Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.
10.1109/ACCESS.2020.DOI

This work was supported by the National Research Foundation of Korea (NRF)
Grant funded by the Korea Government (MSIT) under Grants NRF-2019R1C1C1006143
and NRF-2019R1I1A3A01060518. This work was also supported by Institute of
Information & communications Technology Planning & Evaluation (IITP) grant
funded by the Korea government (MSIT) (No. 2020-0-01450, Artificial
Intelligence Convergence Research Center [Pusan National University]).

Corresponding author: Won-Joo Hwang (e-mail: wjhwang@pusan.ac.kr).

# Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19)
Pandemic:
A Survey on the State-of-the-Arts

QUOC-VIET PHAM1  DINH C. NGUYEN2, THIEN HUYNH-THE3,
WON-JOO HWANG4,5, AND PUBUDU N. PATHIRANA2  Research Institute of Computer,
Information and Communication, Pusan National University, Busan 46241, Korea
(e-mail: vietpq@pusan.ac.kr).  School of Engineering, Deakin University, Waurn
Ponds, VIC 3216, Australia (e-mail: {cdnguyen,
pubudu.pathirana}@deakin.edu.au).  ICT Convergence Research Center, Kumoh
National Institute of Technology, Gyeongsangbuk-do 39177, Korea (e-mail:
thienht@kumoh.ac.kr).  Department of Biomedical Convergence Engineering, Pusan
National University, Busan, 46241 Korea.  Department of Information
Convergence Engineering (Artificial Intelligence), Pusan National University,
Busan 46241, Korea (e-mail: wjhwang@pusan.ac.kr).

###### Abstract

The very first infected novel coronavirus case (COVID-19) was found in Hubei,
China in Dec. 2019. The COVID-19 pandemic has spread over 214 countries and
areas in the world, and has significantly affected every aspect of our daily
lives. At the time of writing this article, the numbers of infected cases and
deaths still increase significantly and have no sign of a well-controlled
situation, e.g., as of 13 July 2020, from a total number of around 13.1
million positive cases, $571,527$ deaths were reported in the world. Motivated
by recent advances and applications of artificial intelligence (AI) and big
data in various areas, this paper aims at emphasizing their importance in
responding to the COVID-19 outbreak and preventing the severe effects of the
COVID-19 pandemic. We firstly present an overview of AI and big data, then
identify the applications aimed at fighting against COVID-19, next highlight
challenges and issues associated with state-of-the-art solutions, and finally
come up with recommendations for the communications to effectively control the
COVID-19 situation. It is expected that this paper provides researchers and
communities with new insights into the ways AI and big data improve the
COVID-19 situation, and drives further studies in stopping the COVID-19
outbreak.

###### Index Terms:

Artificial intelligence (AI), big data, COVID-19, coronavirus, epidemic
outbreak, deep learning, data analytics, machine learning.

=-15pt

##  I Introduction

Coronavirus disease-19 (COVID-19), caused by a novel coronavirus, has changed
the world significantly, not only in the health care space, but also in many
aspects of human life such as education, transportation, politics, supply
chain, etc. Infected COVID-19 people normally experience respiratory illness
and can recover with effective and appropriate treatment methods. What makes
COVID-19 much more dangerous and easily spread than other Coronavirus families
is that the COVID-19 coronavirus has become highly efficient in human-to-human
transmissions. As the writing of this paper, the COVID-19 virus has spread
rapidly in $214$ countries. The United State of America (USA) currently
records with the highest COVID-19 cases, more than $3.4$ million confirmed
cases and nearly $137,782$ deaths (July 13, 2020). Some other countries like
Brazil, India, Russia, and Spain are also enormously influenced. However,
there are no clinical vaccines to prevent the COVID-19 virus and specific
drugs/therapeutic protocols to combat this communicable disease.

As the leaders in the war against the novel coronavirus, the World Health
Organization (WHO) and Centers for Disease Control and Prevention (CDC) have
released a set of public advices and technical guidelines [1, 2]. The
cooperation between and efforts from national governments and large
corporations are expected to significantly reduce risks from the spread of
COVID-19 outbreak. For example, as a search engine giant, Google launched a
COVID-19 portal (www.google.com/covid19), where we can find useful
information, such as coronavirus map, latest statistics, and most common
questions on COVID-19. Another example is that IBM, Amazon, Google, and
Microsoft with White House developed a supercomputing system for researches
relevant to the coronavirus [3]. In response to the pandemic, some publishers
now offer free access to the articles, technical standards, and other
documents related to the COVID-19-like virus, while web archival services like
arXiv, medRxiv, and bioRxiv create a fast link to collected all preprints
related to COVID-19 [4]. On the other hand, artificial intelligence (AI) and
big data have found in a lot of applications in various fields, e.g., AI in
computer science, AI in banking, AI in agriculture, and AI in healthcare.
These technologies are expected and may play important roles in the global
battle against the COVID-19 pandemic.

To better understand and alleviate the COVID-19 pandemic, many papers and
preprints have been published online in the last few months. Our main purpose
is to show the effectiveness of AI and big data to fight against the COVID-19
pandemic and review state-of-the-art solutions using these technologies.
Moreover, two representative examples of the AI and big data-based solutions
are presented for a better understanding. Besides, we highlight challenges and
issues associated with existing AI and big data-based approaches, which
motivate us to produce a set of recommendations for the research communities,
governments, and societies. We note that the papers to be reviewed in this
work are collected from various sources like IEEE Xplore, Nature,
ScienceDirect, and Wiley by using a subsequent of strings (”AI” OR ”artificial
intelligence”) AND (”big COVID-19” OR ”Coronavirus” OR ”SARS-CoV-2”) or (”big
data” OR ”data science”) AND (”big COVID-19” OR ”Coronavirus” OR ”SARS-
CoV-2”). Moreover, as a large number of preprints have been published online
since Dec. 2019, representative preprints from archived websites such as
arXiv, medRxiv, and bioRxiv are also selected.

This work is organized as follows. Section II presents fundamental knowledge
of COVID-19, AI, big data, and shows primary motivations behind the use of AI
and big data. Next, applications of AI and big data in fighting the COVID-19
pandemic, e.g., identifying the infected patients, tracking the COVID-19
outbreak, developing drug researches, and improving the medical treatment, are
reviewed and summarized in Sections III and IV, respectively. In Section V,
two examples of AI and big data-based frameworks for combating the COVID-19
disease are described: a phone-based framework for the COVID-19
detection/surveillance, and a data-driven approach using AI for finding
antibody sequences. Section VI highlights lessons and recommendations learned
from this paper, and discusses the challenges needed to be solved in the
future. Finally, Section VII concludes this paper.

##  II Background and Motivations

This section presents the fundamentals of COVID-19, AI, big data. This section
also shows motivations behind the use of AI and big data in response to the
COVID-19 pandemic.

Figure 1: The global COVID-19 trend (Source: CoronaBoard). Data accurate as of
July 13, 2020.

###  II-A COVID-19 pandemic

COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-
CoV-2), a betacoronavirus [5]. The first COVID-19 infected case was reported
in Wuhan City, Hubei Province of China on 31 December 2019, and has quickly
spread out almost all countries in the world, $214$ countries and areas at the
time of writing this article. There is no sign that the numbers of infected
and dead cases will decrease and the situation will be under control.  In
particular, the numbers of new cases are still very large, around $+139,000$
and $+3,000$ infected and dead cases per day, as report by the
CoronaBoard111https://coronaboard.kr/, whereas the fatality rate is $4.38$%
(accurate as of July 13 2020). The global COVID-19 trend can be found in Fig.
1. Due to the serious situation of COVID-19, WHO has increased the COVID-19
risk assessment to the highest level and declared it as a global pandemic. For
a better understanding of the COVID-19 virus about its structure, etiology and
pathogenesis, characteristics, and recent treatment progress, we refer the
interested readers to refer the paper [6].

As the dramatic impact of COVID-19 on the globe, lots of attempts have been
paid for solutions to fight against the COVID-19 outbreak. Government’s
efforts are mainly responsible to stop the pandemic, e.g., lock down the
(partial) area to limit the spread of infection, ensure that the healthcare
system is able to cope with the outbreak and provide crisis package to
alleviate impacts on the national economics and people, and adopt adaptive
policies according to the COVID-19 situation. At the same time, individuals
are encouraged to stay healthy and protect others by following some advices
like wearing the mask at public locations, washing the hands frequently,
maintaining the social distancing policy, and reporting the latest symptom
information to the regional health center222https://www.who.int/health-
topics/coronavirus. On the other hand, research and development relevant to
COVID-19 are now prioritized, and have been received special interest from
various stakeholders like governments, industries, and academia. For example,
studies in [7, 8] showed huge impacts of the COVID-19 pandemic on the global
supply chain, and considered different aspects of supply chains, including
viability, stability, robustness, and resilience. Besides the global attempt
to develop an effective vaccine and medical treatment for the COVID-19
coronavirus, computer science researchers made initial efforts for the fight
against COVID-19. Motivated by the tremendous success of AI and big data in
various areas, we present state-of-the-art solutions and approaches based on
AI and big data for tackling the COVID-19 coronavirus disease.

