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This is a Technical Report. Cite as: Siddique Latif, Muhammad Usman, Sanaullah Manzoor, Waleed Iqbal, Junaid Qadir, Gareth Tyson, Ignacio Castro, Adeel Razi, Maged N. Kamel Boulos, Adrian Weller and Jon Crowcroft. Leveraging Data Science To Combat COVID-19: A Comprehensive Review (2020). Companion site: https://github.com/Data-Science-and-COVID-19/Leveraging-Data-Science-To-Combat-COVID-19-A-Comprehensive-Review Abstract: COVID-19, an infectious disease caused by the SARS-CoV-2 virus, was declared a pandemic by the World Health Organisation (WHO) in March 2020. At the time of writing, more than 2.8 million people have tested positive. Infections have been growing exponentially and tremendous efforts are being made to fight the disease. In this paper, we attempt to systematise ongoing data science activities in this area. As well as reviewing the rapidly growing body of recent research, we survey public datasets and repositories that can be used for further work to track COVID-19 spread and mitigation strategies. As part of this, we present a bibliometric analysis of the papers produced in this short span of time. Finally, building on these insights, we highlight common challenges and pitfalls observed across the surveyed works. We also created a live resource repository that we intend to keep updated with the latest resources including new papers and datasets. DOI: 10.36227/techrxiv.12212516.v1
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1
Leveraging Data Science To Combat COVID-19:
A Comprehensive Review
Siddique Latif1,2, Muhammad Usman3,4 , Sanaullah Manzoor5, Waleed Iqbal6, Junaid Qadir5, Gareth Tyson6,11,
Ignacio Castro6, Adeel Razi7,8, Maged N. Kamel Boulos9, Adrian Weller10,11, and Jon Crowcroft10,11
1University of Southern Queensland, Australia
2Distributed Sensing Systems Group, Data61, CSIRO Australia
3Seoul National University, Seoul, South Korea
4Center for Artificial Intelligence in Medicine and Imaging, HealthHub Co. Ltd., South Korea
5Information Technology University, Punjab, Pakistan
6Queen Mary University of London, United Kingdom
7Turner Institute for Brain and Mental Health & Monash Biomedical Imaging, Monash University, Australia
8Wellcome Centre for Human Neuroimaging, University College London, United Kingdom
9Sun Yat-sen University, Guangzhou, China
10University of Cambridge, United Kingdom
11Alan Turing Institute, United Kingdom
Abstract—COVID-19, an infectious disease caused by the
SARS-CoV-2 virus, was declared a pandemic by the World Health
Organisation (WHO) in March 2020. At the time of writing, more
than 2.8 million people have tested positive. Infections have been
growing exponentially and tremendous efforts are being made
to fight the disease. In this paper, we attempt to systematise
ongoing data science activities in this area. As well as reviewing
the rapidly growing body of recent research, we survey public
datasets and repositories that can be used for further work to
track COVID-19 spread and mitigation strategies. As part of
this, we present a bibliometric analysis of the papers produced
in this short span of time. Finally, building on these insights,
we highlight common challenges and pitfalls observed across the
surveyed works. We also created a live resource repository1that
we intend to keep updated with the latest resources including
new papers and datasets.
Index Terms—COVID-19, SARS-CoV-2, data science, artificial
intelligence, machine learning, medical image analysis, text min-
ing, speech analysis, bibliometric analysis
IMPAC T STATEM EN T
Data science, defined broadly, will play a central role in
the global response to the COVID-19 pandemic. This review
facilitates the rapid engagement of interested data science and
AI researchers with the breadth of the ongoing research work. In
particular, we identify the major challenges involved, the promis-
ing directions for further work, and the important community
resources. Given the interdisciplinary nature of the challenge,
this review will help data scientists form collaborations across
disciplines. This review also elaborates the benefits of data science
to strategists and policymakers and helps them in coming to grips
with the challenges, opportunities, and pitfalls involved in using
data science to combat the COVID-19 pandemic.
Email: siddique.latif@usq.edu.au
1
https://github.com/Data-Science-and- COVID-19/
Leveraging-Data-Science- To-Combat-COVID-19- A-Comprehensive-Review
I. INTRODUCTION
The SARS-CoV-2 virus and the associated disease (desig-
nated as COVID-19) was first identified in Wuhan city (China)
in December 2019 [1]–[3], and was declared a pandemic by
the World Health Organisation (WHO) on 11 March 2020.
2
At the time of writing,
3
the Centre for Systems Science and
Engineering at Johns Hopkins University reported 2,790,986
confirmed cases, 195,920 deaths, and 781,382 recovered.
Since December 2019, over 24,000 research papers from
peer-reviewed journals as well as sources like medRxiv are
available online [4]. Understanding this rapidly moving research
landscape is particularly challenging since much of this
literature has not been vetted through a peer-review process
yet. This paper tries to overcome this challenge by presenting
a detailed overview and survey of data science research related
to COVID-19. It is intended as a community resource to
facilitate accessibility to the large volume of data and papers
published in recent months. We use the term ‘data science’ as an
umbrella term that encompasses all techniques that use scientific
methods, algorithms, and systems to learn from structured and
unstructured data. We recognise the importance of associated
perspectives from the social sciences, ethics, history, and other
humanities, but those areas are beyond the focus of this work.
In examining this growing landscape of data science research
regarding COVID-19, we make the following five contributions.
First, we present pressing research problems related to COVID-
19, for which data scientists may be able to contribute. Second,
we summarise publicly available COVID-19 datasets that are
being used to drive research, and list how they could be utilised
to address some of the aforementioned problems. Third, we
survey some of the ongoing research in the area, highlighting
2https://tinyurl.com/WHOPandemicAnnouncement
32:31 am Saturday, 25 April 2020 Coordinated Universal Time (UTC)
2
the main topics covered. As our primary audience is computer
scientists and engineers, we theme our discussion around the
types of data analysis. Fourth, we broaden our analysis and
present a bibliometric study of the rapidly growing literature
on COVID-19. Fifth, bringing together our observations, we
highlight some of the common challenges in this fast-moving
space. We intentionally cast a wide net, covering research from
several technical areas surrounding data science.
This paper builds upon recent reviews and perspective
papers [5], [6] to help systematise existing resources and
support the research community in building solutions to the
COVID-19 pandemic. We have attempted in this review to
be comprehensive and provide an up to date literature review,
and highlight important applications, community resources,
publication trends, and challenges of data science research in
COVID-19. However, in a rapidly-evolving field such as this,
it is not possible for us to aim for exhaustiveness. Nonetheless
we hope that our work will provide a very useful introduction
to the field for researchers interested in this area.
The rest of this paper is organised as follows. In Section
II, we present possible use cases where data science can help
address COVID-19 challenges. In Section III, we present the
details of available datasets and resources. In Section IV, we
review contributions made by data scientists including image
analysis, textual data mining, audio analysis, and embedded
sensing. In Section Vwe present a bibliometric analysis of
the COVID-19 related papers. Next, we discuss common
challenges facing this research in Section VI. Finally, Section
VII concludes the paper. The detailed organisation of the paper
including details of the subsections can be seen in Table I.
II. AP PL IC ATIO NS O F DATA SCIENCE FOR COVID-19
Data science is a broad term covering topics such as Machine
Learning (ML), statistical learning, time-series modelling, data
visualisation, expert systems and probabilistic reasoning. In
this section, we summarise some of the key research use cases
to which data scientists may be able to contribute in countering
COVID-19.
A. Risk Assessment and Patient Prioritisation
Healthcare systems around the world are facing unprece-
dented pressures on their resources (e.g., availability of inten-
sive care beds, respirators). This creates the need to rapidly
assess and manage patient risk, while allocating resources
appropriately. In periods of peak load, this must be done rapidly
and accurately, creating a substantial challenge for healthcare
professionals who may not even have access to historical patient
data. Various studies have already proposed algorithmic risk
assessments of diseases such as cancer [7], diabetes [8], and
cardiac-related diseases [9] with Artificial Neural Networks
(ANNs). Due to diverse symptoms and disease trajectories,
researching technologies for data-driven risk assessment and
management in individual COVID-19 patients would be useful.
For instance, traits like age, gender, or health state can be
utilised to provide an estimate of mortality risk. This is
particularly important when resources are limited, e.g., for
patient prioritisation when Intensive Care Unit (ICU) resources
are insufficient.
B. Screening and Diagnosis
A major issue facing countries with growing COVID-19
infection rates is the lack of proper screening and diagnosis
facilities. This further complicates capacity management as
well as social distancing measures, since those with mild
symptoms are often unaware they carry the disease. A key
use case is to develop remote computational diagnosis tools.
Some already exist, which could be expanded, e.g., Babylon
is a mobile app that provides medical advice via questioning.
Other solutions could rely on valuable data from wearable or
other monitoring devices, e.g. for audio, COVID-19 Sounds
is a mobile app collecting audio of breathing symptoms to
help perform diagnosis.
4
We posit that such research will
be particularly useful in developing countries that have a
shortage of healthcare facilities [10]. Automated tools can
also be developed to facilitate screening in larger groups of
people (e.g., at airports), e.g., using computer vision based
thermal imaging to detect fever [11].
C. Simulation and Modelling
Currie et al. [12] provide a detailed review of how models
and simulations can help reduce the impact of COVID-19. For
example, accurate epidemiological models are indispensable
for planning and decision making. Here we discuss some of
the potential modelling and simulation use cases.
1) Epidemic Models: Epidemic models are used to predict
the macroscopic behaviour of an infectious disease. A key
use case is developing and parameterising such models. For
example, in epidemiology, compartmental models are widely
used [13]. In these models, populations are divided into
compartments and the flow of people among compartments is
modelled using (ordinary) differential equations. For example,
COVID-19’s spread has recently been modelled using the SEIR
model [14], [15], which models the flow of people between
four states (or compartments): susceptible (S), exposed (E),
infected (I), and recovered (R).
Generative models represent another broad class of models
which proceed by generating consequences from causes (using
hidden states and parameters). An example generative model
is based upon ensemble or population dynamics that generate
outcomes (new cases of COVID-19 over time) [15]. Such
approaches can capture the effects of interventions (e.g., social
distancing) and differences among populations (e.g., herd immu-
nity) to predict what might happen in different circumstances in
a single region [16]. Using (Bayesian) hierarchical modelling,
one can combine several of these (epidemic) models to create
a (pandemic) model of viral spread among regions [17]. For
interested readers, websites offering COVID-19 forecasting
have emerged
5
, each using a different model (although they
should be treated with caution due to the uncertainty of such
predictions [18] [19]).
Parameterising the above models requires up-to-date infor-
mation on the virus spread. Thus, an important use case is
4http://www.covid-19-sounds.org/
5
For example: (1) COVID-19 worldwide peak forecasting method (https:
//www.people.vcu.edu/
tndinh/covid19/en/) and (2) COVID-19 forecasting
(http://epidemicforecasting.org/)
3
TABLE I: Organisation of paper and summary of different sections.
Sections Subsection
(§II) Applications of Data Science for COVID-19
This section highlights different use cases related to the application of
data-driven methodologies for addressing COVID-19. It also discusses
some examples of these use cases.
(§II-A) Risk Assessment and Patient Prioritisation
(§II-B) Screening and Diagnosis
(§II-C) Simulation and Modelling
(§II-D) Contact Tracing
(§II-E) Understanding Social Interventions
(§II-F) Logistical Planning and Economic Interventions
(§II-G) Automated Primary Care
(§II-H) Supporting Drug Discovery and Treatment
(§III) Datasets and Resources
This section provides information about numerous datasets related to
COVID-19. It also gives information about ongoing data science
competitions, and online resources.
(§III-A) COVID-19 Case Data
(§III-B) COVID-19 Textual Data
(§III-C) COVID-19 Biomedical Data
(§III-D) Other Supportive Datasets
(§III-E) COVID-19 Competition Datasets
(§IV) Survey of Ongoing Research
This section surveys ongoing work across several types of data.
It also provides brief summaries of outcomes and methodologies.
(§IV-A) Image Data Analysis
(§IV-B) Textual Data Analysis
(§IV-C) Voice Sound Data Analysis
(§IV-D) Embedded Data Analysis
(§IV-E) Pharmaceutical Research
(§V) Bibliometric Analysis of COVID-19 Research
This section presents a bibliometric analysis of COVID-19 research.
(§V-A) Bibliometric Data Collection
(§V-B) Peer-reviewed vs. Non-peer-reviewed publications
(§V-C) Research Topics
(§V-D) COVID-19 vs. Earlier Epidemics
(§VI) Cross-Cutting Challenges
This section highlights challenges that researchers may face
when performing data-driven research related to COVID-19.
(§VI-A) Data Limitations
(§VI-B) Correctness of Results vs. Urgency
(§VI-C) Security, Privacy, and Ethics
(§VI-D) The Need For Multidisciplinary Collaboration
(§VI-E) New Data Modalities
(§VI-F) Solutions for the Developing World
(§VII) Conclusions
finding ways to better capture such data. For instance, this could
be done by processing social media information to identify
people who are likely to have been infected, or even analysing
ambulance call out data [20]. Another beneficial use case
would be to develop ways to more accurately evaluate “what-
if ” scenarios with these models [18]. As an example, the initial
policy of the UK government (of adopting almost no social
isolation measures) was later changed based on results from an
extended SEIR model from Imperial College London [21]. This
model projected that without interventions there would be up to
half a million fatalities, highlighting the importance of accurate
predictions. A comprehensive review focused on modelling
infectious disease dynamics in the complex landscape of global
health can be seen at [22]. It is also worth mentioning that
there are several national level modelling efforts underway in
the UK, for e.g., The Royal Society is coordinating to bring
together experts from several fields. They have established
the Rapid Assistance in Modelling the Pandemic (RAMP)
6
initiative which is focusing on mechanistic modelling of disease
spread and outcomes and Data Evaluation and Learning for
Viral Epidemic (DELVE)
7
initiative which, on the other hand,
is focusing on data-driven and inferential modelling of COVID-
19.
2) Simulation Models: Simulation models have broad ap-
plicability and can be used in a variety of settings [12],
including decisions that affect disease transmission—e.g.,
decisions related to quarantine and social distancing strategies;
decisions regarding resource management—e.g., decisions
related to capacity of in-patient hospital beds, critical care units,
6https://royalsociety.org/topics-policy/health-and-wellbeing/ramp/
7https://tinyurl.com/y99c33dc
staffing, and resource allocation within and across regions; and
decisions about care—e.g., deciding thresholds for admission
and discharge of patients and minimising the impact on other
patients. In particular, pandemics generate a large number of
questions all of which cannot be answered by epidemiological
models alone. A key use case is integrating a diversity of
models into simulations that can be used to answer diverse
questions. This might range from understanding disease spread
to predicting the consumption of medical supplies for hospital
management. By considering the range of model outputs, an
additional benefit is that an estimate of uncertainty can be
produced, which may help policy makers gauge expected
benefits against risks. For interested readers, the review paper
[12] provides an overview of the use of various simulation
modelling methods—including those based on system dynamics
[23], agent based models, discrete event simulations, and hybrid
simulations—to reduce the impact of COVID-19.
D. Contact Tracing
Most countries reacted to the early stages of COVID-19
with containment measures. This typically involves rapidly
identifying infected individuals, followed by quarantine and
contact tracing. Countries, such as South Korea, conducted
rigorous testing campaigns, which allowed other potentially
infected contacts to be quickly quarantined. This approach has
been seemingly successful in containing the outbreak [24]. A
valuable use case can therefore be facilitating more rapid and
comprehensive contact tracing at scale [25]. Smartphone contact
sensing, online surveys and automated diagnosis have all
been proposed to rapidly identify exposure [26]. For example,
there are ongoing efforts to survey general populations via
4
social media to learn of symptoms within individuals’ social
networks [27]. Even prior to COVID-19, FluPhone [28] used
Bluetooth communications to identify contacts between people,
and BlueDot monitored outbreaks of infectious diseases to alert
governments, hospitals, and businesses [29].
If data from contact tracing is augmented with personal
information such as geolocation, health characteristics and test
results (both for viral infection and post infection antibodies),
there is the potential to update continually probabilistic
estimates of the inferred states of individuals (never exposed,
infectious, recovered and hence perhaps immune) as well as the
sensitivity and specificity of different types and makes of test,
the patterns of disease progress for an individual and spatially
for the population or how immunity declines over time and
when and where significant new strains of COVID-19 emerge.
While this information could be very helpful, the benefits will
have to be weighed against concerns about loss of individual
privacy (see Section VI-C).
E. Understanding Social Interventions
Governments have taken steps to manage social interactions
as part of their response to COVID-19. We highlight two main
use cases of relevance.
1) Monitoring of Social Distancing: Many governments
have implemented social distancing strategies to mitigate the
spread of COVID-19. This is a non-pharmaceutical intervention
that reduces human contact within the population [30] and
therefore constrains the spread of COVID-19 [31]. Data science
can support contact tracing for the monitoring of social
distancing, for instance by extracting social media data and
using language processing techniques [32], [33]. These analyses
could also help in keeping record of interactions to be used
as individuals develop symptoms. Furthermore, these could be
used for general population tracking to understand compliance
with social distancing. This could then be complemented
with other datasets (e.g., cellular trace data or air pollution
monitoring [34]) to better understand human mobility patterns
in the context of social distancing. Similar to the previous
case, these solutions present complex trade offs with regards
to privacy (see Section VI-C).
2) Controlling Misinformation & Online Harms: The spread
of misinformation can undermine public health strategies [35]
and has potentially dangerous consequences [36], [37]. For
example, online rumours accusing 5G deployments of causing
COVID-19 led to mobile phone masts being attacked in the UK
[38]. Wikipedia maintains an up-to-date list of misinformation
surrounding COVID-19 [39]. This confirms the spread of
a number of dangerous forms of misinformation, e.g., that
vinegar is more effective than hand sanitiser against COVID-
19. Naturally, users who believe such misinformation could
proceed to undermine public health. One important use case
would therefore be to develop classifiers and techniques to stem
this flow. For example, Pennycook et al. [40] are testing simple
interventions to reduce the spread of COVID-19 misinformation.
An infodemic observatory analysing digital responses in online
social media to COVID-19 has been created by CoMuNe lab
at Fondazione Bruno Kessler (FBK) institute in Italy, and is
available online.
8
The observatory uses Twitter data to quantify
collective sentiment, social bot pollution, and news reliability
and displays this visually.
F. Logistical Planning and Economic Interventions
COVID-19 has created serious challenges for healthcare sup-
ply chains and provisioning. This includes personal protective
equipment such as masks and gowns, alongside intensive care
equipment like test kits, beds, and ventilators. There is a history
of applying machine learning to logistical planning, e.g., by
Amazon Fulfilment.
9
A simple use case would be to apply
data science techniques to help supply chain management for
healthcare provisioning. This can also be used to preemptively
allocate resources, e.g., researchers from the University of
Cambridge are using depersonalised data (like lab results
and hospitalisation details) to predict the need for ventilation
equipment.
10
This use case could be critical for ensuring
appropriate equipment is available on time.
