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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 control. 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 comparatively 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.
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Epidemiology and Infection
Original Paper
*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, 110.
Received: 8 June 2020
Revised: 5 September 2020
Accepted: 18 September 2020
Key words:
Analysis of data; contact tracing; COVID-19;
infectious disease epidemiology; social
network analysis
Author for correspondence:
A. Mukhopadhyay,
© The Author(s), 2020. Published by
Cambridge University Press. This is an Open
Access article, distributed under the terms of
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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
derivative work.
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,
India and
Independent researcher
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 1140, 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 147 for 199 (17.35%) nodes, and betweenness, 0.587 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 [1]. 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 [2]. In the initial phases of the infections 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 [5]. 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 [68] 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 [13], and clustering patterns
within the broader patient network [14].
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 forces 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 forces 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?
Study area
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 [15].
Data source
The government of Karnataka initiated measures to control the
spread of COVID-19 in early February 2020 [16]. 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 [17]. 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 [5].
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.
Study design
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 [18].
We wanted to combine the capabilities of two of the leading SNA
software tools [19], Gephi and Cytoscape, and utilise the features
missing in one but available in the other. The use of Gephisnetwork
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 [20]. 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.
Ethical considerations
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 [5]. 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.
Important definitions
(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
count [21].
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 [22]. 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
nodes [23].
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 [23].
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 [24].
Clustering coefficient measures the degree to which nodes in a
graph tend to cluster together [25].
Network density is the number of existing ties between nodes,
divided by the number of possible ties [26].
Network diameter is the shortest path between the two most dis-
tant nodes in a network [25].
Mean path length is the average of the shortest path lengths
between all possible node pairs [25].
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 [12].
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.
1140 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 [27].
Network parameters
We found 948 nodes with zero outdegree. The remaining 199
(17.35%) nodes had an outdegree range of 147 and were the source
of infection to 657 targets through 706 links (edges). Among the tar-
get nodes, 36 had indegree >1 (range 25), 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 0.587 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. Agesex 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 047 0.616 (2.78) 047 (1.013) 015 (1.111)
Indegree 05 0.616 (0.60) 02 (1.013) 03 (1.111)
Degree 047 1.231 (2.79) 147 (2.027) 115 (2.222)
Betweenness 087 0.469 (3.69) 09 (0.426) 012 (1.133)
Harmonic closeness 1 (n= 143)
0.161 (0.35) 01 (0.231) 01 (0.263)
0<HC<1 (n= 56)
0(n= 949)
Eccentricity 04 0.242 (0.61) 03 (0.307) 03 (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
Age groups
Mean outdegree Mean betweenness
Women Men Combined Women Men Combined
010 0.000 0.140 0.079 0.000 0.233 0.132
1120 0.463 0.368 0.407 0.179 0.242 0.216
2130 0.655 0.289 0.396 1.561 0.095 0.527
3140 0.500 0.994 0.822 0.114 1.159 0.795
4150 0.870 0.480 0.601 0.783 0.216 0.392
5160 1.158 1.145 1.150 0.474 0.210 0.310
6170 0.467 1.083 0.846 0.033 0.406 0.263
7199 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 [17].
Bangalore, the capital of Karnataka, is a densely populated
metropolis, housing one-sixth of Karnatakas 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
Bangalores relatively heavy burden of COVID-19 cases (229/
1147). Despite accounting for nearly a fifth of the states 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.
Bangalores 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. Agesex 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 [31], pub-
lic health authorities started precautionary screening and quaran-
tine of passengers arriving from areas of concern at Bangalore
International Airport [32]. 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 [33]in
the wake of a mass religious gathering that was found to be linked
to nearly a third of the countrys 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
[34]. 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 [35]. Another recent
analysis of detailed contact tracing data from Hunan, China,
traced 80% of secondary cases back to 14% of infections [36].
Network analysis of COVID-19 patients in Henan, China, [14]
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
across populations.
