Epidemiology and Infection
*Formerly with Department of Community
Medicine, Bangalore Medical College and
Research Institute, Bangalore, Karnataka,
Formerly with Henry Ford Health System,
Detroit, Michigan, USA.
Cite this article: Saraswathi S, Mukhopadhyay
A, Shah H, Ranganath TS (2020). Social
network analysis of COVID-19 transmission in
Karnataka, India. Epidemiology and Infection
148, e230, 1–10. https://doi.org/10.1017/
Received: 8 June 2020
Revised: 5 September 2020
Accepted: 18 September 2020
Analysis of data; contact tracing; COVID-19;
infectious disease epidemiology; social
Author for correspondence:
© The Author(s), 2020. Published by
Cambridge University Press. This is an Open
Access article, distributed under the terms of
the Creative Commons Attribution-
NonCommercial-NoDerivatives licence (http://
which permits non-commercial re-use,
distribution, and reproduction in any medium,
provided the original work is unaltered and is
properly cited. The written permission of
Cambridge University Press must be obtained
for commercial re-use or in order to create a
Social network analysis of COVID-19
transmission in Karnataka, India
S. Saraswathi1, A. Mukhopadhyay2,*, H. Shah2,†and T. S. Ranganath1
Department of Community Medicine, Bangalore Medical College and Research Institute, Bangalore, Karnataka,
We used social network analysis (SNA) to study the novel coronavirus (COVID-19) outbreak in
Karnataka, India, and to assess the potential of SNA as a tool for outbreak monitoring and con-
trol. We analysed contact tracing data of 1147 COVID-19 positive cases (mean age 34.91 years,
61.99% aged 11–40, 742 males), anonymised and made public by the Karnataka government.
Software tools, Cytoscape and Gephi, were used to create SNA graphics and determine network
attributes of nodes (cases) and edges (directed links from source to target patients). Outdegree
was 1–47 for 199 (17.35%) nodes, and betweenness, 0.5–87 for 89 (7.76%) nodes. Men had
higher mean outdegree and women, higher mean betweenness. Delhi was the exogenous source
of 17.44% cases. Bangalore city had the highest caseload in the state (229, 20%), but compara-
tively low cluster formation. Thirty-four (2.96%) ‘super-spreaders’(outdegree ⩾5) caused 60%
of the transmissions. Real-time social network visualisation can allow healthcare administrators
to flag evolving hotspots and pinpoint key actors in transmission. Prioritising these areas and
individuals for rigorous containment could help minimise resource outlay and potentially
achieve a significant reduction in COVID-19 transmission.
The novel coronavirus (COVID-19) outbreak has attained the proportions of a global calamity. Not
only has the virus sickened millions, it has also affected economic growth worldwide. Researchers
have pointed to overpopulation, globalisation, and hyper-connectivity as factors responsible for
intensifying the spread of infection, turning the outbreak into a pandemic . The first case of cor-
onavirus disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in
India was reported on 30 January 2020, and the first case in Karnataka, a southern state of India, was
reported on 9 March 2020 . In the initial phases of the infection’s spread through the country,
Karnataka reported fewer cases than other Indian states. It was among the earliest states to deploy
modern technology tools as part of its logistics and containment measures [3,4]. As of 17 May 2020,
Karnataka had declared 1147 diagnosed cases and 18 648 individuals underobservation . Among
the 1147, there were 600 active cases, 509 who had recovered and 37 who died due to COVID (a
fatality rate of 3.2%); one person died by suicide after being diagnosed.
Researchers from several countries have used mathematical modelling to predict the trans-
mission of COVID-19 [6–8] and to identify predictors of mortality [9,10]. However, social
network analysis (SNA) has not, thus far, been optimally utilised in the endeavour to under-
stand the characteristics of this disease.
SNA is a technique to study the configurations of social relations between individuals or
other social units. Social network models can be used to measure variables that shape relation-
ships between social actors and the extent to which they affect health-related outcomes
[11,12]. Researchers are exploring the use of SNA to study various facets of the COVID-19
pandemic, such as the role of public figures in communication , and clustering patterns
within the broader patient network .
Since the pandemic is imposing a considerable burden on healthcare delivery systems, any
solution that can potentially aid in controlling its spread deserves serious exploration. One
such approach was the Karnataka healthcare task force’s extensive use of contact tracing.
