Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions

Abstract

Artificial intelligence (AI) has been applied widely in our daily lives in a variety of ways with numerous successful stories. AI has also contributed to dealing with the coronavirus disease (COVID-19) pandemic, which has been happening around the globe. This paper presents a survey of AI methods being used in various applications in the fight against the COVID-19 outbreak and outlines the crucial roles of AI research in this unprecedented battle. We touch on a number of areas where AI plays as an essential component, from medical image processing, data analytics, text mining and natural language processing, the Internet of Things, to computational biology and medicine. A summary of COVID-19 related data sources that are available for research purposes is also presented. Research directions on exploring the potentials of AI and enhancing its capabilities and power in the battle are thoroughly discussed. We highlight 13 groups of problems related to the COVID-19 pandemic and point out promising AI methods and tools that can be used to solve those problems. It is envisaged that this study will provide AI researchers and the wider community an overview of the current status of AI applications and motivate researchers in harnessing AI potentials in the fight against COVID-19.

Full text

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Artificial Intelligence in the Battle against Coronavirus (COVID-19): A
Survey and Future Research Directions
Thanh Thi Nguyen
Abstract—Artificial intelligence (AI) has been applied widely
in our daily lives in a variety of ways with numerous successful
stories. AI has also contributed to dealing with the coronavirus
disease (COVID-19) pandemic, which has been happening around
the globe. This paper presents a survey of AI methods being
used in various applications in the fight against the COVID-19
outbreak and outlines the crucial roles of AI research in this
unprecedented battle. We touch on a number of areas where AI
plays as an essential component, from medical image processing,
data analytics, text mining and natural language processing, the
Internet of Things, to computational biology and medicine. A
summary of COVID-19 related data sources that are available
for research purposes is also presented. Research directions on
exploring the potentials of AI and enhancing its capabilities and
power in the battle are thoroughly discussed. We highlight 13
groups of problems related to the COVID-19 pandemic and point
out promising AI methods and tools that can be used to solve
those problems. It is envisaged that this study will provide AI
researchers and the wider community an overview of the current
status of AI applications and motivate researchers in harnessing
AI potentials in the fight against COVID-19.
Index Terms—Artificial intelligence; AI; machine learning;
coronavirus; COVID-19; SARS-CoV-2; pandemic; epidemic; out-
break; survey; review; overview; future research directions
I. INTRODUCTION
THE novel coronavirus disease (COVID-19) has created
tremendous chaos around the world, affecting people’s
lives and causing a large number of deaths. Since the first
cases were detected, the disease has spread to almost ev-
ery country, causing deaths of over 580,000 people among
nearly 13,379,000 confirmed cases based on statistics of the
World Health Organization in the middle of July 2020 [1].
Governments of many countries have proposed intervention
policies to mitigate the impacts of the COVID-19 pandemic.
Science and technology have contributed significantly to the
implementations of these policies during this unprecedented
and chaotic time. For example, robots are used in hospitals to
deliver food and medicine to coronavirus patients or drones
are used to disinfect streets and public spaces. Many medical
researchers are rushing to investigate drugs and medicines to
treat infected patients whilst others are attempting to develop
vaccines to prevent the virus. Computer science researchers
on the other hand have managed to early detect infectious
patients using techniques that can process and understand
medical imaging data such as X-ray images and computed
tomography (CT) scans. These computational techniques are
part of artificial intelligence (AI), which has been applied
successfully in various fields. This paper focuses on the
roles of AI technologies in the battle against the COVID-
19 pandemic. We provide a comprehensive survey of AI
T. T. Nguyen is with the School of Information Technology, Deakin
University, Victoria, 3216, Australia. E-mail: thanh.nguyen@deakin.edu.au.
applications that support humans to reduce and suppress the
substantial impacts of the outbreak. Recent advances in AI
have contributed significantly to improving humans’ lives and
thus there is a strong belief that proper AI research plans will
fully exploit the power of AI in helping humans to defeat this
challenging battle. We discuss about these possible plans and
highlight AI research areas that could bring great benefits and
contributions to overcome the battle. In addition, we present a
summary of COVID-19 related data sources to facilitate future
studies using AI methods to deal with the pandemic.
An overview of common AI methods is presented in Fig. 1
where recent AI development is highlighted. Machine learn-
ing, especially deep learning, has made great advances and
substantial progress in long-standing fields such as computer
vision, natural language processing (NLP), speech recognition,
and video games. A significant advantage of deep learning
over traditional machine learning techniques is its ability to
deal with and make sense of different types of data, especially
big and unstructured data, e.g. text, image, video and audio
data. A number of industries, e.g. electronics, automotive, se-
curity, retail, agriculture, healthcare and medical research, have
achieved better outcomes and benefits by using deep learning
and AI methods. It is thus expected that AI technologies can
contribute to the fight against the COVID-19 pandemic, such
as those surveyed in the next section.
II. AI AGA IN ST COVID-19: A SURVEY
We separate surveyed papers into different groups that in-
clude: deep learning algorithms for medical image processing,
data science methods for pandemic modelling, AI and the
Internet of Things (IoT), AI for text mining and NLP, and
AI in computational biology and medicine.
A. Medical Image Processing with Deep Learning
While radiologists and clinical doctors can learn to detect
COVID-19 cases based on chest CT examinations, their tasks
are manual and time consuming, especially when required to
examine a lot of cases. Bai et al. [7] convenes three Chinese
and four United States radiologists to differentiate COVID-
19 from other viral pneumonia based on chest CT images
obtained from a cohort of 424 cases, in which 205 cases are
from the United States with non-COVID-19 pneumonia whilst
219 cases are from China positive with COVID-19. Results
obtained show that radiologists can achieve high specificity
(which refers to the proportion of actual positives that are
correctly identified as such) in distinguishing COVID-19 from
other causes of viral pneumonia using chest CT imaging data.