###  II-B Artificial Intelligence

AI is a thriving technology for many intelligent applications in various
fields. Some high-profile examples of AI are autonomous vehicles (e.g., self-
driving car and drones) in automotive, medical diagnosis and telehealth in
healthcare, cybersecurity systems (e.g., malware and botnet detection), AI
banking in finance, image processing and natural language processing in
computer vision. Among many branches of AI, machine learning (ML) and deep
learning (DL) are two important approaches. Generally, ML refers to the
ability to learn and extract meaningful patterns from the data, and the
performance of ML-based algorithms and systems are heavily dependent on the
representative features. In the meanwhile, DL is able to solve complex systems
by learning from simple representations. According to [9, 10], DL has two main
features: 1) the ability to learn the right representations shows one feature
of DL and 2) DL allows the system to learn the data from a deep manner,
multiple layers are used sequentially to learn increasingly meaningful
representations.

AI offers a powerful tool to fight against the COVID-19 pandemic [11]. For
example, the scientists in [12] developed a DL model to identify existing and
commercial drugs for the “drug-repurposing” (also known as drug
repositioning), i.e., finding a rapid drug strategy using existing drugs that
can be immediately applied to the infected patients. This study is motivated
by the fact that newly developed drugs usually take years to be successfully
tested before coming to the market. Although findings in this study are
currently not clinically approved, they still open new ways to fight the
COVID-19 disease. The work in [13] proposed using the deep generative model
for drug discovery (which is defined as the process of identifying new
medicines). The COVID-19 protease structures generated from the DL model in
this work would be further used for computer modeling and simulations for the
purpose of obtaining new molecular entity compounds against the COVID-19
coronavirus. Utilizing DL for computed tomography (CT) image processing, the
authors in [14] showed that their proposed DL model when training with $499$
CT volumes and testing on $131$ CT volumes, can achieve an accuracy of $0.901$
with a positive predictive value of 0.840 and a negative predictive value of
$0.982$. This study offers a fast approach to identify the infected COVID-19
patient, which may provide great helps in timely quarantine and medical
treatment. The last example is the use of AI (i.e., an autoencoder based
method) in real-time predicting dynamics (e.g., the number of infected cases,
ending time and trajectory of the COVID-19 pandemic) of the COVID-19 outbreak
in China [15]. The high accuracy of the AI-based approach proposed in [15] is
helpful in monitoring the COVID-19 outbreak and improving the health and
policy strategies. Along with the applications mentioned above, the
involvement of giant techs is needed because researchers, doctors and
scientists can be effectively supported to expedite the research and
development of COVID-19 virus. Recently, IBM announced that they are now
providing a cloud-based research resource that has been trained on a COVID-19
open dataset (CORD-19) [16], which is a collection of research articles
related to COVID-19. Moreover, IBM has adopted their proposed AI technology
for drug discovery, from which 3000 novel COVID-19 molecules have been
obtained, officially reported in [17]. Another support is from the White House
Office of Science and Technology Policy, the U.S. Department of Energy and IBM
with the development of COVID-19 HPC Consortium (https://covid19-hpc-
consortium.org/), which is open for research proposals concerning COVID-19.
Another example is the Coronavirus International Research Team (COV-IRT)
(https://www.cov-irt.org/), a group of scientists who are developing vaccines
and therapeutic solutions against COVID-19.

###  II-C Big Data

####  II-C1 Definition and Characteristics

The rapid development of the Internet of Things (IoT) results in a massive
explosion of data generated from ubiquitous wearable devices and sensors [18].
The unprecedented increase of data volumes associated with advances of
analytic techniques empowered from AI has led to the emergence of a big data
era [19, 18]. Big data has been employed in a wide range of industrial
application domains, including healthcare where electronic healthcare records
(EHRs) are exploited by using intelligent analytics for facilitating medical
services. For example, health big data potentially supports patient health
analysis, diagnosis assistance, and drug manufacturing [20]. Big data can be
generated from a number of sources which may include online social graphs,
mobile devices (i.e. smartphones), IoT devices (i.e. sensors), and public data
[21] in various formats such as text or video. In the context of COVID-19, big
data refers to the patient care data such as physician notes, X-Ray reports,
case history, list of doctors and nurses, and information of outbreak areas.
In general, big data is the information asset characterized by such a high
volume, velocity and variety to acquire specific technology and analytical
methods for its transformation into useful information to serve the end users,
i.e, big data in digital twin technologies [22, 23, 24, 25]. The three
characteristics of big data are summarized as follows.

  * •

Volume: This feature shows the huge amount of data that can range from
terabytes to exabytes. According to a Cisco’s forecast, the data traffic is
expected to reach $930$ exabytes by 2020, a seven-fold growth from 2017 [26].

  * •

Variety: It refers to the diversity and heterogeneity of big data. For
example, big data in healthcare can be produced from healthcare users (i.e.
doctors, patients), medical IoT devices, and healthcare organizations. Data
can be formatted in text, images, videos with structured or un-structured
dataset types [27].

  * •

Velocity: It expresses the data generation rate that can be calculated in time
or frequency domain. In fact, in industrial applications like healthcare, data
generated from devices is always updated in real-time, which is of significant
importance for time-sensitive applications such as health monitoring or
diagnosis [28].

####  II-C2 Big data for COVID-19 fighting

Big data has been proved its capability to support fighting infectious
diseases like COVID-19 [29], [30]. Big data potentially provide a number of
promising solutions to help combat COVID-19 epidemic. By combining with AI
analytics, big data helps us to understand the COVID-19 in terms of outbreak
tracking, virus structure, disease treatment, and vaccine manufacturing [31].
For example, big data associated with intelligent AI-based tools can build
complex simulation models using coronavirus data streams for outbreak
estimation. This would aid health agencies in monitoring the coronavirus
spread and preparing better preventive measurements [32]. Models from big data
also supports future prediction of COVID-19 epidemic by its data aggregation
ability to leverage large amounts of data for early detection. Moreover, the
big data analytics from a variety of real-world sources including infected
patients can help implement large-scale COVID-19 investigations to develop
comprehensive treatment solutions with high reliability [33], [34]. This would
also help healthcare providers to understand the virus development for better
response to the various treatment and diagnoses.

Based on the above analysis, we want to highlight that big data analytics is
the process of collecting and analyzing the large volume of data sets to
discover useful hidden patterns and other information, e.g., COVID-19 data
discovering. Moreover, AI (and explainable AI [35]) aims to apply logic and
reasoning to build human intelligence that can mimic the function of a machine
for learning, classifying, and estimating possible outcomes, e.g., COVID-19
symptom classifications. The potential applications of each technology in
fighting COVID-19 pandemic will be explained and discussed in the following
sections through a number of practical use cases.

##  III Applications of AI for Fighting Covid-19

This section presents representative applications of AI in fighting the
Covid-19 outbreak.

###  III-A AI for COVID-19 detection and diagnosis

As one of the most effective solutions to combat the COVID-19 pandemic, early
treatment and prediction are of importance. Currently, the standard method for
classifying respiratory viruses is the reverse transcription polymerase chain
reaction (RT-PCR) detection technique. In response to the COVID-19 virus, some
efforts have been dedicated to improve this technique [36] and for other
alternatives [37]. These techniques are, however, usually costly and time-
consuming, have low true positive rate, and require specific materials,
equipment and instruments. Moreover, most countries are suffering from a lack
of testing kits due to the limitation on budget and techniques. Thus, the
standard method is not suitable to meet the requirements of fast detection and
tracking during the COVID-19 pandemic. A simple and low-cost solution for
COVID-19 identification is using smart devices together with AI frameworks
[38, 39]. This is referred to as _mobile health_ or _mHealth_ in the
literature [40]. These works are advantageous since smart devices are daily
used for multi-purposes. Moreover, the emergence of cloud and edge computing
can effectively overcome the limitation of batter, storage, and computing
capabilities [41].