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 [37]. Fractal dimensions
are being explored to determine the vulnerability of complex net-
works [38]. 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.
[39]. 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 [40]. A dynamic
simulation of this nature, such as was done by the Hunan
researchers [36], 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
propagating them.
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
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.
Author contributions.
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. under the heading Health Department Bulletins.
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10 S. Saraswathi et al.
... These approaches include the implementation of various machine learning models in traditional data mining based on time series data, 11,12 mathematical modeling methods, 13,14 deep learning algorithms, 15 Real-time analysis techniques, 16,17 and the SNA approach. 18,19 In this study, we investigated the relationship between COVID-19 cases and passengers transported in Iran provinces. We used the SNA technique to assess different factors in the passengers' network and evaluate their influence on the COVID-19 outbreak. ...
... Researchers in several studies used network analysis metrics to track and control the prevalence of COVID-19 in India. 3,18 In these studies, the network is defined based on the contacts of patients. The researchers analyzed network parameters and identified cases that play an important role in the disease outbreak using out-degree and betweenness centralities. ...
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The emergence of the new coronavirus in late 2019 further highlighted the human need for solutions to explore various aspects of deadly pandemics. Providing these solutions will enable humans to be more prepared for dealing with possible future pandemics. In addition, it helps governments implement strategies to tackle and control infectious diseases similar to COVID-19 faster than ever before. In this article, we used the social network analysis (SNA) method to identify high-risk areas of the new coronavirus in Iran. First, we developed the mobility network through the transfer of passengers (edges) between the provinces (nodes) of Iran and then evaluated the in-degree and page rank centralities of the network. Next, we developed 2 Poisson regression (PR) models to predict high-risk areas of the disease in different populations (moderator) using the mobility network centralities (independent variables) and the number of patients (dependent variable). The P-value of .001 for both prediction models confirmed a meaningful interaction between our variables. Besides, the PR models revealed that in higher populations, with the increase of network centralities, the number of patients increases at a higher rate than in lower populations, and vice versa. In conclusion, our method helps governments impose more restrictions on high-risk areas to handle the COVID-19 outbreak and provides a viable solution for accelerating operations against future pandemics similar to the coronavirus.
... As seen in Table 3 [13]. This is in contrast to findings from the first wave in 2020, when a social network analysis of COVID-19 transmission in Karnataka by Saraswathi et al. revealed that a limited number of source cases had infected close to 60% of all target cases, indicating negligible community transmission [14]. Thus, the results of our study, in conjunction with findings from other authors, shed light on the evolution of transmission characteristics between the first and second waves in Karnataka. ...
... Analysing the basic properties of a virus network can help to identify the main transmission nodes. In addition, community detection can reveal high-risk clusters and virus propagation patterns [11]. Social network analysis helps to better develop measures as well as control the spread of viruses. ...
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Social network analysis is a well-established research method in sociology. Some scholars also refer to this quantitative approach to graph theory as network science. As research intensified in the 1970s, it gained the attention of researchers in many other fields and has become an interdisciplinary research method. Social network analysis collects data from multiple sources and unearths new information by building network structures.The constructed network topology diagram and the calculated network properties are eventually analysed and generalised to produce further research results. The use of social network analysis (SNA) in a few different fieldstourism, society, transportation, citation, viral communication, medical education, and economic geographyis reviewed in this essay. Social network analysis is a quantitative analysis method that blends multiple disciplines. Various relationships are quantified through the construction of networks and property analysis. The paper is categorized according to the different fields of application. The different applications of social network analysis in various fields show its general and specific analytical approach.
... In [126], the author used SNA and its tools to monitor and control the COVID-19 outbreak in the Indian state of Karnataka. The author collected publicly available data from 1147 patients of different ages and genders. ...