We expanded on this approach by applying SNA to the corpus of contact tracing data gener-
ated by the task force’s efforts. We had two main research questions in mind. First, can SNA
improve our understanding of the transmission patterns of SARS-CoV-2? Second, can SNA
produce actionable findings that can help in timely control of the spread of this disease?
Karnataka is a southern state of India, consisting of 30 administrative districts with a population
of over 60 million, accounting for approximately 5% of the total Indian population. Bangalore, a
densely populated metropolis, is the capital city of Karnataka .
The government of Karnataka initiated measures to control the
spread of COVID-19 in early February 2020 . A government
appointed task force formulated guidelines for quarantine and con-
tact tracing. Field workers, trained to elicit travel and contact his-
tory, carried out telephonic and house-to-house surveys to
identify primary and secondary contacts of positive cases. On aver-
age, 47.4 contacts were tested for each confirmed case . Data
collected at the community level was collated by the State War
Room. Daily consolidated bulletins, containing anonymised patient
and contact data, were uploaded by the government to the portal it
created to share information on COVID-19 .
As a part of its effort to contain the outbreak, the Karnataka gov-
ernment implemented a phased lockdown, closing shops and offices,
and shutting down interdistrict and interstate travel. Phase 1 of the
lockdown, with the most stringent curbs on travel and socialisation,
was from 24 March to 14 April. The second phase was from
15 April to 3 May, and the third phase was from 4 May to 17 May.
For our analysis, we downloaded the daily bulletins containing infor-
mation for all cases reported positive for COVID-19 from 9 March
to 17 May 2020, spanning the period from detection of the first case
in the state to the end of phase-3 of the preventive lockdown. We
extracted relevant demographic and contact data from the bulletins
and created a dataset consisting of anonymised data of 1147
COVID-19 patients. We tabulated and summarised demographic
details such as age, district of residence and history of travel, using
Microsoft Excel. We created a nodes and edges datasheet in Excel,
with each node representing a patient and each edge, a confirmed
link or contact between a source and a target patient. We imported
this dataset into Gephi version 0.9.2 and applied the following sequence
of layout algorithms: Yifan Hu Proportional, Fruchterman Reingold
and No Overlap, to achieve a visual representation in which the
more connected nodes are placed centrally, and ones with lower con-
nectivity are placed towards the periphery of the network .
We wanted to combine the capabilities of two of the leading SNA
software tools , Gephi and Cytoscape, and utilise the features
missing in one but available in the other. The use of Gephi’snetwork
analysis tools results in the nodes and edges datasheet being popu-
lated with additional attribute variables. These values, such as node
betweenness and edge betweenness, can then be displayed as visual
features of the network elements in other software tools such as
Cytoscape . We reformatted the data exported from Gephi to
make it compatible with the data model acceptable to Cytoscape ver-
sion 3.8.0, which we used to create network graphics highlighting
pertinent demographic characteristics of the nodes. Layout algo-
rithms provided in Cytoscape were applied in the following sequence:
Compound Spring Embedder (CoSE) and yFiles Remove Overlap,
followed by a few manual adjustments to improve clarity.
We analysed the network attributes generated by Gephi using
MS Excel to explain relevant aspects of the network and its com-
ponents. We discussed the characteristics and evolution of the
graphs and attempted to explain them in the context of facts
and events on the ground.
We have used anonymised, secondary data in the public domain,
available at the COVID-19 information portal of the Karnataka
state government, the copyright policy of which indicates that
material featured on the site may be reproduced free of charge
in any format or media without requiring specific permission,
subject to acknowledgement of the source . The authors assert
that all procedures contributing to this work comply with the eth-
ical standards of the relevant national and institutional commit-
tees, and with the Helsinki Declaration of 1975, as revised in 2008.
(Also displayed in Supplementary Table S1):
Degree centrality is a measure of the number of social connec-
tions or links that a node has. It is expressed as an integer or
The indegree of a node is the number of incoming links to it from
source nodes and refers to the number of infectious patients
who had confirmed contact with a given target patient.