Another directive for COVID-19 detection is to use AI techniques for medical
image processing, which recently appeared in many research works on
coronavirus [42, 43, 44, 45, 46, 47, 48, 49, 50, 51]. As we limit this paper
to the COVID-19 virus, the interested readers are invited to read the surveys
in [52, 53] for other applications of DL in medical image analysis. It is
noted from these works that X-ray images and computed tomography (CT) scans
are widely used as the input of DL models so as to automatically detect the
infected COVID-19 case. Motivated by an important finding that infected
COVID-19 patients normally reveal abnormalities in chest radiography images,
the authors in [54] designed a deep convolutional neural network (CNN) model
for the detection of COVID-19 cases. The three-class classification (normal,
COVID-19 infected, and non-COVID-19 infected) in this work is helpful if the
medical staff needs to decide which cases should be tested with standard
methods (between normal and COVID-19 infected cases), and which treatment
strategies should be taken (between non- and COVID-19 infected cases). By
training on an open source dataset with $13,975$ images of $13,870$ patients,
the proposed CNN model can achieve an accuracy of $93.3$%. The use of ML and
DL techniques with chest CT scans for COVID-19 detection were considered in
[42, 55, 43, 56, 57, 58], respectively. These works show a high performance as
they can achieve a high classification accuracy, e.g., $99.68$% in [42], an
area under curve (AUC) score of $0.994$ in [43], AUC of $0.996$ in [55], and
accuracy of $82.9$% ($98.27$%) with specificity of $80.5$% ($97.60$%) and
sensitivity of $84$% ($98.93$%) in [56] and [57]. As the cost of X-ray scans
is usually cheaper than CT images, a large portion of research works for
COVID-19 detection utilize DL models with X-ray images. For example, the work
in [46] leveraged a deep CNN model, called Decompose, Transfer, and Compose
(DeTraC), to process chest X-ray images for the classification of COVID-19.
The main purpose of the decomposition layer is to reduce the feature space,
thus yielding more sub-classes but improving the training efficiency, whereas
the composition layer is to combine sub-classes from the previous layer so as
to produce the final classification result. Besides the decomposition and
composition layers, a transfer layer is positioned in the middle to speed up
the training time, reduce the computation costs, and make the DL model
trainable with small datasets. The importance of transfer learning made it a
wide consideration in many works, e.g., [49, 45, 48]. To summarize this
paragraph, a general representation of DL-based models for the COVID-19
detection and diagnosis can be showed in Fig. 2, where we present the output
as a binary classification: COVID-19 infected and normal.

Figure 2: An illustration of DL-based frameworks for the COVID-19 detection
and diagnosis.

In the fight against the COVID-19 pandemic, developing efficient diagnostic
and treatment methods plays an important role in mitigating the impact of
COVID-19 virus. The work in [59] introduces a method based on DL and deep
reinforcement learning (DL) to quantify abnormalities in the COVID-19 disease.
The input into the proposed learning model is non-contrasted chest CT images,
while the output is severity scores, including percentage of opacity (POO),
lung severity score (LSS), percentage of high opacity (POHO), and lung high
opacity score (LHOS). The proposed learning model, when trained and tested on
a dataset of $568$ CT images and $100$ samples respectively, shows promising
results as the Pearson’s correlation coefficient between the ground truth and
predicted output is $0.97$ for POO, $0.98$ for POHO, $0.96$ for LSS, and
$0.96$ for LHOS. Another work leveraging DL with CT images is in [60], where
two measurement metrics: volume of infection and percentage of infection are
quantified. In contrast with other studies, the work in [51] exploited a pre-
trained ResNet50V2 model [61] with the Bayesian DL classifier to estimate the
diagnostic uncertainty. A random forest (RF) model was investigated in [62] to
assess the COVID-19 severity, where $63$ features, e.g., infection ratio of
the lung and the ground class opacity lesions, are extracted from chest CT
images. The result is very promising as it can achieve sensitivity of $0.933$,
selectivity of $0.745$, accuracy of $0.875$, and AUC score of $0.91$. The key
finding from this study is that the severity level is more dependent on the
features extracted from the right lung. As an effort to develop a scalable and
cost-effective solution for COVID-19 diagnosis, the authors in [63] proposed
an AI-based framework called AI4COVID-19, which takes into account the deep
domain knowledge of medical experts, and uses smart phones to record
cough/sound signals as the input data. In particular, the method relies on the
fact that COVID-19 disease is likely to have idiosyncrasies from the
underlying pathomorphological alterations, e.g., ground-glass opacity ($91$%
for COVID-19 versus $68$% for non-COVID-19), vascular thickening ($59$% for
COVID-19 versus $22$% for non-COVID-19). Despite interesting results (e.g.,
average accuracy of $97.91$% (93.56%) for cough (COVID-19) detection, and
response time within $60$ seconds), the implementation of AI4COVID-19 at a
larger scale is currently limited by several issues, such as quantity and
quality of the datasets, and lack of clinical validation.

###  III-B Identifying, tracking, and predicting the outbreak

A traditional compartmental model to predict the infectious disease is the SIR
(Susceptible-Infected-Removed) model. This SIR model can be expressed by the
following non-linear differential equations [64]:

$\displaystyle dS/dt=-\beta SI,\;\;dI/dt=\beta SI-\gamma I,\;\;dR/dt=-\gamma
I,$ (1)

where $S$, $I$, and $R$ denote susceptible, infected, and removed individuals,
respectively, $\beta$ is the transmission rate, and $\gamma$ is the recovering
rate. However, this model is not suitable for the COVID-19 pandemic because of
its assumptions, which are 1) the recovered cases will not get infected again
and 2) the model simply ignores the time-varying nature of two parameters
$\beta$ and $\gamma$. To overcome issues associated with the traditional SIR
model, some efforts have been paid for properly adapting the COVID-19
pandemic. For example, a time-dependent SIR model was proposed in [65], which
can adapt to the change of infectious disease control and prevention laws such
as city lock downs and traffic halt, as the control parameters $\beta$ and
$\gamma$ are modeled as time-variant variables. This model can be further
improved by having two types of infected patients: detectable and
undetectable, which the former has a lower transmission rate than that of the
latter. Numerical results in [65] show the proposed time-dependent model can
accurately predict the number of confirmed cases in China, and the outbreak
time of some countries like Republic of Korea, Italy, and Iran. Several
variants of the SIR model other studies, e.g., space-time dependence of the
COVID-19 pandemic in [66], SIQR model in [67], where an additional
compartment, namely Quarantined, is considered in the traditional SIR model,
and stochastic SIR model in [68]. As a summary, these models can predict the
outbreak of COVID-19 and send alarm messages to the governments and policy
makers so that some actions should be taken in advance prior to the outbreak.

To model the outbreak size of COVID-19, over the last few months, a number of
research works have been done using ML and DL models. For example, the authors
in [69] argued that the traditional SIR model is not able to capture the
effects of more granular interactions such as social distancing and quarantine
policies. The authors proposed encoding the quarantine policy as a strength
function, which is then included in a neural network for predicting the
outbreak size in in Wuhan, China. The experimental results using the publicly
available data from the Chinese National Health Commission demonstrate that
the quarantine policy plays an important role in controlling the COVID-19
outbreak, and the number of infected cases can increase exponentially without
the right quarantine policy. Applying the proposed model with quarantine for
the United State of America (USA), the infection of COVID-19 may show a
stagnation by 20 April if the current COVID-19 policies are executed without
any changes. The authors also applied their proposed learning model in [70] to
estimate the global outbreak size and obtained similar results. Very recently,
some scholars proposed working directly with the empirical data to estimate
the outbreak size, for example, logistic model in [71], Bayesian non-linear
model in [72], Prophet forecasting procedure in [73], and autoregressive
integrated moving average (ARIMA) model in [74]. The study in [75] presents a
data-driven approach to learn the phenomenology of COVID-19. The proposed
model enables us to read asymptomatic information, e.g., a lag (also known as
incubation period) of about $10$ days and virulence of $0.14$%.

Another interesting work in [76] proposed a modified auto-encoder (MAE) for
modeling the transmission dynamics of COVID-19 in the world. Using the data
collected from the WHO situation reports, it is shown that the proposed MAE
model can predict the outbreak size accurately with an average error less than
$2.5$%. The experimental results also show that the COVID-19 outbreak can be
prevented effectively with fast public health intervention, e.g., compared
with one-month intervention, one-week case can reduce the maximum number of
cumulative and dead cases by around 166.89 times. Applying the MAE model for
the surveillance data of the confirmed Covid-19 cases in China [15], the
authors also showed high forecasting capabilities of the proposed MAE model
for the transmission dynamics and plateau of COVID-19 in China. The authors in
[77] proposed combining medical information (e.g., susceptible and dead cases)
and local weather data to predict the risk level of the country. Specifically,
a shallow long short-term memory (LSTM) neural network is used to overcome the
challenges of small dataset, and the risk level (high, medium, and recovering)
of a country is classified by using the fuzzy rule. The experiment results
show that the proposed learning model can achieve an average accuracy of $78$%
over 170 countries.

###  III-C AI for Infodemiology and Infoveillance

To date, most reliable information on the COVID-19 pandemic is disseminated
through the official websites and social channels of the health organizations
like the WHO, and the ministry of health and welfare in each country. However,
electronic medium and online platforms (e.g., Facebook, Twitter, YouTube, and
Instagram) have showed their importance in distributing information related to
COVID-19 like pandemic. Regardless of the quality and source, information from
the media platform and the Internet is highly accessible and timely, so
further analyses can be performed if the data can be collected and processed
properly. As a powerful tool to deal with a vast amount of data, AI have been
utilized to have a better understanding of the social network dynamics and to
improve the COVID-19 situation. To illustrate the applications of AI during
the disease pandemic, the work in [78] presented some real-life examples: 1)
using Twitter data to track the public behavior, 2) examining the health-
seeking behavior of the Ebola outbreak, and 3) public reaction towards the
Chikungunya outbreak.