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Coronaviruses are a well-established and deadly group of viruses that cause illness in both humans and animals. The novel type of this virus group, named COVID-19, was firstly reported in December 2019, and, with the passage of time, coronavirus has spread to almost all parts of the world. Coronavirus has been the cause of millions of deaths around the world. Furthermore, many countries are struggling with COVID-19 and have experimented with various kinds of vaccines to eliminate the deadly virus and its variants. This survey deals with COVID-19 data analysis and its impact on human social life. Data analysis and information related to coronavirus can greatly help scientists and governments in controlling the spread and symptoms of the deadly coronavirus. In this survey, we cover many areas of discussion related to COVID-19 data analysis, such as how artificial intelligence, along with machine learning, deep learning, and IoT, have worked together to fight against COVID-19. We also discuss artificial intelligence and IoT techniques used to forecast, detect, and diagnose patients of the novel coronavirus. Moreover, this survey also describes how fake news, doctored results, and conspiracy theories were spread over social media sites, such as Twitter, by applying various social network analysis and sentimental analysis techniques. A comprehensive comparative analysis of existing techniques has also been conducted. In the end, the Discussion section presents different data analysis techniques, provides future directions for research, and suggests general guidelines for handling coronavirus, as well as changing work and life conditions.
... The first case of SARS-CoV-2 in Bangalore, Karnataka was reported on 9 March 2020 [8]. A rapid increase in the number of positive cases in the "first wave" of the COVID-19 pandemic was witnessed in the period between August and October 2020. ...
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Background: We assess the efficacy of orientation programmes for doctors of proper donning, doffing techniques for personal protective equipment (PPE) and safe practices inside the COVID-19 hospital in reducing the COVID-19 infection rate among doctors. Methods: A total of 767 resident doctors and 197 faculty visits on weekly rotation were recorded over a six month period. Doctors were guided through orientation sessions before their entry into the COVID-19 hospital from 1 August 2020. The infection rate among doctors was used to study the efficacy of the programme. McNemars Chi-square test was used to compare the infection rate in the two groups before and after orientation sessions were commenced. Discussion: A statistically significant reduction in SARS-CoV-2 infection was seen among resident doctors after orientation programmes and infrastructure modification (3% vs 7.4%, P=0.03). Twenty-eight of 32 (87.5%) doctors who tested positive developed asymptomatic to mild infection. The infection rate was 3.65% and 2.1% among residents and faculty respectively. There was no mortality recorded. Conclusion: Orientation programme for healthcare workers for PPE donning and doffing protocols with practical demonstration and trial of PPE usage can significantly reduce COVID-19 infection. Such sessions should be mandatory for all workers on deputation in designated area for Infectious Diseases and in pandemic situations.
... Saraswathi et al. [59] used software tools, Cytoscape and Gephi, to create social network visualizations and explore common transmission patterns. The visualization approach enabled the identification of evolving hotspots, such as those associated with international travel and principal cities. ...
It is crucial to develop spatiotemporal analysis tools to mitigate risks during a pandemic. Many dashboards encountered in the literature do not consider how the geolocation characteristics and travel patterns may influence the spread of the virus. This work brings an interactive tool that is capable of crossing information about mobility patterns, geolocation characteristics and epidemiologic variables. To do so, our system uses a mobility network, generated through anonymized mobile location data, which enables the division of a region into representative clusters. The clusters' aggregated socioeconomic, and epidemiologic indicators can be analyzed through multiple coordinated views. The proposal is to enable users to understand how different locations commute citizens, monitor risk over time, and understand what locations need more assistance, considering different layers of visualization, such as clusters and individual locations. The main novelty is the interactive way to construct the mobility network that defines the social distancing level and the way that risks are managed, since many different geolocation characteristics can be considered and visualized, such as socioeconomic indicators of a location, the economic importance of a set of locations, and the connection of important neighborhoods of a city with other cities. The proposed tool was built and verified by experts assembled to give scientific recommendations to the city administration of Recife, the capital city of Pernambuco. Our analysis shows how a policymaker could use the tool to evaluate different isolation scenarios considering the trade-off between economic activity and contamination risk, where the practical insights can also be used to tighten and relax mitigation measures in other phases of a pandemic.