Outdegree is the number of links to target nodes from a source
node and is a measure of the number of secondary cases
infected by a given patient. The direction of the links is denoted
by arrowheads at the target ends, in our visual representations.
Betweenness centrality is a measure of the number of times a node
appears on the shortest path between other nodes . It
reflects the role a patient plays in creating a bridge of infectious
transmission between patients who would not have had direct
contact with each other.
Closeness centrality is the average of the shortest path lengths from
a node to every other node in the network. It is calculated as
the inverted sum of the distances from the node to all other
We used harmonic closeness to measure closeness centrality, due to
the presence of unconnected nodes in our network. It is calculated
as the sum of the inverted distances from a node to all other
nodes, instead of the reciprocal of the sum of all distances .
Edge betweenness is the number of the shortest paths that go
through an edge in a graph or network, with a high score indi-
cative of a bridge-like connection between two parts of a net-
work, crucial to transmission between many pairs of nodes .
Clustering coefficient measures the degree to which nodes in a
graph tend to cluster together .
Network density is the number of existing ties between nodes,
divided by the number of possible ties .
Network diameter is the shortest path between the two most dis-
tant nodes in a network .
Mean path length is the average of the shortest path lengths
between all possible node pairs .
Network component is an island of interlinked nodes that are dis-
connected from other nodes of the network. Many networks
consist of one large component, sometimes together with sev-
eral smaller ones and singleton actors .
Super-spreader (operational definition): Any node with an outde-
gree ⩾5 was considered a super-spreader. Individuals repre-
sented by these nodes would have infected five or more contacts.
We analysed 1147 patients of whom 742 (64.69%) were males, aged
34.91 years on average (standard deviation 17.34 years) (Fig. 1).
Most of these individuals (711/1147; 61.99%) belonged to the
2 S. Saraswathi et al.
11–40 years age range. Most deaths, however, occurred among
older patients. We observed maximum mortality in patients aged
over 70 years. There were 34 patients in this age-group, of whom
10 (29.41%) had died. Further socio-demographical details of
these patients are available in the public domain .
We found 948 nodes with zero outdegree. The remaining 199
(17.35%) nodes had an outdegree range of 1–47 and were the source
of infection to 657 targets through 706 links (edges). Among the tar-
get nodes, 36 had indegree >1 (range 2–5), implying more than one
source. We noted equal means, but widely differing standard devia-
tions for outdegree and indegree centralities. This difference is due
to the wider range of outdegree compared to indegree (Table 1).
There were 490 nodes with zero indegree, of which 383 had
zero outdegree. The latter were isolated nodes, with degree cen-
trality value of zero.
The range of betweenness centrality was 0.5–87 for 89 (7.76%)
nodes. The network had 143 nodes with a harmonic closeness
centrality (HCC) of one, and 56 nodes with HCC between zero
and one. Our network density was 0.001, network diameter was
4 and the clustering coefficient was 0.004.
Table 2 shows that men had a higher mean outdegree (0.628,
Mvs. 0.593, F) and women, higher mean betweenness (0.573, F
vs. 0.412, M).
The 95th percentile cut-off values for outdegree and between-
ness were 3 and 2, respectively. There were 77 (6.71% of 1147)
nodes with outdegree ⩾3, and combined, they accounted for 556
(78.75% of 706) edges. More than two-thirds of these 77 nodes
were men (54, 70.13%). The average HCC for the 77 nodes with
outdegree ⩾3 was 0.887, compared to 0.161 for the entire network.
Of the 59 nodes with betweenness ⩾2, more than half (33, 55.93%)
were men, though women had a higher mean betweenness overall.
We noted 34 super-spreaders with outdegree ⩾5, with a cumu-
lative outdegree of 410, and after deducting 17 duplicate edges for
target nodes with indegree >1, they accounted for 393 (59.81%) of
the 657 target cases.
The aggregate network graphic (Fig. 2), created using Gephi,
shows nodes representing patients, and components representing
case clusters. The nodes are coloured according to district and
sized by outdegree, making the larger nodes represent individuals
who infected a greater number of targets. The largest node, located
at the centre of a major component, denotes a patient from Mysuru
district who infected 47 target nodes. Transmission between districts
was limited, occurring chiefly from Mysuru to Mandya, a geograph-
ically adjacent district. The network figure also has two large-sized
grey nodes that represent two patients with outdegree 29 and 25,
from districts Vijayapura and Uttara Kannada, respectively.
