Data Analytics and Resource Planning Using Deep Learning for Overcoming Challenges of Covid-19

Abstract

Statistical disease modeling, data analysis and planning of resources are  important aspects for overcoming the challenges of COVID-19. The proposed pandemic modelling   involves pre-screening of virus affected peoples and resource allocation using deep learning frameworks. Since most of the virus infected peoples are asymptomatic in nature, hence it is very difficult to recognize and isolate them from the society. In this paper a new pre-screening methodology is introduced to classify peoples who are more likely to be virus infected based on image analytics. The pre- screening techniques consists of classifying x ray images and coughing sounds by using Convolutional Neural Networks(CNN). The pre-screening results of deep learning frame works are used to prepare a risk score, i.e., higher risk score higher probability of infected and vice-versa. The proposed method has good classification accuracy for predicting various lung diseases and also can be used in pre-screening covid infected individuals. However, results alone cannot be used to confirm the COVID-19 virus, whereas it helps to distinguish people more prone to get virus infected based on a risk score. The real time results  with an accuracy of 90% indicate the competency of  proposed technique.