... Social media analysis has already been used in digital epidemiology, 18,19 and in the case of CoViD-19 it has been applied even for contact tracing and outbreak control. 20 Particularly, Twitter high-speed communication capability allows super-fast spread of information, and its "follow model" can be described as an interest graph, differently from Facebook. 21 Thus, as a very dynamic platform, Twitter is the ideal probe to study CoViD-19 social context. ...
The Covid-19 pandemic has spread across the world at a rate never seen before, affecting different countries and having a huge impact not only on health care systems but also on economic systems. Never as in this situation the continuous exchange of views between scientists of different disciplines must be considered the keystone to overcome this emergency. The dramatic global situation has prompted many researchers from different fields to focus on studying the Covid-19 pandemic and its economic and social implications in a multi-facet fashion. This volume collects the contributions to the COVid-19 Empirical Research (COVER) Conference, organized by the Centre of Excellence in Economics and Data Science of the Department of Economics, Management and Quantitative Methods, University of Milan, Italy, October 30th, 2020. This conference aimed to collect different points of view by opening an interdisciplinary discussion on the possible developments of the pandemic. The conference contributions ranged in the social, economic and mathematical-statistical areas
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Background The Covid-19 pandemic stretched health systems globally including in Iran. Hospital demand and performance was affected both directly and indirectly as a result of the pandemic. Analyzing hospital indicators can provide insights to deal with the consequences and challenges related to various aspects of future pandemics. Objective This study aimed to investigate the impact of the Covid-19 pandemic on key performance indicators of public hospitals in Iran. Methods We used time-series analysis to examine six key indicators of hospital performance: average length of stay, hospital mortality rate, number of surgeries, hospitalization rate, emergency visits, and bed occupancy rate. Data were extracted from four public hospitals in Yazd at two time intervals, 15 months before and after the outbreak of COVID-19. Data were analysed using interrupted time series analysis models with STATA17. Results Average length of stay (p = 0.02) and hospital mortality rate (p < 0.01) increased significantly following the outbreak of COVID-19, while the mean of other indicators such as number of surgeries (p = 0.01), hospitalization rate (p < 0.01), emergency visits (p < 0.01) and bed occupancy rate (p < 0.01) decreased significantly. The Covid-19 pandemic had an immediately reverse significant impact on the level changes of "hospitalization rate", "emergency visits" and "bed occupancy rate" indicators (p < 0.05). although the trend of surgeries indicator was affected significantly (p = 0.01) after the covid-19 outbreak. Conclusion We showed significant changes in most hospital indicators after the Covid-19 pandemic, reflecting the effect of this pandemic on the performance of hospitals. Understanding the impact of a pandemic on hospital indicators is necessary for decision-makers to effectively plan an effective pandemic response and to inform resource allocation decisions.
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Today, the emergence of social media is helpful for the healthcare system where everyone is closely connected. Large numbers of people can be reached by using seed nodes to provide medical advice, facilities, new changes in the treatment, and any health ministry guidelines. As today’s world is dealing with COVID-19, the main objective is to provide healthcare services to many people irrespective of time and locality. As people suffering from corona are dealing with mental health issues, in order to deal with it, a seed pick framework using machine learning for the influence maximization technique is proposed, which will be helpful to provide pervasive healthcare. For pervasive healthcare, an effectual seed pick framework is required focusing on influence maximization using machine learning. The proposed algorithm Fuzzy-VIKOR is helpful to identify the targeted node to spread information at a high rate. Consequently, the proposed structure effectively addresses different issues related to a large number of patients, and thus, increased influence maximization using seed nodes is helpful for pervasive healthcare. The experiments show that the proposed framework has high precision, accuracy, F1-score, and recall compared to other existing algorithms employed to find influence maximization seeds.