Bangalore had the highest number of cases, followed by Belagavi,
Kalaburagi and Mysuru districts (Supplementary Table S2).
The aggregate network contained 93 clusters of connected
nodes (components), of which 37 components, made up of five
or more nodes each, had more than half of all the nodes (613,
53.44%) and four-fifths of the edges (611, 86.54%) concentrated
within them (Fig. 3). The distribution of these clusters by district
and type of origin is shown in Supplementary Table S3.
Figure 4 shows nodes coloured by age group and sized by out-
degree. Figure 5 shows nodes coloured by source of infection and
sized by betweenness centrality. We have considered patients with
a history of travel from Delhi in a separate category as their count
was comparable to the combined number of travellers from all
other states of India. It is noteworthy that travellers from abroad
did not contribute to the formation of any major cluster.
Fig. 1. Age–sex distribution of cases and deaths. Case counts are inclusive of deaths. Death counts are also shown separately.
Epidemiology and Infection 3
Comparing Figures 4(nodes sized by outdegree) and 5(nodes
sized by betweenness), we find that in clusters with nodes that had
multiple interconnections, relatively low outdegree and high
betweenness, the key nodes were females. This indicates that
women played a significant bridging role in these clusters. This
differs from clusters with edges radiating from a central node
with high outdegree and low betweenness, where typically, a
young male was the nidus. The largest and second-largest compo-
nents illustrate this difference in transmission (Fig. 6). The largest
component had 75 nodes and 76 edges, and the second-largest
component had 45 nodes and 50 edges. The largest cluster origi-
nated in the district of Mysuru; its source node was a male with
high outdegree who spread the infection to many contacts.
However, secondary transmission from those contacts was lim-
ited. This cluster is star shaped. The second-largest component
resembles a spiderweb with multiple interconnected nodes and
many female actors. This cluster was in Belagavi, and its network
density was nearly twice that of the largest cluster (0.025 vs.
0.014), with a shorter average path length (1.314 vs. 1.321).
Dynamic evolution of the network
Figure 7 shows how the network began with the first detected
cases and how it expanded in each phase of the lockdown. We
Table 1. Network parameters
Network C1 C2
Nodes 1147 75 45
Edges 706 76 50
Node attributes Range Mean (S.D.) Range (mean)
Outdegree 0–47 0.616 (2.78) 0–47 (1.013) 0–15 (1.111)
Indegree 0–5 0.616 (0.60) 0–2 (1.013) 0–3 (1.111)
Degree 0–47 1.231 (2.79) 1–47 (2.027) 1–15 (2.222)
Betweenness 0–87 0.469 (3.69) 0–9 (0.426) 0–12 (1.133)
Harmonic closeness 1 (n= 143)
0.161 (0.35) 0–1 (0.231) 0–1 (0.263)
0<HC<1 (n= 56)
Eccentricity 0–4 0.242 (0.61) 0–3 (0.307) 0–3 (0.4)
Network attributes Value
Diameter 4 3 3
Radius 1 1 1
Mean path length 1.623 1.321 1.314
Mean no. of neighbours 1.231 2.027 2.222
Density 0.001 0.025 0.014
Clustering coefficient 0.004 0.011 0.000
C1, largest component; C2, second-largest component; S.D., standard deviation; HC, harmonic closeness.
Table 2. Mean outdegree and betweenness by sex and age group
Mean outdegree Mean betweenness
Women Men Combined Women Men Combined
0–10 0.000 0.140 0.079 0.000 0.233 0.132
11–20 0.463 0.368 0.407 0.179 0.242 0.216
21–30 0.655 0.289 0.396 1.561 0.095 0.527
31–40 0.500 0.994 0.822 0.114 1.159 0.795
41–50 0.870 0.480 0.601 0.783 0.216 0.392
51–60 1.158 1.145 1.150 0.474 0.210 0.310
61–70 0.467 1.083 0.846 0.033 0.406 0.263
71–99 0.667 1.263 1.000 1.572 0.000 0.694
Combined 0.593 0.628 0.616 0.573 0.412 0.469
4 S. Saraswathi et al.
Fig. 2. Aggregate network graphic created in Gephi. Arrowheads indicate direction of transmission from source node to target node. Node size determined by
outdegree. Edges inherit colour from parent nodes.
