Visualising COVID-19 Research

Abstract

The world has seen in 2020 an unprecedented global outbreak of SARS-CoV-2, a new strain of coronavirus, causing the COVID-19 pandemic, and radically changing our lives and work conditions. Many scientists are working tirelessly to find a treatment and a possible vaccine. Furthermore, governments, scientific institutions and companies are acting quickly to make resources available, including funds and the opening of large-volume data repositories, to accelerate innovation and discovery aimed at solving this pandemic. In this paper, we develop a novel automated theme-based visualisation method, combining advanced data modelling of large corpora, information mapping and trend analysis, to provide a top-down and bottom-up browsing and search interface for quick discovery of topics and research resources. We apply this method on two recently released publications datasets (Dimensions' COVID-19 dataset and the Allen Institute for AI's CORD-19). The results reveal intriguing information including increased efforts in topics such as social distancing; cross-domain initiatives (e.g. mental health and education); evolving research in medical topics; and the unfolding trajectory of the virus in different territories through publications. The results also demonstrate the need to quickly and automatically enable search and browsing of large corpora. We believe our methodology will improve future large volume visualisation and discovery systems but also hope our visualisation interfaces will currently aid scientists, researchers, and the general public to tackle the numerous issues in the fight against the COVID-19 pandemic.

Full text

Visualising COVID-19 Research
Pierre Le Bras, Azimeh Gharavi, David A. Robb, Ana F. Vidal, Stefano Padilla, and Mike J. Chantler
Strategic Futures Laboratory, School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, UK.
{ P.Le_Bras, AG72, D.A.Robb, AF69, S.Padilla, M.J.Chantler }@hw.ac.uk
12 May 2020
ABSTRACT
The world has seen in 2020 an unprecedented global outbreak of SARS-CoV-2, a new strain of coronavirus, causing the
COVID-19 pandemic, and radically changing our lives and work conditions. Many scientists are working tirelessly to
nd a treatment and a possible vaccine. Furthermore, governments, scientic institutions and companies are acting
quickly to make resources available, including funds and the opening of large-volume data repositories, to accelerate
innovation and discovery aimed at solving this pandemic. In this paper, we develop a novel automated theme-based
visualisation method, combining advanced data modelling of large corpora, information mapping and trend analysis, to
provide a top-down and bottom-up browsing and search interface for quick discovery of topics and research resources.
We apply this method on two recently released publications datasets (DimensionsâĂŹ COVID-19 dataset and the Allen
Institute for AIâĂŹs CORD-19). The results reveal intriguing information including increased eorts in topics such as
social distancing; cross-domain initiatives (e.g. mental health and education); evolving research in medical topics; and
the unfolding trajectory of the virus in dierent territories through publications. The results also demonstrate the need
to quickly and automatically enable search and browsing of large corpora. We believe our methodology will improve
future large volume visualisation and discovery systems but also hope our visualisation interfaces will currently aid
scientists, researchers, and the general public to tackle the numerous issues in the ght against the COVID-19 pandemic.
Keywords: COVID-19; Coronavirus; SARS-COV-2; Information Visualisation; Research Corpora; Topic Modelling.
Fig. 1. Our online system demonstrates the hierarchical topic visualisation of the DimensionsâĂŹ COVID-19 dataset. The principal
panel on the le shows the at-a-glance overview of all the resources in the dataset. The panels on the right visualise the sub-topics
when selecting a main topic, the topic descriptions, the trend analysis, and the drill-down to available resources. Moreover, the system
includes a search capability for the boom-up discovery of known areas and support for advanced users. The interactive visualisation
is available on our research lab pages: hp://strategicfutures.org/TopicMaps/COVID-19/dimensions.html.
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arXiv:2005.06380v1 [cs.IR] 13 May 2020

Visualising COVID-19 Research Strategic Futures Lab, Heriot-Wa University, Edinburgh
1 INTRODUCTION
As the scientic community grapples with the search for solutions to the COVID-19 pandemic, it also needs to accelerate
the pace of innovation [
5
]. Globally, research funding organisations are seeking to further drive an acceleration in
research with additional short duration calls, schemes to re-purpose existing funding and opening research resources,
e.g. [
1
,
2
]. With this need for faster-paced research comes a need for timely processing, assimilation and appreciation of
the continually growing and evolving body of research literature that is originating rapidly from the global research eort.