China, the world's largest developing country, faces a severe water shortage. As the government has set a goal of limiting water use to 7000 × 10 ⁸ m ³ by 2035, how to control the increase in water use will be a thorny issue for China. Unbalanced and uncoordinated regional socio-economic development is an important feature of China. Research on the interaction between provincial water use will help to optimize the rational allocation of water resources and control of water use. In this paper, SNA (social network analysis) method is first used to explore the characteristics of social network relationship between inter-provincial water use, construct a two-stage model of SNA–LMDI, and decompose the driving factors of inter-provincial water use evolution. We found the following points. (1) From 2000 to 2018, the spatial correlation network structure of water use is tending to be stable, and the stability and risk resistance of the whole network are enhanced. (2) From different angles to quantify the centricity analysis, can be found that eastern provinces located right in the heart of water network, obviously larger impact on water resources utilization in other provinces, Shanghai and Beijing is located in the former two, and central and western provinces in the edge position. (3) The national water use spatial correlation network can be divided into four blocks, net beneficial block, bidirectional spillover block, brokers block, and net spillover block. (4) Technological progress and industrial structure adjustment were the primary and secondary factors inhibiting the increase of total water use, while income increase was the main factor promoting the increase of total water use, population scale expansion had a weak role in promoting the increase of total water use. Some policy implications are put forward related to our research conclusions.
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Gephi is an open source software for graph and network analysis. It uses a 3D render engine to display large networks in real-time and to speed up the exploration. A flexible and multi-task architecture brings new possibilities to work with complex data sets and produce valuable visual results. We present several key features of Gephi in the context of interactive exploration and interpretation of networks. It provides easy and broad access to network data and allows for spatializing, filtering, navigating, manipulating and clustering. Finally, by presenting dynamic features of Gephi, we highlight key aspects of dynamic network visualization.
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Time and intimacy drive transmission A minority of people infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmit most infections. How does this happen? Sun et al. reconstructed transmission in Hunan, China, up to April 2020. Such detailed data can be used to separate out the relative contribution of transmission control measures aimed at isolating individuals relative to population-level distancing measures. The authors found that most of the secondary transmissions could be traced back to a minority of infected individuals, and well over half of transmission occurred in the presymptomatic phase. Furthermore, the duration of exposure to an infected person combined with closeness and number of household contacts constituted the greatest risks for transmission, particularly when lockdown conditions prevailed. These findings could help in the design of infection control policies that have the potential to minimize both virus transmission and economic strain. Science , this issue p. eabe2424
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This study aimed to identify clinical features for prognosing mortality risk using machine-learning methods in patients with coronavirus disease 2019 (COVID-19). A retrospective study of the inpatients with COVID-19 admitted from 15 January to 15 March 2020 in Wuhan is reported. The data of symptoms, comorbidity, demographic, vital sign, CT scans results and laboratory test results on admission were collected. Machine-learning methods (Random Forest and XGboost) were used to rank clinical features for mortality risk. Multivariate logistic regression models were applied to identify clinical features with statistical significance. The predictors of mortality were lactate dehydrogenase (LDH), C-reactive protein (CRP) and age based on 500 bootstrapped samples. A multivariate logistic regression model was formed to predict mortality 292 in-sample patients with area under the receiver operating characteristics (AUROC) of 0.9521, which was better than CURB-65 (AUROC of 0.8501) and the machine-learning-based model (AUROC of 0.4530). An out-sample data set of 13 patients was further tested to show our model (AUROC of 0.6061) was also better than CURB-65 (AUROC of 0.4608) and the machine-learning-based model (AUROC of 0.2292). LDH, CRP and age can be used to identify severe patients with COVID-19 on hospital admission.