Fig. 3. Major network components organised by size, created in Cytoscape. Arrowheads indicate direction of transmission from source node to target node. Edge
betweenness determines the thickness and colour intensity of the edges.
Epidemiology and Infection 5
see that in the initial, pre-lockdown phase, the cases were mostly
isolated nodes with minimal occurrence of secondary cases. They
are all travellers returning from abroad (red nodes, source type
‘International Travel’). By the time the first and strictest phase
of the lockdown (24 March to 14 April) was declared, however,
cluster formation had already begun and most of the new cases
had a history of travel to Delhi or contact with returnees from
Delhi (green nodes, source type ‘Delhi Hotspot’). The origin of
the largest cluster, which was labelled as a Karnataka (in-state)
hotspot (blue nodes), could not be traced either to travel or
to contact with any known positive case. Clusters continued to
form and grow during the second phase of the lockdown (15
April to 3 May). However, due to continuing curbs on travel
and transportation, the fresh cases were mostly found among con-
tacts of existing cases. In the beginning of May 2020, the govern-
ment arranged special transportation facilities by road and rail, so
that migrant labourers in distress could return to their home
states. This resulted in many new cases with source traced to tra-
vel from out of state (orange nodes) in phase 3 of the lockdown (4
May to 17 May). We have shown the weekly increase in cases in
the form of a line graph in Supplementary Figure S1.
While we have performed a basic conventional analysis of the
data, our chief objectives were to create social network graphics
from the empirical contact tracing data, and derive insights into
disease transmission therefrom.
Our study reveals that most cases of COVID-19 in Karnataka
were young and middle-aged men. Deaths, however, occurred
overwhelmingly among elderly patients. The age and sex profile
of our study set matches nationwide surveillance data from
India, with median age and age-distribution close to our sample,
and a similar high attack rate in males .
Bangalore, the capital of Karnataka, is a densely populated
metropolis, housing one-sixth of Karnataka’s population in 1%
of its area [15,28]. The city airport is a major transit point for
domestic and international travellers. These factors may explain
Bangalore’s relatively heavy burden of COVID-19 cases (229/
1147). Despite accounting for nearly a fifth of the state’s caseload,
however, Bangalore did not have notably large or numerous clus-
ters compared to other districts (Fig. 2 and Supplementary Tables
S2 and S3). Most of the cases detected here were isolated nodes.
Bangalore’s low transmission may have been due to the disci-
plined observance of lockdown measures, and rigorous con-
tact tracing and quarantine activities by its healthcare workforce
The presence of two large nodes (where size denotes outde-
gree) in districts that had a minor contribution to the total case-
load (Fig. 2) points to the risk of cluster formation even in
relatively unaffected areas if, for example, physical distancing
measures are not followed scrupulously.
Fig. 4. Age–sex attributes of nodes and clusters, created in Cytoscape. Node size determined by outdegree. Arrowheads indicate direction of transmission from
source node to target node. Edge betweenness determines the thickness and colour intensity of the edges.
6 S. Saraswathi et al.
Shortly after the World Health Organization confirmed the
novel coronavirus as the cause of the outbreak in China , pub-
lic health authorities started precautionary screening and quaran-
tine of passengers arriving from areas of concern at Bangalore
International Airport . These early steps may explain why
we found no major clusters originating from international
travellers. Conversely, we noted several clusters formed by people
with a history of travel to the national capital, Delhi (Fig. 5 and
Supplementary Tables S2 and S3). By 19 April 2020, the entire
city of Delhi had been declared a COVID-19 hotspot in
the wake of a mass religious gathering that was found to be linked
to nearly a third of the country’s caseload earlier in the month
Fig. 5. Network analysis by sources of infection (Cytoscape). Node size determined by betweenness. Arrowheads indicate direction of transmission from source
node to target node. Edge betweenness determines the thickness and colour intensity of the edges.
Fig. 6. Comparing the two largest components
(Cytoscape). Node size determined by betweenness.
Arrowheads indicate direction of transmission from
source node to target node. Edge betweenness deter-
mines the thickness and colour intensity of the edges.