Social distancing measures are also having a major impact
on the global economy [41], [42]. As organisations emerge
from economic hibernation they will be challenged to return
to normal levels of service and operation given disruptions
to their supply chains and workforce. Data scientists might
be able to assist in identifying problems limiting recovery.
For instance, governments can use data science techniques
to identify optimal economic interventions at a high level
of granularity and companies can use data science to detect
unusual patterns of behaviour in the market or in their own
customer base.
G. Automated Patient Care
The pandemic has triggered a shortage of healthcare workers
(e.g., in primary care). To alleviate this, automated primary
care tools, such as remote chatbots and expert systems,
could be developed and/or improved. Such systems can help
people in providing information about the outbreak, symptoms,
precautionary measures, etc. For instance, an interactive chatbot
by the WHO and Rakuten Viber aims to provide accurate
information about COVID-19 to people in multiple languages
[43]. Automated healthcare methods could also be utilised to
help monitor the conditions of COVID-19 patients in emergency
care [44].
Another use case would be to gather and collate observational
data to monitor the efficacy of treatments for certain patient
types, enabling decision support for better personalised patient
treatment given limited resources. For example, the DeCOVID
project at the Alan Turing Institute is attempting to use clinical
data to identify factors and generate insights that can lead
to more effective clinical strategies. Similarly, physical and
psychological (self) recordings could be used to augment
personal plans. Due to the need to rapidly discharge patients
from hospitals, further monitoring could continue remotely
with the help of various existing remote care devices. For
8COVID19 Infodemics Observatory: https://covid19obs.fbk.eu/
9
https://services.amazon.co.uk/services/fulfilment-by- amazon/
features-benefits.html
10https://tinyurl.com/CambridgeCenterAIMedicineCOVID
5
instance, AliveCor [45] Kardia Mobile 6L device can assist
healthcare professionals in remotely managing COVID-19
patients by measuring QTc (heart rate corrected interval)
through a six-lead personal ECG [46]. CLEW’s TeleICU
solution is another remote care solution that can help identify
respiratory deterioration [47]. FreeStyle Libre [48] is a secure
cloud-based working app that connects people with their doctor
remotely without any extra cost. Home pulse oximetry [49]
can also help decrease COVID-related mortality by performing
an earlier detection remotely. Data scientists could contribute
to these remote care solutions, which are particularly useful
in developion regions. The USA Department of Health and
Human Services (HHS) has recently announced an expansion of
the coverage of Medicare for tele-health visits in a bid to better
manage the COVID-19 outbreak by [50]. Similarly, the COVID-
19 Telehealth Program in the USA [51] announced a $200
million funding in response to the COVID-19 pandemic for the
provision of virtual health services by healthcare practitioners
to patients at their homes or remote locations [52].
H. Supporting Vaccine Discovery and New Treatments
The international effort to discover or re-purpose drug
treatments and vaccines can also benefit from extensive
data science work predating COVID-19 [53]. For example,
computational methods can reduce the time spent on examining
data, predicting protein structures and genomes [54], [55].
It can also assist in identifying eligible patients for clinical
trials [56], which is often a time-consuming and costly part
of drug development. There is also substantial scope for
applying advanced methods to managing trials, such as applying
Bayesian clinical trials to adapt treatments based on information
that accrues during the trial [57]. This may be critical in
expediting the delivery of drug treatments, and we argue this is
another area where data scientists can contribute. The field of
network medicine, which applies techniques and insights from
network science to medicine, is also being actively pursued
for the purpose of developing and validating computational
tools that can help identify drug repurposing opportunities [58].
We limit our discussion as a deeper foray into the science of
vaccine discovery and drug re-purposing is outside the scope
of this paper.
III. DATASETS AND RESOURCES
To enable research by the community, it is vital that datasets
are made available. Next, we survey public datasets, some of
which we summarise in Table II.
A. COVID-19 Case Data
The number of COVID-19 cases along with their geo-
locations can help to track the growth of the pandemic and
the geographical distribution of patients. Many countries are
collecting and sharing infection information. One of the most
used datasets is collated by John Hopkins University, which
contains the daily number of positive cases, the number of
cured patients and the mortality rates at a country as well as
state/province level [71]. A further source of daily COVID-
19 case data is available at Kaggle [76]. This dataset is
annotated with other attributes such as patient demographics,
case reporting date and location. Another epidemiological
dataset, nCOV2019 [75], contains national and municipal health
reports of COVID-19 patients. The key attributes are geo-
location, date of confirmation, symptoms, and travel history.
Similarly, the New York Times is compiling a state-wise dataset
consisting of the number of positive cases and death count
[77]. Whereas the above datasets are mostly based on statistics
compiled by governmental administrations, other datasets are
being collated using community surveys, requesting people
to report infection rates among their social networks [27].
Common data science applications used with such data in the
literature include data visualisation and predictive analytics
[81].
A key limitation in these datasets is the divergence of testing
regimes, which makes it challenging to compare results across
countries [82]. It is estimated in one study
11
that the average
detection rate of SARS-CoV-2 infections is just 6% worldwide.
Similarly, variations in interventions, population densities and
demographics have a major impact, as can be seen when
contrasting, for example, Japan vs. USA.
12
As such, regional
prediction tasks are non-trivial, and we posit that temporal
models such as Auto Regressive Integrated Moving Average
(ARIMA) [83] and Long Short Term Memory (LSTM) [84]
neural networks may be effective here.
B. COVID-19 Textual Data
The availability of rich textual data from various online
sources can be used to understand the growth, nature and
spread of COVID-19.
One prominent source is social media, for which datasets
are already available covering COVID-19 discussions. There
are open Twitter datasets covering Tweet IDs [78] and tweet
text data [66]. These were gathered using Twitter’s Streaming
API to record tweets containing a series of related keywords,
including “Coronavirus”, “COVID-19”, “N95”, “Pandemic”,
etc. Another dataset of 2.2 million tweets, alongside the code
to collect more data is available [85]. This data could be
used to monitor the spread of COVID-19, as well as people’s
reactions (e.g., to social distancing measures) using existing
natural language processing techniques [86]–[88]. Sharma et
al. [89] also made a public dashboard
13
available summarising
data across more than 5 million real-time tweets. There are also
textual datasets that include image content: Zarei et al. [90]
provide 5.3K Instagram posts related to COVID-19, including
18.5K comments.
The wealth of academic publications in recent weeks is
also creating a deluge of textual information. Information
extraction from clinical studies is already being performed
[91] using language processing models such as [92]. These
bibliometric datasets can easily be collected from pre-print
services such as arXiv, medRxiv, and biorXiv [93]–[95]. The
White House has also released an open research articles dataset
[64]. This dataset contains nearly
45,000
articles related
11https://tinyurl.com/cov6percent
12http://nrg.cs.ucl.ac.uk/mjh/covid19/index.html
13https://usc- melady.github.io/COVID-19-Tweet-Analysis/
6
TABLE II: A List of Prominent COVID-19 Datasets
Dataset Name Country/Region Modality Attributes Ref.
BSTI Imaging Database United Kingdom CT scans data Patient CT scans [59]
COVID Chestxray Dataset Italy Chest X-ray scans and reports X-Ray Image, date, patient, demographics, findings, loca-
tion and survival information
[60]
COVID-CT-Dataset All Countries Chest CT-scans Scans with associated labels [61]
COVID-19 CT segmentation dataset Italy Lungs CT scans JPG image scans with segmentation and label report [61]
COVID-19 Community Mobility Reports 131 Countries Mobility statistics with textual
reports
Presence of people at grocery stores, pharmacies, recre-
ational spots, parks, transit stations, workplaces, and resi-
dences
[62]
COVID-19 DATABASE Italy Radiology data Xrays and demographics [63]
COVID-19 Open Research Challenge All Countries Research articles dataset Published date, author list, journal name, full text [64]
Coronavirus Source Data All Countries Case statistics Time series of confirmed daily COVID-19 cases for coun-
tries around the world
[65]
Coronavirus COVID19 Tweets All Countries Public Tweets on COVID-19 UserID, location, hashtags, tweet text [66]
COVID-19 Korea Dataset Korea Case statistics Patient routes, age, gender, diagnosed date [67]
CHIME All Countries Case statistics Daily number of susceptible, infected and recovered pa-
tient
[68]
Global research on COVID-19 All Countries Database of research articles Date, location, authors and journal [69]
hCOV-19 All Countries Genomic epidemiology Genetic sequence and metadata [70]
JHU CSSE COVID-19 Data All Countries Case statistics Number of infections, number of cured patients, total
mortality count, location
[71]
Kinsa Smart Thermometer Weather Map USA U.S. Health Weather Map Temperature readings from internet-connected thermome-
ters made by Kinsa Health.
[72]
LitCovid All Countries Dataset of research articles Up-to-date research topics and geographic locations [73], [74]
nCoV2019 Dataset
China, Japan,
South Korea,
Hong Kong, Taiwan,
Thailand, Singapore
Epidemiological data Patient demographics, case reporting date, location, brief
history
[75]
Novel Corona-virus 2019 dataset All Countries Case statistics Patient demographics, case reporting date, location, brief
history
[76]
New York Times dataset USA State-wise cumulative cases Date, state name, number of cases, death count [77]
Public Corona-virus Twitter Dataset All Countries Tweet IDs Twitter ID with location [78]
RCSB Protein Data Bank All Countries Clinical and pathology Gemonic sequences [79]
RKI COVID19 Germany Cases data Number of infection cases [80]
to COVID-19, SAR-CoV-2 and other coronaviruses. These
activities are mirrored across other organisations. For instance,
in the US, The National Center for Biotechnology Information
(NCBI) is providing up-to-date COVID-19 scientific literature
[73], and WHO is compiling a database of recent research
publications [69]. Closely related is the wealth of activity on
Wikipedia, a community-driven encylopedia, which already
contains substantial information about COVID-19. The entirety
of Wikipedia can be downloaded for offline analysis [96], and
there are already pre-processed Wikipedia datasets focussing
on COVID-19 available.14
C. COVID-19 Biomedical Data
Biomedical data can be used to support diagnosis, prognosis
and treatment. A major source of data are physical medical
reports (such as X-rays) or clinical pathology reports (genomic
sequencing). As the current diagnosis and prognosis of COVID-
19 often requires human interpretations, there is potential
for applications of computer vision research,
e
.
g
., automated
diagnosis from chest X-rays. Currently, there are some open-
source COVID-19 X-ray scans such as the COVIDx dataset
[97]. These can be used for training COVID-19 infection
assessment and diagnosis models (exploiting known computer
vision techniques [98]). Other X-ray datasets that are publicly
available for research are [60], [99]. The latter contains
date, patient, demographics, findings, location and survival
information. However, there are some intrinsic challenges
related to these X-ray datasets, such as the requirement of
radiologists or clinicians for data labelling and annotation
14http://covid-data.wmflabs.org/
(before training models). As such, the datasets are still small,
limiting the application of methods like convolutional neural
networks.
Lung Computed Tomography (CT) scans can also be used
for COVID-19 diagnosis and prognosis. Currently, there are
datasets of lungs CT scans available. One of the datasets
[61] covers
60
patients and comprises three class labels:
ground glass, consolidation, and pleural effusion. The dataset
is collected from 6, March to 13 March, 2020. A larger dataset
of
288
CT scans collected from
19
January to
25
March, 2020
[100]. The dataset has
275
CT scans of COVID-19 patients,
which to the best of our knowledge, is the largest publicly
available.
Besides the above physical scans, there are important
genomic sequencing datasets available. The study of drug
impact, protein-protein interactions and RNA structure in
genomic data is an essential part of diagnosis test evaluations.
Available datasets related to epidemiological and clinical
data include RCSBdata [79] and GHDDI [101]. However, as
COVID-19 has emerged very recently, these datasets are mostly
incomplete or too small. For example, the biomedical datasets
(see [100]) range from just a few up to 300 patients.
D. Other Supportive Datasets
As part of monitoring secondary factors related to COVID-
19 and the surrounding interventions, there are several other
relevant datasets. For example, air quality index statistics can
be used as an indirect measure of social distancing polices,
i.e., if movements are restricted there will be fewer vehicles
(and pollution). For example, a recent study showed that the
air quality of six populous world cities has improved between
7
February and March 2020 due to the measures to combat
COVID-19 [34]. The data is publicly available [102] as well
as the related COVID-19 case data [71]. Mobility trace data
[103] can also serve a similar purpose—a collection of such
logs is available here [104]. Note that mobility datasets have
already been re-purposed: Google has released community
mobility reports for public health officials in 131 countries
[62]. These reports are compiled using Google Maps and
describe how busy places such as grocery stores, transit stations,
and workplaces are. In a recent not-yet-peer-reviewed study
[105], an indirect COVID-19 spread correlation is reported
with wastewater samples. The wastewater sample data consists
of
23
raw and
8
treated samples which is collected from three
major wastewater treatment plants of Parsian, France region
during
5
, March to
7
, April,
2020
. It is found that all the raw
samples, and
6
out of
8
treated samples, tested positive for
SARS C oV 2
which demonstrated the correlation of number
of COVID-19 cases in that region.
E. COVID-19 Competition Datasets
To facilitate and promote research in this area, there are
several recent open data science competitions established on
Kaggle (summarised in Table III). These are mostly based on
the previously discussed data. For instance, the White House
in coalition with some leading research groups (e.g., Kaggle
and SGS Digicomply) has opened a new challenge using the
earlier mentioned dataset of
45,000
research articles [64]. For
this, there a few questions posed; for example, “What do we
know about virus genetics, origin, and evolution?” For each
task, there is an associated prize of $1000.
TABLE III: COVID-19 Related Kaggle Competitions
Challenges Aims
Answer 9 key questions
This challenge asks data scientists to understand
COVID-19 faster by exploring 47,000 scholarly
articles about COVID-19 and related coronaviruses.
COVID19 Global Forecasting
This challenge asks data scientists to predict
the number of cases and fatalities by city between
April 9 and May 7.
UNCOVER COVID-19
This challenge asks data scientists to use exploratory
analysis to answer research questions that help support
frontline responders.
The Roche Data Science Coalition (RDSC) also established
the challenge “UNCOVER COVID-19” [106]. RDSC has
rolled-out a multi-modal dataset collected from
20
sources and
has posed questions prepared by front-line healthcare experts,
medical staff, WHO and governmental policymakers. This
dataset is mainly collected from John Hopkins, the WHO, New
York Times and the World Bank. It includes local and national
COVID-19 cases, geo-spatial data and social distancing polices.
Participants are required to design solutions to address questions
like “which populations are at risk of contracting COVID-19?
and “which populations have contracted COVID-19 and require
ventilators?
Finally, the White House Office of Science and Technology
Policy (OSTP) has opened a weekly challenge to predict the
number of COVID-19 cases and fatalities at particular locations
around the world [107]. Competitors are also required to unveil
the factors associated with COVID-19 transmission rate. At
the time of writing, participants are required to forecast the
number of COVID-19 cases and deaths between 1-April-20 to
07-May-20.
For those wishing to engage in these competitions, there
are several helpful tools and guideline blogs available. These
resources provide support for data preprocessing, visualisations,
and the implementation of different frameworks. We provide a
list in Table IV.
TABLE IV: Prominent COVID-19 Community Resources.
Resources Details
AI against COVID-19 This webpage contains information related to recent papers,
projects, and datasets for COVID-19.
AitsLab-Corona This is an NLP toolbox and related text processing resources
for SARS-CoV-2 and COVID-19 NLP research.
CORD-19
Allen Institute for AI with partners has prepared the
COVID-19 Open Research Dataset (CORD-19),
a free resource of over 52,000 scholarly articles
Amazon AWS Amazon AWS public data lake for COVID-19 data analysis
CDC, USA Centers for Disease Control and Prevention (CDC)
COVID-19 research articles downloadable database.
ChemML [108]
ChemML is a machine learning and informatics program
that support and advance the data-driven research in the
domain of chemical and materials.
COVID-19 Graphs This repository provides the tools to visualise the different
statistics of COVID-19 using case data.
COVID-19 Data Portal
European Molecular Biology Lab-European Bioinformatics
Institute (EMBL-EBI) and partners have set up this portal
to bring together relevant datasets for biomedical data.
JHU’s CSSE
Coronavirus COVID-19 Resource Page by the
Center for Systems Science and Engineering (CSSE)
at Johns Hopkins University (JHU).
NIH NLM LitCovid
LitCovid is a curated literature hub for tracking up-to-date
scientific information about COVID-19. It provides central
access to more than 3558 relevant articles in PubMed.
MATLAB resource
MATLAB based tutorial on deep learning based
techniques for detecting COVID-19 using chest radiographs
(in MATLAB).
MONTREAL.AI This contains the details of multiple open source codes
and tools to model different aspects of COVID-19.
Partnership on AI Page on AI- and Technology- Related COVID-19 Efforts
Vector Institute This is a webpage that provides information about various
resources and research tools for COVID-19.
WHO resource This is a webpage of the WHO which contains updated
details on the global research on COVID-19.
Telehealth Toolbox This toolbox is providing an online treatment and
telemedicine platform to combat COVID-19.
IV. SURV EY O F ONGOING DATA SCIENCE RELATE D
COVID-19 RESEARCH
The above provides an overview of publicly available datasets
that could be used by researchers wishing to contribute to the
COVID-19 crisis. Next, we detail some of the ongoing research
in this space. We theme this section around the above datasets
and summarise key studies in Table V.
A. Image Data Analysis
Various studies [109]–[111] have used computer vision
algorithms to speed up the process of disease detection across
several imaging modalities with some studies demonstrating
that image analysis techniques have the potential to outperform
expert radiologists [112], [113]. To diagnose COVID-19,
two medical imaging modalities (CT and X-ray) have been
experimented with, which we discuss below.
1) Computed Tomography (CT) Scans: Recent studies have
found that radiologists can diagnose COVID-19 using Chest
CT scans with lower false positive rates [114], [115] than
other imaging modalities such as X-ray and Ultrasound scans.
Thus, many deep learning (DL) techniques related to CT scans
have been proposed to expedite the diagnosis process. Wang et
8
al. [116] utilise DL methods to detect radiographical changes
in COVID-19 patients. They evaluate the proposed model on
the CT scans of pathogen-confirmed COVID-19 cases and
show that DL can extract radiological features suitable for
COVID-19 diagnosis. Xiaowei et al. [117] present a method
for the automatic screening of COVID-19 in pulmonary CT
scans using a 3D DL model with location-attention. They
achieve promising accuracy to identify COVID-19 infected
patients scans from other well-known infections. Chen et
al. [118] exploit the UNet++ architecture [119] to detect
suspicious lesions on CT scans. They trained their model on
289 scans and test on 600 scans. They achieve 100% accuracy
in identifying the suspicious areas in CT scans of COVID-
19 patients, which suggests their techniques have potential
for clinical utilisation. Ophir et al. [120] employ 2D and 3D
convolutional neural networks (CNNs) to calculate the Corona
score (which represents the evolution of the disease in the
lungs). They estimate the presence of the virus in each slice of
CT scan with a 2D CNN and detect other lung diseases (i.e.,
lung nodule) by using a 3D CNN. Similarly in [121], a neural
network (COVNet), is developed to extract visual features
from volumetric chest CT exams for the detection of COVID-
19. The study suggests that DL-based models can accurately
detect COVID-19 and differentiate it from community acquired
pneumonia and other lung diseases.