However, their performance in terms of sensitivity (which
arXiv:2008.07343v1 [cs.CY] 30 Jul 2020

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Fig. 1. An overview of common AI methods where machine learning constitutes a great proportion. The development of deep learning, a subset of machine
learning, has contributed significantly to improving the power and capabilities of recent AI applications. A number of deep learning-based convolutional neural
network (CNN) architectures, e.g. LeNet [2], AlexNet [3], GoogLeNet [4], Visual Geometry Group (VGG) Net [5] and ResNet [6], have been proposed and
applied successfully in different areas, especially in the computer vision domain. Other techniques such as autoencoders and recurrent neural networks are
crucial components of many prominent natural language processing tools. The deep learning methods in particular and AI in general may thus be employed
to create useful applications to deal with various aspects of the COVID-19 pandemic.
refers to the proportion of actual negatives that are correctly
identified as such) is just moderate for the same task. AI
methods, especially deep learning, have been used to process
and analyse medical imaging data to support radiologists and
doctors to improve diagnosis performance. Likewise, the cur-
rent COVID-19 pandemic has witnessed a number of studies
focusing on automatic detection of COVID-19 using deep
learning systems.
A three-dimensional deep learning method, namely COVID-
19 detection neural network (COVNet), is introduced in [8]
to detect COVID-19 based on volumetric chest CT images.
Three kinds of CT images, including COVID-19, community
acquired pneumonia (CAP) and other non-pneumonia cases,
are mixed to test the robustness of the proposed model, which
is illustrated in Fig. 2. These images are collected from 6
hospitals in China and the detection method is evaluated by the
area under the receiver operating characteristic curve (AUC).
COVNet is a convolutional ResNet-50 model [6] that takes a
series of CT slices as inputs and predicts the class labels of
the CT images via its outputs. The AUC value obtained is at
0.96, which shows a great ability of the proposed model for
detecting COVID-19 cases.
Another deep learning method based on the concatena-
tion between the location-attention mechanism and the three-
dimensional CNN ResNet-18 network [6] is proposed in [9] to
detect coronavirus cases using pulmonary CT images. Distinct
manifestations of CT images of COVID-19 found in previous
Fig. 2. Illustrative architecture of the COVNet model proposed in [8] for
COVID-19 detection using CT images. Max pooling operation is used to
combine features extracted by ResNet-50 CNNs whose inputs are CT slices.
The combined features are fed into a fully connected layer to compute proba-
bilities for three classes, i.e. non-pneumonia, community acquired pneumonia
(CAP) and COVID-19. Predicted class is the one that has highest probability
among the three classes.
studies [10], [11] and their differences with those of other
types of viral pneumonia such as influenza-A are exploited
through the proposed deep learning system. A dataset com-
prising CT images of COVID-19 cases, influenza-A viral
pneumonia patients and healthy cases is used to validate the
performance of the proposed method. The method’s overall
accuracy of approximately 86% is obtained on this dataset,

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TABLE I
SUMMARY OF DEEP LEARNING METHODS FOR COVID-19 DIAGNOSIS USING RADIOLOGY IMAGES
Papers Data AI Methods Results
[8] 4,356 chest CT exams from 3,322 patients from 6 medical centers:
1,296 exams for COVID-19, 1,735 for CAP and 1,325 for non-
pneumonia
A 3D convolutional ResNet-50 [6],
namely COVNet
AUC for detecting COVID-19 is
of 0.96
[9] 618 CT samples: 219 from 110 COVID-19 patients, 224 CT
samples from 224 patients with influenza-A viral pneumonia, and
175 CT samples from healthy people
Location-attention network and
ResNet-18 [6]
Accuracy of 86.7%
[12]
5,941 Posterior-anterior chest radiography images across 4 classes
(normal: 1,583, bacterial pneumonia: 2,786, non-COVID-19 viral
pneumonia: 1,504, and COVID-19: 68)
Drop-weights based Bayesian CNNs Accuracy of 89.92%
[13]
1,065 CT images (325 COVID-19 and 740 viral pneumonia) Modified inception transfer-learning
model
Accuracy of 79.3% with speci-
ficity of 0.83 and sensitivity of
0.67
[14]
Clinical data and a series of chest CT data collected at differ-
ent times on 133 patients of which 54 patients progressed to
severe/critical periods whilst the rest did not
Multilayer perceptron and LSTM [45] AUC of 0.954
[15]
970 CT volumes of 496 patients with confirmed COVID-19 and
1,385 negative cases
2D deep CNN Accuracy of 94.98% and AUC of
97.91%
[16]
CT images of 1,136 training cases (723 positives for COVID-19)
from 5 hospitals
A combination of 3D UNet++ [17] and
ResNet-50 [6]
Sensitivity of 0.974 and specificity
of 0.922
[18]
Chest X-ray images of 50 normal and 50 COVID-19 patients Pre-trained ResNet-50 Accuracy of 98%
[19]
16,756 chest radiography images across 13,645 patient cases from
two open access data repositories
A deep CNN, namely COVID-Net Accuracy of 92.4%
[20]
CT images obtained from 157 international patients (China and
U.S.)
ResNet-50 AUC of 0.996
[21]
1,341 normal, 1,345 viral pneumonia, and 190 COVID19 chest
Xray images
AlexNet [3], ResNet-18 [6], DenseNet-
201 [23], SqueezeNet [25]
Accuracy of 98.3%
[22]
170 X-ray images and 361 CT images of COVID-19 from 5
different sources
A new CNN and pre-trained AlexNet
[3] with transfer learning
Accuracy of 98% on X-ray images
and 94.1% on CT images
which exhibits its ability to help clinical doctors to early screen
COVID-19 patients using chest CT images.