Epidemiology and Infection 7
. Clusters of cases that originated from Delhi tended to be
closely interconnected, with women playing an active transmis-
sion role. This could reflect close community ties between these
individuals, or residence in underprivileged areas where strict
physical distancing may not have been observed.
Most of the clusters in our network had a man with high out-
degree as the nidus. Women, however, played an important role in
transmission by bridging multiple nodes within clusters even
though men outnumbered women in the 95th percentile region
of betweenness. Further study is warranted into the social and
behavioural characteristics of men and women that drive these
The low density of our network, the presence of 948 nodes
with zero outdegree, and the fact that only 34 source cases had
infected close to two-thirds of all target cases, indicate that com-
munity transmission was negligible. A similar transmission pat-
tern was reported from Shenzhen, China, where 8.9% of the
cases had caused 80% of all infections . Another recent
analysis of detailed contact tracing data from Hunan, China,
traced 80% of secondary cases back to 14% of infections .
Network analysis of COVID-19 patients in Henan, China, 
revealed a non-uniform pattern of clustering (208/1105 patients
in clusters) with a skewed distribution of patients in different cit-
ies. The Henan study also indicated a strong correlation of con-
firmed cases with travel to Wuhan (the epicentre of the
pandemic), which corresponds to our observation that a fair pro-
portion (17.44%) of the Karnataka patients had travelled to Delhi.
These similarities indicate that our findings may be generalisable
Researchers have analysed network properties from different
perspectives, depending on the type and complexity of networks.
Entropy-based analysis has been used to identify influential nodes
using local information dimensionality . Fractal dimensions
are being explored to determine the vulnerability of complex net-
works . Mathematical modelling has been used to simulate
and predict transmission dynamics in various types of networks
Fig. 7. Evolution of the network at each phase of lockdown. Node colour denotes infection source type. Arrowheads indicate direction of transmission from source
node to target node.
8 S. Saraswathi et al.
. These models are informed, and their predictions are influ-
enced, by the types of data processing decisions that are made
prior to collecting and analysing the contact data . A dynamic
simulation of this nature, such as was done by the Hunan
researchers , would require data at a granular level, including
educational, occupational and socioeconomic status of patients,
their mobility patterns, severity of infection, and the duration
and intensity of contact events. This information was unavailable
in the anonymised secondary data that we used. The data avail-
able to us allowed only a limited dynamic analysis to be done.
Our SNA findings may not universally reflect field realities. Some
findings such as eccentricity and mean path length are theoretical
constructs computed by software algorithms, but in practice, these
metrics remain indeterminate as our network had very few inter-
district connections and many isolated nodes and components.
Our dataset included many patients with contact history still
under investigation at the time of analysis. We were not able to
analyse the role of type and duration of contact, as these data
were unavailable for many patients. Although we have attempted
to faithfully reproduce all the information that we could extract
from the daily bulletins, the quality of our data is necessarily lim-
ited by the constraints of secondary data sources.
Our conventional analysis indicates that mortality due to
COVID-19 is highest among senior citizens. We recommend
that the elderly should be advised strict physical distancing, and
older patients from rural or underserved areas should be pre-
emptively transferred to tertiary centres with intensive care
facilities. This may help in early detection and treatment of com-
plications, mitigating their mortality risk.
The findings from our network analysis suggest that geograph-
ical, demographical and community characteristics could
influence the spread of COVID-19. Gender influences the morph-
ology of clusters, with men seeding the clusters and women
Our results also highlight the need for recording, on an
ongoing basis, high granularity contact tracing data in a uniform
format. We believe that outbreak control task forces should be
provided with requisite software and training in SNA techniques,
and should directly receive contact tracing information from
workers in the field. This would enable SNA in real time with
the ability to visualise and flag evolving networks with alacrity.
It would also help pinpoint nodes with high outdegree, between-
ness and closeness scores, which imply an active role in the trans-
mission and bridging of infection. Real-time SNA could thus help
identify the super-spreaders responsible for a large proportion of
transmission. In particular, close tracking of betweenness scores
would allow detection of individuals who might be missed by
conventional tracing methods. These actors may not themselves
spread the infection to many contacts, but their bridging charac-
teristic accelerates transmission in the community. Public health
authorities could prioritise these individuals and clusters for
immediate and rigorous containment, and formulate control mea-
sures tailored to the network characteristics of each locality. These
measures could help minimise resource outlay, and potentially
facilitate a significant reduction in the spread of COVID-19.