2) X-ray Scans: Ongoing image processing work is not
limited to CT scans, and there has been work on other
modalities, namely X-rays scans. Although less sensitive than
CT scans, they are less invasive, have a lower ionising radiation
dose, and are more portable. Following the IR(ME)R 17
guidelines, ionising radiation dose should be kept As Low
As Reasonably Achievable (ALARA) whilst still producing an
image of diagnostic quality. [122]. Ezz et al. [123] propose a
DL-based framework (COVIDX-Net) to automatically diagnose
COVID-19 in X-ray images. COVIDX-Net includes seven
different CNN models, such as VGG19 [124] and Google
MobileNet [125]. The models can classify the patient status as
either COVID-19 negative or positive. However, due to a lack of
data, the technique is validated on only 50 X-ray images, among
which 25 were of confirmed corona patients. Linda et al. [97]
introduce another DL-based solution tailored for the detection
of COVID-19 cases from chest X-ray images. They also develop
a dataset named COVIDx and leverage it to train a deep CNN.
In [126], three different CNN-based models (i.e., ResNet-50,
Inception and InceptionResNet) are employed to detect COVID-
19 in X-rays of pneumonia infected patients. The results show
that the pre-trained ResNet-50 model [127] performs well,
achieving 98% accuracy. Similarly, Farooq et al. [128] provide
the steps to fine-tune a pre-trained ResNet-50 [127] architecture
to improve model performance for detecting COVID-19 related
abnormalities (called COVID-ResNet). Prabira et al. [129] use
DL to extract the meaningful features from chest X-rays, and
then trained a support vector machine (using the extracted
features) to detect infected patients.
We also briefly note that several companies have released
commercial solutions, some of which are freely available, e.g.,
Lunit [130] CXR solution for COVID-19 and VUNO Med
[131] solution for chest CT and X-ray scans. Such solutions
help to expedite the initial screening of COVID-19.
B. Textual Data Analysis
Researchers are currently utilising text mining to explore
different aspects of COVID-19, mainly from social media
and bibliometric data. To assist in this, Kazemi et al. [162]
have developed a toolbox for processing textual data related to
COVID-19. This toolbox comprises English dictionaries related
to the disease, virus, symptoms and protein/gene terms.
In terms of social media research, Lopez et al. [163]
explore the discourse around the COVID-19 pandemic and
government policies being implemented. They use Twitter data
from different countries in multiple languages and identify the
popular responses to the pandemic using text mining. Similarly,
Saire and Navarro [164] use text mining on Twitter data to show
the epidemiological impact of COVID-19 on press publications
in Bogota, Colombia. Intuitively, they find that the number
of tweets is positively correlated with the number of infected
people in the city. Schild et al. [150] inspect Twitter and
4Chan data to measure how sinophobic behaviour, driven by the
pandemic, has evolved. This includes studying the impact that
real world events, such as regional containment measures, have
on online hate. Cinelli et al. [165] analyse Twitter, Instagram,
YouTube, Reddit and Gab data about COVID-19. They find
different volumes of misinformation on each platform. Singh
et al. [166] are also monitoring the flow of (mis)information
flow across 2.7M tweets, and correlating it with infection
rates to find that misinformation and myths are discussed, but
at lower volume than other conversations. For those seeking
easy acceess to this information, FBK institute is collecting
COVID-19 related tweets to visualise the presence of bots and
misinformation.15
In terms of bibliometric analysis, Li et al. [167] analyse
research publications on other coronaviruses (e.g, SARS,
MERS). This is used to build a network-based drug re-
purposing platform to identify drugs for the treatment of
COVID-2019. Using module detection and drug prioritisation
algorithms, the authors identify 24 disease-related human
pathways, five modules and suggest 78 drugs to re-purpose.
The rapid growth in COVID-19 related literature further led
Hossain et al. [168] to perform a bibliometric analysis of
COVID-19 related studies published since the outbreak. They
review relationships, citations and keywords, which could be
useful to new researchers in the area.
Finally, there is work processing text data from patient
records. Roquette et al. [169] train a deep neural network to
forecast patient admission rates using the unstructured text data
available for triage. There are also other studies that utilise
text data mining techniques to explore the important aspect of
current situation.
C. Voice Sound Data Analysis
The most common symptoms of COVID-19 are linked
to pneumonia, and the main mortality risk is cardiovascular
disease followed by chronic respiratory disease. Hence, audio
15https://covid19obs.fbk.eu/
9
TABLE V: Summary of data science work related to COVID-19. Papers are categorised based on the dataset used.
Authors Area Modality/Data Type Technique Methodology
Wang et al. [116]
Image Analysis
Chest CT scans InceptionNet
on random ROIs
InceptionNet is used to detect the anomalies related
to COVID-19 infection in lungs CT scan.
Xu et al. [117]Chest CT scans 3D CNNs 3-D CNN models used to classify the COVID-19
infected regions in CT scans.
Chen et al. [118]Chest CT scans UNet++ UNet++ architecture has been used to identify
the suspicious areas in CT scans.
Gozes et al. [120]Chest CT scans 2D + 3D CNNs 2D and 3D CNNs models have been simultaneously employed
to quantify the infection in the lungs of COVID19 patients.
Lin et al. [121]Chest CT scans CNN COVNet; CNN-based model is developed to detect
COVID-19 in chest CT scans.
Shan et al. [132]Chest CT scans DNN DL-based segmentation system is developed
to quantify infected ROIs in lung CT scans.
Zhang et al. [133]Chest CT scans DenseNet Used DenseNet-like architecture and optimised it for
classification task to detect COVID-19 infection.
Wang et al. [134]Chest CT scans Pre-training + DNN Pre-trained DNN has been used
to improve detection of COVID-19 in lungs scans.
Mucahid et al. [135]Chest CT scans Conventional Feature
Extraction techniques + SVM
GLCM, LDP, GLRLM, GLSZM, and DWT algorithms are used
as feature extraction and SVM for classification.
Zhao et al. [100]Chest CT scans CNN Developed a public dataset and employed CNN
for COVID-19 detection on chest CT scans.
Gozes et al. [136]Chest CT scans U-Net + ResNet Used UNet for lung segmentation, ResNet for 2D slice classification
and fine grain localisation for detection of infected regions in lungs.
Asnaui et al. [137]Chest X-rays
and CT images Fine tuning + CNNs Various CNN-based models used for binary classification in
COVID-19 detection on pneumonia affected X-ray and CT images.
Ezz et al. [123]Chest X-rays CNN-based models Introduced COVIDX-Net, which includes seven different CNN
models for classification of COVID-19 infected X-rays.
Linda et al. [97]Chest X-rays ResNet An open source solution (COVID-Net) utilized ResNet
to detect COVID-19.
Narin et al. [126]Chest X-rays ResNet50, InceptionV3
and InceptionResNetV2
Different CNN-based models are used to detect COVID-19
pneumonia infected patients chest X-rays.
Prabira et al. [129]Chest X-rays DNN + SVM Used DNN to extract meaningful information from X-rays
and SVM for classification of corona affected X-rays.
Farooq et al. [128]Chest X-rays Fine-tuning + ResNet Devised multi-stage fine-tuning scheme to improve
performance and training time.
Abbas et al. [138]Chest X-rays Transfer learning (TL) + CNN Employed TL and used previously developed CNN,
called Decompose, Transfer, and Compose (DeTraC).
Chowdhury et al. [139]Chest X-rays CNN + Image
argumentation
An image argumentation technique has been proposed to create
the chest X-ray images for fine-tuing of pre-trained models.
Alqudah et al. [140]Chest X-rays CNN, SVM, and
Random Forest (RF)
Applied various ML techniques for classification of
COVID-19 infected X-rays.
Goshal et al. [141]Chest X-rays Bayesian Convolutional
Neural Networks (BCNN) Investigated the significance of dropping weights BCNN.
Fatima et al. [142]Chest X-rays CNN Trained CNN for COVID-10 detection in X-rays.
Xin et al. [143]Chest X-rays DenseNet Used DenseNet Architecture [144] for COVID-19 detection in X-rays.
Karim et al. [145]Chest X-rays DNN Used neural ensemble method for classification and provided
human-interpretable explanations of the predictions of COVID-19.
Ioannis et al. [146]Chest X-rays TL + CNN TL is used for extracting patterns from common bacterial
pneumonia patients X-rays using CNN to detect COVID-19.
Jahanbin et al. [147]
Text data
Mining
Twitter data Evolutionary
algorithm
Fuzzy rule-based evolutionary algorithm was used to timely detect
outbreaks of the COVID-19 by using Twitter data.
Zhao et al. [148]Sina Microblog
hot search list
Content mining
algorithms
This work investigates the public’s response at the beginning
(December 31, 2019, to February 20, 2020) of the
COVID-19 epidemic in China.
Li et al. [149]Weibo data
SVM,
Na¨
ıve Bayes (NB),
Random Forest (RF)
Weibo data was used to characterise the propagation of
situational information in social media during COVID-19.
Schild et al. [150]Twitter & 4Chan data word2vec Authors look at rise of COVID-19 related sinophobic abuse on
Twitter and 4Chan.
Prabhakar et al. [151]Twitter data Topic modelling In this work, the information flow on twitter during
COVID-19 pandemic was studies using topic modelling.
Stephany et al. [152]Risk reports data Multiple text
mining algorithms
Text mining methods are used to identify industry-specific risk
assessments related to COVID-19 in real-time.
Zhavoronkov et al. [101]
Pharmaceutical
Research
Crystal structure,
homology modelling,
and co-crystallised
ligands
Generative models Generative models were used to generate the molecules for the 3C-
like protease that can act as potential inhibitors for SARS-CoV-2.
Hofmarcher et al. [153]Drug-discovery
databases DNNs
Used ChemAI [154], [155], a DNN trained on million of data pints
across 3.2 million of molecules, for screening favourable
inhibitors from the ZINC database [156] for SARS-CoV-2.
Beck et al. [157]
SMILES strings,
amino acid
sequences
Deep learning model Authors utilise a pre-trained drug-target interaction model
to predict commercially available antiviral drugs for COVID-19.
Kim et al. [158]
SMILES strings,
amino acid
sequences
AI-based prediction
platform
A binding affinity prediction platform is used
to detect available FDA approved drugs that can block
SARS-CoV-2 from entering cells.
Richardson et al. [159]Biomedical data AI-driven knowledge
graph
Authors use BenevolentAI to search for approved
drugs that can block the viral infection process.
Stebbing et al. [160]Biomedical data AI-driven knowledge
graph
This study examines approved antiviral and anti-inflammatory
treatments for COVID-19.
Vijil et al. [161]SMILES Generative models Design drug candidates specific to a given target protein sequence.
They release around 3000 COVID-19 drug candidates.
10
analysis has been considered a potential means for lightweight
diagnosis. There is work performing diagnosis with respiratory
and lung sound analysis [170], which can work even with low-
cost smartphones [171]. High mortality risk groups, including
the elderly, can also be continuously monitored using speech
analysis [172]. The patterns of coughs [173], [174], sneezing
[173], throat clearing and swallowing sounds [175] can all
be analysed using speech and sound processing. At present,
COVID-19 related speech data has limited availability, although
the potential benefits are highlighted in [170]. Thus, mobile
apps like COVID-19 Sounds are attempting to collect large
audio datasets. In [176], the authors present an app called
AI4COVID-19 for the preliminary diagnosis of COVID-19. It
requires a 2 second cough sample and provides the preliminary
diagnosis within a minute. This work confirms the feasibility
of COVID-19 detection using cough samples with promising
results (90% detection rate). Similarly, FluSense [177] is a
portable device that can detect coughing and crowd size in real
time. It can then monitor flu-like illnesses and influenza trends
by analysing the data. The authors aims to use this device to
forecast seasonal flu and other viral respiratory outbreaks like
COVID-19.
D. Embedded Sensor Data Analysis
Embedded data (e.g., from smartphones and sensors) is
being used for remote patient care and diagnosis [178]. This
can include mobility data, physiological vital signs, blood
glucose, body temperature, and various other movement-related
signals. In [179], the authors develop a system utilising real-
time information, including demographic data, mobility data,
disease-related data, and user-generated information from social
media. The proposed system, called
α
-Satellite, can provide
hierarchical community-level risk assessment that can inform
the development of strategies against the COVID-19 pandemic.
Google has also been using location data from smartphones to
show people’s movement during the pandemic [180]. Another
study [181] presents the design of a low-cost framework for
the detection of COVID-19 using smartphone sensors. They
propose the use of the mobile phones of radiologists for virus
detection. They highlight that the proposed framework is more
reliable as it uses multi-readings from different sensing devices
that can capture symptoms related to the disease.
Another recent study [25] concluded that COVID-19’s
“spread is too fast to be contained by manual contact tracing”.
To address this, disease tracking apps [28] use contact/location
sensor data. The simplest ones aim to understand the spread
of the disease, particularly mild cases that are not routinely
lab tested. For example, the COVID Symptom Tracker app
16
and COVID Near You
17
service. Others, like Hong Kong’s
StayHomeSafe and Poland’s Home Quarantine app [182], try
to monitor if people obey quarantine rules (via geofencing).
More advanced solutions can notify users if they have come
into contact with somebody infected. Examples include China’s
Close Contact Detector app [183], China’s complementary QR
health code system [184]), Singapore’s TraceTogether [185]
16https://covid.joinzoe.com/
17https://www.covidnearyou.org/
app, and Israel’s HaMagen [186] app. The UK is also planning
to launch a similar app [187].
We note that one critical challenge in the above apps is
protecting user privacy [188], [189]. For instance, uploading
contact data for server-side computation could create a nation-
wide database of social relationships, particularly in countries
where usage is mandatory. To address this, Decentralised
Privacy-Preserving Proximity Tracing (DP-3T) [190] was
proposed. This is a mobile app that offers privacy-preserving
alerts for people who may have recently been in contact
with an infected person. TraceSecure [191] supports similar
features based on homomorhpic encryption, whereas [192]
offers privacy guarantees via private set intersection. Apple
and Google have announced a partnership to develop their
own privacy-preserving contact tracing specifications based on
Bluetooth.18
E. Pharmaceutical Research
There is extensive ongoing work in using new experimental
technologies to support the search for COVID-19 pharmaceuti-
cals. This has received substantial attention in recent months
in an attempt to build models to explore the 3D structure of
SARS-CoV-2 (the virus that causes COVID-19). In [193], the
authors use the AlphaFold model to predict the structures of
six proteins related to SARS-CoV-2. AlphaFold [194] is a DL
model based on a dilated ResNet architecture [127], which
predicts the distance and the distribution of angles between
amino acid residing on protein structure. In [195], the authors
use a DNN-based model for de novo design of new small
molecules capable of inhibiting the chymotrypsin-like (3CL)
protease—the protein targets for corona-viruses. Based on the
results they were able to identify 31 potential compounds as
ideal candidates for testing and synthesis against SARS-CoV-2.
Studies also attempt to improve the RT-PCR test by utilising
ML and novel genome technologies. Metsky et al. [196] employ
CRISPR
19
to develop assay designs for the detection of 67
respiratory viruses including SARS-CoV-2.
As well as the above, studies have utilised ML models to
speed up drug development. Hu et al. [197] exploit a multi-
task DNN for the prediction of potential inhibitors against
SARS-CoV-2. They aim to identify existing drugs that can
be re-purposed. Based on the results, they list 10 potential
inhibitors for SARS-CoV-2. Zhang et al. [198] perform DL-
based drug screening against 4 chemical compound databases
and tripeptides for SARS-CoV-2. Based on the results, they
provide a list of potential inhibitors that can help facilitate
drug development for COVID-19. Tang et al. [199] propose the
use of reinforcement learning (RL) models to predict potential
lead compounds targeting SARS-CoV-2. Similarly, in [200] the
authors propose a collaborative and open antiviral discovery
approach using deep RL technique to discover new molecules
to fight COVID-19.
Finally, pharmaceutical interventions must go through
clinical trials before being deployed. Accelerated clearance
18https://www.apple.com/covid19/contacttracing/
19
A tool that uses an enzyme to edit genomes by cleaving specific strands
of genetic code
11
pathways for COVID-19 studies have been established by
several regulators including the WHO, the European Medicines
Agency, the UK Medicines and Healthcare products Regulatory
Agency and the US Food and Drug Administration [201]. As
of March 24, 2020, 536 relevant clinical trials were registered.
A major barrier though is recruiting suitable patients. Data-
driven solutions are available to rapidly identify eligible trial
participants [56], [202], and data collection platforms already
exist for monitoring symptoms remotely [203]
V. BIBLIOMETRIC ANALYS IS O F COVID-19 RESEARCH
We next augment the survey in the previous section with a
brief bibliometric analysis of the literature related to COVID-
19. This gives a broader understanding of how publications
have evolved across the short lifespan of the pandemic.
A. Bibliometric Data Collection
There are many data repositories which contain COVID-19
research articles, both peer-reviewed [73], [204], [205] and
non-peer reviewed [93]–[95]. We use Scopus to crawl peer
reviewed articles, and arXiv, medRxiv, and biorXiv for non-
peer reviewed articles. Peer-reviewed articles in our dataset are
from top venues, including Nature [206], Science [207], the
Lancet [208], and the British Medical Journal (BMJ) [209]. See
Table VI for a complete list of peer-reviewed articles and the
number of articles in our dataset of COVID-19 publications. We
developed scripts to gather this data from pre-print archives and
database queries to fetch data from the Scopus database. Each
entry includes title, authors, journal, publication date, etc. Our
dataset covers papers on COVID-19 from all of the mentioned
sources till April 23, 2020. We extracted these papers from the
corpus of papers using keyword matching on titles and abstract
of the paper. We use “COVID-19”, “COVID”, “CoronaVirus”,
“Corona Virus”, “Pandemic”, “Epidemic”, and “SARS-CoV-
2” as candidate keywords. Finally we did a manual check to
confirm that extracted papers do not include any unrelated
papers. In total, the dataset covers 5755 publications, of which
2797 are pre-prints and 2958 are from peer reviewed journals.
B. Peer-reviewed vs. Non-peer-reviewed publications
The pandemic has resulted in the rapid production of
academic material, much of which is yet to go through the
peer review process due to the urgency of dissemination.
Figure 1presents the cumulative number of COVID-19
related papers published since December, 2019 including non-
peer-reviewed COVID-19 literature. We see that the number
of papers has increased dramatically since the beginning of
January. To date, non-peer-reviewed articles are the most
numerous (bioRxiv, medRxiv and arXiv combined), whereas
peer reviewed articles are also increasing equally. By far the
most active outlet is medRxiv, which has published 61% of
all non-peer reviewed papers in our dataset. Table VI presents
the number of publications from each journal covered. We see
a highly skewed distribution: the top five journals based on
publication count i.e. The Lancet, Nature, the BMJ, Science and
Journal of Medical Virology, Viruses, and The JAMA contribute
around 1/3 of peer-reviewed publications of our dataset. Of
course, we anticipate this will change in the long-term, as more
pre-prints move into peer reviewed journals.