Similar to the previous pandemics, the recent emergence of COVID-19 has called
for several studies from the infodemiology and infoveillance perspectives. The
authors in [79] analyzed the data collected from three popular social
platforms in China: Sina Weibo, Baidu search engine, and Ali e-commerce 29
marketplace for assessing public concerns, risk perception, and tracking
public behaviors in response to the COVID-19 outbreak. More specifically,
public emotion is evaluated by using a text analysis program, called
Linguistic Inquiry and Word Count (LIWC), whereas public attention and
awareness, and misinformation are assessed by constructing a Weibo daily
index, which is the number of posts with keywords related to the COVID-19
pandemic. Moreover, the Baidu and Ali daily indices are used to evaluate the
intention and behaviors to follow the recommended protection measures and/or
rumors about ineffective treatments during the COVID-19 outbreak. The results
show that quickly classifying rumors and misinformation can greatly alleviate
the impact of irrational behavior. Applications computer audition (CA), i.e.,
speech and sound analysis with AI, to contribute to the COVID-19 pandemic,
were considered in [80]. Similar to textual cues in [79], it is possible to
collect spoken conversations from news, advertising videos, and social medias
for further analyses. Several potential use-cases are also presented,
including risk assessment, diagnosis, monitoring the spread and social
distancing effects, checking the treatment and recovery, generation of speech
and sound. Along the potentials of CA in the fight against COVID-19, there are
some challenges needed to be addressed, for example, how to collected COVID-19
patient data, how to process the audio and speech data effectively and in
real-time, and how to explain the results obtained from the CA-based
solutions.

Another application of AI can be found in [81], where the COVID-19 related
data is collected from heterogeneous sources at multiple levels, including
official public health organizations (e.g., WHO and county government
websites), demographic data, mobility data (e.g., traffic density from Google
map), and user generated data from social media platforms. By utilizing the
conditional generative adversarial nets to enrich the limited data and a novel
heterogeneous graph auto-encoder to estimate the risk in an hierarchical
fashion. The proposed $\alpha$-Satellite system allows us to be aware of the
COVID-19 risk in a specific location, thus enabling the selection and
appropriate actions to minimize the impact of COVID-19. Recently, the authors
in [82] proposed a novel method, namely Augmented ARGONet, to estimate the
number confirmed COVID-19 cases in two days ahead. The data is collected from
multiple sources, including official health reports from China CDC, COVID-19
Internet search activities from Baidu, news media activities from Media Cloud,
and daily forecasts achieved by the model proposed in [83]. To enrich the
dataset, each data point is also augmented by adding a random Gaussian noise
with mean $0$ and standard deviation $1$. The Lasso regression model is then
applied to predict the number of confirmed cases for 32 provinces in China.
The results show that the proposed Augmented ARGONet is able to outperform the
baseline models for most testing scenarios.

###  III-D AI for biomedicine and pharmacotherapy

The world has seen a race for finding effective vaccines and medical
treatments in order to combat the COVID-19 virus, which demands for
substantial efforts from not only health science but also computer science,
with the help of AI and emerging technologies. The authors in [84] have
answered the question “Why AI may benefit biomedical research”. First, the
vast amount of biomedical data has triggered the use of AI in various areas of
biomedicine and pharmaceutical industry. DL with deep neural networks is able
to handle high-dimensional and non-structured data with nonlinear
relationships, which are in line with biomedical data like transcriptomics and
proteomics. Lastly, high capabilities in extracting higher-level features
makes DL a potential candidate to analyze, bind, and interpret heterogeneous
medical data such as DNA microarray data. The past few years have seen a wide
range of AI applications to biomedicine researches, for example, medical image
classification, genomic sequence analysis, classification and prediction,
biomarkers, structural biology and chemistry, multiplatform data processing,
drug discovery and repurposing [85, 86]. Due to the severity of COVID-19
pandemic, AI has recently found medicine applications to curb the spread of
the coronavirus, which mainly focus on protein structure prediction, drug
discovery, and drug repositioning.

TABLE I: Summary of the state-of-the-art studies on AI applications for
COVID-19.

Category | Paper |  Highlights & Contributions
---|---|---
Detection and diagnosis | [46] |  A CNN-based DeTraC framework is proposed. In particular, the transfer learning concept is used to utilize well-performed deep models.
For the pre-trained ResNet18 model, the DeTraC method achieves competitive
performance, accuracy of 95.12%, sensitivity of 97.91%, and specificity of
91.87%.
[54] |  A deep CNN model for classification of COVID-19 and the dataset is designed by collecting $13,975$ chest X-ray images across $13,870$ patients. The proposed CNN model can achieve the test accuracy of 93.3%.
[62] |  Using chest CT images, 63 quantitative features of COVID-19 are analyzed by an RF model.
The proposed method can obtain promising results, e.g., accuracy of 0.875 and
AUC score of 0.91.
[63] |  The AI4COVID-19 framework is proposed to consider domain knowledge of medical experts. The input data is cough/sound signals, which may be recorded by smartphones.
The performance is very promising, the classification accuracy of 97.91%
(93.56%) is obtained for cough (COVID-19) detection.
Identifying, tracking, and predicting the outbreak | [65] |  A time-dependent SIR model is proposed to dynamically adjust the control parameters according to the outbreak policies. The model is also extended to consider undetectable infected cases.
[76] |  A modified autoencoder framework is investigated to model the transmission dynamics of COVID-19. Using the empirical data from the WHO, the model can achieve an average error of less than 2.5%.
An interesting observation is that a faster intervention can significantly
reduce the numbers of infected and dead cases.
Infodemiology and infoveillance | [79] |  Data is collected from Sina Weibo, Baidu search engine, and Ali e-commerce 29 marketplace to evaluate public concerns/behaviors and risk perception to the COVID-19 outbreak.
The result shows that fast reaction to quickly classify rumors and
misinformation can well mitigate impacts of irrational behaviors.
[80] |  Applications of computer visions for combating the COVID-19 pandemic are presented. Potential use cases (e.g., risk assessment and diagnosis) and challenges (e.g., data collection and model sharing) are also discussed.
[81] |  An AI-driven system, namely $\alpha$-satellite, is proposed to estimate the risk of COVID-19 in an hierarchical manner. Data is collected from heterogeneous sources, e.g., WHO, demographic and mobility data, and social platforms.
Biomedicine and pharmacotherapy | [87] |  A pre-trained deep learning model is utilized to train a dataset of $4,895$ commercially available drugs.
After learning and manual refinement, 10 drugs are selected as potential
COVID-19 inhibitors.
[88] |  For drug repurposing, a data-driven approach is examined over $6,000$ candidate drugs. The key finding is that the inhibitor CVL218 is very promising and has a safety profile in monkeys and rats.
[89] |  A deep generative model, namely CogMol, is proposed to find potential molecules that can blind three relevant protein structures of coronavirus.
Also, in silico screening experiments are conducted to assess the toxicity of
the generated molecules.

Using AI to discover new medicines and/or existing drugs to be immediately
used to treat the COVID-19 virus is beneficial from both the economic and
scientific perspectives, especially when clinically verified medicines and
vaccines are not available. The work in [87] leveraged a COVID-19 virus-
specific dataset to train a DL model. Then, this trained model is used to
check $4,895$ commercially available drugs and find potential inhibitors with
high affinities. Overall, ten existing drugs are listed as potential
inhibitors, for example, the HIV inhibitor abacavir, and respiratory
stimulants almitrine mesylate and roflumilast. The work in [88] proposed a
data-driven model for drug repurposing through the combination of ML and
statistical analysis methods. Initially, the authors selected a list of $6225$
candidate drugs, which are then narrowed through consecutive phases, including
a network-based knowledge mining algorithm, a DL based relation extraction
method, and a connectivity map analysis approach. After the in silico and in
vitro studies, a novel1 inhibitor, namely CVL218, has been found to be a drug
candidate to treat the COVID-19 virus. The potential of this finding is that
the inhibitor CVL218 is verified to have a safety profile in monkeys and rats.
Another interesting work for drug repurposing was in [90], which leverages a
Siamese neural network (SNN) to identify the COVID-19 protein structure versus
Ebola and HIV-1 viruses. Advantages this work is that the proposed DL model
can be trained without the need for a large dataset and can work directly with
available biological dataset instead of public datasets, which are not
specific about the COVID-19 virus. Besides drug repurposing, many have been
devoted to drug discovery [91, 89, 92, 93] and protein structure prediction
[94, 95, 96] for combating against the COVID-19 pandemic. For example, the
work in [89] considered a DL model named controlled generation of molecules
(CogMol) to learn candidate molecules that can bind protein targets of the
COVID-19 virus, which are then used to generate candidate drugs for the
treatment of the COVID-19 virus. Moreover, a multitask deep neural network is
used to predict the toxicity of the generated molecules, thus improving the in
silico screening process and increasing the success rate of the drug
candidates.