Supplementary material. The supplementary material for this article can
be found at https://doi.org/10.1017/S095026882000223X.
Acknowledgements. We thank the Government of Karnataka for their
punctual and detailed bulletins summarising data of COVID-19 cases in the
state. We are deeply grateful to the grassroots health workers who selflessly
carried out the contact tracing surveys that made our analysis possible. We
extend our sincere thanks to the reviewers and the editor for their valuable
suggestions and support.
S. Saraswathi: conceptualisation, study design, data retrieval from government bulletins;
A. Mukhopadhyay and H. Shah: data analysis; all authors: writing, editing, review and
final approval of manuscript.
Financial support. We received no financial support for this study.
Conflict of interest. We have no conflict of interest to declare.
Data availability statement. Our datasets were constructed using contact
tracing details available in the daily bulletins released online by the
Government of Karnataka. The complete archives of these bulletins can be
accessed at the COVID-19 portal at the address https://covid19.karnataka.
gov.in/govt_bulletin/en under the heading ‘Health Department Bulletins’.
1. Cheong KH and Jones MC (2020) Introducing the 21st century’s
New four horsemen of the coronapocalypse. BioEssays 42, 2000063.
2. India Today. Coronavirus in India: Tracking country’s first 50 COVID-19
cases; what numbers tell. https://www.indiatoday.in/india/story/corona-
tell-1654468-2020-03-12 (Accessed 26 August 2020).
3. The New Indian Express. Karnataka fares well in war against COVID-19.
fares-well-in-war-against-covid-19-2129751.html (Accessed 17 May 2020).
4. The Times of India. New tech solutions aiding Karnataka’s battle against
cms (Accessed 17 May 2020).
5. Health department bulletin –COVID-19 information portal. https://
(Accessed 18 May 2020).
6. Ndaïrou F et al.(2020) Mathematical modeling of COVID-19 transmis-
sion dynamics with a case study of Wuhan. Chaos, Solitons & Fractals 135,
7. Yang HM et al.(2020) Mathematical model describing CoViD-19 in São
Paulo, Brazil –evaluating isolation as control mechanism and forecasting
epidemiological scenarios of release. Epidemiology and Infection 148, e155.
8. Singh A, Dey J and Bhardwaj S (2020) Is this the beginning or the end of
COVID-19 outbreak in India? A data driven mathematical model-based
analysis. medRxiv. https://doi.org/10.1101/2020.04.27.20081422.
9. Ma X et al.(2020) Development and validation of prognosis model of mor-
tality risk in patients with COVID-19. Epidemiology and Infection 148,e168.
10. Sousa GJB et al.(2020) Mortality and survival of COVID-19.
Epidemiology and Infection 148, e123.
11. Newman MEJ (2003) The structure and function of complex networks.
SIAM Review 45, 167–256.
12. O’Malley AJ and Marsden PV (2008) The analysis of social networks.
Health Services & Outcomes Research Methodology 8, 222–269.
13. Yum S (2020) Social network analysis for coronavirus (COVID-19) in the
United States. Social Science Quarterly 101, 1642–1647. doi: 10.1111/
14. Wang P et al.(2020) Statistical and network analysis of 1212 COVID-19
patients in Henan, China. International Journal of Infectious Diseases 95,
15. Karnataka Government.https://karnataka.gov.in/new-page/Karnataka%
20at%20a%20Glance/en (Accessed 26 August 2020).
Epidemiology and Infection 9
16. Government circulars –COVID-19 information portal. https://covid19.
karnataka.gov.in/new-page/Government%20Circulars/en (Accessed 26
17. ICMR COVID Study Group et al.(2020) Laboratory surveillance for
SARS-CoV-2 in India: performance of testing & descriptive epidemiology
of detected COVID-19, January 22–April 30, 2020. Indian Journal of
Medical Research 151, 424–437. doi: 10.4103/ijmr.IJMR_1896_20.