December January February March April
Publication Month
0
500
1000
1500
2000
2500
3000
bioRxiv
medRxiv
arXiv
Peer reviewed
Fig. 1: Cumulative distribution of publications per month on
COVID-19 (data gathered till April 23, 2020).
Figure 2complements the above analysis by presenting the
geo-distribution of both groups of publications. As the initial
epicentre of COVID-19 pandemic, a major part of COVID-19
research has been contributed by China. The USA holds the
second position in terms of research contributions. China has
1378 peer reviewed article and 882 non peer reviewed articles
on COVID-19 in our dataset.
C. Research Topics
We next use topic modelling to identify core sub-topics
within the publications. For this, we use Latent Dirichlet
Allocation (LDA) [210]. This algorithm extracts and clusters
abstract topics that exist within the papers. We divide our
dataset into two groups: (1) all papers, and (2) data science
related paper. We have tagged these papers manually based on
their title and abstract.
Table VII shows the latent clusters of topics discussed in all
papers in our dataset. Note that we split the results into peer
reviewed vs. pre-print publications. The results are intuitive,
covering many important aspects of COVID-19 research, e.g.,
disease cure, transmission of COVID-19, the role of different
animals, social distancing, the impact of COVID-19 on crime
rates, and the impact of age on patients. In contrast, Table
VIII, shows the list of topics observed in data science related
COVID-19 papers. These topics show that data science research
on COVID-19 is being carried out using various techniques
and algorithms. Noteworthy algorithms and techniques include
multidimensional kernel estimation, Bayesian learning, and
deep learning based epidemic forecasting with synthetic
information (TDFESI). We hope that these results will be
useful to the community in identifying key topics receiving
coverage.
12
TABLE VI: Peer-reviewed journals in our dataset and number of COVID-19 articles published therein.
Sr. No. Journal Name Articles Sr. No. Journal Name Articles
1 The Lancet 290 70 Epidemiology And Infection 11
2 BMJ 226 71 Frontiers Of Medicine 11
3 Nature 150 72 Influenza And Other Respiratory Viruses 11
4 Journal Of Medical Virology 135 73 Korean Journal Of Radiology 11
5 Viruses 84 74 Medical Journal Of Wuhan University 11
6 JAMA 71 75 Microbes And Infection 11
7 Travel Medicine And Infectious Disease 66 76 Zhonghua Xin Xue Guan Bing Za Zhi 11
8 Clinical Infectious Diseases 56 77 American Journal Of Hematology 10
9 Journal Of Infection 50 78 Chinese General Practice 10
10 Emerging Infectious Diseases 49 79 Clinical Chemistry And Laboratory Medicine 10
11 Journal Of Virology 45 80 CMAJ 10
12 New England Journal Of Medicine 44 81 European Communicable Disease Bulletin 10
13 Infection Control And Hospital Epidemiology 38 82 Morbidity And Mortality Weekly Report 10
14 Emerging Microbes And Infections 37 83 Revue Medicale Suisse 10
15 Eurosurveillance 37 84 Singapore Medical Journal 10
16 International Journal Of Infectious Diseases 37 85 Vaccine 10
17 Science 36 86 World Journal Of Pediatrics 10
18 Journal Of Microbiology Immunology And Infection 29 87 Chinese Journal Of Pediatrics 10
19 Asian Journal Of Psychiatry 28 88 American Journal Of Infection Control 9
20 Psychiatry Research 28 89 Bioscience Trends 9
21 Disaster Medicine And Public Health Preparedness 26 90 European Urology 9
22 Radiology 26 91 International Journal Of Molecular Sciences 9
23 Chinese Medical Journal 25 92 Irish Medical Journal 9
24 International Journal Of Environmental Research And Public Health 25 93 Journal Of Infection In Developing Countries 9
25 PLOS One 25 94 Journal Of Rural Health Official 9
26 Veterinary Record 25 95 Journal Of The American College Of Surgeons 9
27 Veterinary Microbiology 24 96 Journal Of Veterinary Medical Science 9
28 Chinese Traditional And Herbal Drugs 23 97 One Health 9
29 Journal Of Korean Medical Science 22 98 Pharmacological Research 9
30 Journal Of Travel Medicine 21 99 Virus Genes 9
31 Transboundary And Emerging Diseases 21 100 Chinese Journal Of Contemporary Pediatrics 9
32 Anesthesia And Analgesia 20 101 China Journal Of Chinese Materia Medica 9
33 Antiviral Research 20 102 Chinese Journal Of Experimental Ophthalmology 9
34 Intensive Care Medicine 20 103 American Journal Of Transplantation 8
35 Swiss Medical Weekly 20 104 Anesthesiology 8
36 Virology 20 105 Of The Rheumatic Diseases 8
37 Journal Of Clinical Virology 19 106 Clinical Radiology 8
38 Journal Of Hospital Infection 19 107 European Journal Of Nuclear Medicine And Molecular Imaging 8
39 European Respiratory Journal 18 108 International Journal Of Surgery 8
40 International Journal Of Antimicrobial Agents 18 109 Journal Of Clinical Microbiology 8
41 Chinese Journal Of Preventive Medicine 18 110 Microbiology Resource Announcements 8
42 BMC Veterinary Research 17 111 Microbes And New Infections 8
43 Critical Care 17 112 Osong Public Health And Research Perspectives 8
44 Diabetes And Metabolic Syndrome Clinical Research And Reviews 17 113 Pediatric Pulmonology 8
45 MMW Fortschritte Der Medizin 17 114 Poultry Science 8
46 American Journal Of Roentgenology 15 115 Proceedings Of The National Academy Of Sciences Of The United States Of America 8
47 Archives Of Virology 15 116 QJM Monthly Journal Of The Association Of Physicians 8
48 Infection Genetics And Evolution 15 117 Journal Of Zhejiang University Medical Sciences 8
49 Archives Of Iranian Medicine 14 118 Academic Radiology 7
50 Dermatologic Therapy 14 119 American Journal Of Emergency Medicine 7
51 European Review For Medical And Pharmacological Sciences 14 120 Archives Des Maladies Professionnelles Et De L Environnement 7
52 Journal Of Infection And Public Health 14 121 Asian Pacific Journal Of Tropical Medicine 6
53 European Heart Journal 13 122 British Journal Of Anaesthesia 5
54 Frontiers In Microbiology 13 123 European Radiology 5
55 International Journal Of Biological Sciences 13 124 Fudan University Journal Of Medical Sciences 4
56 Journal Of The American Academy Of Dermatology 13 125 Indian Journal Of Medical Research 4
57 Methods In Molecular Biology 13 126 Infectious Disease Modelling 4
58 Research In Social And Administrative Pharmacy 13 127 Japanese Journal Of Radiology 4
59 Science Of The Total Environment 13 128 Journal Of Cardiothoracic And Vascular Anesthesia 3
60 Virus Research 13 129 Journal Of Clinical Nursing 2
61 American Journal Of Transplantation 12 130 Journal Of Dermatological Treatment 2
62 Brain Behavior And Immunity 12 131 Journal Of Feline Medicine And Surgery 2
63 Deutsche Apotheker Zeitung 12 132 Journal Of Forensic Medicine 1
64 Journal Of Infectious Diseases 12 133 Journal Of General Virology 1
65 Journal Of The Formosan Medical Association 12 134 Journal Of Microbiology And Biotechnology 1
66 Pathogens 12 135 Journal Of Virological Methods 1
67 American Journal Of Respiratory And Critical Care Medicine 11 136 Medecine Et Maladies Infectieuses 1
68 Annals Of Internal Medicine 11 137 Science China Life Sciences 1
69 Canadian Journal Of Anesthesia 11 138 Chinese Journal Of Gastrointestinal Surgery 1
D. COVID-19 vs. Earlier Epidemics
We conclude our bibliometric analysis by briefly comparing
the rate of publication for COVID-19 research vs. prior
epidemics. For this, we select Ebola and SARS-CoV-1. Figure
3presents a time series for the first 3 years of peer reviewed
publications. Note that the X-range differs and, naturally, we
only have data since December 2019 for COVID-19.
We see that COVID-19 literature is growing faster than
any prior epidemic. There have been more peer-reviewed
publications (
2.9K) in around 4 months for COVID-19 than
there were in 3 years for SARS-COV-1 and Ebola. Furthermore,
as noted in the earlier subsection, there are even more pre-
prints being released which means that COVID-19 has rapidly
overtaken other epidemics in terms of academic attention. Of
course, this is driven in-part by the wider geographic coverage
of COVID-19, impacting numerous highly research active
countries (e.g., China, USA, UK, Germany).
VI. CHALLENGES IN DATA SCIENCE RELATED COVID-19
RESEARCH
In this section, we highlight some of the most important
data science challenges. We specifically focus on cross-cutting
challenges that impact all of the previously discussed use cases.
A. Data Limitations
Data science systems typically learn and improve as more
data is gathered over time. Ideally, the data should be of
high fidelity and voluminous. For many of the above use cases,
extensive labelled datasets are not yet available, e.g., for speech
analysis. Although there are a few publicly available datasets
13
(a) Peer reviewed papers
(b) Pre-print (non-peer-reviewed) papers
Fig. 2: Publication count of different countries on COVID-19
(data gathered till April 23, 2020).
TABLE VII: Top topics discussed in overall COVID-19
research literature
(a) Top topics discussed in non-peer-reviewed papers on COVID-19
Topic No. Extracted Topics
1(risk, grade, shed, infect, effect, strong,
parturients, viral, favipiravir, arbidol, monkey, glucose)
2(coronavirus, sequence, genome, virus, infect, viral, bat,
human, novel, outbreak)
3(antibodies, patient, sample, infect, detect, serology,
assay, negative, serum, swab)
4(estimate, transmission, countries, outbreak, number,
infect, epidemic, spread, travel, reproduction)
5(specimen, sputum, influenza, throat, nasal, heat, primer-prob,
respiration, inactive, psychiatry)
6(virus, vaccine, epitope, genome, coronavirus, mutate, sequence,
strain, differ, region, viral, human, immune, high, peptide, develop)
7(intervention, health, public, social, populate, reduce, quarantine,
epidemic, outbreak, distance)
8(temperature, treatment, hypoxemia, meteorology, coronavirus,
effect, degree, receive, progress, screen)
9(drug, effect, virus, antivirus, potential, inhibitor,
protease, coronavirus, model, compound )
10 (positive, age, data, negative, differ, rate,
older, associate, variant, fatal)
(b) Top topics discussed in peer-reviewed papers on COVID-19
Topic No. Extracted Topics
1(patient, pneumonia, severe, infect, treatment, lung, symptom,
coronavirus, chest, hospital)
2(coronavirus, sequence, virus, human,bat, genome, respiration, gene,
animal, origin)
3(health, medic, care, patient, staff, pandemic, social,
service, score, protect)
4(infect, coronavirus, disease, outbreak, case, spread,
virus, health, transmission, respiratories)
5(infect, effect, strong, viral, glucose, genome, virus,
antibodies, patient, sample)
6(viral, bat, human, novel, outbreak, nasal, sequence,
strain, inhibitor, infect)
7(patient, sample, infect, detect, serum, swab, antibodies,
influenza, test, sputum)
8(countries, outbreak, number, infect, epidemic, spread,
travel, reproduction)
9(effect, virus, protease, coronavirus, model, compound, degree,
receive, potential, inhibitor)
10 (intervention, health, public, social, data, negative, differ,
rate, outbreak, distance)
TABLE VIII: Top topics discussed in COVID-19 data science
based research papers
(a) Top topics in non-peer-reviewed data science based COVID-19
papers
Topic No. Extracted Topics
1(model, number, use, countries, passengers, access, china,
reduction, outbreak, result)
2(dimensions, kernel, complex, structure, spectral, time, network,
distance, base, infection-link)
3(learn, image, covid-19, detect, dataset, feature, patient,
predict, risk, death)
4(sample, network, estimate, image, detrace, mean, transfer,
covid-19, x-ray, medics)
5(epidemic, risk, data, detect, method, health, influence,
outbreak, measure, covid-19)
6(model, graph, number, mixture, rate, predict, infect,
algorithm, china, covid-19)
7(sepsis, learn, feature, clinic, severe, treatment, differ,
disease, auroc, automate)
8(forecast, data, epidemic, high-resolute, tdefsi, method, ili, disease,
mds, perform)
9(data, world, period, trend, death, register, pandemic,
epidemic, model, covid-19)
10 (crime, virus, sars-cov-2, genotype, isolate, mutate, global,
genome, public, policies)
(b) Top topics in peer-reviewed data science based COVID-19 papers
Topic No. Extracted Topics
1(estimate, number, outbreak, model, epidemic, method, data, rate,
dynamics, coronavirus)
2(infect, estimate, death, risk, disease, quarantine, asymptomatic,
coronavirus, intervene, individual)
3(number, case, infect, model, data, epidemic, patient, control,
peak, forecast)
4(report, forecast, use, cumulative, predict, growth, data,
outbreak, transmission, improve)
5(coronavirus, quarantine, countries, data, suspect, measure,
effect, ratio, intervention, transmission)
6(outbreak, coronavirus, period, transmission, peak, predict,
reproduction, mean, intervention)
7(case, cities, model, number, outbreak, fit, dynamics,
prevent, trend, predict)
8(outside, travel, cause, viral, range, detect, phase,
pneumonia, incubate, quarantine)
9(case, estimate, epidemic, global, export, forecast,
risk, incident, reproduction, severe)
10 (control, outbreak, trace, isolate, transmission, symptom,
prevent, model, onset, strategies)
for medical images and textual analysis, these datasets are
small compared to the requirements of deep learning models.
For example, in the case of biomedical data, sample sizes
range from a few up to
60
patients (see [61]). The scarcity of
measured data is frequently due to the distributed nature of
many data sources. For example, electronic healthcare records
are often segregated on a national, regional, or even per-hospital
level. A key challenge is therefore federating these sources, and
overcoming practical differences across each source, e.g., in
terms of schemas. Thus, better and more automated approaches
to data munging, data wrangling etc. may be critical in attaining
fast, reliable and robust outcomes. Common standards and
international collaboration will help.
Beyond these challenges regarding availability of data, there
are also major challenges within the data itself. The time-critical
nature of this research is causing hurdles in developing certain
types of high-quality dataset. For instance, by the time social
media data is collected, curated and annotated it can become
out-of-date. Due to this, COVID-19 datasets and their causal
interpretations often contain poorly quantified biases [82]. For
example, daily infection rates in Japan exhibit few similarities to
those in the Italy. Training models on unrepresentative datasets
will lead to poor (and even dangerous) outcomes. Whereas
techniques such as transfer learning could allow models to be
14
Fig. 3: Cumulative publication rates for peer-reviewed publi-
cations in COVID-19, SARS-CoV-1, Ebola (data gathered till
April 23, 2020). Note the different X-ranges.
specialised with regional characteristics, the fast-moving nature
of the problem can make it difficult to perform informed model
selection and parameterisation. A key challenge is devising
analytical approaches that can work with these data limitations.
B. Correctness of Results vs. Urgency
There is a clear need for rapid results, yet the methods
surveyed in this paper are largely based on statistical learning
using (rapidly produced) datasets. In a recent systematic review
of prediction models for diagnosis and prognosis of COVID-
19, Wynants et al. [211] report that all 31 reviewed prediction
models have a high risk of bias (due to non-representative
selection of control patients and model overfitting). The
reported models are therefore susceptible to errors. This is an
inherent risk in all scientific work but, given the fast-moving
nature of the situation, errors can have severe consequences.
It should further be remembered that the outcomes of research
may impact healthcare policy. For example, predictions may be
used by governments to decide the extent of social distancing.
Yet political actors are often less well placed to understand
the nuance of scientific studies. We therefore posit that a key
challenge is balancing exigency vs. the need for well-evidenced
and reproducible results that can inform policy.
Due to the above, another clear challenge is finding ways to
capture and represent the uncertainty of conclusions produced
within the flurry of research. Bayesian methods can be
used to capture uncertainty, although we have seen limited
quantification of uncertainty in studies so far [212]. To ensure
the correctness of data analysis, researchers must also describe
their goals and process, and facilitate reproducible conclusions,
e.g., sharing code, data and documentation. This, again, can
create challenges as such requirements are balanced against the
need for urgency. Another potential avenue is ‘Explainable AI’
[213], which can be used to provide context to results. That
said, it is not clear if this will protect against problems such
as unintentional bias [214] or even adversarial scenarios [215].
C. Security, Privacy, and Ethics
Most of the works that we discussed imply the sharing
and/or use of potentially personal and sensitive data. Devising
solutions that exhibit good results but also protect privacy
and adhere to high ethical standards is a key challenge. We
argue that this could be vital for encouraging uptake among
populations, particularly as infrastructure setup may persist
beyond the pandemic [216]. There are already substantial efforts
to build privacy-preserving medical analytics. For example,
MedCo [217] uses homomorphic encryption to allow sites to
federate datasets with privacy guarantees. Drynx [218] supports
privacy-conscious statistical analysis on distributed datasets.
This links closely into the availability of data (see
§VI-A
), as
often data can only be shared when robust privacy guarantees
are in place.
Broadly speaking, there is some consensus as outlined in
Floridi et al. [219] on the five main “AI ethics principles”:
(1) beneficence, (2) non-maleficence, (3) autonomy, (4) justice,
and (5) explicability. However, in the situation imposed by
COVID-19, decisions may need to choose a tradeoff between
these AI ethics virtues [220], [221]. For example, to what
extent does the current situation warrants the prioritisation of
“public health” and “beneficence” over “individual privacy” and
“autonomy”. And even if this is warranted in the short-term,
how can we ensure that these compromises do not become
permanent and it is possible to roll back these tradeoffs in the
future as the situation changes. Other difficult questions include
the issue of allocation of scarce resources and the tradeoffs
involved therein. As highlighted in the Call for Action presented
in March 2020 by a coalition of experts on data governance
[222], there is also a need for data sharing between the public
and private sectors to ensure that data is used for “beneficence”.
In effect, the failure to share data in such contexts may be
considered maleficence since withholding critical data may
block an opportunity to bring potential benefit. That said, good
governance mechanisms with suitable regulations should be in
place to oversee ethical use of data as much as possible.
Privacy may also become particularly challenging when
considering the roll-out of interventions (e.g., targeted social
distancing measures), as the intervention itself may expose
sensitive information [223]. This, for example, may apply to
contact tracing apps, which strive to notify users when they
have been in contact with an infected person. Although privacy-
preserving implementations exist (e.g., DP-3T, TraceSecure),
notifications may still allow users to guess who the infected
person is (see [224] for a discussion of security issues in tracing
apps).
