  Several researches and evidence show the increasing likelihood of pandemics
(large-scale outbreaks of infectious disease) which has far reaching sequels in
all aspects of human lives ranging from rapid mortality rates to economic and
social disruption across the world. In the recent time, COVID-19 (Coronavirus
Disease 2019) pandemic disrupted normal human lives, and motivated by the
urgent need of combating COVID-19, researchers have put significant efforts in
modelling and analysing the disease spread patterns for effective preventive
measures (in addition to developing pharmaceutical solutions, like vaccine). In
this regards, it is absolutely necessary to develop an analytics framework by
extracting and incorporating the knowledge of heterogeneous datasources 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 any infectious disease. In this direction, this paper proposes a
spatio-temporal knowledge mining framework, named STOPPAGE to model the impact
of human mobility and other contextual information over large geographic area
in different temporal scales. The framework has two major modules: (i)
Spatio-temporal data and computing infrastructure using fog/edge based
architecture; and (ii) Spatio-temporal data analytics module to efficiently
extract knowledge from heterogeneous data sources. Typically, we develop a
Pandemic-knowledge graph to discover correlations among mobility information
and disease spread, a deep learning architecture to predict the next hot-spot
zones; and provide necessary support in home-health monitoring utilizing
Femtolet and fog/edge based solutions. The experimental evaluations on
real-life datasets related to COVID-19 in India illustrate the efficacy of the
proposed methods.