18. Bastian M, Heymann S and Jacomy M (2009)Gephi:anopensourcesoft-
ware for exploring and manipulating networks. International AAAI
Conference on Web and Social Media; Third International AAAI Conference
on Weblogs and Social Media.Publishedonline:2009.https://www.aaai.org/
ocs/index.php/ICWSM/09/paper/view/154 (Accessed 31 May 2020).
19. Bhatia R (2018) Top 7 Network Analysis Tools For Data Visualisation.
Analytics India Magazine. https://analyticsindiamag.com/top-7-network-
analysis-tools-for-data-visualisation/ (Accessed 18 May 2020).
20. Shannon P et al.(2003) Cytoscape: a software environment for integrated
models of biomolecular interaction networks. Genome Research 13, 2498–
21. Degree Centrality –an overview, ScienceDirect Topics. https://www.
14 May 2020).
22. Betweenness Centrality –an overview, ScienceDirect Topics. https://www.
14 May 2020).
23. Rochat Y. Closeness centrality extended to unconnected graphs: the har-
monic centrality index. https://core.ac.uk/download/pdf/148005918.pdf
(Accessed 30 August 2020).
24. Girvan M and Newman MEJ (2002) Community structure in social and
biological networks. Proceedings of the National Academy of Sciences of the
United States of America 99, 7821–7826.
25. Ouyang F and Reilly C. Term inology –social network analysis. https://sites.
google.com/a/umn.edu/social-network-analysis/terminology (Accessed 3
26. Hanneman RA and Riddle M. Introduction to social network methods:
chapter 8: more properties of networks and actors. https://faculty.ucr.
edu/∼hanneman/nettext/C8_Embedding.html (Accessed 3 June 2020).
27. Karnataka COVID+ analysis: 17 May 2020. https://covid19.karnataka.gov.in/
storage/pdf-files/GOK_Scorecard_V3%20(1).pdf (Accessed 28 August 2020).
28. Karnataka government.https://karnataka.gov.in/district/en (Accessed 26
29. The Times of India. Welcome indicator: Only 2% of COVID-19 beds
occupied in Karnataka. https://timesofindia.indiatimes.com/city/benga-
articleshow/75653647.cms (Accessed 2 June 2020).
30. News18 (2020) Trace, test and treat: armed with 3ts, how Bengaluru is on
course to flattening the curve of COVID-19. https://www.news18.com/
to-flattening-the-curve-of-covid-19-2619117.html (Accessed 2 June 2020).
31. World Health Organization. WHO statement regarding cluster of pneu-
monia cases in Wuhan, China. https://www.who.int/china/news/detail/09-
china (Accessed 3 June 2020).
32. The News Minute. Screening begins at Bengaluru airport over Coronavirus
airport-over-coronavirus-scare-116502 (Accessed 3 June 2020).
33. Business Insider. No lockdown relaxation in Delhi as all 11 districts
are coronavirus hotspots. https://www.businessinsider.in/india/news/no-
articleshow/75232679.cms (Accessed 2 June 2020).
34. The Times of India. 1,445 cases linked to Tablighi Jamaat event; total
cases rise to 4,281, death toll 111. https://timesofindia.indiatimes.com/
death-toll-109/articleshow/75010939.cms (Accessed 2 June 2020).
35. Bi Q et al.(2020) Epidemiology and transmission of COVID-19 in 391
cases and 1286 of their close contacts in Shenzhen, China: a retrospective
cohort study. The Lancet Infectious Diseases 20, 911–919. doi: 10.1016/
36. Sun K et al.(2020) Transmission heterogeneities, kinetics, and controllability
of SARS-CoV-2. medRxiv. https://doi.org/10.1101/2020.08.09.20171132.
37. Wen T and Deng Y (2020) Identification of influencers in complex net-
works by local information dimensionality. Information Sciences 512,
38. Wen T, Song M and Jiang W (2018) Evaluating topological vulnerability
based on fuzzy fractal dimension. International Journal of Fuzzy Systems
39. Wang C et al.(2017) A rumor spreading model based on information
entropy. Scientific Reports 7, 9615.
40. Dawson DE, et al.(2019) Transmission on empirical dynamic con-
tact networks is influenced by data processing decisions. Epidemics 26,
10 S. Saraswathi et al.