To move ahead, simple measures can be adopted to help
ensure ethical data science research. For example, data collected
should be transparent (the users should be informed about
what data is being collected) and stewarded with a limited
purpose (even when it is anonymised) and governed with
ethical oversight and appropriate safeguards (e.g., with time
limits and sunset provisions). Interested readers are referred to
several comprehensive resources on data ethics [219], [225]–
[229], and to a recent report from the TUM Institute for Ethics
in Artificial Intelligence [220] and the IEEE Global Initiative
15
on Ethics of Autonomous and Intelligent Systems [221] on
the ethical challenges involved in using AI for managing the
COVID-19 outbreak.
D. The Need For Multidisciplinary Collaboration
Our understanding of COVID-19’s long-term impact is
preliminary. Contributing serious insights will require a mix
of domain expertise from multiple fields, and there is already
a push for better international collaboration and tracking of
COVID-19 [230]. For example, the use of black-box models
might yield a superficially practical solution, but could be
useless without the involvement of (international) medical
and biotechnology expert interpretations. This will further
have implications for licensing technologies and engendering
uptake (as healthcare professionals are unlikely to engage with
technologies developed without medical expertise). Rapidly
bringing together cohorts of complementary expertise is there-
fore important. This also brings many further challenges, e.g.,
ensuring a team’s interpretation of things like ethics, benefits
and risks are coherent.
E. New Data Modalities
The data science community has limited exposure to certain
modalities of data that may prove critical in combating COVID-
19. A natural challenge is rapidly adapting existing techniques
to reflect these new data types. For example, whereas the
community has substantial expertise in computer vision tasks,
there is less experience in processing ultrasound scans. Yet
these have shown good results that are similar to chest CT scans
and superior to standard chest radiography for the evaluation of
pneumonia and/or acute respiratory distress syndrome (ARDS)
in corona patients [231], [232]. They also have the benefit of
greater ease of use, absence of radiation, and low cost. Despite
these advantages, to the best of our knowledge, no study has
yet explored the potential of automatically detecting COVID-19
infections via ultrasound scans. Similarly, magnetic resonance
imaging (MRI) is considered the safest imaging modality as it
is a non-invasive and non-ionising technique, which provides a
high resolution image and excellent soft tissue contrast [110].
Some studies like [233] have described the significance of
MRI in fighting against COVID-19 infections. Yet the modality
remained under-explored by the computer vision community
due to a lack of sufficient training data. Thus, a challenge is
to rapidly develop a well-annotated dataset of such medical
imaging modalities.
F. Solutions for the Developing World
The COVID-19 pandemic poses unique challenges to popula-
tions that have limited access to healthcare (e.g. in developing
countries), particularly as such people are disproportionately
affected by limited access to information [234]. A key challenge
is developing technologies that are designed so that they
are globally inclusive. This requires considering how such
technologies will impact different communities, and exploring
how they could be deployed in both rural and economically
deprived regions [235]–[237], as well as how they might be
misused in certain contexts. This subsumes several practical
challenges that naturally vary based on the specific use case.
For example, if building a mobile app for contact tracing, it
should be low cost and require limited resources; it should
be designed with limited network connectivity in-mind; it
should also support multiple languages and be accessible to
illiterate users or those with disabilities. We emphasise that
ensuring wide accessibility of technological solutions is critical
for addressing this global pandemic.
VII. CONCLUSIONS
Data scientists have been active in addressing the emerging
challenges related to COVID-19. This paper has been written
to rapidly make available a summary of ongoing work for
the wider community. We have attempted to make five broad
contributions. We first presented relevant use cases of data
science, which have the potential to help in the pandemic. This
is by no means a comprehensive list and we expect the set to
expand in the coming months. We then focused on summarising
publicly available datasets for use by researchers. Again, this
is intended as a community resource to shorten the time taken
to discover relevant data. Following this, we surveyed some
of the ongoing research in this area. As the paper is mainly
intended for a computer science and engineering audience,
we again themed our analysis around the different types of
datasets available. Following this, we broadened our analysis
and presented a bibliometric study of thousands of publications
in recent months. Finally, we highlighted some of the common
challenges we observed as part of our systematic review, e.g.,
availability of data and privacy concerns. We also note that
many of the systems discussed in this paper are not operational
yet. In view of this, we intend to update the paper repeatedly
with new information.
REFERENCES
[1]
C. Huang, Y. Wang, X. Li, L. Ren, J. Zhao, Y. Hu, L. Zhang, G. Fan,
J. Xu, X. Gu et al., “Clinical features of patients infected with 2019
novel coronavirus in Wuhan, China,” The Lancet, vol. 395, no. 10223,
pp. 497–506, 2020.
[2]
J. T. Wu, K. Leung, and G. M. Leung, “Nowcasting and forecasting the
potential domestic and international spread of the 2019-ncov outbreak
originating in Wuhan, China: a modelling study,” The Lancet, vol. 395,
no. 10225, pp. 689 – 697, 2020.
[3]
N. Zhu, D. Zhang, W. Wang, X. Li, B. Yang, J. Song, X. Zhao, B. Huang,
W. Shi, R. Lu et al., “A novel coronavirus from patients with pneumonia
in China, 2019,” New England Journal of Medicine, 2020.
[4]
K. Hao, “Over 24,000 coronavirus research papers are now available
in one place, published on 16-March-2020; accessed on: 6-April-2020.”
[Online]. Available: https://tinyurl.com/MITTECHREV24000papers
[5]
J. Bullock, K. H. Pham, C. S. N. Lam, M. Luengo-Oroz et al., “Mapping
the landscape of artificial intelligence applications against COVID-19,
arXiv preprint arXiv:2003.11336, 2020.
[6]
M. van der Schaar, A. Alaa, A. Floto, A. Gimson, S. Scholtes,
A. Wood, E. McKinney, D. Jarrett, P. Lio, and A. Ercole, “How artificial
intelligence and machine learning can help healthcare systems respond
to COVID-19, published on 27-March-2020; accessed on: 1-April-2020.
[7]
A. A. Khorana, “Artificial intelligence for cancer-associated thrombosis
risk assessment–author’s reply,” The Lancet Haematology, vol. 5, no. 9,
pp. e391–e392, 2018.
[8]
J. Dave, V. N. Dubey, D. Coppini, and J. Beavis, “Predicting diabetic
neuropathy risk level using artificial neural network based on clinical
characteristics of subjects with diabetes,” Diabetic Medicine, pp. 144–
144, 2019.
16
[9]
K. Wattanakit, G. Harshavardhan, S. Mungee, and M. Imtiaz, “Artificial
intelligence based clinical risk assessment in predicting cardiac related
chest pain in patients presenting to emergency room,Circulation, vol.
140, no. Suppl 1, pp. A14 100–A14 100, 2019.
[10]
S. Latif, M. Y. Khan, A. Qayyum, J. Qadir, M. Usman, S. M. Ali, Q. H.
Abbasi, and M. A. Imran, “Mobile technologies for managing non-
communicable diseases in developing countries,” in Mobile applications
and solutions for social inclusion. IGI Global, 2018, pp. 261–287.
[11]
Startup Uses Fever Detection Technology To Stop Spread of
Coronavirus, accessed on: 4-April-2020,” 2020. [Online]. Available:
https://tinyurl.com/feverdetectiontechnology
[12]
C. S. Currie, J. W. Fowler, K. Kotiadis, T. Monks, B. S. Onggo, D. A.
Robertson, and A. A. Tako, “How simulation modelling can help reduce
the impact of COVID-19,Journal of Simulation, 2020.
[13]
W. O. Kermack, A. G. McKendrick, and G. T. Walker, “A contribution
to the mathematical theory of epidemics,” Proceedings of the Royal
Society of London. Series A, Containing Papers of a Mathematical and
Physical Character, vol. 115, no. 772, pp. 700–721, 1927.
[14]
A. J. Kucharski, T. W. Russell, C. Diamond, Y. Liu, J. Edmunds,
S. Funk, R. M. Eggo, F. Sun, M. Jit, J. D. Munday et al., “Early
dynamics of transmission and control of COVID-19: a mathematical
modelling study,The Lancet Infectious Diseases, 2020.
[15]
K. J. Friston, T. Parr, P. Zeidman, A. Razi, G. Flandin, J. Daunizeau,
O. Hulme, A. J. Billig, V. Litvak, R. J. Moran, C. J. Price, and
C. Lambert, “Dynamic causal modelling of COVID-19,arXiv preprint
arXiv:2004.04463, 2020.
[16]
J. Dehning, J. Zierenberg, F. P. Spitzner, M. Wibral, J. P.
Neto, M. Wilczek, and V. Priesemann, “Inferring COVID-19
spreading rates and potential change points for case number
forecasts,” arXiv preprint arXiv:2004.01105, 2020. [Online]. Available:
http://dx.doi.org/10.1101/2020.04.02.20050922
[17]
K. J. Friston, T. Parr, P. Zeidman, A. Razi, G. Flandin, J. Daunizeau,
O. Hulme, A. J. Billig, V. Litvak, R. J. Moran, C. J. Price, and
C. Lambert, “Second waves, social distancing, and the spread of
Covid-19 across America,” University College London, Tech. Rep.,
2020. [Online]. Available: https://www.fil.ion.ucl.ac.uk/spm/covid-19
[18]
Z. Tufekci, “Don’t Believe the COVID-19 Models: That’s not what
they’re for, The Atlantic, Data Published: April 2, 2020,” 2020.
[Online]. Available: https://www.theatlantic.com/technology/archive/
2020/04/coronavirus-models-arent-supposed-be-right/609271/
[19]
M. Koerth, L. Bronner, and J. Mithani, “Why It’s So Freaking Hard To
Make A Good COVID-19 Model,https://fivethirtyeight.com/features/
why-its- so-freaking- hard-to- make-a-good-covid-19-model/, 2020.
[20]
A. Noulas, C. Moffatt, D. Hristova, and B. Gon
c¸
alves, “Foursquare to the
rescue: Predicting ambulance calls across geographies,” in Proceedings
of the 2018 International Conference on Digital Health, 2018, pp.
100–109.
[21]
N. Ferguson, D. Laydon, G. Nedjati Gilani, N. Imai, K. Ainslie,
M. Baguelin, S. Bhatia, A. Boonyasiri, Z. Cucunuba Perez, G. Cuomo-
Dannenburg et al., “Report 9: Impact of non-pharmaceutical interven-
tions (npis) to reduce covid19 mortality and healthcare demand,” 2020.
[22]
H. Heesterbeek, R. M. Anderson, V. Andreasen, S. Bansal, D. De An-
gelis, C. Dye, K. T. Eames, W. J. Edmunds, S. D. Frost, S. Funk et al.,
“Modeling infectious disease dynamics in the complex landscape of
global health,” Science, vol. 347, no. 6227, p. aaa4339, 2015.
[23]
N. Ghaffarzadegan and H. Rahmandad, “Simulation-based estimation of
the spread of covid-19 in Iran,” medRxiv, 2020. [Online]. Available: https:
//www.medrxiv.org/content/early/2020/03/27/2020.03.22.20040956
[24]
J. Hellewell, S. Abbott, A. Gimma, N. I. Bosse, C. I. Jarvis, T. W.
Russell, J. D. Munday, A. J. Kucharski, W. J. Edmunds, F. Sun et al.,
“Feasibility of controlling COVID-19 outbreaks by isolation of cases
and contacts,” The Lancet Global Health, 2020.
[25]
L. Ferretti, C. Wymant, M. Kendall, L. Zhao, A. Nurtay, L. Abeler-
D
¨
orner, M. Parker, D. Bonsall, and C. Fraser, “Quantifying SARS-CoV-2
transmission suggests epidemic control with digital contact tracing,”
Science, 2020.
[26]
A. S. S. Rao and J. A. Vazquez, “Identification of COVID-19 can be
quicker through artificial intelligence framework using a mobile phone-
based survey in the populations when cities/towns are under quarantine,
Infection Control & Hospital Epidemiology, pp. 1–18, 2020.
[27]
C. B. et al., “Coronasurveys: Monitoring COVID-19 incidence via
open polls,” 2020. [Online]. Available: http://coronasurveys.com/
[28]
E. Yoneki and J. Crowcroft, “Epimap: Towards quantifying contact
networks for understanding epidemiology in developing countries,Ad
Hoc Networks, vol. 13, pp. 83–93, 2014.
[29]
AI could help with the next pandemic—but not with this
one, MIT Technology Review, accessed on: 1-April-2020.
[Online]. Available: https://www.technologyreview.com/s/615351/
ai-could- help-with- the-next-pandemicbut-not-with- this-one/
[30]
A. Wilder-Smith and D. Freedman, “Isolation, quarantine, social
distancing and community containment: pivotal role for old-style
public health measures in the novel Coronavirus (2019-ncov) outbreak,
Journal of Travel Medicine, vol. 27, no. 2, 2020.
[31]
F.-J. Schmitt, “A simplified model for expected development of the
SARS-CoV-2 (corona) spread in germany and us after social distancing,”
arXiv preprint arXiv:2003.10891, 2020.
[32]
C. St Louis and G. Zorlu, “Can twitter predict disease outbreaks?” Bmj,
vol. 344, p. e2353, 2012.
[33]
A. Signorini, A. M. Segre, and P. M. Polgreen, “The use of Twitter to
track levels of disease activity and public concern in the US during the
influenza A H1N1 pandemic,” PloS one, vol. 6, no. 5, 2011.
[34]
M. Cadotte, “Early evidence that COVID-19 government policies reduce
urban air pollution,” Mar 2020. [Online]. Available: eartharxiv.org/nhgj3
[35]
J. Zarocostas, “How to fight an infodemic,The Lancet, vol. 395, no.
10225, p. 676, 2020.
[36]
L. Bode and E. K. Vraga, “See something, say something: Correction of
global health misinformation on social media,” Health communication,
vol. 33, no. 9, pp. 1131–1140, 2018.
[37]
P. M. Waszak, W. Kasprzycka-Waszak, and A. Kubanek, “The spread of
medical fake news in social media–the pilot quantitative study,” Health
policy and technology, vol. 7, no. 2, pp. 115–118, 2018.
[38]
N. Parveen and J. Waterson, “Uk phone masts
attacked amid 5g-coronavirus conspiracy theory,”
https://www.theguardian.com/uk-news/2020/apr/04/
uk-phone- masts-attacked-amid-5g-coronavirus-conspiracy-theory,
2020.
[39]
“Misinformation related to the 2019–20 coronavirus pandemic,”
https://en.wikipedia.org/wiki/Misinformation related to the 2019%
E2%80%9320 coronavirus pandemic, 2020.
[40]
G. Pennycook, J. McPhetres, Y. Zhang, and D. Rand, “Fighting COVID-
19 misinformation on social media: Experimental evidence for a scalable
accuracy nudge intervention,” 2020.
[41]
W. J. McKibbin and R. Fernando, “The global macroeconomic impacts
of COVID-19: Seven scenarios,” 2020.
[42]
A. Atkeson, “What will be the economic impact of COVID-19 in
the US? rough estimates of disease scenarios,” National Bureau of
Economic Research, Tech. Rep., 2020.
[43]
WHO and Rakuten Viber fight COVID-19 misinformation with
interactive chatbot, Accessed on: 4-April-2020.” [Online]. Available:
https://tinyurl.com/WHORatukenChatBot
[44]
CPR News, “Health Care Workers’ Stress Compounded By Long Days
And Concerns About People Not Taking COVID-19 Seriously, accessed
on: 1-April-2020.” [Online]. Available: https://www.cpr.org/2020/03/23/
colorado-coronavirus-stress-healthcare-workers-covid-19- spread/
[45]
“AliveCor, accessed on: 1-April-2020.” [Online]. Available: https:
//www.alivecor.com/
[46]
AliveCor, “New FDA Guidance Allows Use of KardiaMobile 6L to
Measure QTc in COVID-19 Patients, accessed on: 27-April-2020.”
[Online]. Available: https://www.alivecor.com/press/press release/
new-fda-guidance-allows-use- of-kardiamobile- 6l-to- measure-qtc- in-covid- 19-patients/
[47]
“CLEW Medical, accessed on: 1-April-2020.” [Online]. Available:
https://clewmed.com/
[48]
FreeStyle, “Diabetes manegmet and COVID-19, accessed on: 27-
April-2020.” [Online]. Available: https://www.freestylelibre.co.uk/libre/
freestyle-libre- blog/Managing-diabetes- and-covid-19.html
[49]
Kate Johnson, “COVID-19: Home Pulse Oximetry Could Be Game
Changer, Says ER Doc, accessed on: 27-April-2020.” [Online].
Available: https://www.medscape.com/viewarticle/929309?src=soc tw
200424 mscpedt news mdscp pulseoximeter&faf=1
[50]
HHS News, “Secretary Azar Announces Historic Expansion of
Telehealth Access to Combat COVID-19, accessed on: 27-April-
2020.” [Online]. Available: https://www.hhs.gov/about/news/2020/03/17/
secretary-azar-announces-historic-expansion-of-telehealth-access-to-combat-covid-19.
html/
[51]
Federal Communications Commission, “COVID-19 Telehealth Program,
accessed on: 27-April-2020.” [Online]. Available: https://www.fcc.gov/
covid-19-telehealth-program/
[52]
P. Webster, “Virtual health care in the era of covid-19,The Lancet,
vol. 395, no. 10231, pp. 1180–1181, 2020.
[53]
J. B. Mitchell, “Artificial intelligence in pharmaceutical research and
development,” 2018.
[54]
K.-K. Mak and M. R. Pichika, “Artificial intelligence in drug devel-
opment: present status and future prospects,” Drug discovery today,
vol. 24, no. 3, pp. 773–780, 2019.
17
[55]
A. Zhavoronkov, “Artificial intelligence for drug discovery, biomarker
development, and generation of novel chemistry,” 2018.
[56]
G. Tyson, A. Taweel, S. Miles, M. Luck, T. Van Staa, and B. Delaney,
“An agent-based approach to real-time patient identification for clinical
trials,” in International Conference on Electronic Healthcare. Springer,
2011, pp. 138–145.
[57]
D. A. Berry, “Bayesian clinical trials,Nature reviews Drug discovery,
vol. 5, no. 1, pp. 27–36, 2006.
[58]
D. M. Gysi,
´
I. D. Valle, M. Zitnik, A. Ameli, X. Gan, O. Varol,
H. Sanchez, R. M. Baron, D. Ghiassian, J. Loscalzo et al., “Network
medicine framework for identifying drug repurposing opportunities for
covid-19,” arXiv preprint arXiv:2004.07229, 2020.
[59]
SIRM, “Covid-19-BSTI Imaging Database,” 2020. [On-
line]. Available: https://www.bsti.org.uk/training-and-education/
covid-19-bsti-imaging-database/
[60]
J. P. Cohen, P. Morrison, and L. Dao, “COVID-19 image
data collection,” arXiv 2003.11597, 2020. [Online]. Available:
https://github.com/ieee8023/covid-chestxray-dataset
[61]
MegSeg, “COVID-19 CT segmentation dataset,” Mar 2020. [Online].