###  III-E Summary of AI Applications for COVID-19

In this section, we review the applications of AI for COVID-19 detection and
diagnosis, tracking and identification of the outbreak, infodemiology and
infoveillance, biomedicine and pharmacotherapy. We observe that the AI-based
framework is highly suitable for mitigating the impact of the COVID-19
pandemic as a vast amount of COVID-19 data is becoming available thanks to
various technologies and efforts. Through the AI studies are not implemented
at a large scale and/or tested clinically, they are still helpful as they can
provide fast response and hidden meaningful information to medical staffs and
policy makers. However, we face many challenges in designing AI algorithms as
the quality and quantity of COVID-19 datasets should be further improved,
which call for constant effort from the research communities and the help from
official organizations with more reliable and high-quality data. A summary of
state-of-the-art studies on AI techniques for is summarized in Table I.

##  IV Applications of Big Data for Fighting COVID-19

In the sense of COVID-19 data storage and analysis, big data analytics for
COVID-19 is mainly characterized via some key techniques from the big data
concept, such as multi-domain dataset analysis (i.e. combined COVID-19 image-
time series data), high-dimensional analysis (i.e. 3-D COVID-19 images), deep
analysis (i.e. using deep Boltzmann machine, hidden Markov model, deep neural
network), and parallel computing (i.e. bit-, instruction-, data-, and task-
parallelism). In this section, we summarize some key big data use cases and
applications enabled by these techniques, including outbreak prediction, virus
spread tracking, coronavirus diagnosis/treatment, and vaccine/drug discovery
that are summarized in Fig. 3.

Figure 3: Big data and its applications for fighting COVID-19 pandemic.

###  IV-A Outbreak prediction

Big data plays an important role in combating COVID-19 through its capability
to predict the outbreak from large-scale data analytics. For example, the work
in [97] leveraged real datasets collected from the COVID-19 pandemic in Italy
to estimate the outbreak possibility which is of significant importance to
plan effective disease control strategies. Instead of using a simple and
deterministic model based on human transmission [98], the authors developed
more complex models that can accurately formulate the dynamics of the pandemic
based on huge data sets from Italian Civil Protection sources. Another data
source for outbreak prediction is the public dataset that can be used to
visualize the geographical areas with possible outbreak [99]. A first trial is
an investigation in Wuhan, aiming to monitor the people migration from and to
Wuhan city so that the health agencies can predict the population infected
with COVID-19 for quarantine.

Meanwhile, the work in [100] employed the COVID-19 outbreak-related data from
authoritative sources such as the National, provincial and municipal Health
Commissions of China (http://www.nhc.gov.cn/). This big data source can help
implement pandemic modeling to interpret the cumulative numbers of infected
people, recovered cases in five different regions, i.e., the Mainland, Hubei,
Wuhan, Beijing, and Shanghai. More importantly, it allows us to perform
simulations to predict the tendency of the COVID-19 outbreak, i.e. identifying
the areas at high risks of pandemic and detecting the population with
increasing infected cases, all of which contribute to the success of anti-
pandemic campaigns.

Big data also enables outbreak prediction on the global scope. From the data
analysis point of view, the outbreak is predicted using available data points
that put the accuracy of the fitting for reliable estimation under doubt due
to the lack of comprehensive investigations. Accuracy may depend on the number
of contributed factors, ranging from infected cases, population, living
conditions, environments, etc. Motivated by this, a research effort in [101]
leveraged a large dataset from various regions and countries such as Korea,
China, to estimate the pandemic based on a logistic model that can adjudge the
reliability of the predictions. Another trial in [102] used Google Trends as a
data engineering tool to collect coronavirus-related information in China,
South Korea, Italy, and Iran. The data comes from a textual search with five
geographical settings: 1) worldwide, to investigate the global interest in
coronaviruses; 2) China, where there is currently the highest number of
infected cases; 3) South Korea, where interest has increased since 19 February
because hundreds of new infected cases were confirmed; 4) Italy; and 5) Iran,
where since 22 February hundreds of new cases have been detected. This data
source aggregation would help visualize the outbreak tendency and estimate the
possible outbreak. The government reports collected from the coronavirus
pandemics in China, South Korea, Italy, and Iran are also collected and
analyzed by [103] with a data optimization model, aiming to generate an
accurate prediction of the daily infected cases, which potentially long-term
predictions on the future outbreak.

Moreover, the work in [104] employed datasets from John Hopkins University
repository, which are obtained from confirmed cases, death cases and recovered
cases of all countries, to build prediction models using data learning. The
trial is implemented in India, indicating that the proposed data analytic can
estimate the outbreak in short-term intervals, i.e. two-week duration, which
potentially expands to build larger models for long-term predictions.
Meanwhile, another data analytic method in [105] was investigated in the US
with the large-scale datasets collected from American cities. The key purpose
of the study is learning from recent data to calculate prediction errors to
optimize the data modeling model for improving the quality of future
estimation, possibly for coronavirus-like pandemics.

###  IV-B Virus spread tracking

Another role of big data is the tracking of COVID-19 spread, which is of
paramount importance for healthcare organizations and governments in
controlling successfully the coronavirus pandemic [106]. A number of newly
emerging solutions using big data have been proposed to support tracking of
the COVID-19 spread. For instance, the study in [107] suggested a big data-
based methodology for tracking the COVID-19 spread. A large dataset is
collected from China National Health Commission with 854,424 air passengers
left 55 Wuhan Airport to 49 cities in China during the period of December
2019-January 2020. A multiple linear model is built using local population and
air passengers as estimated variables that are useful to quantify the variance
of reported cases in China cities. More specifically, the authors employed a
Spearman correlation analysis for the daily user traffic from Wuhan and the
total user traffic in this period with the number of 49 confirmed cases. The
analytic results show a high correlation between the positive infection cases
and the population size. The authors in [108] considered leveraging both big
data for spatial analysis methods and Geographic Information Systems (GIS)
technology which would facilitate data acquisition and the integration of
heterogeneous data from health data resources such as governments, patients,
clinical labs and the public.

Another research effort in [109] combined datasets collected from China,
Singapore, South Korea, and Italy to build a comprehensive analytic model for
virus spread tracking. Based on data learning and modelling, a macroscopic
growth law is derived that allows us to estimate the maximum number of
infected patients in a certain area. This is important for effective
assessment of COVID-19 prevention and monitoring the potential spread of
COVID-19 disease, especially the nearby regions of the central epidemic.
Different from [109], the study in [110] proposed a temperature-based model
that evaluates the relationship between the number of infected cases and the
average temperature in different countries necessary for coronavirus tracking.
A large dataset is collected from 42 countries which developed the epidemic
earlier and built a dataset of 88 countries. The analysis results from
COVID-19 tracking show that in northern hemisphere countries, the growth rate
should significantly decrease due to the warmer weather and lockdown policies,
compared to the southern hemisphere countries.

The previous works have focused on big data solutions, where ground truth
data, in the form of historical syndromic surveillance reports, can be
utilized to build data models for disease tracking. However, the accuracy and
reliability of the modelling models are under questions. The authors in [111]
suggested an unsupervised model for COVID-19 spread tracking from online data.
They selected a wide range of related symptoms as identified and collected by
a survey from the National Health Service (NHS) in the United Kingdom (UK) by
incorporating a basic news media coverage metric associated with confirmed
COVID-19 cases. Then, a transfer learning method is proposed for mapping
supervised COVID-19 models from a country to another country where the
COVID-19 epidemic possibly spreads.

###  IV-C Coronavirus diagnosis/treatment

In addition to outbreak prediction and spread tracking applications, big data
has the potential to support COVID-19 diagnosis and treatment processes. In
fact, the potential of big data for diagnosing infectious diseases like
COVID-19 has been proved via recent successes, from early diagnosis [112],
prediction of treatment outcomes, and building supportive tools for surgery.
Regarding COVID-19 diagnosis and treatment, big data has provided various
solutions as reported in the literature. A study in [113] introduced a robust,
sensitive, specific and highly quantitative solution based on multiplex
polymerase chain reactions that are able to diagnose the SARS-CoV-2. The model
consists of comprised of 172 pairs of specific primers associated with the
genome of SARS-CoV-2 that can be collected from the Chinese National Center
for Biological information (https://bigd.big.ac.cn/ncov). The proposed
Multiplex PCR scheme has been shown to be an efficient and low-cost method to
diagnose Plasmodium falciparum infections, with high coverage (median 99%) and
specificity (99.8%). Another research in [114] implemented a molecular
diagnostic method for genomic analyses of SARS-CoV-2 strains, with a focus on
studying on Australian returned travelers with COVID-19 disease using genome
data available at https://www.gisaid.org/. This study may provide important
insights into viral diversity and support COVID-19 diagnosis in areas with
lacking genomic data.