In line with the studies described above, we have found a
number of papers also applying deep learning for COVID-
19 diagnosis using radiology images. They are summarized in
Table I for comparisons.
B. AI-based Data Science Methods for COVID-19 Modelling
A modified stacked autoencoder deep learning model is used
in [26] to forecast in real-time the COVID-19 confirmed cases
across China. This modified autoencoder network includes
four layers, i.e. input, first latent layer, second latent layer
and output layer, with the number of nodes correspondingly
is 8, 32, 4 and 1. A series of 8 data points (8 days) are used as
inputs of the network. The latent variables obtained from the
second latent layer of the autoencoder model are processed
by the singular value decomposition method before being fed
into clustering algorithms in order to group the cases into
provinces or cities to investigate the transmission dynamics of
the pandemic. The resultant errors of the model are low, which
give confidence that it can be applied to forecast accurately
the transmission dynamics of the virus as a helpful tool for
public health planning and policy-making.
On the other hand, a prototype of an AI-based system,
namely α-Satellite, is proposed in [27] to assess the infectious
risk of a given geographical area at community levels. The
system collects various types of large-scale and real-time data
from heterogeneous sources, such as number of cases and
deaths, demographic data, traffic density and social media
data, e.g., Reddit posts. The social media data available for
a given area may be limited so that they are enriched by the
conditional generative adversarial networks (GANs) [28] to
learn the public awareness of COVID-19. A heterogeneous
graph autoencoder model is then devised to aggregate infor-
mation from neighbourhood areas of the given area in order
to estimate its risk indexes. This risk information enables
residents to select appropriate actions to prevent them from
the virus infection with minimum disruptions in their daily
lives. It is also useful for authorities to implement appropriate
mitigation strategies to combat the fast evolving pandemic.
Chang et al. [29] modify a discrete-time and stochastic
agent-based model, namely ACEMod (Australian Census-
based Epidemic Model), previously used for influenza pan-
demic simulation [30], [31], for modelling the COVID-19
pandemic across Australia over time. Each agent exemplifies
an individual characterized by a number of attributes such
as age, occupation, gender, susceptibility and immunity to
diseases and contact rates. The ACEMod is calibrated to model
specifics of the COVID-19 pandemic based on key disease
transmission parameters. Several intervention strategies in-
cluding social distancing, school closures, travel bans, and case
isolation are then evaluated using this tuned model. Results
obtained from the experiments show that a combination of
several strategies is needed to mitigate and suppress the
COVID-19 pandemic. The best approach suggested by the
model is to combine international arrival restrictions, case
isolation and social distancing in at least 13 weeks with the
compliance level of 80% or above.

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C. AI and the Internet of Things
A framework for COVID-19 detection using data obtained
from smartphones’ onboard sensors such as cameras, micro-
phones, temperature and inertial sensors is proposed in [32].
Machine learning methods are employed for learning and
acquiring knowledge about the disease symptoms based on
the collected data. This offers a low-cost and quick approach
to coronavirus detection compared to medical Kits or CT scan
methods. This is arguably plausible because data obtained
from the smartphones’ sensors have been utilized effectively
in different individual applications and the proposed approach
integrates these applications together in a unique framework.
For instance, data obtained from the temperature-fingerprint
sensor can be used for fever level prediction [33]. Images and
videos taken by smartphones’ camera or data collected by the
onboard inertial sensors can be used for human fatigue detec-
tion [34], [35]. Likewise, Story et al. [36] use smartphone’s
videos for nausea prediction whilst Lawanont et al. [37] use
camera images and inertial sensors’ measurements for neck
posture monitoring and human headache level prediction. Al-
ternatively, audio data obtained from smartphone’s microphone
are used for cough type detection in [38], [39].
An approach to collecting individuals’ basic travel history
and their common manifestations using a phone-based online
survey is proposed in [40]. These data are valuable for machine
learning algorithms to learn and predict the infection risk of
each individual, thus help to early identify high-risk cases for
quarantine purpose. This contributes to reducing the spread
of the virus to the susceptible populations. In another work,
Allam and Jones [41] suggest the use of AI and data sharing
standardization protocols for better global understanding and
management of urban health during the COVID-19 pandemic.
For example, added benefits can be obtained if AI is integrated
with thermal cameras, which might have been installed in
many smart cities, for early detection of the outbreak. AI meth-
ods can also demonstrate their great effectiveness in supporting
managers to make better decisions for virus containment when
loads of urban health data are collected by data sharing across
and between smart cities using the proposed protocols.
D. AI for Text Mining and NLP
A hybrid AI model for COVID-19 infection rate forecasting
is proposed in [42], which combines the epidemic susceptible
infected (SI) model, NLP and deep learning tools. The SI
model and its extension, i.e. susceptible infected recovered
(SIR), are traditional epidemic models for modelling and
predicting the development of infectious diseases where S
represents the number of susceptible people, Idenotes the
number of infected people and Rspecifies the recovered cases.
Using differential equations to characterize the relationship
between I,Sand R, these models have been used to predict
successfully SARS and Ebola infected cases, as reported
in [43] and [44] respectively. NLP is employed to extract
semantic features from related news such as epidemic control
measures of governments or residents’ disease prevention
awareness. These features are then served as inputs to the
long short-term memory (LSTM) deep learning model [45] to
revise the infection rate predictions of the SI model (detailed
in Fig. 3). Epidemic data of Wuhan, Beijing, Shanghai and the
whole China are used for experiments, which demonstrate the
great accuracy of the proposed hybrid model. It can be applied
to predict the COVID-19 transmission law and development
trend, and thus useful for establishing prevention and control
measures for future pandemics. That study also shows the
importance of public awareness of governmental epidemic
prevention policies and the significant role of transparency
and openness of epidemic reports and news in containing the
development of infectious diseases.