Available: http://medicalsegmentation.com/covid19/
[62]
G. Inc., “COVID-19 Community Mobility Reports,” Mar 2020.
[Online]. Available: https://www.google.com/covid19/mobility/
[63]
SIRM, “COVID-19 DATABASE,” 2020. [Online]. Available: https:
//www.sirm.org/category/senza-categoria/covid-19/
[64]
Allen-Institute, “CORD-19 research challenge,” Mar 2020.
[Online]. Available: https://www.kaggle.com/allen-institute-for- ai/
CORD-19- research-challenge
[65]
ECDC, “European Centre for Disease Prevention and Control
(ECDC),” 2020. [Online]. Available: https://ourworldindata.org/
coronavirus-source-data
[66]
Smith, “Coronavirus (covid19) tweets,” Mar 2020. [Online]. Available:
www.kaggle.com/smid80/coronavirus-covid19-tweets
[67]
“COVID-19 Korea Dataset with Patient Routes,” 2020. [Online].
Available: https://github.com/ThisIsIsaac/Data-Science-for-COVID-19
[68]
CHIME, “COVID-19 Hospital Impact Model for Epidemics,” 2020.
[Online]. Available: https://github.com/CodeForPhilly/chime
[69]
WHO, “Global research on novel coronavirus-
2019,” 2020. [Online]. Available: https://
www.who.int/emergencies/diseases/novel-coronavirus-2019/
global-research- on-novel-coronavirus-2019-ncov
[70]
GISAID, “Genomic epidemiology of hCoV-19,” 2020. [Online].
Available: https://www.gisaid.org/epiflu-applications/next-hcov-19-app/
[71]
CSSEGISandData, “CSSEGISandData/COVID-19,” Mar 2020. [Online].
Available: https://github.com/CSSEGISandData/COVID-19
[72]
Kinsa Health, “U.S. Health Weather Map,” Mar 2020. [Online].
Available: https://healthweather.us/?mode=Atypical
[73]
NCBI, “LitCovid,” 2020. [Online]. Available: https://www.ncbi.nlm.nih.
gov/research/coronavirus/
[74]
Q. Chen, A. Allot, and Z. Lu, “Keep up with the latest coronavirus
research,” Nature, vol. 579, no. 7798, p. 193, 2020.
[75]
nCoV2019Data, “ncov2019 epidemiological data,” Mar 2020. [Online].
Available: https://github.com/beoutbreakprepared/nCoV2019
[76]
sudalairajkumar Data, “Novel corona-virus dataset,” Mar
2020. [Online]. Available: https://www.kaggle.com/sudalairajkumar/
novel-corona-virus-2019-dataset
[77]
NewYork-Times, “New york times dataset,” Mar 2020. [Online].
Available: https://github.com/nytimes/covid-19-data
[78]
E. Chen, K. Lerman, and E. Ferrara, “COVID-19: The first public
Coronavirus Twitter dataset,” arXiv preprint arXiv:2003.07372, 2020.
[79]
D. S. Goodsell, C. Zardecki, L. Di Costanzo, J. M. Duarte, B. P. Hudson,
I. Persikova, J. Segura, C. Shao, M. Voigt, J. D. Westbrook et al., “RCSB
protein data bank: Enabling biomedical research and drug discovery,
Protein Science, vol. 29, no. 1, pp. 52–65, 2020.
[80]
NPGEO, “Dataset of infections in germany,” 2020. [On-
line]. Available: https://npgeo-corona- npgeo-de.hub.arcgis.com/datasets/
dd4580c810204019a7b8eb3e0b329dd6 0/data
[81]
A. Koubaa, “Understanding the covid19 outbreak: A comparative data
analytics and study,arXiv preprint arXiv:2003.14150, 2020.
[82]
N. Fenton, G. A. Hitman, M. Neil, M. Osman, and S. McLachlan,
“Causal explanations, error rates, and human judgment biases missing
from the covid-19 narrative and statistics.
[83]
G. P. Zhang, “Time series forecasting using a hybrid ARIMA and neural
network model,” Neurocomputing, vol. 50, pp. 159–175, 2003.
[84]
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural
computation, vol. 9, no. 8, pp. 1735–1780, 1997.
[85]
C. Jacobs, “Coronada,” 2020. [Online]. Available: https://github.com/
BayesForDays/coronada
[86]
E. Aramaki, S. Maskawa, and M. Morita, “Twitter catches the flu:
detecting influenza epidemics using Twitter,” in Proceedings of the
conference on empirical methods in natural language processing.
Association for Computational Linguistics, 2011, pp. 1568–1576.
[87]
A. Culotta, “Towards detecting influenza epidemics by analyzing Twitter
messages,” in Proceedings of the first workshop on social media
analytics, 2010, pp. 115–122.
[88]
V. Lampos, T. De Bie, and N. Cristianini, “Flu detector-tracking
epidemics on Twitter,” in Joint European conference on machine
learning and knowledge discovery in databases. Springer, 2010,
pp. 599–602.
[89]
K. Sharma, S. Seo, C. Meng, S. Rambhatla, A. Dua, and Y. Liu,
“Coronavirus on social media: Analyzing misinformation in Twitter
conversations,arXiv preprint arXiv:2003.12309, 2020.
[90]
K. Zarei, R. Farahbakhsh, N. Crespi, and G. Tyson, “A first instagram
dataset on covid-19,” arxiv, 2020.
[91] D. Zhao, F. Yao, L. Wang, L. Zheng, Y. Gao, J. Ye, F. Guo, H. Zhao,
and R. Gao, “A comparative study on the clinical features of COVID-19
pneumonia to other pneumonias,” Clinical Infectious Diseases, 2020.
[92]
C. Manning, “Understanding human language: Can NLP and deep
learning help?” in Proceedings of the 39th International ACM SIGIR
conference on Research and Development in Information Retrieval,
2016, pp. 1–1.
[93]
“arXiv, accessed on: 12-April-2020.” [Online]. Available: https:
//arxiv.org/
[94]
“medRxiv, accessed on: 12-April-2020.” [Online]. Available: https:
//www.medrxiv.org/
[95]
“bioRXiv, accessed on: 12-April-2020.” [Online]. Available: https:
//www.biorxiv.org/
[96]
“Wikipedia database downloaod,https://en.wikipedia.org/wiki/
Wikipedia:Database download, 2020.
[97]
L. Wang and A. Wong, “COVID-Net: A tailored deep convolutional
neural network design for detection of COVID-19 cases from chest
radiography images,” 2020.
[98]
D. Mery, “Computer vision for X-Ray testing,Switzerland: Springer
International Publishing.–2015, vol. 10, pp. 978–3, 2015.
[99]
“COVID Chest-Xray Dataset,” Mar 2020. [Online]. Available:
https://github.com/ieee8023/covid-chestxray-dataset
[100]
J. Zhao, Y. Zhang, X. He, and P. Xie, “COVID-CT-Dataset: a CT scan
dataset about COVID-19,arXiv preprint arXiv:2003.13865, 2020.
[101]
A. Zhavoronkov, V. Aladinskiy, A. Zhebrak, B. Zagribelnyy, V. Terentiev,
D. S. Bezrukov, D. Polykovskiy, R. Shayakhmetov, A. Filimonov,
P. Orekhov et al., “Potential COVID-2019 3c-like protease inhibitors
designed using generative deep learning approaches,Insilico Medicine
Hong Kong Ltd A, vol. 307, p. E1, 2020.
[102]
W. Inc., “World’s air pollution: Real-time air quality index,” 2020.
[Online]. Available: https://waqi.info
[103]
N. Oliver, E. Letouz
´
e, H. Sterly, S. Delataille, M. De Nadai, B. Lepri,
R. Lambiotte, R. Benjamins, C. Cattuto, V. Colizza et al., “Mobile
phone data and COVID-19: Missing an opportunity?” arXiv preprint
arXiv:2003.12347, 2020.
[104]
“Data world,” 2020. [Online]. Available: https://data.world/datasets/
mobile
[105]
S. Wurtzer, V. Marechal, J.-M. Mouchel, and L. Moulin, “Time course
quantitative detection of sars-cov-2 in parisian wastewaters correlates
with covid-19 confirmed cases,” medRxiv, 2020.
[106]
R. data science coalition, “Uncover covid19 challenge,” Mar 2020. [On-
line]. Available: https://www.kaggle.com/roche-data-science- coalition/
uncover
[107]
“Covid19 Global Forecasting Challenge, The White House Office
of Science and Technology,” Mar 2020. [Online]. Available: https:
//www.kaggle.com/c/covid19-global-forecasting-week-2/overview
[108]
M. Haghighatlari, G. Vishwakarma, D. Altarawy, R. Subramanian, B. U.
Kota, A. Sonpal, S. Setlur, and J. Hachmann, “Chemml: A machine
learning and informatics program package for the analysis, mining,
and modeling of chemical and materials data,” Wiley Interdisciplinary
Reviews: Computational Molecular Science, p. e1458, 2019.
[109]
S. Robertson, H. Azizpour, K. Smith, and J. Hartman, “Digital image
analysis in breast pathology—from image processing techniques to
artificial intelligence,” Translational Research, vol. 194, pp. 19–35,
2018.
[110]
M. Usman, S. Latif, M. Asim, B.-D. Lee, and J. Qadir, “Retrospective
motion correction in multishot MRI using generative adversarial
network,” Scientific Reports, vol. 10, no. 1, pp. 1–11, 2020.
[111]
M. Usman, B.-D. Lee, S. S. Byon, S. H. Kim, and B. IlLee, “Volumetric
lung nodule segmentation using adaptive roi with multi-view residual
learning,” arXiv preprint arXiv:1912.13335, 2019.
18
[112]
Z. Z. Qin, M. S. Sander, B. Rai, C. N. Titahong, S. Sudrungrot, S. N.
Laah, L. M. Adhikari, E. J. Carter, L. Puri, A. J. Codlin et al., “Using
artificial intelligence to read chest radiographs for tuberculosis detection:
A multi-site evaluation of the diagnostic accuracy of three deep learning
systems,” Scientific reports, vol. 9, no. 1, pp. 1–10, 2019.
[113]
R. Singh, M. K. Kalra, C. Nitiwarangkul, J. A. Patti, F. Homayounieh,
A. Padole, P. Rao, P. Putha, V. V. Muse, A. Sharma et al., “Deep
learning in chest radiography: detection of findings and presence of
change,” PloS one, vol. 13, no. 10, 2018.
[114]
T. Ai, Z. Yang, H. Hou, C. Zhan, C. Chen, W. Lv, Q. Tao, Z. Sun, and
L. Xia, “Correlation of chest CT and RT-PCR testing in Coronavirus
disease 2019 (COVID-19) in China: a report of 1014 cases,Radiology,
p. 200642, 2020.
[115]
Y. Li and L. Xia, “Coronavirus disease 2019 (covid-19): Role of chest
ct in diagnosis and management,” American Journal of Roentgenology,
pp. 1–7, 2020.
[116]
S. Wang, B. Kang, J. Ma, X. Zeng, M. Xiao, J. Guo, M. Cai, J. Yang,
Y. Li, X. Meng et al., “A deep learning algorithm using CT images to
screen for corona virus disease (COVID-19),medRxiv, 2020.
[117]
X. Xu, X. Jiang, C. Ma, P. Du, X. Li, S. Lv, L. Yu, Y. Chen, J. Su,
G. Lang et al., “Deep learning system to screen Coronavirus disease
2019 pneumonia,” arXiv preprint arXiv:2002.09334, 2020.
[118]
J. Chen, L. Wu, J. Zhang, L. Zhang, D. Gong, Y. Zhao, S. Hu, Y. Wang,
X. Hu, B. Zheng et al., “Deep learning-based model for detecting 2019
novel Coronavirus pneumonia on high-resolution computed tomography:
a prospective study,” medRxiv, 2020.
[119]
Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, and J. Liang, “Unet++:
A nested u-net architecture for medical image segmentation,” in Deep
Learning in Medical Image Analysis and Multimodal Learning for
Clinical Decision Support. Springer, 2018, pp. 3–11.
[120]
O. Gozes, M. Frid-Adar, H. Greenspan, P. D. Browning, H. Zhang,
W. Ji, A. Bernheim, and E. Siegel, “Rapid AI development cycle for
the Coronavirus (COVID-19) pandemic: Initial results for automated
detection & patient monitoring using deep learning CT image analysis,”
arXiv preprint arXiv:2003.05037, 2020.
[121]
L. Li, L. Qin, Z. Xu, Y. Yin, X. Wang, B. Kong, J. Bai, Y. Lu, Z. Fang,
Q. Song et al., “Artificial intelligence distinguishes COVID-19 from
community acquired pneumonia on chest CT,” Radiology, p. 200905,
2020.
[122]
D. of Health., “The ionising radiation (medical exposure) regulations
2017,” 2017.
[123]
E. El-Din Hemdan, M. A. Shouman, and M. E. Karar, “COVIDX-Net:
A framework of deep learning classifiers to diagnose COVID-19 in
X-Ray images,” arXiv, pp. arXiv–2003, 2020.
[124]
K. Simonyan and A. Zisserman, “Very deep convolutional networks for
large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014.
[125]
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang,
T. Weyand, M. Andreetto, and H. Adam, “Mobilenets: Efficient
convolutional neural networks for mobile vision applications,arXiv
preprint arXiv:1704.04861, 2017.
[126]
A. Narin, C. Kaya, and Z. Pamuk, “Automatic detection of Coronavirus
disease (COVID-19) using x-ray images and deep convolutional neural
networks,” arXiv preprint arXiv:2003.10849, 2020.
[127]
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image
recognition,” in Proceedings of the IEEE conference on computer vision
and pattern recognition, 2016, pp. 770–778.
[128]
M. Farooq and A. Hafeez, “COVID-ResNet: A deep learning frame-
work for screening of COVID19 from radiographs,arXiv preprint
arXiv:2003.14395, 2020.
[129]
P. K. Sethy and S. K. Behera, “Detection of coronavirus disease (covid-
19) based on deep features,” Preprints, 2020.
[130]
L. Inc., “Lunit CXR for COVID-19; Chest X-ray solutions,” 2020.
[Online]. Available: https://insight.lunit.io/covid19/
[131]
V. Inc., “VUNO Med-LungQuant & Chest X-ray solutions,” 2020.
[Online]. Available: https://covid19.vunomed.com/
[132]
F. Shan+, Y. Gao+, J. Wang, W. Shi, N. Shi, M. Han, Z. Xue, D. Shen,
and Y. Shi, “Lung infection quantification of COVID-19 in ct images
with deep learning,” arXiv preprint arXiv:2003.04655, 2020.
[133] J. Zhang, Y. Xie, Y. Li, C. Shen, and Y. Xia, “Covid-19 screening on
chest x-ray images using deep learning based anomaly detection,” arXiv
preprint arXiv:2003.12338, 2020.
[134]
S. Wang, Y. Zha, W. Li, Q. Wu, X. Li, M. Niu, M. Wang, X. Qiu, H. Li,
H. Yu et al., “A fully automatic deep learning system for COVID-19
diagnostic and prognostic analysis,” medRxiv, 2020.
[135]
M. Barstugan, U. Ozkaya, and S. Ozturk, “Coronavirus (covid-19)
classification using ct images by machine learning methods,” arXiv
preprint arXiv:2003.09424, 2020.
[136]
O. Gozes, M. Frid-Adar, N. Sagie, H. Zhang, W. Ji, and H. Greenspan,
“Coronavirus detection and analysis on chest ct with deep learning,”
arXiv preprint arXiv:2004.02640, 2020.
[137]
K. E. Asnaoui, Y. Chawki, and A. Idri, “Automated methods for
detection and classification pneumonia based on x-ray images using
deep learning,” arXiv preprint arXiv:2003.14363, 2020.
[138]
A. Abbas, M. M. Abdelsamea, and M. M. Gaber, “Classification of
COVID-19 in chest x-ray images using DeTraC deep convolutional
neural network,” arXiv preprint arXiv:2003.13815, 2020.
[139]
M. E. Chowdhury, T. Rahman, A. Khandakar, R. Mazhar, M. A. Kadir,
Z. B. Mahbub, K. R. Islam, M. S. Khan, A. Iqbal, N. Al-Emadi et al.,
“Can AI help in screening viral and COVID-19 pneumonia?” arXiv
preprint arXiv:2003.13145, 2020.
[140]
A. M. Alqudah, S. Qazan, H. Alquran, I. A. Qasmieh, and A. Alqudah,
“COVID-2019 detection using x-ray images and artificial intelligence
hybrid systems.”
[141]
B. Ghoshal and A. Tucker, “Estimating uncertainty and interpretability
in deep learning for Coronavirus (COVID-19) detection,arXiv preprint
arXiv:2003.10769, 2020.
[142]
F. M. Salman, S. S. Abu-Naser, E. Alajrami, B. S. Abu-Nasser, and
B. A. Ashqar, “Covid-19 detection using artificial intelligence,” 2020.
[143]
X. Li and D. Zhu, “Covid-xpert: An ai powered population screening
of covid-19 cases using chest radiography images,” arXiv preprint
arXiv:2004.03042, 2020.
[144]
Y. Zhu and S. Newsam, “Densenet for dense flow,” in 2017 IEEE
international conference on image processing (ICIP). IEEE, 2017, pp.
790–794.
[145]
M. Karim, T. D
¨
ohmen, D. Rebholz-Schuhmann, S. Decker, M. Cochez,
O. Beyan et al., “Deepcovidexplainer: Explainable covid-19 predictions
based on chest x-ray images,” arXiv preprint arXiv:2004.04582, 2020.
[146]
I. D. Apostolopoulos and T. A. Mpesiana, “Covid-19: automatic detec-
tion from x-ray images utilizing transfer learning with convolutional
neural networks,” Physical and Engineering Sciences in Medicine, p. 1,
2020.
[147]
K. Jahanbin and V. Rahmanian, “Using Twitter and web news mining
to predict COVID-19 outbreak,” 2020.
[148]
Y. Zhao and H. Xu, “Chinese public attention to COVID-19 epidemic:
Based on social media,” medRxiv, 2020.
[149]
L. Li, Q. Zhang, X. Wang, J. Zhang, T. Wang, T.-L. Gao, W. Duan,
K. K.-f. Tsoi, and F.-Y. Wang, “Characterizing the propagation of
situational information in social media during COVID-19 epidemic:
A case study on weibo,” IEEE Transactions on Computational Social
Systems, 2020.
[150]
L. Schild, C. Ling, J. Blackburn, G. Stringhini, Y. Zhang, and
S. Zannettou, ““go eat a bat, chang!”: An early look on the emergence
of Sinophobic behavior on web communities in the face of COVID-19,
2020.
[151]
D. Prabhakar Kaila, D. A. Prasad et al., “Informational flow on Twitter–
Corona virus outbreak–topic modelling approach,” International Journal
of Advanced Research in Engineering and Technology (IJARET), vol. 11,
no. 3, 2020.
[152]
F. Stephany, N. Stoehr, P. Darius, L. Neuh
¨
auser, O. Teutloff, and
F. Braesemann, “The CoRisk-index: A data-mining approach to identify
industry-specific risk assessments related to COVID-19 in real-time,
arXiv preprint arXiv:2003.12432, 2020.