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In São Paulo, Brazil, the first case of coronavirus disease 2019 (CoViD-19) was confirmed on 26 February, the first death due to CoViD-19 was registered on 16 March, and on 24 March, São Paulo implemented the isolation of persons in non-essential activities. A mathematical model was formulated based on non-linear ordinary differential equations considering young (60 years old or less) and elder (60 years old or more) subpopulations, aiming to describe the introduction and dissemination of the new coronavirus in São Paulo. This deterministic model used the data collected from São Paulo to estimate the model parameters, obtaining R0 = 6.8 for the basic reproduction number. The model also allowed to estimate that 50% of the population of São Paulo was in isolation, which permitted to describe the current epidemiological status. The goal of isolation implemented in São Paulo to control the rapid increase of the new coronavirus epidemic was partially succeeded, concluding that if isolation of at least 80% of the population had been implemented, the collapse in the health care system could be avoided. Nevertheless, the isolated persons must be released one day. Based on this model, we studied the potential epidemiological scenarios of release by varying the proportions of the release of young and elder persons. We also evaluated three different strategies of release: All isolated persons are released simultaneously, two and three releases divided in equal proportions. The better scenarios occurred when young persons are released, but maintaining elder persons isolated for a while. When compared with the epidemic without isolation, all strategies of release did not attain the goal of reducing substantially the number of hospitalisations due to severe CoViD-19. Hence, we concluded that the best decision must be postponing the beginning of the release.
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This study aims to identify the risk factors associated with mortality and survival of COVID-19 cases in a state of the Brazilian Northeast. It is a historical cohort with a secondary database of 2070 people that presented flu-like symptoms, sought health assistance in the state and tested positive to COVID-19 until 14 April 2020, only moderate and severe cases were hospitalised. The main outcome was death as a binary variable (yes/no). It also investigated the main factors related to mortality and survival of the disease. Time since the beginning of symptoms until death/end of the survey (14 April 2020) was the time variable of this study. Mortality was analysed by robust Poisson regression, and survival by Kaplan-Meier and Cox regression. From the 2070 people that tested positive to COVID-19, 131 (6.3%) died and 1939 (93.7%) survived, the overall survival probability was 87.7% from the 24th day of infection. Mortality was enhanced by the variables: elderly (HR 3.6; 95% CI 2.3-5.8; P < 0.001), neurological diseases (HR 3.9; 95% CI 1.9-7.8; P < 0.001), pneumopathies (HR 2.6; 95% CI 1.4-4.7; P < 0.001) and cardiovascular diseases (HR 8.9; 95% CI 5.4-14.5; P < 0.001). In conclusion, mortality by COVID-19 in Ceará is similar to countries with a large number of cases of the disease, although deaths occur later. Elderly people and comorbidities presented a greater risk of death.
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Background & objectives: India has been reporting the cases of coronavirus disease 2019 (COVID-19) since January 30, 2020. The Indian Council of Medical Research (ICMR) formulated and established laboratory surveillance for COVID-19. In this study, an analysis of the surveillance data was done to describe the testing performance and descriptive epidemiology of COVID-19 cases by time, place and person. Methods: The data were extracted from January 22 to April 30, 2020. The frequencies of testing performance were described over time and by place. We described cases by time (epidemic curve by date of specimen collection; seven-day moving average), place (area map) and person (attack rate by age, sex and contact status), and trends were represented along with public health measures and events. Results: Between January 22 and April 30, 2020, a total of 1,021,518 individuals were tested for severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). Testing increased from about 250 individuals per day in the beginning of March to 50,000 specimens per day by the end of April 2020. Overall, 40,184 (3.9%) tests were reported positive. The proportion of positive cases was highest among symptomatic and asymptomatic contacts, 2-3-fold higher than among those with severe acute respiratory infection, or those with an international travel history or healthcare workers. The attack rate (per million) by age was highest among those aged 50-69 yr (63.3) and was lowest among those under 10 yr (6.1). The attack rate was higher among males (41.6) than females (24.3). The secondary attack rate was 6.0 per cent. Overall, 99.0 per cent of 736 districts reported testing and 71.1 per cent reported COVID-19 cases. Interpretation & conclusions: The coverage and frequency of ICMR's laboratory surveillance for SARS-CoV-2 improved over time. COVID-19 was reported from most parts of India, and the attack rate was more among men and the elderly and common among close contacts. Analysis of the data indicates that for further insight, additional surveillance tools and strategies at the national and sub-national levels are needed.