Fig. 3. An AI-based approach to COVID-19 prediction that combines
traditional epidemic SI model, NLP and machine learning tools as introduced
in [42]. A pre-trained NLP model is used to extract NLP features from text
data, i.e. the pandemic news, reports, prevention and control measures. These
features are integrated with infection rate features obtained from the SI model
via multilayer perceptron (MLP) networks before being fed into LSTM model
for COVID-19 case modelling and prediction.
In another work, Lopez et al. [46] recommend the use of
network analysis techniques as well as NLP and text mining
to analyse a multilanguage Twitter dataset to understand
changing policies and common responses to the COVID-
19 outbreak across time and countries. Since the beginning
of the pandemic, governments of many countries have tried
to implement policies to mitigate the spread of the virus.
Responses of people to the pandemic and to the governmental
policies may be collected from social media platforms such
as Twitter. Much of information and misinformation is posted
through these platforms. When stricter policies such as social
distancing and country lockdowns are applied, people’s lives
are changed considerably and part of that can be observed and
captured via people’s reflections on social media platforms
as well. Analysis results of these data can be helpful for
governmental decision makers to mitigate the impacts of the
current pandemic and prepare better policies for possible
future pandemics.
Likewise, three machine learning methods including support
vector machine (SVM), naive Bayes and random forest are
used in [47] to classify 3,000 COVID-19 related posts col-
lected from Sina Weibo, which is the Chinese equivalent of
Twitter, into seven types of situational information. Identifying
situational information is important for authorities because it
helps them to predict its propagation scale, sense the mood of
the public and understand the situation during the crisis. This
contributes to creating proper response strategies throughout

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the COVID-19 pandemic.
E. AI in Computational Biology and Medicine
Being able to predict structures of a protein will help
understand its functions. Google DeepMind is using the latest
version of their protein structure prediction system, namely
AlphaFold [48], to predict structures of several proteins as-
sociated with COVID-19 based on their corresponding amino
acid sequences. They have released the predicted structures
in [49], but these structures still need to be experimentally
verified. Nevertheless, it is expected that these predictions will
help understand how the coronavirus functions and potentially
lead to future development of therapeutics against COVID-19.
An AI-based generative chemistry approach to design novel
molecules that can inhibit COVID-19 is proposed in [50].
Several generative machine learning models, e.g. generative
autoencoders, GANs, genetic algorithms and language models,
are used to exploit molecular representations to generate
structures, which are then optimized using reinforcement
learning methods. This is an ongoing work as the authors are
synthesising and testing the obtained molecules. However, it
is a promising approach because these AI methods can exploit
the large drug-like chemical space and automatically extract
useful information from high-dimensional data. It is thus able
to construct molecules without manually designing features
and learning the relationships between molecular structures
and their pharmacological properties. The proposed approach
is cost-effective and time-efficient and has a great potential to
generate novel drug compounds in the COVID-19 fight.
On the other hand, Randhawa et al. [51] aim to predict the
taxonomy of COVID-19 based on an alignment-free machine
learning method [52] using genomic signatures and a decision
tree approach. The alignment-free method is a computationally
inexpensive approach that can give rapid taxonomic classi-
fication of novel pathogens by processing only raw DNA
sequence data. By analysing over 5000 unique viral sequences,
the authors are able to confirm the taxonomy of COVID-
19 as belonging to the subgenus Sarbecovirus of the genus
Betacoronavirus, as previously found in [53]. The proposed
method also provides quantitative evidence that supports a
hypothesis about a bat origin for COVID-19 as indicated
in [53], [54]. Recently, Nguyen et al. [55] propose the use
of AI-based clustering methods and more than 300 genome
sequences to search for the origin of the COVID-19 virus.
Numerous clustering experiments are performed on datasets
that combine sequences of the COVID-19 virus and those
of reference viruses of various types. Results obtained show
that COVID-19 virus genomes consistently form a cluster
with those of bat and pangolin coronaviruses. That provides
quantitative evidences to support the hypotheses that bats and
pangolins may have served as the hosts for the COVID-
19 virus. Their findings also suggest that bats are the more
probable origin of the virus than pangolins. AI methods thus
have demonstrated their capabilities and power for mining big
biological datasets in an efficient and intelligent manner, which
contributes to the progress of finding vaccines, therapeutics or
medicines for COVID-19.
III. COVID-19 DATA SOURCES AND POTE NTIAL
MODELLING APP ROAC HE S
This section summarises available data sources relevant to
COVID-19, ranging from numerical data of infection cases,
radiology images [56], Twitter, text, natural language to bi-
ological sequence data (Table II), and highlights potential
AI methods for modelling different types of data. The data
are helpful for research purposes to exploit the capabilities
and power of AI technologies in the battle against COVID-
19. Different data types have different characteristics and
thus require different AI methods to handle. For example,
numerical time series data of infection cases can be dealt with
by traditional machine learning methods such as naive Bayes,
logistic regression, k-nearest neighbors (KNN), SVM, MLP,
fuzzy logic system [57], nonparametric Gaussian process [58],
decision tree, random forest, and ensemble learning algorithms
[59]. Deep learning recurrent neural networks such as LSTM
[45] can be used for regression prediction problems if a
large amount of training data are available. The deeper the
models, the more data are needed to enable the models to learn
effectively from data. Based on their ability to characterize
temporal dynamic behaviours, recurrent networks are well
suited for modelling infection case time series data.
