Project information

• Category: Framework Design/ System
• Project date: Work in Progress

The global outbreak of infectious disease COVID-19 (Coronavirus Disease 2019), caused by SARS-Cov-2, has swept 200+ countries or territories and contracted more than 35+ million people (as on the first week of October 2020). This human-to-human disease transmission is highly contagious and there is yet no standard treatment protocol for the affected people. It is already observed that traditional infection-control or public-health measures to combat COVID-19 are inadequate. It is absolutely necessary to develop an analytics framework by extracting and incorporating the knowledge of heterogeneous data-sources to deliver insights in improving administrative policy and enhance the preparedness to combat the pandemic. Specifically, human mobility, travel history and other transport statistics have significant impacts on the spread of infectious disease. In this direction, this work proposes a spatio-temporal knowledge mining framework to model the impact of human mobility and other contextual information over large geographic area in different temporal scales. The framework has three salient features, namely, (i) COVID-knowledge graph to discover correlations among mobility information and disease spread; (ii) Deep learning architecture to predict the next hot-spot zones; and (iii) providing necessary support in home-health monitoring utilizing Femtolet and fog/edge based solutions. Another objective of this work is to design a low cost wearable device consisting of heterogeneous sensors to realize the proposed framework