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Objectives This study explores how public key players play an important role in social networks for coronavirus (COVID‐19). Methods This study employs social network analyses based on 2,864 Twitter users and 2,775 communications of Twitter. Results This study finds that President Trump plays the most important role in social networks among the top 20 key players for both in‐degree centrality and content in tweets. Second, Donald Trump and Barak Obama show the opposite result for the in‐degree centrality and follower analysis. The result shows that the topic‐based networks and the person‐based networks play a different role in social networks. This study demonstrates that the presidents, the World Health Organization (WHO) and its regional offices, the Centers for Disease Control, and news channels play a crucial role in the news of COVID‐19 for people. Key players, such as Donald Trump, Barack Obama, and BBC, are located in the central networks. In contrast, U.S. news channels and WHO and its regional offices have independent channels. Conclusions Governments should understand the characteristics of public key players to provide information for COVID‐19 in a timely manner.
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India has experienced an early and harshest lockdown from 25th March 2020 in response to the outbreak. However, an accurate estimation of the progression of the spread of infection and the level of preparedness to combat this disease are urgently needed. Using a data-based mathematical model, our study has made predictions on the number of cases that are expected to rise in India till 14th June 2020. The epidemiological data of daily cases have been utilized from 25th March (i.e., the first day of lockdown) to 23rd April 2020. In the study, we have stimulated two possible scenarios (optimistic and pessimistic) for the prediction. As per the optimistic approach of modelling, COVID-19 may end in the first week of June 2020 with a total of 77,900 infected cases including 2,442 fatalities. However, the results under the pessimistic scenario are a bit scary as it shows that a total of 283,300 infected cases with 10,180 fatalities till 14th June. To win the battle, 10 weeks of complete lockdown is much needed at least in the infected states and the union territories of India. Alternatively, the isolation of clusters (hotspot regions) is required if India wants a resume of some essential activities.
As the world struggles through the COVID-19 pandemic, we should also be asking what systems-level measures will be needed to prevent this or even worse disasters from happening in the future. We argue that the pandemic is merely one of potentially myriad and pleiomorphic future global disasters generated by the same underlying dynamical system. We explain that there are four broad but easily identifiable systemic, pathologically networked conditions that are hurtling civilization toward potential self-destruction. As long as these conditions are not resolved, we should consider catastrophe as an inevitable emergent endpoint from the dynamics. All four conditions can be reversed with collective action to begin creating an enduring and thriving post-COVID-19 world. This will require maximal application of the precautionary principle.
A long-standing question in infectious disease dynamics concerns the role of transmission heterogeneities, driven by demography, behavior and interventions. Based on detailed patient and contact tracing data in Hunan, China we find 80% of secondary infections traced back to 15% of SARS-CoV-2 primary infections, indicating substantial transmission heterogeneities. Transmission risk scales positively with the duration of exposure and the closeness of social interactions and is modulated by demographic and clinical factors. The lockdown period increases transmission risk in the family and households, while isolation and quarantine reduce risks across all types of contacts. The reconstructed infectiousness profile of a typical SARS-CoV-2 patient peaks just before symptom presentation. Modeling indicates SARS-CoV-2 control requires the synergistic efforts of case isolation, contact quarantine, and population-level interventions, owing to the specific transmission kinetics of this virus. One Sentence Summary Public health measures to control SARS-CoV-2 could be designed to block the specific transmission characteristics of the virus.