Radiology images such as chest X-ray and CT scans are
high-dimensional data that require processing capabilities of
deep learning methods in which CNN-based models are
common and most suitable (e.g. LeNet [2], AlexNet [3],
GoogLeNet [4], VGG Net [5] and ResNet [6]). CNNs were
inspired by biological processes of visual cortex of human
and animal brains where each cortical neuron is activated
within its receptive field when stimulated. A receptive field
of a neuron covers a specific subarea of the visual field and
thus the entire visual field can be captured by a partial overlap
of receptive fields. A CNN consists of multiple layers where
each neuron of a subsequent (higher) layer connects to a
subset of neurons in the previous (lower) layer. This allows
the receptive field of a neuron of a higher layer covers a
larger portion of images compared to that of a lower layer.
The higher layer is able to learn more abstract features of
images than the lower layer by taking into account the spatial
relationships between different receptive fields. This use of
receptive fields enables CNNs to recognize visual patterns
and capture features from images without prior knowledge
or making hand-crafted features as in traditional machine
learning approaches. This principle is applied to different CNN
architectures although they may differ in the number of layers,
number of neurons in each layer, the use of activation and
loss functions as well as regularization and learning algorithms
[60]. Transfer learning methods can be used to customize CNN
models, which have been pretrained on large medical image
datasets, for the COVID-19 diagnosis problem. This would
avoid training a CNN from scratch and thus reduce training
time and the need for COVID-19 radiology images, which may
not be sufficiently available in the early stage of the pandemic.
Alternatively, unstructured natural language data need text
mining tools, e.g. Natural Language ToolKit (NLTK) [61],
and advanced NLP and natural language generation (NLG)

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TABLE II
AVAI LA BLE DATA SO UR CES A BO UT COVID-19 NUMBER OF CASES,RADIOLOGY IMAGES,TEXT A ND TW ITT ER DATA,AND BIOLOGICAL SEQUENCES
Sources Data Type Links
Johns Hopkins University [78] Web-based mapping global cases https://systems.jhu.edu/research/public-health/ncov/
C. R. Wells’s GitHub [79] Daily incidence data and airport connectiv-
ity from China
https://github.com/WellsRC/Coronavirus-2019
Conference of State Bank Super-
visors
U.S. county-level map of coronavirus cases
(updated hourly)
https://www.csbs.org/information-covid-19-coronavirus
DataHub Time series data on cases https://datahub.io/core/covid-19
China CDC (CCDC) Daily number of cases in China http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm
U.S. CDC Cases in U.S. https://www.cdc.gov/coronavirus/2019-ncov/index.html
https://www.coronavirus.gov/
U.S. National Institutes of Health Cases in U.S. https://www.nih.gov/health-information/coronavirus
Italy Ministry of Health Cases in Italy http://www.salute.gov.it/nuovocoronavirus
Kaggle Cases in South Korea https://www.kaggle.com/kimjihoo/coronavirusdataset
W. Zeng’s website Global cases and country cases http://open-source-covid-19.weileizeng.com/
J. P. Cohen’s GitHub [80] Chest X-ray and CT images https://github.com/ieee8023/covid-chestxray-dataset
European Society of Radiology Chest X-ray and CT images https://www.eurorad.org/advanced-search?search=COVID
Italian Society of Medical Radi-
ology (SIRM)
Chest X-ray and CT images https://www.sirm.org/category/senza-categoria/covid-19/
British Society of Thoracic Imag-
ing (BSTI)
Chest X-ray and CT images https://bit.ly/BSTICovid19 Teaching Library
Kaggle Chest X-ray and CT images https://www.kaggle.com/bachrr/covid-chest-xray
UCSD-AI4H [81] CT images https://github.com/UCSD-AI4H/COVID-CT
MedSeg (medseg.ai) CT images http://medicalsegmentation.com/covid19/
Point-of-Care Ultrasound
(POCUS) [82]
Lung ultrasound images and videos https://github.com/jannisborn/covid19 pocus ultrasound/tree/master/
data
COVID-19 Radiography
Database [21]
Chest X-ray images https://www.kaggle.com/tawsifurrahman/covid19-radiography-
database
A. G. Chung’s GitHub - Ac-
tualmed Initiative
Chest X-ray images https://github.com/agchung/Actualmed-COVID-chestxray-
dataset/tree/master/images
A. G. Chung’s GitHub - Figure 1
Initiative
Chest X-ray images https://github.com/agchung/Figure1-COVID-chestxray-
dataset/tree/master/images
Georgia State University’s
Panacea Lab [83]
Twitter chatter dataset in many languages http://www.panacealab.org/covid19/
COVID-19 Open Research
Dataset (CORD-19) [84]
45,000 scholarly articles about COVID-19
and the coronavirus family
https://pages.semanticscholar.org/coronavirus-research
https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-
challenge
World Health Organization Latest scientific findings and knowledge on
COVID-19
https://www.who.int/emergencies/diseases/novel-coronavirus-
2019/global-research-on-novel-coronavirus-2019-ncov
NCBI GenBank SARS-CoV-2 sequences https://www.ncbi.nlm.nih.gov/genbank/sars-cov-2-seqs/
The GISAID Initiative SARS-CoV-2 sequences https://www.gisaid.org/
China National GeneBank COVID-19 sequence database https://db.cngb.org/datamart/disease/DATAdis19/
EMBL-EBI Sequences, gene and protein expression,
protein structures
https://www.covid19dataportal.org/
tools for various tasks such as text classification, text sum-
marization, machine translation, named entity recognition,
speech recognition, and question and answering. These tools
may include Embeddings from Language Models (ELMo)
[62], Universal Language Model Fine-Tuning (ULMFiT) [63],
Transformer [64], Googles Bidirectional Encoder Representa-
tions from Transformers (BERT) [65], Transformer-XL [66],
XLNet [67], Enhanced Representation through kNowledge
IntEgration (ERNIE) [68], Text-to-Text Transfer Transformer
(T5) [69], Binary-Partitioning Transformer (BPT) [70] and
OpenAIs Generative Pretrained Transformer 2 (GPT-2) [71].