Recent studies have examined the growing literature on COVID-19 and employed various methods and techniques to
analyse it. Haghani et al. [
15
] use bibliometric analysis with heat maps, pie charts, and bar graphs to describe COVID-19
research areas and their relative importance. Others combine bibliometrics with text mining. Hossain [
16
] does this, using
bibliometrics combined with text mining for word co-occurrence and factorial analysis of the top keywords visualised
in network diagrams and dendrograms. Similarly (to Hossain) Aguado-CortÃľs & CastaÃśo [
4
] use bibliometrics and
concurrence of keywords with network diagrams to visualise the analysis. Fister et al. [
10
] use association rule text
mining [
3
], then analyse word relationships and employ bar chart and word cloud visualisations. Wang et al. [
24
] create
an application which uses distantly supervised named entity recognition [
25
] and facilitates text queries. It visualises
query results with a doughnut chart. One study by Domingo-Fernandez et al. [
9
] took a subset of the literature focusing
on drug targets, generated a network graph by manually annotating evidence text from the corpus with Biological
Expression Language (BEL) and explored the network graph with web applications built for the task. Lastly, Ahamed &
Samad [
5
] use a method of topic analysis where topics are identied using betweenness centrality measurement [
12
].
Subgraphs are then generated for the inuential topics, and they examine several topics in detail through these subgraphs.
In this paper, we introduce a COVID-19 research knowledge visualisation designed to help researchers and research
strategists to get an at-a-glance overview of the research landscape (Figure 1). This overview is laid out by topics, and it
is valuable to (a) identify connections between research areas, (b) recognise trends over time by visualising research
volume in specic topics, and (c) nd available resources from these large volume datasets of research. We believe our
approach to visualise the relationships between the concepts in the COVID-19 research oers a more suitable pipeline
for a frequently updateable visualisation.
We use Latent Dirichlet Allocation (LDA) [
6
] to build a topic model from the research corpus titles and abstracts in a
similar manner to Padilla et al. [
21
]. LDA allows control of the number of topics and thus the ability to choose a suitable
level of abstraction for both overview and detailed sub-topics. The topics can be interrogated intuitively to reveal more
detail, and further visualised with a word cloud and a trend chart showing how the volume of research in that topic has
changed over time. Our method which we describe in Section 2, uses a pipeline perfected over recent years allowing a
rapid and ecient generation of a newly updated visualisation as the corpus grows and develops, thus allowing ecient
and timely updating of the visualisation.
We rst describe, in this paper, the methodology we use to create our visualisation for analysing the COVID-19
research landscape and present the open visualisation itself, providing the web address where it can be accessed. Then
with the aid of screenshots from our interactive visualisations of two research corpora, we describe four research trends
which illustrate the benets that our techniques bring to the understanding of COVID-19 and Coronavirus research.
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Visualising COVID-19 Research Strategic Futures Lab, Heriot-Wa University, Edinburgh
Fig. 2. Our approach to visualising research publications. Starting from the publication titles and abstracts, we generate a hierarchical
topic model: a main topic model with tens of topics, and a sub-topic model with hundreds of topics. Each sub-topic is assigned to one
main topic based on their document vector similarity. Topic trends are then extracted, and topics are mapped into bubble maps: the
top-level map containing the main topics, and tens of sub-maps, one for each main topic, containing sub-topics.
Finally, we conclude by summarising the benets of our techniques and our new COVID-19 research landscape analysis
and visualisation tool.
In summary, the contributions of this paper are:
(1)
We explore a novel automated theme-based visualisation methodology combined with data modelling of large
corpora of COVID-19 resources.
(2)
We develop COVID-19 research information mapping and trend analysis to provide a top-down and bottom-up
browsing and search interface for quick discovery of topics and resources.
(3) We reveal interesting information from open COVID-19 research datasets using our visualisation and method.
(4)
We provide an open interface for discovering COVID-19 research with the aim to aid in solving various issues of
the current pandemic.
2 METHODOLOGY
In this section, we present our methodology for visualising research information of a large volume. In particular, we
use topic modelling to abstract thousands of research documents into a smaller hierarchical set of themes. We then
estimate the trends of these topics and group these into simplied bubble treemaps to create semantic overviews. Figure 2
illustrates this automated process.
2.1 Datasets
Our methods and visualisations are automatically generated from research corpora. This automated approach is particu-
larly suited for rapidly evolving knowledge datasets as it requires little oversight, manipulation, and can avoid common
delays frequently encountered in manual classications and processes.