[153]
M. Hofmarcher, A. Mayr, E. Rumetshofer, P. Ruch, P. Renz,
J. Schimunek, P. Seidl, A. Vall, M. Widrich, S. Hochreiter et al.,
“Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors
using deep neural networks,” Available at SSRN 3561442, 2020.
[154]
A. Mayr, G. Klambauer, T. Unterthiner, M. Steijaert, J. K. Wegner,
H. Ceulemans, D.-A. Clevert, and S. Hochreiter, “Large-scale com-
parison of machine learning methods for drug target prediction on
ChEMBL,” Chemical science, vol. 9, no. 24, pp. 5441–5451, 2018.
[155]
K. Preuer, G. Klambauer, F. Rippmann, S. Hochreiter, and T. Unterthiner,
“Interpretable deep learning in drug discovery,” in Explainable AI:
Interpreting, Explaining and Visualizing Deep Learning. Springer,
2019, pp. 331–345.
[156]
T. Sterling and J. J. Irwin, “ZINC 15–ligand discovery for everyone,
Journal of chemical information and modeling, vol. 55, no. 11, pp.
2324–2337, 2015.
[157]
B. R. Beck, B. Shin, Y. Choi, S. Park, and K. Kang, “Predicting
commercially available antiviral drugs that may act on the novel
coronavirus (SARS-CoV-2) through a drug-target interaction deep
learning model,” Computational and Structural Biotechnology Journal,
2020.
[158]
J. Kim, Y. Cha, S. Kolitz, J. Funt, R. Escalante Chong, S. Barrett,
B. Zeskind, R. Kusko, H. Kaufman et al., “Advanced bioinformatics
19
rapidly identifies existing therapeutics for patients with coronavirus
disease-2019 (COVID-19),” 2020.
[159]
P. Richardson, I. Griffin, C. Tucker, D. Smith, O. Oechsle, A. Phelan,
and J. Stebbing, “Baricitinib as potential treatment for 2019-ncov acute
respiratory disease,” The lancet, vol. 395, no. 10223, pp. e30–e31, 2020.
[160]
J. Stebbing, A. Phelan, I. Griffin, C. Tucker, O. Oechsle, D. Smith, and
P. Richardson, “Covid-19: combining antiviral and anti-inflammatory
treatments,” The Lancet Infectious Diseases, 2020.
[161]
V. Chenthamarakshan, P. Das, I. Padhi, H. Strobelt, K. W. Lim,
B. Hoover, S. C. Hoffman, and A. Mojsilovic, “Target-specific and
selective drug design for covid-19 using deep generative models,” arXiv
preprint arXiv:2004.01215, 2020.
[162]
S. Kazemi Rashed, J. Frid, and S. Aits, “English dictionaries, gold
and silver standard corpora for biomedical natural language processing
related to SARS-CoV-2 and COVID-19,arXiv, pp. arXiv–2003, 2020.
[163]
C. E. Lopez, M. Vasu, and C. Gallemore, “Understanding the perception
of COVID-19 policies by mining a multilanguage Twitter dataset,” arXiv
preprint arXiv:2003.10359, 2020.
[164]
J. E. C. Saire and R. C. Navarro, “What is the people posting about
symptoms related to Coronavirus in Bogota, Colombia?” arXiv preprint
arXiv:2003.11159, 2020.
[165]
M. Cinelli, W. Quattrociocchi, A. Galeazzi, C. M. Valensise, E. Brugnoli,
A. L. Schmidt, P. Zola, F. Zollo, and A. Scala, “The COVID-19 social
media infodemic,” arXiv preprint arXiv:2003.05004, 2020.
[166]
L. Singh, S. Bansal, L. Bode, C. Budak, G. Chi, K. Kawintiranon,
C. Padden, R. Vanarsdall, E. Vraga, and Y. Wang, “A first look at
COVID-19 information and misinformation sharing on Twitter,” arXiv
preprint arXiv:2003.13907, 2020.
[167]
X. Li, J. Yu, Z. Zhang, J. Ren, A. E. Peluffo, W. Zhang, Y. Zhao,
K. Yan, D. Cohen, and W. Wang, “Network bioinformatics analysis
provides insight into drug repurposing for COVID-2019,” 2020.
[168]
M. M. Hossain, “Current status of global research on novel Coronavirus
disease (COVID-19) : A bibliometric analysis and knowledge mapping,
Available at SSRN 3547824, 2020.
[169]
B. P. Roquette, H. Nagano, E. C. Marujo, and A. C. Maiorano,
“Prediction of admission in pediatric emergency department with deep
neural networks and triage textual data,Neural Networks, 2020.
[170]
B. W. Schuller, D. M. Schuller, K. Qian, J. Liu, H. Zheng, and X. Li,
“Covid-19 and computer audition: An overview on what speech & sound
analysis could contribute in the SARS-CoV-2 Corona crisis,” arXiv
preprint arXiv:2003.11117, 2020.
[171]
I. Song, “Diagnosis of pneumonia from sounds collected using low
cost cell phones,” in 2015 International Joint Conference on Neural
Networks (IJCNN), 2015, pp. 1–8.
[172]
R. Rana, S. Latif, R. Gururajan, A. Gray, G. Mackenzie, G. Humphris,
and J. Dunn, “Automated screening for distress: A perspective for the
future,” European journal of cancer care, vol. 28, no. 4, p. e13033,
2019.
[173]
S. Amiriparian, S. Pugachevskiy, N. Cummins, S. Hantke, J. Pohjalainen,
G. Keren, and B. Schuller, “Cast a database: Rapid targeted large-
scale big data acquisition via small-world modelling of social media
platforms,” in 2017 Seventh International Conference on Affective
Computing and Intelligent Interaction (ACII). IEEE, 2017, pp. 340–345.
[174]
P. Moradshahi, H. Chatrzarrin, and R. Goubran, “Improving the
performance of cough sound discriminator in reverberant environments
using microphone array,” in 2012 IEEE International Instrumentation
and Measurement Technology Conference Proceedings, 2012, pp. 20–23.
[175]
T. Olubanjo and M. Ghovanloo, “Tracheal activity recognition based
on acoustic signals,” in 2014 36th Annual International Conference
of the IEEE Engineering in Medicine and Biology Society, 2014, pp.
1436–1439.
[176]
A. Imran, I. Posokhova, H. N. Qureshi, U. Masood, S. Riaz, K. Ali,
C. N. John, and M. Nabeel, “AI4COVID-19: AI Enabled Preliminary
Diagnosis for COVID-19 from Cough Samples via an App,arXiv
preprint arXiv:2004.01275, 2020.
[177]
F. Al Hossain, A. A. Lover, G. A. Corey, N. G. Reich, and T. Rahman,
“Flusense: A contactless syndromic surveillance platform for influenza-
like illness in hospital waiting areas,” Proc. ACM Interact. Mob.
Wearable Ubiquitous Technol., vol. 4, no. 1, 2020.
[178]
S. Latif, J. Qadir, S. Farooq, and M. A. Imran, “How 5g wireless
(and concomitant technologies) will revolutionize healthcare?” Future
Internet, vol. 9, no. 4, p. 93, 2017.
[179]
Y. Ye, S. Hou, Y. Fan, Y. Qian, Y. Zhang, S. Sun, Q. Peng, and
K. Laparo, “
α
-satellite: An AI-driven system and benchmark datasets
for hierarchical community-level risk assessment to help combat COVID-
19,” arXiv preprint arXiv:2003.12232, 2020.
[180]
“Google uses location data to show which places are complying with
stay-at-home orders — and which aren’t, The Verge, accessed on:
4-April-2020.” [Online]. Available: https://www.theverge.com/2020/4/3/
21206318/google-location- data-mobility- reports-covid-19-privacy
[181]
H. S. Maghdid, K. Z. Ghafoor, A. S. Sadiq, K. Curran, and K. Rabie,
“A novel AI-enabled framework to diagnose Coronavirus COVID 19
using smartphone embedded sensors: Design study,arXiv preprint
arXiv:2003.07434, 2020.
[182]
Poland: App helps police monitor home quarantine, accessed on:
1-April-2020.” [Online]. Available: https://privacyinternational.org/
examples/3473/poland-app-helps-police-monitor-home-quarantine/
[183]
M. N. Kamel Boulos and E. M. Geraghty, “Geographical tracking and
mapping of coronavirus disease COVID-19/severe acute respiratory
syndrome coronavirus 2 (SARS-CoV-2) epidemic and associated events
around the world: how 21st century GIS technologies are supporting
the global fight against outbreaks and epidemics,” International Journal
of Health Geographics, vol. 19, no. 8, 2020.
[184]
J. Ye, “China’s QR health code, published on 19-Feb-2020; accessed on:
6-April-2020.” [Online]. Available: https://www.abacusnews.com/tech/
chinas-qr-health-code-system-brings-relief-some-and-new-problems/
article/3051020
[185]
GovTech, “TraceTogether - behind the scenes look at its development
process, published on 25-March-2020; accessed on: 6-April-2020.
[186]
T. Cohen, “Israelis using voluntary coronavirus monitoring app,
published on 01-April-2020; accessed on: 6-April-2020.” [Online].
Available: https://tinyurl.com/reutersCoronaIsraelApps
[187]
L. Kelion, “Coronavirus: UK considers virus-tracing app to ease
lockdown, published on 31-March-2020; accessed on: 6-April-2020.”
[Online]. Available: https://www.bbc.com/news/technology-52095331
[188]
H. Cho, D. Ippolito, and Y. W. Yu, “Contact tracing mobile apps
for COVID-19: Privacy considerations and related trade-offs,” arXiv
preprint arXiv:2003.11511, 2020.
[189]
R. A. Calvo, S. Deterding, and R. M. Ryan, “Health surveillance
during COVID-19 pandemic,BMJ, vol. 369, 2020. [Online]. Available:
https://www.bmj.com/content/369/bmj.m1373
[190]
“Dp-3t documentation,” 2020. [Online]. Available: https://github.com/
DP-3T/documents
[191]
J. Bell, D. Butler, C. Hicks, and J. Crowcroft, “Tracesecure: Towards
privacy preserving contact tracing,” 2020.
[192]
A. Berke, M. Bakker, P. Vepakomma, R. Raskar, K. Larson, and
A. Pentland, “Assessing disease exposure risk with location histories
and protecting privacy: A cryptographic approach in response to a
global pandemic,” arXiv preprint arXiv:2003.14412, 2020.
[193]
J. John, T. Kathryn, K. Pushmeet, H. Demis,
and A. Team, “Computational predictions of protein
structures associated with COVID-19,” March 2020.
[Online]. Available: https://deepmind.com/research/open-source/
computational-predictions- of-protein- structures-associated- with-COVID-19
[194]
A. W. Senior, R. Evans, J. Jumper, J. Kirkpatrick, L. Sifre, T. Green,
C. Qin, A.
ˇ
Z
´
ıdek, A. W. Nelson, A. Bridgland et al., “Protein
structure prediction using multiple deep neural networks in the 13th
critical assessment of protein structure prediction (CASP13),” Proteins:
Structure, Function, and Bioinformatics, vol. 87, no. 12, pp. 1141–1148,
2019.
[195]
N. Bung, S. R. Krishnan, G. Bulusu, and A. Roy, “De novo
design of new chemical entities (NCEs) for SARS-CoV-2
using artificial intelligence,” 2020. [Online]. Available: https:
//chemrxiv.org/articles/De Novo Design of New Chemical Entities
NCEs for SARS-CoV-2 Using Artificial Intelligence/11998347/2
[196]
H. C. Metsky, C. A. Freije, T.-S. F. Kosoko-Thoroddsen, P. C. Sabeti,
and C. Myhrvold, “CRISPR-based surveillance for COVID-19 using
genomically-comprehensive machine learning design,bioRxiv, 2020.
[Online]. Available: https://www.biorxiv.org/content/early/2020/03/02/
2020.02.26.967026.1
[197]
F. Hu, J. Jiang, and P. Yin, “Prediction of potential commercially
inhibitors against SARS-CoV-2 by multi-task deep model,” arXiv
preprint arXiv:2003.00728, 2020.
[198]
H. Zhang, K. M. Saravanan, Y. Yang, M. T. Hossain, J. Li,
X. Ren, and Y. Wei, “Deep learning based drug screening
for novel coronavirus 2019-nCov,” 2020. [Online]. Available:
https://www.preprints.org/manuscript/202002.0061/v1
[199]
B. Tang, F. He, D. Liu, M. Fang, Z. Wu, and D. Xu, “AI-aided design
of novel targeted covalent inhibitors against SARS-CoV-2,” bioRxiv,
2020.
[200]
V. Boucher, “Open and Collaborative De Novo Discovery of Antiviral
Agents for COVID-19 with Deep Reinforcement Learning and OpenAI
20
Gym, MONTREAL.AI, accessed on: 7-April-2020.” [Online]. Available:
https://montrealartificialintelligence.com/covid19/
[201]
“Global coalition to accelerate COVID-19 clinical research in
resource-limited settings,” The Lancet, 2020. [Online]. Available:
https://doi.org/10.1016/S0140-6736(20)30798-4
[202]
S. Mahmoud, G. Tyson, S. Miles, A. Taweel, T. Vanstaa, M. Luck,
and B. Delaney, “Multi-agent system for recruiting patients for clinical
trials,” Kings College London, Tech. Rep., 2014.
[203]
J. A. Anguera, J. T. Jordan, D. Castaneda, A. Gazzaley, and P. A. Are
´
an,
“Conducting a fully mobile and randomised clinical trial for depression:
access, engagement and expense,” BMJ innovations, vol. 2, no. 1, pp.
14–21, 2016.
[204]
“Scopus, accessed on: 12-April-2020.” [Online]. Available: https:
//www.scopus.com/home.uri
[205]
CDC, “CDC research Repositories, accessed on: 12-April-
2020.” [Online]. Available: https://www.cdc.gov/library/researchguides/
2019novelcoronavirus/databasesjournals.html
[206]
“Nature, accessed on: 12-April-2020.” [Online]. Available: https:
//nature.com/
[207]
“Science, accessed on: 12-April-2020.” [Online]. Available: https:
//sciencemag.org/
[208]
“The Lancet, accessed on: 12-April-2020.” [Online]. Available:
https://thelancet.com/
[209]
“BMJ, accessed on: 12-April-2020.” [Online]. Available: https:
//bmj.com/
[210]
D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,”
Journal of machine Learning research, vol. 3, no. Jan, pp. 993–1022,
2003.
[211]
L. Wynants, B. Van Calster, M. M. Bonten, G. S. Collins, T. P. Debray,
M. De Vos, M. C. Haller, G. Heinze, K. G. Moons, R. D. Riley et al.,
“Prediction models for diagnosis and prognosis of COVID-19 infection:
systematic review and critical appraisal,BMJ, vol. 369, 2020.
[212]
N. Fenton and M. Neil, “The use of Bayes and causal modelling in
decision making, uncertainty and risk,” CEPIS Upgrade, vol. 12, no. 5,
pp. 10–21, 2011.
[213]
D. Gunning, “Explainable artificial intelligence (XAI),” Defense Ad-
vanced Research Projects Agency (DARPA), nd Web, vol. 2, 2017.
[214]
S. Latif, A. Qayyum, M. Usama, J. Qadir, A. Zwitter, and M. Shahzad,
“Caveat emptor: the risks of using big data for human development,
IEEE Technology and Society Magazine, vol. 38, no. 3, pp. 82–90,
2019.
[215]
A. Qayyum, J. Qadir, M. Bilal, and A. Al-Fuqaha, “Secure and
robust machine learning for healthcare: A survey,” arXiv preprint
arXiv:2001.08103, 2020.
[216]
Y. N. Harari, “The world after coronavirus, financial times, accessed
on: 1-April-2020.” [Online]. Available: https://www.ft.com/content/
19d90308-6858- 11ea-a3c9- 1fe6fedcca75
[217]
J. L. Raisaro, J. R. Troncoso-Pastoriza, M. Misbach, J. S. Sousa,
S. Pradervand, E. Missiaglia, O. Michielin, B. Ford, and J.-P. Hubaux,
“Medco: Enabling secure and privacy-preserving exploration of dis-
tributed clinical and genomic data,” IEEE/ACM transactions on com-
putational biology and bioinformatics, vol. 16, no. 4, pp. 1328–1341,
2018.
[218]
D. Froelicher, J. R. Troncoso-Pastoriza, J. S. Sousa, and J.-P. Hubaux,
“Drynx: Decentralized, secure, verifiable system for statistical queries
and machine learning on distributed datasets,” IEEE Transactions on
Information Forensics and Security, 2020.
[219]
L. Floridi, J. Cowls, M. Beltrametti, R. Chatila, P. Chazerand, V. Dignum,
C. Luetge, R. Madelin, U. Pagallo, F. Rossi et al., “AI4People—An
ethical framework for a good AI society: Opportunities, risks, principles,
and recommendations,” Minds and Machines, vol. 28, no. 4, pp. 689–
707, 2018.
[220]
“Ethical Implications of the Use of AI to Man-
age the COVID-19 Outbreak,” April 2020. [On-
line]. Available: https://ieai.mcts.tum.de/wp-content/uploads/2020/04/
April-2020- IEAI-Research- Brief-Covid-19-FINAL.pdf
[221]
Statement Regarding the Ethical Implementation of Artificial
Intelligence Systems (AIS) for Addressing the COVID-19 Pandemic,
The Executive Committee of The IEEE Global Initiative
on Ethics of Autonomous and Intelligent Systems,” April
2020. [Online]. Available: https://standards.ieee.org/content/dam/
ieee-standards/standards/web/documents/other/gieais- covid.pdf
[222]
Call for Action: Toward Building the Data Infrastructure and
Ecosystem We Need to Tackle Pandemics and Other Dynamic Society
and Environmental Threats. The Gov Lab, New York University,
Mar 2020. [Online]. Available: http://www.thegovlab.org/static/files/
publications/ACallForActionCOVID19.pdf
[223]
“Pandemic data challenges,” Nature Machine Intelligence, vol. 2, no. 4,
pp. 193–193, Apr 2020. [Online]. Available: https://doi.org/10.1038/
s42256-020- 0172-7
[224]
R. Anderson, “Contact tracing in the real world,” https://www.
lightbluetouchpaper.org/2020/04/12/contact-tracing-in-the-real-world/,
2020.
[225]
A. Jobin, M. Ienca, and E. Vayena, “The global landscape of AI ethics
guidelines,” Nature Machine Intelligence, vol. 1, no. 9, pp. 389–399,
2019.
[226]
T. Hagendorff, “The ethics of AI ethics: An evaluation of guidelines,
Minds and Machines, pp. 1–22, 2020.
[227]
N. Bostrom and E. Yudkowsky, “The ethics of artificial intelligence,”
The Cambridge handbook of artificial intelligence, vol. 1, pp. 316–334,
2014.
[228]
C. Tucker, A. Agrawal, J. Gans, and A. Goldfarb, “Privacy, algorithms,
and artificial intelligence,” The Economics of Artificial Intelligence: An
Agenda, pp. 423–437, 2018.