The core components of these tools are deep learning and
transfer learning methods. For example, ELMo and ULM-
FiT are built using LSTM-based language models while
Transformer utilizes an encoder-decoder structure. Likewise,
BERT and ERNIE use multi-layer Transformer as a basic
encoder while XLNet is a generalized autoregressive pretrain-
ing method inherited from Transformer-XL. Transformer also
serves as a basic model for T5, BPT and GPT-2. These are
excellent tools for many NLP and NLG tasks to handle text
and natural language data related to COVID-19.
Analysing biological sequence data such as viral genomic
and proteomic sequences requires either traditional machine
learning or advanced deep learning or a combination of both
depending on problems being addressed and data pipelines
used. As an example, traditional clustering methods, e.g.
hierarchical clustering and density-based spatial clustering of
applications with noise (DBSCAN) [72], can be employed
to find the virus origin using genomic sequences [55]. Al-
ternatively, a fuzzy logic system can be used to predict
protein secondary structures based on quantitative properties
of amino acids, which are used to encode the twenty common
amino acids [73]. A combination between principal component
analysis and lasso (least absolute shrinkage and selection
operator) can be used as a supervised approach for analysing
single-nucleotide polymorphism genetic variation data [74].

7
Advances in deep learning may be utilized for protein structure
prediction using protein amino acid sequences as in [48], [75].
An overview on the use of various types of machine learning
and deep learning methods for analysing genetic and genomic
data can be referred to [76], [77]. Typical applications may
include, for example, recognizing the locations of transcription
start sites, identifying splice sites, promoters, enhancers, or
positioned nucleosomes in a genome sequence, analysing
gene expression data for finding disease biomarkers, assigning
functional annotations to genes, predicting the expression of a
gene [76], identifying splicing junction at the DNA level, pre-
dicting the sequence specificities of DNA- and RNA-binding
proteins, modelling structural features of RNA-binding protein
targets, predicting DNA-protein binding, or annotating the
pathogenicity of genetic variants [77]. These applications can
be utilized for analysing genomic and genetic data of severe
acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the
highly pathogenic virus that has caused the global COVID-19
pandemic.
IV. CONCLUSIONS AND FUTURE RESEARCH DIRECTIONS
The COVID-19 pandemic has considerably affected lives of
people around the globe and the number of deaths related to
the disease keeps increasing worldwide. While AI technologies
have penetrated into our daily lives with many successes, they
have also contributed to helping humans in the tough fight
against COVID-19. This paper has presented a survey of AI
applications so far in the literature relevant to the COVID-
19 crisis’s responses and control strategies. These applications
range from medical diagnosis based on chest radiology images,
virus transmission modelling and forecasting based on number
of cases time series and IoT data, text mining and NLP to
capture the public awareness of virus prevention measures, to
biological data analysis for drug discovery. Although various
studies have been published, we observe that there are still
relatively limited applications and contributions of AI in this
battle. This is partly due to the limited availability of data
about COVID-19 whilst AI methods normally require large
amounts of data for computational models to learn and acquire
knowledge. However, we expect that the number of AI studies
related to COVID-19 will increase significantly in the months
to come when more COVID-19 data such as medical im-
ages and biological sequences are available. Current available
datasets as summarized in Table II are stored in various
formats and standards that would hinder the development of
COVID-19 related AI research. A future work on creating,
hosting and benchmarking COVID-19 related datasets is es-
sential because it will help to accelerate discoveries useful
for tackling the disease. Repositories for this goal should be
created following standardized protocols and allow researchers
and scientists across the world to contribute to and utilize them
freely for research purposes.
Among the published works, the use of deep learning
techniques for COVID-19 diagnosis based on radiology imag-
ing data appears to be dominant. As summarized in Table
1, numerous studies have used various deep learning meth-
ods, applying on different datasets and utilizing a number
of evaluation criteria. This creates an immediate concern
about difficulties when utilizing these approaches to the real-
world clinical practice. Accordingly, there is a demand for
a future work on developing a benchmark framework to
validate and compare the existing methods. This framework
should facilitate the same computing hardware infrastructure,
(universal) datasets covering same patient cohorts, same data
pre-processing procedures and evaluation criteria across AI
methods being evaluated.
Furthermore, as Li et al. [8] pointed out, although their
model obtained great accuracy in distinguishing COVID-19
with other types of viral pneumonia using radiology images,
it still lacks of transparency and interpretability. For example,
they do not know which imaging features have unique effects
on the output computation. The benefit that black box deep
learning methods can provide to clinical doctors is therefore
questionable. A future study on explainable AI to explain
the deep learning models’ performance as well as features of
images that contribute to the distinction between COVID-19
and other types of pneumonia is necessary. This would help
radiologists and clinical doctors to gain insights about the virus
and examine future coronavirus CT and X-ray images more
effectively.
In the field of computational biology and medicine, AI
has been used to partially understand COVID-19 or discover
novel drug compounds against the virus [49], [50]. These
are just initial results and thus there is a great demand for
AI research in this field, e.g., to investigate genetics and
chemistry of the virus and suggest ways to quickly produce
vaccines and treatment drugs. With a strong computational
power that is able to deal with large amounts of data, AI
can help scientists to gain knowledge about the coronavirus
quickly. For example, by exploring and analyzing protein
structures of virus, medical researchers would be able to find
components necessary for a vaccine or drug more effectively.