Meanwhile, the authors in [115] used the proteomics cells that get infected
with COVID-19 virus for diagnosis. More specifically, data includes 6381
proteins in human cells with the COVID-19 infection. Based on that, a
cooperative analysis of an impact pathway analysis and a network analysis has
been derived to analyze the data gathered from the Kyoto Genes storage.
Another solution in [116] leveraged publicly available mouse and nonhuman
primate for non-neural expression of SARS-CoV-2 entry genes. As a trial, a
bulk and single-cell RNA-Seq dataset is tested to detect all types of cells
that got infected with COVID-19 virus, which is necessary for diagnosis and/or
prognosis in COVID-19.

Due to the limited dataset for COVID-19 diagnosis and treatment, more
experimental data has been generated in [117] where all the 6 epitopes (A, B,
C, E, F/G and H) can induce the body to produce corresponding antibodies and
generate specific humoral immunity. The sequence of S, E, M protein and its
proximal sequences have been employed, which produced 420, 334 and 329
sequences in total from NCBI database to build the final dataset after genome
annotation. Based on that, the authors focus on a diagnosis procedure by the
prediction of the 3D structure of target protein and prediction of
conformational B cell epitopes of target protein of SARS-CoV-2 coupled with an
analysis of epitope conservation.

Interestingly, the recent work in [118] presented a comprehensive guideline
with useful tools to serve the diagnosis and treatment of COVID-19. This
guideline consists of the guideline methodology, epidemiological
characteristics, population prevention, diagnosis, treatment of COVID-19
disease. As a first trial, a large-scale data of Zhongnan Hospital of Wuhan
University has been analyzed from a collection system where 11,500 persons
were screened, and 276 were identified as suspected infectious victims, and
170 were diagnosed. An array of clinical tests have been implemented from the
big dataset, from Typical and Atypical CT/X-ray imaging manifestation to
hematology examination and detection of pathogens in the respiratory tract.

###  IV-D Vaccine/drug discovery

Developing a novel vaccine is very crucial to defending the rapid endless of
global burden of the COVID-19 pandemic. Big data can gain insights for
vaccine/drug discovery against the COVID-19 pandemic. Few attempts have been
made to develop a suitable vaccine for COVID-19 using big data within this
short period of time. The work in [119] used the GISAID database
(www.gisaid.org/CoV2020/) that has been used to extract the amino acid
residues. The research is expected to seek potent targets for developing
future vaccines against the COVID-19 pandemic. Another effort for vaccine
development is in [120] that focused on investigating the spike proteins of
SARS CoV, MERS CoV and SARS-CoV-2 and four other earlier out-breaking human
coronavirus strains. This analysis would enable critical screening of the
spike sequence and structure from SARS CoV-2 that may help to develop a
suitable vaccine.

The approaches of reverse vaccinology and immune informatics can be useful to
develop subunit vaccines towards COVID-19. [121]. The strain of the SARS-CoV-2
has been selected by reviewing numerous entries of the online database of the
National Center for Biotechnology Information, while the online epitope
prediction server Immune Epitope Database was utilized for T-cell and B-cell
epitope prediction. An array of steps may be needed to analyze from the built
dataset, including antigenicity, allergenicity and physicochemical property
analysis, aiming to identify the possible vaccine constructs. A huge dataset
has been also collected from the National Center of Biotechnology Information
for facilitating vaccine production [122]. Different peptides were proposed
for developing a new vaccine against COVID-19 via two steps. First, the whole
genome of COVID-19 was analyzed by a comparative genomic approach to identify
the potential antigenic target. Then, an Artemis Comparative Tool has been
used to analyze human coronavirus reference sequence that allows us to encode
the four major structural proteins in coronavirus including envelope (E)
protein, nucleocapsid (N) protein, membrane (M) protein, and spike (S).

Big data also helps provide strategies for drug manufacturing for fighting
COVID-19. For example, a solution based on molecular docking was proposed in
[123] for drug investigations. More than 2500 small molecules in the FDA-
approved drug database were first screened and validated through a molecular
docking program called Glide. As a result, fifteen out of twenty-five drugs
validated in exhibited significant inhibitory potencies, inhibiting signaling
pathway and inflammatory responses and prompting drug repositioning against
COVID-19. A big data-driven drug repositioning scheme was also introduced in
[124]. The key purpose of this project is to apply ML to combine both
knowledge graph and literature for COVID-19 vaccine development.

TABLE II: Summary of the state-of-the-art studies on big data applications for
COVID-19.

Category | Paper |  Highlights & Contributions
---|---|---
Outbreak prediction | [97] |  A big data platform is proposed to estimate the outbreak possibility using the huge data sets from Italian Civil Protection sources.
The first trial is implemented in Wuhan to predict the population infected
with COVID-19 for quarantine.
[100] |  A big data-based solution is proposed to implement pandemic modeling to interpret the cumulative numbers of infected people, recovered cases in different regions, i.e., Wuhan, Beijing, and Shanghai.
This scheme is able to predict the tendency of the COVID-19 outbreak in the
areas at high risks of pandemic.
[101] |  A framework is introduced using a large dataset from various regions and countries such as Korea, China, to estimate the pandemic based on a logistic model that can adjudge the reliability of the predictions.
[105] |  A big data analytic method is investigated in the US with the large-scale datasets collected from American cities.
The approach enables to calculate prediction errors to optimize the data
modeling model for improving estimation accuracy.
Virus spread tracking | [107] |  A big data-based analytic methodology for tracking the COVID-19 spread is considered using a large dataset collected from China National Health Commission with 854,424 people.
The analytic results show a high correlation between the positive infection
cases and the population size.
[109] |  A big data-based analytic model is built using datasets collected from China, Singapore, South Korea, and Italy for virus spread tracking.
This model can estimate the maximum number of infected patients in a certain
area.
[110] |  A temperature-based model is proposed to evaluate the relationship between the number of infected cases and the average temperature in different countries necessary for coronavirus tracking.
[111] |  A big data-based unsupervised model is designed for COVID-19 spread tracking from online data by incorporating a basic news media coverage metric associated with confirmed COVID-19 cases.
The work is in progress for coronavirus tracking tasks.
Coronavirus diagnosis/ treatment | [113] |  A robust, sensitive, specific and highly quantitative solution based on multiplex polymerase chain reactions is proposed to diagnose the SARS-CoV-2.
The proposed scheme has been shown to be an efficient and low-cost method to
diagnose Plasmodium falciparum infections.
[115] |  A method is proposed using 6381 proteins in human cells that get infected with COVID-19 virus.
This aims to analyze the data gathered from the Kyoto Genes storage to serve
COVID-19 diagnosis.
[118] |  An array of clinical tests have been implemented from the big dataset, from Typical and Atypical CT/X-ray imaging manifestation to hematology examination and detection of pathogens in the respiratory tract.
These tests provide a comprehensive guideline with useful tools to serve the
diagnosis and treatment of COVID-19.
Vaccine/drug discovery | [120] |  A method is proposed to investigate the spike proteins of SARS CoV, MERS CoV and SARS-CoV- 2 and four other earlier out-breaking human coronavirus strains.
It enables critical screening of the spike sequence and structure from SARS
CoV-2 for vaccine development.
[122] |  A project is built using a huge dataset collected from the National Center of Biotechnology Information for facilitating vaccine production. Different peptides were proposed for developing a new vaccine against COVID-19.
[123] |  A solution is proposed based on molecular docking for drug investigations with over 2500 small molecules, which aims prompting drug repositioning against COVID-19.

###  IV-E Summary of Big Data Applications for COVID-19

Reviewing from the rapidly emerging literature, we find that big data plays an
important role in combating the COVID-19 pandemic through a number of
promising applications, including outbreak prediction, virus spread tracking,
coronavirus diagnosis/treatment, and vaccine/drug discovery. In fact, big data
potentially enables outbreak prediction on the global scope using data
analytic tools on huge datasets collected from available sources such as
health organizations (e.g. WHO), healthcare institutes (i.e. China National
Health Commission) [101], [102]. Big data has also emerged as a promising
solution for coronavirus spread tracking by combining with intelligent tools
such as ML and DL [109] for building prediction models that is very useful for
governments in monitoring the potential COVID-19 outbreak in the future.
Moreover, big data has the potential to support COVID-19 diagnosis and
treatment processes. The research results from the literature studies prove
that big data can help healthcare provide various medical operations from
early diagnosis, disease analysis, and prediction of treatment outcomes [113],
[114]. Finally, data learning from big datasets also helps finding potential
targets for an effective vaccine against SARS-CoV-2 [120], and integrating
large-scale knowledge graph, literature and transcriptome data, supporting to
discover the potential drug candidates against SARS-CoV-2 [124]. A summary of
big data applications for COVID-19 is shown in Table II.

