A novel predictive model for capturing threats for facilitating effective social distancing in COVID-19

Salma Firdose (Christ Academy Institute of Advanced Studies Christ Nagar)
Surendran Swapna Kumar (Vidya Academy of Science and Technology)
Ravinda Gayan Narendra Meegama (University of Sri Jayewardenepura Gangodawila)

International Journal of Electrical and Computer Engineering
Vol 12, No 1: February 2022

Social distancing is one of the simple and effective shields for every individual to control spreading of virus in present scenario of pandemic coronavirus disease (COVID-19). However, existing application of social distancing is a basic model and it is also characterized by various pitfalls in case of dynamic monitoring of infected individual accurately. Review of existing literature shows that there has been various dedicated research attempt towards social distancing using available technologies, however, there are further scope of improvement too. This paper has introduced a novel framework which is capable of computing the level of threat with much higher degree of accuracy using distance and duration of stay as elementary parameters. Finally, the model can successfully classify the level of threats using deep learning. The study outcome shows that proposed system offers better predictive performance in contrast to other approaches.