IEEE Consumer Electronics Magazine - July/August 2021 - 26

Healthcare Technology
helps identify potential hotspots, super-spreader
events (SSEs), or individuals of the disease and
have been used in the past for viral infections
like MERS.
We use the notation of C1, C2, so on to refer
to a different group of nodes. We analyze the
derived COVID-19 transmission networks in Kerala
based upon two centrality measures.
1) Degree centrality: In our context, the value for
the degree of a node specifies the number of
individuals to which one individual of the
network is directly associated. More an individual
having direct relationships with other
individuals, more are its chances of spreading
the disease if he/she is already infected-
also, the vice versa-more the connections,
more the chances of acquiring the infection.
2) Betweenness centrality: In our context, this is
the number of infections that would not have
happened if an infected individual was not
there in the pathway to the newly infected
ones in the network.
Kerala was the state where the first case in
the country was identified in the Thrissur district.
A total of 11 out of 14 districts of the states
have witnessed COVID-19 cases till 11th April
2020. Out of 379 detected cases, 11 cases had a
direct association with SSE (Nizamuddin Markaz
congregation). A series of local transmission
from the Pathanamthitta district, which also
expanded further to Kottayam district, is
observed in C6 of Figure 7(a). An imported case
from Italy did infect its relatives and friends, and
they, in turn, infected their friends. Figure 7(b)
shows the degree centrality network, whereas
Figure 7(c) shows the betweenness centrality in
the state. There were a total of 364 nodes with
57 connections in the centrality networks. A
total of 15.3% of the nodes in the system were
having connections to some other nodes in the
network. A total of 84.7% of the nodes remained
isolated. Nodes in the C6 can be seen to have
dominated in terms of betweenness centrality.
With the network centrality measures,
namely the degree centrality and the betweenness
centrality, we analyzed the connections
among the patients of COVID-19 in the Kerala
state. We found Nizamuddin Markaz congregation
to be an SSE, which significantly increased
26
Figure 7. ICT for COVID-19 transmission network
analysis in Kerala, State of India.
the state's number of cases. Also, we identified
super spreader individuals throughout the state
using the centrality measures. Such as the case
of Pathanamthitta (Kerala) people returning
from Italy. Family and friends component infections
display higher interconnectivity than other
scenarios of disease transmission as seen in C6
of Figure 7(a). With the betweenness centrality,
we find while a patient/individual is directly
associated with others, it is not always the pathway
to further infections in the network.
A majority of India's households, especially
the rural ones, live in a joint family setting in
much-limited space. This shared space environment
was also responsible for the infection
spread in Kerala's Pathanamthitta to Kottayam
districts. Thus, much emphasis is required on
monitoring and accessing the progress of homequarantined
individuals. Proper tracking of such
incoming cases is much needed to prevent new
epicenters of the outbreak. A limitation in this
study lies in the data accuracy and completeness
of the information. The crowd-sourced data
comes from sources such as Twitter handles of
health ministry (both central and states). Thus,
proper contact tracing and accurate and complete
information availability are very crucial at
the early stage.
CONCLUSION AND RESEARCH
SCOPE
Various solutions have been developed using
powerful technologies (IoT, AI, robots, drones,
blockchain), and many are still under development
to combat the spread of the COVID-19.
These technology-driven solutions can help to a
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