##  V Examples of AI and Big Data Frameworks for Covid-19

Figure 4: An AI-based framework using mobile phones for COVID-19 diagnosis and
surveillance. Adapted from [125].

This section presents two examples of AI- and big data based frameworks of how
AI and big data are currently helping in the fight against the COVID-19
pandemic

###  V-A Phone-based solution for detection and surveillance

Thanks to recent advancements in hardware capabilities, evolution of wireless
and mobile communications, and emergence of edge/cloud computing, mobile phone
have many opportunities during the outbreak, e.g., outbreak identification,
detection and diagnosis, treatment and infected case management, and disease
elimination. Adapted from [125], we propose a mobile phone based framework for
COVID-19 detection and surveillance, as shown in Fig. 4. Wireless
connectivity, e.g., WiFi, 3G, 4G, and 5G, has been widely deployed in almost
all the countries, so every individual with a mobile phone can connect to the
Internet and can do many activities online. The DL model can be trained in the
cloud, even at a server collocated at the network edge, which is then pushed
to mobile phone for further purpose. For example, by using the embedded
cameras and biosensors, a mobile phone can collect personal information, for
example, X-ray and CT images, cough sound, and heart rate, which may be
encrypted and compressed before sending to the cloud for training [63, 39].
Review on recent advances in biosensors for mobile health platforms can be
found in [126]. The massive number of mobile devices connected to the Internet
may relax the limited data sent from an individual phone. According to the
latest annual Internet report from Cisco [127], by 2023 around 29.3 billion
networked devices will be connected to the Internet, where mobile phones and
tablets account for $23$% and $3$% respectively, and over 70% of the world
population will have mobile connectivity.

Despite many potentials, a number of challenges need to be addressed for the
success of AI-based mHealth [128]. The very first challenge is from the
capability and reliability of hardware and software, which are used in mobile
devices to the medical purpose. This challenge can be overcome by developing
standard mobile devices with specific computing/sensing components and system
on chip (SoC). Another challenge arises from the protocol to collect/store the
diagnosis results, and the path way to improve the patients’ trust, especially
when they normally prefer to have a face-to-face talk with doctors and
clinicians. For example, depending on local and national policies, the
diagnostic results collected from individuals in a region are required to
transmit to and store at the local server instead of a cloud server, which
would be used to train the DL model for global communication. Lastly, a
challenge in utilizing mobile devices for the detection and surveillance is
how to evaluate the clinical cost and effectiveness. This is reasonable since
the results using phone-based frameworks are usually questionable and not
comparable with those of the direct tests.

###  V-B AI and big data for neutralizing antibody discovery

Under the current situation that specific medicines and vaccines for COVID-19
virus are not available, it is urgent to find efficient and fast solution to
combat the COVID-19 disease and prevent the virus outbreak. Motivated by this,
the authors in [129] introduced a data-driven framework that combines AI, big
data, and medical knowledge to identify antibody sequences that can inhibit
the growth of COVID-19 virus. The schematic illustration of the framework
proposed in [129] is pictorially shown in Fig. 5. The initial dataset is
consisted of 1831 antigen and antibody sequences of various viruses, for
example, HIV, H1N1, Dengue, SARS, and Ebola from the CATNAP tool [130], and
their corresponding half maximal inhibitory concentration (IC50) values333IC50
is a quantitative measure that denotes the concentration of a substance
required for $50$% inhibition [131].. Moreover, the authors proposed mining
more $102$ data samples of from the RCSP protein data bank [132] (URL:
https://www.rcsb.org/), which is then added to the initial data set, thus
creating the final dataset of $1933$ samples.

Figure 5: Illustration of a data-driven framework for discovering antibody
sequences to treat the COVID-19 disease [129].

The next phase is extracting the features from the dataset of $1933$ samples.
In doing so, the authors exploited an open-source cheminformatics software
(available at https://www.rdkit.org/), namely RDKit, to represent the
molecular graph. Moreover, to reduce computational complexity and variance of
the training data and, the authors applied an average-pool layer over the
extracted features. Five ML models, including random forest, XGBoost,
multilayer perceptron, support vector machines, and logistic regression, are
then applied to evaluate their performance in terms of five-fold cross
validation. The experiment results show that the XGBoost model is the most
superior one, which is therefore selected to find the potential antibody
candidates for COVID-19 virus. More importantly, the XGBoost model is highly
promising as it can achieve an outstanding out-of-class prediction over
various virus families, for example, $100$% for SARS and Dengue, $84.61$% for
Influenza, and $75$% Ebola and Hepatitis. After that, since COVID-19 virus has
similar characteristics ($88$-$89$% similarity [133]) with SARS-CoV-2, the
authors proposed generating a set of $2589$ hypothetical candidates based on
neutralizing antibodies and sequence features against SARS. Out of all the
candidates, finally 8 structures are selected as potential antibody sequences
for neutralizing the COVID-19 disease. The results discovered in this study
would be used for further academic researches as well as find effective
medicines and vaccines for the treatment of COVID-19.

##  VI Challenges, Lessons, and Recommendations

As reviewed in the previous sections, AI and big data have found their great
potentials in the globe battle against the COVID-19 pandemic. Apart from the
obvious advantages, there are still challenges needed to be discussed and
addressed in the future. Additionally, we highlights some lessons stemmed from
this paper and give some recommendations for the research communities and
authorities.

###  VI-A Challenges and solutions

####  VI-A1 Regulation

As the outbreak is booming and the daily number of confirmed cases (infected
and dead) increases considerably, various approaches have been taken to the
control this outbreak, e.g., lockdown, social distancing, screen and testing
at a large scale. In this way, regulatory authorities occupy a crucial role in
defining policies that can encourage the involvement of residents, scientists
and researchers, industry, giant techs and large firms, as well as harmonizing
the approaches executed by different entities to avoid any barriers and
obstacles in the way of preventing the COVID-19 disease.

Regarding this challenge, many attempts have been made from the first
confirmed COVID-19 to current situation. An example is from the quarantine
policy in Korea, effective from 1st April 2020. More specifically, all
passengers entering Korea are required to be quarantined for 14 days at
registered addresses or designated facilities. Moreover, all passengers are
required to take daily self-diagnosis twice a day and send the reports using
self-diagnosis apps installed in their mobile phones. An “AI monitoring
calling system” has been implemented by the Seoul Metropolitan Government to
automatically check the health conditions of the people who do not have any
mobile phones and/or have not installed the self-diagnosis apps [134]. Another
effort is from the collaboration between the Zhejiang Provincial Government
and Alibaba DAMO Academy to develop an AI platform for automatic the COVID-19
testing and analysis [135].

####  VI-A2 Lack of standard datasets

In order to make AI and big data platforms and applications a trustful
solution to fight the COVID-19 virus, a critical challenge arises from the
lack of standard datasets. As surveyed in the previous sections, many AI
algorithms and big data platforms have been proposed, but they are not tested
using the same dataset. For example, the algorithms in [56] and [57] are
verified to respectively achieve accuracy of $82.9$%/$98.27$%, specificity of
$80.5$% and $97.60$%, sensitivity of $84$% and $98.93$%. However, we cannot
decide which algorithm is better for the virus detection since two datasets
with different numbers of samples are used. Furthermore, most datasets found
in the literature have been made thanks to individual efforts, e.g., the
authors collect some datasets available in the Internet, then unify them to
create their own dataset and evaluate their proposed algorithms.

To overcome this challenge, government, giant firms, and health organizations
(e.g., WHO and CDC) play a key role as they can collaboratively work for high-
quality and big datasets. A variety of data sources can be provided by these
entities, e.g., X-ray and CT scans from the hospitals, satellite data,
personal information and reports from self-diagnosis apps. For example, the
CORD-19 dataset [16] has been made and led by the Georgetown’s Center for
Security and other partners like Allen Institute for AI, Chan Zuckerberg
Initiative, Microsoft Research, and National Institutes of Health.
Alternatively, Alibaba DAMO Academy has worked with many Hospitals in China to
build AI systems for detecting the COVID-19 infected cases, where Alibaba DAMO
Academy is responsible for designing AI algorithms and hospitals are
responsible for providing CT scans of more than $5,000$ confirmed COVID-19
cases. As reported in [136], this system has been used by more than 20
hospitals in China thanks to its remarkable performance: an accuracy of $96$%
within $20$ seconds only. As individual efforts, an initiative to manage
public datasets of COVID-19 medical images is available at [137] and a global
collection of open-source projects on COVID-19 is available at http://open-
source-covid-19.weileizeng.com/.