We have chosen to rst work on the Dimensions COVID-19 research dataset [
22
] as it curates, from multiple sources,
the details of publications submitted in the last four months investigating the COVID-19 outbreak. We extracted
the publications’ title and abstract; from our experience, we have found this information is sucient for generating
meaningful representative topics (Figure 3in red). In total, 17
,
015 publications were retrieved from the 16
th
version of
this dataset, dated from the 23
rd
of April 2020. Moreover, our process can scale to any corpus input, and we have also
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Visualising COVID-19 Research Strategic Futures Lab, Heriot-Wa University, Edinburgh
Fig. 3. The two datasets chosen for the visualisation system. On the le and in red is the representation of the Dimensions COVID-19
research dataset, and on the right in blue is the representation of the COVID-19 Open Research Dataset (CORD-19).
chosen to separately visualise the COVID-19 Open Research Dataset (CORD-19) [
11
], as it adds a historical perspective
on coronaviruses research (Figure 3in blue). In total, 59
,
875 publications’ titles and abstracts were retrieved from the
12
th
version of this dataset, dated from the 4
th
of May 2020. The rest of the process is entirely automated using an
algorithmic pipeline which consists of three main phases: modelling topics,estimating trends, and mapping topics. These
stages are explained in detail in the next subsections.
2.2 Modelling Topics
We produced a two-level hierarchical topic model to allow for fast visual discovery, cognitive processing, and cognitive
retention of the topics in users. We did so by separately modelling 30 topics and 200 topics from the same publication data
(Dimensions), using MALLETâĂŹs implementation of Gibbs Sampling for Latent Dirichlet Allocation (LDA) [
6
,
14
,
18
].
Care was taken to process the datasets appropriately; this includes removing general stop words, lemmatising terms
and choosing the most benecial parameters for the implementation. To do so, we used knowledge from previous work
[
17
] and followed recommendations from Boyd-Graber et al. [
7
]. Each sub-topic (200) was then assigned to the one
main topic (30) with the least cosine distance between their document vectors. Because the CORD-19 dataset contained
more historical publications (e.g. SARS and MERS), we used larger numbers of topics to model: 50 main topics and 400
sub-topics.
2.3 Estimating Trends
We believe it is valuable to understand the evolution of the themes. While topic modelling oers the ability to abstract
thousands of documents into a more digestible set of themes, it is also essential to represent the time trajectory of the
resources to enable researchers to compare current trends of research quickly. Using the publication date information,
and the topic weights in each publication, we were able to construct the distribution of each topic over time. This
distribution details, per topic and across dates, the sum of this topic’s weights for publications of that date
1
. These trend
charts are displayed when a user interacts with a topic as shown in Figure 1for the Social-Measure-Intervention sub-topic.
1
It was noted that, in the Dimensions dataset, some publications where scrapped with only the information “2020”. As a result, they were defaulted to the
1st of January, making this date showing an unusual volume of publications. To simplify the reading of trend charts, we have excluded this date.
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Visualising COVID-19 Research Strategic Futures Lab, Heriot-Wa University, Edinburgh
Fig. 4. Top-level overview of the COVID-19 research publications in the Dimensions dataset. Each bubble contains the top labels of the
extracted topics, and the bubble size show the proportion of the topic. The placement and clustering of bubbles depict the relative
similarities between topics. We have found that three significant regions emerged: Patient Care at the top right-hand side; Virus and
Micro-Biology at the le-hand side; and Pandemic at the boom.
2.4 Mapping Topics
We use a novel implementation of bubble treemaps to visualise our sets of topics [
13
]. We choose this representation as
it allows us to show (a) the topics relative similarities with the placement of bubbles, (b) the importance of the topic
with the size of bubbles, and (c) clusters of topics to facilitate cognitive recognition and memory. We rst computed
the hierarchical topic similarities using the cosine distance between the topics’ document vectors, and agglomerative
clustering with a complete linkage criterion [
23
]. Then, the bubble treemap placement algorithm on this hierarchy
allowed us to position similar topics next to one another. We used the sum of each topic weights across publications to
set the bubble sizes, showing the topics relative importance in the dataset. To represent our two-level topic hierarchy, we
rst mapped the main topic model, the resulting visualisations for both datasets are shown in Figure 3. Then, for each
main topic, we grouped the associated sub-topics and mapped these groups independently from one another. It resulted
as a set of sub-maps, each accessible from their main topic. Finally, this automated methodology allowed us to quickly
abstract and visualise the content of thousands of research publications on COVID-19 and coronaviruses. In the next
sections, we present these visualisations and highlight compelling ndings within them.
