineering, Gitam Institute of Technology, GITAM Deemed to be University, Rushikonda, Visakhapatnam 530045, India
Abstract: COVID-19 is a novel corona virus which is infectious and communicable disease and it is originated from Wuhan, China. As the virus is mutating, the world is suffering from its spread again and again. However, the spread of communicable diseases can be predicted in advance so the proper preventive measures can be taken before it become life-taking. In this paper, mathematical model (SEIR) for the prediction of infectious diseases, which is modification of conventional SIR model is described and modelled which can be used to predict the cases in advance. A novel framework to detect COVID-19 from home is also proposed using artificial intelligence, machine learning and smartphone embedded sensors. The various smartphone embedded sensors such as proximity sensor, light sensor, accelerometer, gyroscope and fingerprint sensors are used to read the symptoms or activity and scan the CT images, and can be used to detect COVID-19.
Keywords: COVID-19; mathematical model; susceptible-exposed-infectious-removed; SEIR; detection; smartphones; artificial intelligence.
International Journal of Modelling, Identification and Control, 2022 Vol.41 No.1/2, pp.43 - 52
Received: 02 Jul 2021
Accepted: 24 Sep 2021
Published online: 22 Nov 2022 *