This process would be very time consuming and expensive
with conventional methods [85]. Recent astonishing success of
deep learning in identifying powerful new kinds of antibiotic
from a pool of more than 100 million molecules as published
in [86] gives a strong hope to this line of research in the battle
against COVID-9.
Compared to the 1918 Spanish flu pandemic [87], we are
now fortunately living in the age of exponential technology.
When everybody, organization and government try their best
in the battle against the pandemic, the power of AI should be
fully exploited and employed to support humans to combat this
battle. AI can be utilized for the preparedness and response
activities against the unprecedented national and global cri-
sis. For example, AI can be used to create more effective
robots and autonomous machines for disinfection, working
in hospitals, delivering food and medicine to patients. AI-
based NLP tools can be used to create systems that help
understand the public responses to intervention strategies, e.g.
lockdown and physical distancing, to detect problems such as
mental health and social anxiety, and to aid governments in
making better public policies. NLP technologies can also be
employed to develop chatbot systems that are able to remotely
communicate and provide consultations to people and patients

8
about the coronavirus. AI can be used to eradicate fake news
on social media platforms to ensure clear, responsible and
reliable information about the pandemic such as scientific
evidences relevant to the virus, governmental social distancing
policies or other pandemic prevention and control measures.
In Table III, we point out 13 groups of problems related to
COVID-19 along with types of data needed and potential AI
methods that can be used to solve those problems. We do not
aim to cover all possible AI applications but emphasize on
realistic applications that can be achieved along with their
technical challenges. Those challenges need to be addressed
effectively for AI methods to bring satisfactory results.
It is great to observe the increasing number of AI applica-
tions against the COVID-19 pandemic. AI methods however
are not silver bullets but they have limitations and challenges
such as inadequate training and validation data or when data
are abundantly available, they are normally in poor quality.
Huge efforts are needed for an AI system to be effective
and useful. They may include appropriate data processing
pipelines, model selection, efficient algorithm development,
remodelling and retraining, continuous performance monitor-
ing and validation to facilitate continuous deployment and
so on. There are AI ethics principles and guidelines [88],
[89] that each phase of the AI system lifecycle, i.e. design,
TABLE III
SUMMARY OF EXISTING AND POTENTIAL AI APP LIC ATIO NS TO D EA L WIT H TH E COVID-19 PANDEMIC,DATA NEE DED ,CH ALL EN GES N EED T O BE
ADDRESSED,AN D AI ME THO DS T HAT CA N BE US ED
Applications Types of Data Challenges Related AI Methods
Screen and triage patients,
identify effective personalized
medicines and treatments, risk
evaluation, survival prediction,
healthcare and medical re-
source planning.
Clinical symptoms, routine
laboratory tests, blood exams,
electronic health records,
heart rate, respiratory
rate, data observed from
previous patients, e.g. clinical
information, administered
treatments, patients’ case
history.
- Challenging to collect physiological
characteristics and therapeutic out-
comes of patients.
- Low-quality data would make biased
and inaccurate predictions.
- Uncertainty of AI models outcomes.
- Privacy and confidentiality issues.
[90]–[96] Machine learning
techniques, e.g. naive
Bayes, logistic regression,
KNN, SVM, MLP, fuzzy
logic system, ElasticNet
regression [97], decision
tree, random forest,
nonparametric Gaussian
process [58], deep learning
techniques such as LSTM
[45] and other recurrent
networks, and optimization
methods.
Predict number of infected
cases, infection rate and
spreading trend.
Time series case data, pop-
ulation density, demographic
data.
- Insufficient time series data, leading
to unreliable results.
- Complex models may not be more
reliable than simple models [98].
[26], [99],
[100]
COVID-19 early diagnosis us-
ing medical images.
Radiology images, e.g. chest
X-ray and CT scans.
- Imbalanced datasets due to insuffi-
cient COVID-19 medical image data.
- Long training time and unable to
explain the results.
- Generalisation problem and vulner-
able to false negatives.
[101]–[119]
and works in
Table I.
Deep learning CNN-based
models (e.g. AlexNet [3],
GoogLeNet [4], VGG
network [5], ResNet [6],
DenseNet [23], ResNeXt
[24], and ZFNet [120]),
AIbased computer vision
camera systems, and facial
recognition systems.
Scan crowds for people with
high temperature, and moni-
tor people for social distancing
and mask-wearing or during
lockdown.
Infrared camera images, ther-
mal scans.
- Cannot measure inner-body temper-
ature and a proportion of patients are
asymptomatic, leading to imprecise
results.
- Privacy invasion issues.
[121]–[123]
Analyse viral genomes, create
evolutionary (phylogenetic)
tree, find virus origin, track
physiological and genetic
changes, predict protein
secondary and tertiary
structures.
Viral genome and protein se-
quence data
- Computational expenses are huge
for aligning a large dataset of ge-
nomic or proteomic sequences.
- Deep learning models take long
training time, especially for large
datasets, and are normally unexplain-
able.
[55], [75],
DeepMinds
AlphaFold
[48], [49]
- Sequence alignment, e.g.
dynamic programming,
heuristic and probabilistic
methods.
- Clustering algorithms,
e.g. hierarchical clustering,
k-means, DBSCAN [72] and
supervised deep learning.
Discover vaccine and drug bio-
chemical compounds and can-
didates, and optimize clinical
trials.
Viral genome and protein
sequences, transcriptome
data, drug-target interactions,
protein-protein interactions,
crystal structure of protein, co-
crystalized ligands, homology
model of proteins, and clinical
data.
- Dealing with big genomic and pro-
teomic data.
- Results need to be verified with
experimental studies.
- It can take long time for a promising
candidate to become a viable vaccine
or treatment method.