3 OVERVIEW OF COVID-19 RESEARCH
We present the bubble map overview of COVID-19 Research in Figure 4. Because it focuses specically on the latest
publications regarding COVID-19, this map uses the Dimensions datasets described in the previous section. While this
visualisation depicts eight clusters2, we can identify three major regions in this map
(1)
On the top right-hand side corner, we see topics dealing with patients,treatment, and care. These cover domains
such as common symptoms and clinical trials of possible treatments for COVID-19, modelling of the disease,
hospital management, transmission research, and mental health concerns.
2We have selected this number as it compromised on quantity and quality of clusters.
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Visualising COVID-19 Research Strategic Futures Lab, Heriot-Wa University, Edinburgh
(a) In the main topic Measure-Model-
Social, the sub-topic Social-Measure-
Intervention predominates.
(b) Both main and sub-topics about so-
cial distancing show a steep increase
in terms of volume of research, start-
ing from February 2020.
(c) The word cloud for the sub-topic
Social-Measure-Intervention informs
us on the vocabulary used to describe
this issue.
Fig. 5. In the Dimensions dataset, social distancing is shown as a trending research topic.
(2)
On the left-hand side of the map, the topics seem to represent domains like virology and micro-biology. It is in this
region that we nd research investigating the actual virus SARS-COV-2, its genome, testing, drugs eects, and
possible vaccines.
(3)
At the bottom of the map, we nd bigger topics, all with regards to the pandemic and its consequences. This
includes modelling the pandemic, and discussion about its economic political, and societal impacts.
In the next section, we present four detailed analyses indicative of the emerging and rapidly changing focuses this
pandemic has unfold.
4 DISCUSSION
Virology, vaccine, antiviral, health, and treatment research gratefully constitute the core of the scientic response to
COVID-19. This unprecedented situation has, however, also seen the emergence of research in many other elds. By
modelling these topics, and mapping them in semantic layouts, we were able to discover exciting themes, navigate this
research and highlight interesting trends.
4.1 Unprecedented Social Distancing
Three months after rst being reported, by March 2020, this pandemic has launched an unprecedented global response
to slow down its spread. For many countries, this response meant limiting human contacts (and possible transmission) to
the maximum, hence introducing social distancing. We examine the importance of this issue in research, as illustrated
in Figure 5a, which shows the sub-topic Social-Measure-Intervention predominating other sub-topics in the main topic
Measure-Model-Social. We can also measure the rapid ascent of this theme from February, to March and April 2020 in
Figure 5b, corresponding to the point when many countries followed ChinaâĂŹs suite and put lockdown measures in
action.
To get a broader perspective, we have searched for similar topics in the CORD-19 dataset, which comprise older
research on coronaviruses (from 1951). There, we nd that social distancing is only mentioned along COVID (Figure 6a).
Furthermore, while recent papers constitute 25% of the whole corpus, we can see that the trend for this sub-topic only
shows an increase in 2020 (Figure 6b, lower line). It could be in part due to COVID-19 being categorised in this sub-topic,
however, checking the other labels in the sub-topic (Figure 6c), we only nd a few occurrences of COVID related terms
(Spread,Epidemic,Pandemic) compared to a larger number of labels describing social distancing (Measure,Lockdown,
Quarantine,Intervention,Mitigation,Mobility). It reects that this is the rst time, in peacetime, that a lockdown of this
6

Visualising COVID-19 Research Strategic Futures Lab, Heriot-Wa University, Edinburgh
(a) We only find mention of social dis-
tancing in the topic Model-Epidemic-
Time, and it is only next to COVID.
(b) The historical trend of social dis-
tancing (lower line) only seems to
show an increase in 2020.
(c) Although we could expect the topic
trend to be caused by the term COVID,
we see that most labels of that topic
focus on distancing.
Fig. 6. The CORD-19 dataset interface allows us to explore a historical perspective on social distancing.
scale has been imposed to slow the exponential growth in transmission rate.
We found that the research categorised in these topics can provide a resource to understand and optimise social
distancing measures, such as (a) drawing models of the costs and benets of these measures against their eectiveness;
(b) analysing the impact of asymptomatic carriers; (c) suggesting eective exit strategies; or (d) predicting long term
social and travel behaviours. Measures such as social distancing have also triggered society to adapt and nd new ways
of continuing its activities. We discuss these societal impacts below and how scientists address them.