[229]
R. Shokri and V. Shmatikov, “Privacy-preserving deep learning,” in
Proceedings of the 22nd ACM SIGSAC conference on computer and
communications security, 2015, pp. 1310–1321.
[230]
E. Segal, F. Zhang, X. Lin, G. King, O. Shalem, S. Shilo, W. E.
Allen, Y. H. Grad, C. S. Greene, F. Alquaddoomi et al., “Building an
international consortium for tracking coronavirus health status,” medRxiv,
2020.
[231]
Q.-Y. Peng, X.-T. Wang, L.-N. Zhang, C. C. C. U. S. Group et al.,
“Findings of lung ultrasonography of novel corona virus pneumonia
during the 2019–2020 epidemic,” Intensive Care Medicine, p. 1, 2020.
[232]
E. Poggiali, A. Dacrema, D. Bastoni, V. Tinelli, E. Demichele, P. Ma-
teo Ramos, T. Marcian
`
o, M. Silva, A. Vercelli, and A. Magnacavallo,
“Can lung us help critical care clinicians in the early diagnosis of novel
coronavirus (COVID-19) pneumonia?” Radiology, p. 200847, 2020.
[233]
N. Poyiadji, G. Shahin, D. Noujaim, M. Stone, S. Patel, and B. Griffith,
“COVID-19–associated acute hemorrhagic necrotizing encephalopathy:
CT and MRI features,” Radiology, p. 201187, 2020.
[234]
F. Ahmed, N. Ahmed, C. Pissarides, and J. Stiglitz, “Why inequality
could spread COVID-19,The Lancet Public Health, Apr. 2020.
[Online]. Available: https://doi.org/10.1016/s2468-2667(20)30085- 2
[235]
J. Qadir, M. Mujeeb-U-Rahman, M. H. Rehmani, A.-S. K. Pathan, M. A.
Imran, A. Hussain, R. Rana, and B. Luo, “IEEE access special section
editorial: health informatics for the developing world,IEEE Access,
vol. 5, pp. 27 818–27 823, 2017.
[236]
S. Latif, R. Rana, J. Qadir, A. Ali, M. A. Imran, and M. S. Younis,
“Mobile health in the developing world: Review of literature and lessons
from a case study,IEEE Access, vol. 5, pp. 11 540–11 556, 2017.
[237]
J. Quinn, V. Frias-Martinez, and L. Subramanian, “Computational
sustainability and artificial intelligence in the developing world,AI
Magazine, vol. 35, no. 3, p. 36, 2014.
... Entsprechend ist es kaum verwunderlich, dass bei den internationalen Reichweiten-1 und Nutzungsdaten von Medien-und Technologieanbieter*innen seit dem Frühjahr 2020 eine weltweite Zunahme im Internetverkehr messbar war und die Zahl der Besuche von Nachrichten-und Informationsseiten erkennbar zunahm (Siddique et al. 2020). Der Amazon-Dienst Alexa belegte dies auch für den deutschen Sprachraum (Dreisiebner et al. 2020) und der öffentlich-rechtliche Rundfunk in Österreich -der ORF -berichtete während des ersten Lockdowns über Rekordwerte für seine Nachrichtensendungen. Mitarbeiter*innen der Sendergruppe gaben für den entsprechenden Zeitraum Reichweitenwerte von bis zu Dabei ist Nachrichtenrezeption ein komplexes und vielschichtiges Thema: Denn obwohl in den letzten Jahrzehnten das Angebot sowie die Nutzung von Informationen zum alltäglichen Geschehen stark zunahmen und auch in der Corona-Krise eine erhöhte Nutzungsintensität zu beobachten war, gibt es widersprüchliche Wahrnehmungen bezüglich der Glaubwürdigkeit von Medieninhalten Turcotte et al. 2015). ...
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Zusammenfassung Menschen sind soziale Wesen, weshalb Kontakte zu anderen Personen einen wichtigen Beitrag für das eigene Wohlbefinden leisten. Durch die Corona-Krise dreht sich diese Vorstellung dagegen um – Abstand halten heißt das neue Gebot, um die Gesundheit zu wahren. Vor diesem Hintergrund untersuchen wir in diesem Buchbeitrag den Verlauf von Sozialkontakten und Wohlbefinden, sowie den Zusammenhang zwischen diesen beiden Konstrukten von Ende März bis Anfang Juli 2020. Wie erwartet zeigt sich ein sprunghafter Anstieg physischer sozialer Kontakte seit Anfang Mai und somit seit den damals eingeführten Lockerungsverordnungen. Gleichzeitig verringerten sich die nicht-physischen Sozialkontakte (via Telefon & Internet) seit dem 1. Lockdown kontinuierlich, was auf eine Substitution für physische Sozialkontakte hinweist. Affektives- und kognitives Wohlbefinden zeigen einen kleinen und gleichmäßigen Anstieg über diesen Zeitraum. Weitere Analysen weisen auf einen signifikanten aber geringen Einfluss von physischen Sozialkontakten auf das Wohlbefinden hin – der physische Kontakt zu Freunden und Verwandten führt demnach zu einem geringen Anstieg des Wohlbefindens. Weiterführende Vergleiche zwischen alleinlebenden und nicht-alleinlebenden Österreicher*innen zeigten, dass alleinlebende Personen häufiger auf nicht-physische Sozialkontakte zurückgriffen als physische Sozialkontakte durch restriktive Maßnahmen erschwert wurden.
... Entsprechend ist es kaum verwunderlich, dass bei den internationalen Reichweiten-1 und Nutzungsdaten von Medien-und Technologieanbieter*innen seit dem Frühjahr 2020 eine weltweite Zunahme im Internetverkehr messbar war und die Zahl der Besuche von Nachrichten-und Informationsseiten erkennbar zunahm (Siddique et al. 2020). Der Amazon-Dienst Alexa belegte dies auch für den deutschen Sprachraum (Dreisiebner et al. 2020) und der öffentlich-rechtliche Rundfunk in Österreich -der ORF -berichtete während des ersten Lockdowns über Rekordwerte für seine Nachrichtensendungen. Mitarbeiter*innen der Sendergruppe gaben für den entsprechenden Zeitraum Reichweitenwerte von bis zu Dabei ist Nachrichtenrezeption ein komplexes und vielschichtiges Thema: Denn obwohl in den letzten Jahrzehnten das Angebot sowie die Nutzung von Informationen zum alltäglichen Geschehen stark zunahmen und auch in der Corona-Krise eine erhöhte Nutzungsintensität zu beobachten war, gibt es widersprüchliche Wahrnehmungen bezüglich der Glaubwürdigkeit von Medieninhalten Turcotte et al. 2015). ...
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Zusammenfassung Eine wichtige Funktion der Religion im Verlauf der Menschheitsgeschichte liegt darin, die Menschen bei der Bewältigung existenziell bedrohlicher Lebenssituationen zu unterstützen. In einem prosperierenden Staat wie Österreich, mit einem funktionierenden Gesundheitssystem und einem etablierten Wohlfahrtsstaat, in dem die meisten relativ gut gegen ökonomische Risiken abgesichert sind, könnte die gegenwärtige Krise aber nur bedingt mit einer stärkeren Bedeutung von Religiosität und Spiritualität einhergehen. Andererseits könnten sich religiös und spirituell aktive Personen sowohl in der Krisenbewältigung, als auch in solidarischen Einstellungen von nicht religiösen Personen unterscheiden. Vor diesem Hintergrund werden im Beitrag anhand des Austrian Corona Panel Projekts (ACPP) vier Fragestellungen untersucht? 1) Ist die Religiosität in der Zeit der Covid-19-Pandemie im Vergleich zu den vorhergehenden Jahren in der Gesamtgesellschaft tendenziell gestiegen? 2) Führt eine starke gesundheitliche oder ökonomische Betroffenheit durch die Corona-Krise zu einer höheren Bedeutung von Religion und Spiritualität? 3) Wie wirken sich Religiosität und Spiritualität auf das emotionale Wohlbefinden, die Lebenszufriedenheit und die Strategien der Krisenbewältigung (Coping-Strategien) aus? 4) Unterscheiden sich religiöse, spirituelle und nicht religiöse Menschen in Hinblick auf ihre Haltung zur staatlichen Krisenpolitik, den Umgang mit den sozialen Distanzregelungen sowie dem Solidarverhalten gegenüber Menschen, die besonders von der Krise betroffen sind?
... Entsprechend ist es kaum verwunderlich, dass bei den internationalen Reichweiten-1 und Nutzungsdaten von Medien-und Technologieanbieter*innen seit dem Frühjahr 2020 eine weltweite Zunahme im Internetverkehr messbar war und die Zahl der Besuche von Nachrichten-und Informationsseiten erkennbar zunahm (Siddique et al. 2020). Der Amazon-Dienst Alexa belegte dies auch für den deutschen Sprachraum (Dreisiebner et al. 2020) und der öffentlich-rechtliche Rundfunk in Österreich -der ORF -berichtete während des ersten Lockdowns über Rekordwerte für seine Nachrichtensendungen. Mitarbeiter*innen der Sendergruppe gaben für den entsprechenden Zeitraum Reichweitenwerte von bis zu Dabei ist Nachrichtenrezeption ein komplexes und vielschichtiges Thema: Denn obwohl in den letzten Jahrzehnten das Angebot sowie die Nutzung von Informationen zum alltäglichen Geschehen stark zunahmen und auch in der Corona-Krise eine erhöhte Nutzungsintensität zu beobachten war, gibt es widersprüchliche Wahrnehmungen bezüglich der Glaubwürdigkeit von Medieninhalten Turcotte et al. 2015). ...
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Zusammenfassung In aktuellen sozialwissenschaftlichen Analysen wird vielfach angenommen, dass die Covid-19-Pandemie zu tief greifenden gesellschaftlichen Veränderungen führen könnte. Die Schlussfolgerungen sind jedoch nicht einheitlich. Einerseits wird ein gesellschaftlicher Wandel hin zu mehr Solidarität, zu einem stärkeren Sozialstaat und zu ökologischer und ökonomischer Nachhaltigkeit suggeriert, andererseits wird auch auf das Wiedererstarken des Nationalstaates und soziale Schließungsprozesse hingewiesen. In diesem Beitrag untersuchen wir mit einem Fokus auf die aktuelle Values in Crisis Studie 2020 und in Verbindung mit österreichspezifischen Daten aus weiteren repräsentativen Umfragen (z. B. European Social Survey ), ob sich ein Wertewandel in Hinblick auf die individuellen Grundwerte nach Schwartz beobachten lässt und wie sich dieser nach Generationen unterscheidet. Anschließend werden die Zukunftserwartungen und -wünsche der Österreicher*innen für die Zeit nach Corona analysiert und der Einfluss von soziodemografischen Faktoren, Werten und Einstellungen auf die Zukunftsvorstellungen untersucht.
... Entsprechend ist es kaum verwunderlich, dass bei den internationalen Reichweiten-1 und Nutzungsdaten von Medien-und Technologieanbieter*innen seit dem Frühjahr 2020 eine weltweite Zunahme im Internetverkehr messbar war und die Zahl der Besuche von Nachrichten-und Informationsseiten erkennbar zunahm (Siddique et al. 2020). Der Amazon-Dienst Alexa belegte dies auch für den deutschen Sprachraum (Dreisiebner et al. 2020) und der öffentlich-rechtliche Rundfunk in Österreich -der ORF -berichtete während des ersten Lockdowns über Rekordwerte für seine Nachrichtensendungen. Mitarbeiter*innen der Sendergruppe gaben für den entsprechenden Zeitraum Reichweitenwerte von bis zu Dabei ist Nachrichtenrezeption ein komplexes und vielschichtiges Thema: Denn obwohl in den letzten Jahrzehnten das Angebot sowie die Nutzung von Informationen zum alltäglichen Geschehen stark zunahmen und auch in der Corona-Krise eine erhöhte Nutzungsintensität zu beobachten war, gibt es widersprüchliche Wahrnehmungen bezüglich der Glaubwürdigkeit von Medieninhalten Turcotte et al. 2015). ...
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Zusammenfassung Die Corona-Krise hat deutlich gemacht, welche Berufe für die Aufrechterhaltung der Grundfunktionen in der österreichischen Gesellschaft von besonderer Bedeutung sind. Die sogenannten Systemerhaltenden im Sozial-, Gesundheits- und Pflegebereich sowie im Handel ernteten im ersten Lockdown viel Anerkennung vonseiten der Politik; im Zuge dessen wurden auch Stimmen laut, die für eine entsprechende finanzielle Honorierung des Einsatzes dieser Berufsgruppen eintraten. Der vorliegende Beitrag geht anhand des Vergleichs von Umfragedaten aus dem Jahr 2009 und Daten, die während der Corona-Krise erhoben wurden, der Frage nach, welche Einkommenshöhen die österreichische Bevölkerung für verschiedene Berufsgruppen als gerecht empfindet. Die Ergebnisse zeigen, dass sich die Befragten zu beiden Erhebungszeitpunkten für eine massive Reduktion der Einkommen von Eliteberufen (Manager*innen und Politiker*innen) aussprechen, während die Einkommen von statusniedrigen Berufen im Einzelhandel und in der Industrie, ihrer Ansicht nach, erhöht werden sollten. Während der Corona-Krise tritt diese Tendenz verstärkt zu Tage. Der Berufsgruppe der Allgemeinmediziner*innen wird hingegen zu beiden Zeitpunkten, insbesondere während der Corona-Krise, ein relativ hohes Einkommen zugestanden. Gleichzeitig ist in der Krise auch die Befürwortung eines bedingungslosen Grundeinkommens etwas höher, wobei die Bevölkerung in dieser Frage nach wie vor gespalten ist und sich zunehmend polarisierte.
... Entsprechend ist es kaum verwunderlich, dass bei den internationalen Reichweiten-1 und Nutzungsdaten von Medien-und Technologieanbieter*innen seit dem Frühjahr 2020 eine weltweite Zunahme im Internetverkehr messbar war und die Zahl der Besuche von Nachrichten-und Informationsseiten erkennbar zunahm (Siddique et al. 2020). Der Amazon-Dienst Alexa belegte dies auch für den deutschen Sprachraum (Dreisiebner et al. 2020) und der öffentlich-rechtliche Rundfunk in Österreich -der ORF -berichtete während des ersten Lockdowns über Rekordwerte für seine Nachrichtensendungen. Mitarbeiter*innen der Sendergruppe gaben für den entsprechenden Zeitraum Reichweitenwerte von bis zu Dabei ist Nachrichtenrezeption ein komplexes und vielschichtiges Thema: Denn obwohl in den letzten Jahrzehnten das Angebot sowie die Nutzung von Informationen zum alltäglichen Geschehen stark zunahmen und auch in der Corona-Krise eine erhöhte Nutzungsintensität zu beobachten war, gibt es widersprüchliche Wahrnehmungen bezüglich der Glaubwürdigkeit von Medieninhalten Turcotte et al. 2015). ...
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Zusammenfassung Mit der Covid-19-Pandemie, die das gesellschaftliche Leben ab März 2020 in Österreich massiv eingeschränkt hat, haben wir es mit der größten gesundheitlichen, ökonomischen und sozialen Herausforderung seit Jahrzehnten zu tun. Zunehmend rücken dabei auch die sozialen Folgen der Pandemie in den Fokus der Öffentlichkeit. Zusammenfassend umreißen wir in der Einleitung jene Themen, die in weiterer Folge auch in den Beiträgen im Vordergrund stehen. Es sind dies die weitreichenden Eingriffe der Pandemie in den Lebensalltag der Bevölkerung, Solidaritätspotentiale in der Gesellschaft, Dynamiken sozialer Ungleichheit sowie Werteverschiebungen und Zukunftserwartungen zur weiteren gesellschaftlichen Entwicklung. Das Ziel des vorliegenden Sammelbands ist es, empirische Befunde, die auf mehreren Umfrageprojekten während der Pandemie basieren, in die Gesellschaft zu tragen und dadurch tiefergehende Reflexionen über die österreichische Gesellschaft während der Corona-Pandemie zu ermöglichen.
... Entsprechend ist es kaum verwunderlich, dass bei den internationalen Reichweiten-1 und Nutzungsdaten von Medien-und Technologieanbieter*innen seit dem Frühjahr 2020 eine weltweite Zunahme im Internetverkehr messbar war und die Zahl der Besuche von Nachrichten-und Informationsseiten erkennbar zunahm (Siddique et al. 2020). Der Amazon-Dienst Alexa belegte dies auch für den deutschen Sprachraum (Dreisiebner et al. 2020) und der öffentlich-rechtliche Rundfunk in Österreich -der ORF -berichtete während des ersten Lockdowns über Rekordwerte für seine Nachrichtensendungen. Mitarbeiter*innen der Sendergruppe gaben für den entsprechenden Zeitraum Reichweitenwerte von bis zu Dabei ist Nachrichtenrezeption ein komplexes und vielschichtiges Thema: Denn obwohl in den letzten Jahrzehnten das Angebot sowie die Nutzung von Informationen zum alltäglichen Geschehen stark zunahmen und auch in der Corona-Krise eine erhöhte Nutzungsintensität zu beobachten war, gibt es widersprüchliche Wahrnehmungen bezüglich der Glaubwürdigkeit von Medieninhalten Turcotte et al. 2015). ...
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Zusammenfassung Um den Zugang zu diesem Buch auch für Personen zu erleichtern, die wenig Erfahrung mit quantitativer Sozialforschung haben, wird in diesem Glossar ein grundlegender Einblick in die Praxis der Umfrageforschung sowie in statistische Analysetechniken gegeben. Wir geben Hinweise, wie man soziale und politische Einstellungen in Umfragen erhebt und welche Möglichkeiten der statistischen Analyse vorliegen. Insbesondere komplexere Verfahren wie Regressionsanalyse, Faktorenanalsen, Reliabilitätsanalysen und Kontrastgruppenanalysen werden näher erklärt.
... Entsprechend ist es kaum verwunderlich, dass bei den internationalen Reichweiten-1 und Nutzungsdaten von Medien-und Technologieanbieter*innen seit dem Frühjahr 2020 eine weltweite Zunahme im Internetverkehr messbar war und die Zahl der Besuche von Nachrichten-und Informationsseiten erkennbar zunahm (Siddique et al. 2020). Der Amazon-Dienst Alexa belegte dies auch für den deutschen Sprachraum (Dreisiebner et al. 2020) und der öffentlich-rechtliche Rundfunk in Österreich -der ORF -berichtete während des ersten Lockdowns über Rekordwerte für seine Nachrichtensendungen. Mitarbeiter*innen der Sendergruppe gaben für den entsprechenden Zeitraum Reichweitenwerte von bis zu Dabei ist Nachrichtenrezeption ein komplexes und vielschichtiges Thema: Denn obwohl in den letzten Jahrzehnten das Angebot sowie die Nutzung von Informationen zum alltäglichen Geschehen stark zunahmen und auch in der Corona-Krise eine erhöhte Nutzungsintensität zu beobachten war, gibt es widersprüchliche Wahrnehmungen bezüglich der Glaubwürdigkeit von Medieninhalten Turcotte