[50], [124]–
[132]
Heuristic algorithm, graph
theory, combinatorics, and
machine learning such as
adversarial autoencoders
[50], multitask CNN [124],
GAN [50], [125], deep
reinforcement learning [50],
[126], [127].
Making drones and robots for
disinfection, cleaning, obtain-
ing patients vital signs, dis-
tance treatment, and deliver
medication.
Simulation environments and
demonstration data for training
autonomous agents.
- Safety must be guaranteed at the
highest level.
- Trust in autonomous systems.
- Huge efforts from training agents to
implementing them to real machines.
[133]–[136] Deep learning, computer vi-
sion, optimization and con-
trol, transfer learning, deep
reinforcement learning [137],
learning from demonstrations.
Track and predict economic re-
covery via, e.g. detection of
solar panel installations, count-
ing cars in parking lots.
Satellite images, GPS data
(e.g. daily anonymized data
from mobile phone users to
count the number of com-
muters in cities).
- Difficult to obtain satellite data in
some regions.
- Noise in satellite images.
- Anonymized mobile phone data se-
curity.
[138], [139] Deep learning, e.g. autoen-
coder models for feature ex-
traction and dimensionality
reduction, and CNN-based
models for object detection.

9
Applications Types of Data Challenges Related AI Methods
Real-time spread tracking,
surveillance, early warning
and alerts for particular
geographical locations, like
the global Zika virus spread
model BlueDot [140].
Anonymized location data from
cellphones, flight itinerary data,
ecological data, animal and
plant disease networks, temper-
ature profiles, foreign-language
news reports, public announce-
ments, and population distri-
bution data, e.g. LandScan
datasets [141].
- Insufficient data in some regions of
the world, leading to skewed results.
- Inaccurate predictions may lead to
mass hysteria in public health.
- Privacy issues to ensure cellphone
data remain anonymous.
BlueDot
[142],
Metabiota
Epidemic
Tracker
[143],
HealthMap
[144]
Deep learning (e.g.
autoencoders and recurrent
networks), transfer learning,
and NLG and NLP tools
(e.g. NLTK [61], ELMo
[62], ULMFiT [63],
Transformer [64], Googles
BERT [65], Transformer-
XL [66], XLNet [67],
ERNIE [68], T5 [69],
BPT [70] and OpenAIs
GPT-2 [71]) for various
natural language related
tasks such as terminology
and information extraction,
automatic summarization,
relationship extraction,
text classification, text and
semantic annotation,
sentiment analysis,
named entity recognition,
topic segmentation and
modelling, machine
translation, speech
recognition and synthesis,
automated question and
answering.
Understand communities’ re-
sponses to intervention strate-
gies, e.g. physical distancing
or lockdown, to aid public pol-
icy makers and detect prob-
lems such as mental health.
News outlets, forums, health-
care reports, travel data, and
social media posts in multiple
languages across the world.
- Social media data and news reports
may be low-quality, multidimensional,
and highly unstructured.
- Issues related to language translation.
- Data cannot be collected from popu-
lations with limited internet access.
[145]–[147]
Mining text to obtain
knowledge about COVID-
19 transmission modes,
incubation, risk factors, non-
pharmaceutical interventions,
medical care, virus genetics,
origin, and evolution.
Text data on COVID-19 virus
such as scholarly articles in
CORD-19 dataset [84].
- Dealing with inaccurate and ambigu-
ous information in the text data.
- Large volume of data from heteroge-
neous sources.
- Excessive amount of data make dif-
ficult to extract important pieces of
information.
[148]–[150]
Mining text to discover can-
didates for vaccines, antiviral
drugs, therapeutics, and drug
repurposing through search-
ing for elements similar to
COVID-19 virus.
Text data about treatment effec-
tiveness, therapeutics and vac-
cines on scholarly articles, e.g.
CORD-19 dataset [84] and li-
braries of drug compounds.
- Need to involve medical experts
knowledge.
- Typographical errors in text data need
to be rectified carefully.
[132],
[151]–[155]
Making chatbots to consult pa-
tients and communities, and
combat misinformation (fake
news) about COVID-19.
Medical expert guidelines and
information.
- Unable to deal with unsaved query.
- Require a large amount of data and
information from medical experts.
- Users are uncomfortable with chat-
bots being machines.
- Irregularities in language expression
such as accents and mistakes.
[156]–[164]
development, implementation and ongoing maintenance, may
need to adhere to, especially when most AI applications
against COVID-19 involve or affect human beings. The more
AI applications are proposed, the more these applications need
to ensure fairness, safety, explainability, accountability, privacy
protection and data security, be aligned with human values, and
have positive impacts on societal and environmental wellbeing.
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Thanh Thi Nguyen was a Visiting Scholar with
the Computer Science Department at Stanford Uni-
versity, California, USA in 2015 and the Edge
Computing Lab, John A. Paulson School of Engi-
neering and Applied Sciences, Harvard University,
Massachusetts, USA in 2019. He received an Alfred
Deakin Postdoctoral Research Fellowship in 2016,
a European-Pacific Partnership for ICT Expert Ex-
change Program Award from European Commission
in 2018, and an AustraliaIndia Strategic Research
Fund Early- and Mid-Career Fellowship Awarded by
the Australian Academy of Science in 2020. Dr. Nguyen obtained a PhD in
Mathematics and Statistics from Monash University, Australia in 2013 and has
expertise in various areas, including artificial intelligence, deep learning, deep
reinforcement learning, cyber security, IoT, bioinformatics, medical research
and data science.
Dr. Nguyen has been recognized as a leading researcher in Australia
in the field of Artificial Intelligence by The Australian Newspaper in a
report published in 2018. He is currently a Senior Lecturer in the School
of Information Technology, Deakin University, Victoria, Australia.