4.2 Research Responding to Society’s Concerns
Topic modelling is a technique that allows us to analyse large amounts of information to get an overview of all emerging
issues in text corpora. As such, we can gain access to often-overlooked topics or themes. In the Dimensions dataset, for
example, we can nd a group of sub-topics, under Education-Health-State, all with regards to the societal impacts of this
pandemic. We show these topics in Figure 7a. Although a large portion of publications describes a Pandemic,Crisis, or
Challenge, we nd that they also address issues such as public health and individual rights, mental health, education, and
economy.
Responding to any outbreak requires policymakers to modify or enact new public health laws. The COVID-19 pan-
demic, however, has seen an even greater and more urgent public health response due to the high transmission rate of
the virus. Research has, therefore, emerged to investigate the impact of these measures on individual rights [
19
]. One
common concern is individual liberty faced with forced social distancing and isolation. Another is medical privacy versus
the prevention of virus spread, i.e. by reporting and disclosing patientsâĂŹ name to their employers, or large-scale
surveillance and contact tracing. This latter example is even large enough to have its own sub-topic, where technological
vocabulary meets with ethics and privacy (Figure 7b).
As we have seen before, social distancing has been a signicant consequence of this pandemic. It is therefore natural
for researchers to explore the eects of such measures. We have found three major elds of research in our topic maps.
The rst concerns mental health, and seems to follow two paths. On the one hand, there is research on the eectiveness
of new approaches for pursuing existing therapies remotely, either one-on-one or group support meetings to manage
addiction. On the other hand, we recognise studies focusing on the overall populationâĂŹs stress, anxiety, and depression
facing the pandemic, as well as concerns towards the mental strain on medical and care sta working endlessly to help
7

Visualising COVID-19 Research Strategic Futures Lab, Heriot-Wa University, Edinburgh
(a) With crises and challenging times
emerge new issues regarding rights,
education, or mental health.
(b) Contact tracing and surveillance
can enable beer prevention, but
needs strong ethical considerations.
(c) Mental health can be challenging
in diicult times; it is therefore cru-
cial to understand and address such
issues.
Fig. 7. With Topic Modelling we can explore the emergence of new concerns and research with regards to the impacts of this pandemic
on society.
patients. The importance of that eld has made it one of the main topics in the Dimensions dataset, from which we show
the detailed submap in Figure 7c.
The second addresses the new challenges for education. With schools and universities closing to protect their students
and prevent the virus from spreading, teachers and administrations have to adapt to provide learning resources remotely.
It is then necessary to study the eectiveness of large-scale home-schooling, as well as new online teaching methods
for elementary, higher and university levels. On a more direct response to the outbreak, we have also found a shift in
medical training, where advanced medical students are oered to start their internship early to help treat patient and
relieve stress on the other medical sta.
Finally, there has also been signicant concerns around the economic impacts of this pandemic, present and future. In
particular, we see economists and actuarial scientists analysing its damaging eects on the world market, stock prices,
countries’ local economies; moreover, they are trying to provide solutions to such issues. We also found that research
into modern digital currencies and banking has been carried out to understand their potential as a solution to slow and
prevent the progression of the virus.
4.3 Responding to a New Disease
Looking at terms such as Pneumonia and SARS-CoV-2 probably highlights best the scientic community’s response to a cri-
sis of this sort. Looking at the topic Treatment-Coronavirus-Pneumonia and its large sub-topic about Pneumonia (Figure 8a),
we nd that this theme has peaked in February, only to decline in the following two months (Figure 8b). We suspect that
it reects the rst stage of a new disease outbreak: medical sta report on the observation and study of similar symptoms
rising in number of cases [
8
]. Meanwhile, temporarily only known as 2019-nCoV, the virus responsible for this epidemic
is sparingly used in publications. It is only by March, when it is named by the Coronaviridae Study Group (CSG) as SARS-
CoV-2 [
20
], that we see a net increase of its mention in publication (Figure 8c), taking over the initial reports on Pneumonia.
At the same time, looking at terms like Simulation reveals the massive eort that has gone into developing better
computational models to inform government policy. Many scientists have been working against the clock to use the
limited available data to make predictions about the evolution of the pandemic. The interface we developed can help
speed up progress by helping scientists nd relevant related publications. In Figure 9we show an excerpt of the top
8

Visualising COVID-19 Research Strategic Futures Lab, Heriot-Wa University, Edinburgh
(a) The sub-map of topic Treatment-
Coronavirus-Pneumonia shows how
treating pneumonia has been as crit-
ical as preventing and controlling the
epidemic.