####  VI-A3 Privacy and security challenges

Right now, important things are keeping people healthy and soon controlling
the situation; however, how personal data secure and private is still needed
and should be investigated. A showcase of this challenge is the scandal of the
Zoom videoconferencing app over its security and privacy issues444Zoom admits
user data ‘mistakenly’ routed through China:
https://www.ft.com/content/2fc518e0-26cd-4d5f-8419-fe71f5c55c98. In the
pandemic, authorities may request their people to share their personal
information, for example, GPS location, CT scans, diagnosis reports, travel
trajectory, and daily activities, which is needed to control the situation,
make up-to-date policies, and decide immediate actions. Data is a must to
guarantee the success of any AI and big data platforms; however, normally
people do not want to share their personal information, if not officially
requested. There is a trade-off: privacy/security and performance.

Many technologies are available to solve the privacy and security issues
during the COVID-19 pandemic. Here, we consider some potential solutions
below, which can serve as research directives in the future.

  * •

Blockchain: Basically, blockchain is defined as a decentralized, immutable and
public database, where each transaction is verified by all the nodes in the
network, which is enabled by consensus algorithms [138]. Blockchain has found
its success for various healthcare applications [139], so it is possible to
deploy blockchain based solutions to improve the user security and data
privacy during the COVID-19 outbreak period. MiPasa is one of the projects
that combines two emerging technologies from IBM: blockchain and cloud
computing [140]. The purpose of this project is to provide reliable,
accessible, and high-quality data to the communities.

  * •

Federated learning (FL): Traditionally, the data should be collected and
stored centrally to training DL models. Federated learning offers a new
solution in which a majority of personal data is not required to be
transmitted to the central server [141]. Applying to the AI-based framework
using mobile phones for diagnosing COVID-19 case, each phone can train its own
DL model using local data. Mobile phones transmit their trained models to a
central server, which is responsible for aggregating to make a global model
that is then disseminated to all the mobile phones. We note that a phone is
not limited to mobile phone only, it can be a server at the local health
department, whereas the aggregation server can be a global cloud like
Microsoft Azure and Amazon Web Services.

  * •

Incentive mechanisms: A large and reliable dataset lays a foundation for AI
and big data platforms contend with the COVID-19 outbreak. Therefore, there is
a need for incentive design to call the participation of more people and
entities in contributing their own data. Incentives are needed due to the
following reasons: 1) a massive quantity of data are available from
people/entities, who may be not requested by the governments to provide their
data, and 2) the quality of data should be guaranteed in order to improve the
accuracy and performance of learning models. Such incentive mechanisms for
healthcare, wireless communications, transportation, etc. can be found in
[142].

###  VI-B Lessons and Recommendations

Reviewing the state-of-the-art literature, we find that AI and big data
technologies play a key role in combating the COVID-19 pandemic through a
variety of appealing applications, ranging from outbreak tracking, virus
detection to treatment and diagnosis support. On the one hand, AI is able to
provide viable solutions for fighting the COVID- 19 pandemic in several ways.
For example, AI has proved very useful for supporting outbreak prediction,
coronavirus detection as well as infodemiology and infoveillance by leveraging
learning-based techniques such as ML and DL from COVID-19-centric modeling,
classification, and estimation. Moreover, AI has emerged as an attractive tool
for facilitating vaccine and drug manufacturing. By using the datasets
provided by healthcare organizations, governments, clinical labs and patients,
AI leverages intelligent analytic tools for predicting effective and safe
vaccine/drug against COVID-19, which would be beneficial from both the
economic and scientific perspectives. On the other hand, big data has been
proved its capability to tackle the COVID-19 pandemic. Big data potentially
provides various promising solutions to help fight the COVID-19 pandemic. By
combining with AI analytics, big data helps us to understand the COVID-19 in
terms of virus structure and disease development. Big data can help healthcare
providers in various medical operations from early diagnosis, disease analysis
to prediction of treatment outcomes. With its great potentials, the
integration of AI and big data can be the key enabler for governments in
fighting the potential COVID-19 outbreak in the future.

Some recommendations can be considered to promote the COVID-19 fighting.
First, AI and big data-based algorithms should be optimized further to enhance
the accuracy and reliability of the data analytics for better COVID-19
diagnosis and treatment. Second, AI and big data can be incorporated with
other emerging technologies to offer newly effective solutions for fighting
COVID-19. For example, data analytic tools from Oracle cloud computing have
been leveraged to design Vaccine, a new vaccine candidate against the COVID-19
virus [143]. More interestingly, recent studies have showed that 5G wireless
technologies (e.g., drones, IoT, localization) can be utilized to combat the
COVID-19 pandemic [144] via a number of applications, such as delivery of
testing samples, goods transport, social distancing, and people’s movement
monitoring for outbreak tracking. Final, non-technology measures such as
social distancing restrictions still play a highly important role in slowing
down the virus spread and thus need to be implemented effectively under the
management of government agencies [145], aiming to control the COVID-19
pandemic in the future.

##  VII Conclusion

In this paper, we have presented a survey on the state-of-the-art solutions in
the battle against the COVID-19 pandemic. Firstly, we have provided an
introduction of the COVID-19 virus, the fundamentals and motivations of AI and
big data for finding fast and effective approaches that can effectively combat
the COVID-19 disease. Then, we have reviewed the applications of AI for
detection and diagnosis, tracking and predicting the outbreak, infodemiology
and infoveillance, biomedicine and pharmacotherapy. The applications of big
data for the COVID-19 disease have been also presented, including outbreak
prediction, virus spread tracking, diagnosis and treatment, and vaccine and
drug discovery. Furthermore, we have discussed the challenges needed to
overcome for the success of AI and big data in fighting the COVID-19 pandemic.
Finally, we have highlighted important lessons and recommendations for the
authorities and research communities.

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|  Quoc-Viet Pham (M’18) received the B.S. degree in electronics and
telecommunications engineering from Hanoi University of Science and
Technology, Vietnam, in 2013, and the Ph.D. degree in telecommunications
engineering, from Inje University, South Korea, in 2017. He is currently a
research professor at Research Institute of Computer, Information and
Communication, Pusan National University, South Korea. From Sept. 2017 to Dec.
2019, he was with Kyung Hee University, Changwon National University, and Inje
University on various academic positions. He received the best PhD
dissertation award in Engineering from Inje University in 2017. His research
interests include convex optimization, game theory, and machine learning to
analyze and optimize edge/cloud computing, and 5G and beyond networks. He is a
member of the IEEE.
---|---
|  Dinh C. Nguyen received the B.S. degree (first class honors) in electrical
and electronic engineering from Ho Chi Minh City University of Technology,
Vietnam, in 2015. Currently, he is a Ph.D. scholar in the School of
Engineering, Deakin University, Victoria, Australia. His research interests
focus on security and privacy in Internet of Things (IoT), mobile cloud
computing, and blockchain. He is currently working on adopting blockchain for
secure communication networks including clouds, IoT, and healthcare. He is a
recipient of the Data61 PhD scholarship, CSIRO, Australia.
---|---
|  Thien Huynh-The (S’15, M’19) received the B.S. degree in electronics and
telecommunication engineering from Ho Chi Minh City University of Technology
and Education, Vietnam, in 2011, and the Ph.D. degree in computer science and
engineering from Kyung Hee University (KHU), South Korea, in 2018. He is
awarded with the Superior Thesis Prize by KHU.  He is currently a Postdoctoral
Research Fellow with ICT Convergence Research Center at Kumoh National
Institute of Technology, South Korea. His current research interest includes
radio signal processing, digital image processing, computer vision, machine
learning, and deep learning.
---|---
|  Won-Joo Hwang (S’01-M’03-SM’17) received the B.S. and M.S. degrees in
computer engineering from Pusan National University, Busan, South Korea, in
1998 and 2000, respectively. He received the Ph.D. degree in information
systems engineering from Osaka University, Osaka, Japan, in 2002. From 2002 to
2019, he was a Full Professor at the Inje University, Gimhae, South Korea.
Currently, he is a Full Professor in the Biomedical Convergence Engineering
Department at the Pusan National University. His research interests include
optimization theory, game theory, machine learning and data science for
wireless communications and networking. He is a senior member of the IEEE.
---|---
|  Pubudu N. Pathirana was born in 1970 in Matara, Sri Lanka, and was educated
at RoyalCollege Colombo. He received the B.E. degree (first class honors) in
electrical engineering and the B.Sc. degree in mathematics in 1996, and the
Ph.D. degree in electrical engineering in 2000 from the University ofWestern
Australia, all sponsored by the government of Australia on EMSS and IPRS
scholarships, respectively. He was a Postdoctoral Research Fellow at Oxford
University, Oxford, a Research Fellow at the School of Electrical Engineering
and Telecommunications, University of New South Wales, Sydney, Australia, and
a Consultant to the Defence Science and Technology Organization (DSTO),
Australia, in 2002. He was a visiting associate professor at Yale University
in 2009. Currently, he is a full Professor and the Director of Networked
Sensing and Control group at the School of Engineering, Deakin University,
Geelong, Australia and his current research interests include Bio-Medical
assistive device design, human motion capture, mobile/wireless networks,
rehabilitation robotics and radar array signal processing. He is a senior
member of the IEEE.
---|---