(b) Looking at the trend for topics
around Pneumonia, we see it peaks in
February, before slowly decreasing.
(c) The trends for SARS and COV have
only gone up, and show a steep in-
crease aer February.
Fig. 8. Trend analysis allows us to see evolution in medical research.
(a) The sub-map of topic Epidemic-
Model-Time
(b) An excerpt from the top document list. We highlight, in blue, a relevant
publication that would be diicult to discover just from its title.
Fig. 9. The CORD-19 dataset interface allows researchers to discover relevant related work that might otherwise be diicult to find
from the publication titles alone.
documents for the sub-topic Data-Model-Predict within the topic Epidemic-Model-Time and we highlight in blue one
particular publication which would be otherwise dicult to nd due to the lack of epidemic-related keywords in its title.
4.4 Tracking the International Epidemic
A close examination of the topics in the Pandemic region of the main topic map (see Figure 4) allows us to visualise the
trends in the volume of publication which share topics including particular countries. Upon closer inspection, we found
that we can track the progress of the pandemic around the world by following the volume of publications. We show the
evolution of these trends in Figure 10. In Figure 10a, we see how both main and sub-topics featuring Wuhan and China
display a trend showing publication volumes peaking in March and dropping back in April. Then, if we examine the
sub-topics of the Model-Case-Number main topic, we can see that the publication volumes trend for the sub-topic featuring
Korea,Japan,Iran, and Italy rose and levelled o (Figure 10b). For the Europe sub-topic (Italy-Spain-France-Germany),
the publication volumes rose and have kept rising. Lastly, the India sub-topic shows publication volumes which are just
beginning to rise. Using topic modelling on large corpora, combined with trend analysis, we can see how scientists have
followed the progress of the pandemic through their publications. The interface, moreover, can help predict the trends in
other countries and discover exciting movements in the relevance of other various research topics of the pandemic.
9

Visualising COVID-19 Research Strategic Futures Lab, Heriot-Wa University, Edinburgh
(a) Publications around the out-
break in China where numerous,
but only peaked in March.
(b) Publications around the out-
break in Korea, Japan, and Iran
grew in March and plateaued
through April (lower line).
(c) Publications around the out-
break in Europe (Italy, Spain,
France, and Germany) have
grown steadily from March into
April (lower line).
(d) Publications around the out-
break in India only appeared in
April (lower line).
Fig. 10. The trend analysis allows us to trace the virus spread through research publications (note that Fig. 10a shows both main and
sub-topics about Wuhan and China; Fig. 10b,10c, and 10d show sub-topics nested under the main topic Case-Model-Number).
5 CONCLUSION
In this paper, we have presented one of the rst works combining analysis and visualisation of large-volume literature
datasets to highlight the impact of COVID-19 on many research communities. We show that it is possible to integrate
advanced statistical topic modelling techniques into a visualisation pipeline which quickly: (a) abstracts thousands of
publication entries into smaller themes; (b) extracts trend information; and (c) produces at-a-glance semantic visual
overviews of rapidly changing corpora. This method, its techniques and interfaces, can help scientists browse, search,
and access knowledge faster, and stay abreast of evolving themes.
We have presented analysis, using topic and visual information, through dierent themes to summarise interesting
aspects of the information inside the large volume of research literature. This analysis highlights: (a) the development of
research regarding social distancing for the rst time in 70 years; (b) insights into cross-domain initiatives to understand
the consequences of this unprecedented situation; (c) the evolution in medical topics; and (d) the unfolding of the
pandemic through publications. We hope the methods and ndings may be useful as a reference guide for similar systems,
to stimulate new ideas and directions of research, and to help in the ght against this pandemic.
RESOURCES
The datasets used in this paper can be found here:
•
https://dimensions.gshare.com/articles/Dimensions_COVID-19_publications_datasets_and_clinical_trials/11961063
•https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge
We have made available the visualisation interfaces at the following addresses:
•http://strategicfutures.org/TopicMaps/COVID-19/dimensions.html
•http://strategicfutures.org/TopicMaps/COVID-19/cord19.html
ACKNOWLEDGEMENTS
This work was funded by the ORCA Hub (EPSRC grant: EP/R026173/1, website: orcahub.org) and the Exploiting Impact
Using a Modular Decision-Making Toolset project (EPSRC Impact Acceleration Account: EP/R511535/1).
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Visualising COVID-19 Research Strategic Futures Lab, Heriot-Wa University, Edinburgh
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