Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.
10.1109/ACCESS.2018.DOI

Corresponding author: Dinh C. Nguyen<EMAIL_ADDRESS>
# Blockchain and AI-based Solutions to Combat Coronavirus (COVID-19)-like
Epidemics: A Survey

DINH C. NGUYEN1  (Graduate Student Member  IEEE)  MING DING2  (Senior Member
IEEE)  PUBUDU N. PATHIRANA1  (Senior Member  IEEE)  ARUNA SENEVIRATNE3
(Senior Member  IEEE)  School of Engineering, Deakin University, Waurn Ponds,
VIC 3216, Australia  Data61, CSIRO, Australia  School of Electrical
Engineering and Telecommunications, University of New South Wales (UNSW), NSW,
Australia

###### Abstract

The beginning of 2020 has seen the emergence of coronavirus outbreak caused by
a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled
worldwide spread of COVID-19 show the limitations of existing healthcare
systems in timely handling public health emergencies. In such contexts,
innovative technologies such as blockchain and Artificial Intelligence (AI)
have emerged as promising solutions for fighting coronavirus epidemic. In
particular, blockchain can combat pandemics by enabling early detection of
outbreaks, ensuring the ordering of medical data, and ensuring reliable
medical supply chain during the outbreak tracing. Moreover, AI provides
intelligent solutions for identifying symptoms caused by coronavirus for
treatments and supporting drug manufacturing. Therefore, we present an
extensive survey on the use of blockchain and AI for combating COVID-19
epidemics. First, we introduce a new conceptual architecture which integrates
blockchain and AI for fighting COVID-19. Then, we survey the latest research
efforts on the use of blockchain and AI for fighting COVID-19 in various
applications. The newly emerging projects and use cases enabled by these
technologies to deal with coronavirus pandemic are also presented. A case
study is also provided using federated AI for COVID-19 detection. Finally, we
point out challenges and future directions that motivate more research efforts
to deal with future coronavirus-like epidemics.

###### Index Terms:

Blockchain, Artificial Intelligence (AI), security, privacy, machine learning,
deep learning, coronavirus (COVID-19), SARS-CoV-2, epidemic.

=-15pt

##  I Introduction

The coronavirus (COVID-19) outbreak in late 2019 comprises a serious threat
around the world [1]. The severity of the epidemic was so huge that the World
Health Organization (WHO) was compelled to declare it as a pandemic within a
month of its wide-scale expansion. The virus spread causes the global economic
shock with the massive interruptions of many sectors such as supply chain,
industry, insurance, agriculture, transport, and tourism, forcing governments
and owners to shut stop operations on a worldwide scale [2]. According to the
Organisation for Economic Cooperation and Development (OECD), the global
economy could grow at its slowest rate since 2009 in this year [3] due to the
coronavirus outbreak as the forecasts in Fig. 1. As the number of infections
rises, many governments around the world have instituted drastic lock-downs
and curfews and called for social distancing and work from home for virus
spead fighting[4].

Figure 1: The impact of COVID-19 on global economic growth (Source: OECD).

With the deadly coronavirus spreading globally, every attempt is being made to
ensure help for victims as well as to stop the spread. As governments scramble
to address these problems, technology-empowered solutions can help deal with
the worldwide health crisis. Applications of innovative technologies such as
Blockchain and Artificial Intelligence (AI) could have the answers in response
to coronavirus crisis [5], [6], [7]. Blockchain can support to combat the
COVID-19 pandemic by enabling early detection of outbreaks, fast-tracking drug
delivery, and providing a consensus on the ordering of the COVID-19 data
records. Furthermore,  AI with supervised and unsupervised machine learning
provides intelligent solutions for monitoring real-time epidemic outbreaks,
pandemic trend forecasting, identifying symptoms caused by coronavirus for
treatments, and supporting drug manufacturing [8]. It is also argued that the
present coronavirus crisis should be seen as a ”call to arms for the
technology industry” where blockchain and AI may be the key enablers for
radically changing the landscape of crisis response and the management of the
coronavirus outbreak [9], [10].

###  I-A Motivations of Using Blockchain and AI for Coronavirus Fighting

####  I-A1  Limitations of Current Healthcare Systems

The COVID-19 outbreak could push the existing healthcare systems to their
limits. At present, there is a lack of a reliable data surveillance system
that would instantly give relevant healthcare organizations the information
they need about potential outbreaks. In fact, most of the current coronavirus
information comes from separate sources such as the public, hospitals,
clinical labs with a large amount of inaccurate data without being monitored
thoroughly. The use of unreliable information makes it challenging for
potential outbreak identification and quarantine. Another limitation is the
current time-consuming and in-accuracy coronavirus detection procedure that
often takes several hours to complete the virus tests. Thus, how to accelerate
coronavirus detection speed with high accuracy is an urgent need. Furthermore,
it is very challenging to process coronavirus data with complex patterns and
large volumes by using human-depending medicine tools.

####  I-A2 Why we need blockchain and AI for fighting COVID-19

In this subsection, we will explain why there is a requirement of using
blockchain and AI technologies for COVID-19 disease. More specifically, the
reasons on why we need to use blockchain for such a disease are explained as
follows.

  * •

In such a pandemic scenario, blockchain provides promising security solutions
to support fighting COVID-19. Indeed, the blockchain constructs immutable
ledgers of transactions for medical data sharing systems. Transactions (e.g.,
COVID-19 data) recorded in the blockchain cannot be modified or altered by any
entities. Also, transactions are only written to blockchain while recovery
actions or modifications are not allowed. Moreover, all health data storage
servers, e.g., cloud in the COVID-19 data collection, would be controlled
autonomously by the blockchain and data usage over the network is always
reflected on the blockchain for tracing [11]. More importantly, blockchain
coupled with the smart contract technology eliminates the reliance on central
servers to ensure fairness among transaction parties, e.g., patients,
hospitals, governments and provide effective access control over data usage.
Each entity in the blockchain-based COVID-19 data management system has an
equal right to control all healthcare operations. Specially, blockchain with
its distributed nature can work well when any party fails due to its
decentralized nature [12]. Such traceability and decentralization are among
the key unique features of blockchain that cannot be found in other
traditional security techniques.

  * •

Moreover, blockchain can support well for reliable COVID-19 analytics.
Collecting COVID-19 data is an important step of disease analytics. During the
data collection, how to ensure the reliability of collected data is of
practical significance to ensure the high quality of COVID-19 data analytics
[4]. The use of wrong data or unreliable database sources may result in biased
analytical results, which can lead to fatal consequences like incorrect
COVID-19 diagnosis. Moreover, in the emergency epidemic situation, many
sources of COVID-19 data are collected from hospitals, the public, or media
without being protected which can result in COVID-19 data modifications. These
issues would definitely affect the correctness of the collected data which
consequently reduces the reliability of the COVID-19 analysis process. In such
contexts, blockchain is high of need to ensure the reliability of the
collected data thanks to its security nature. Blockchain also ensures the
correct ordering of the COVID-19 data records from the data sources to the
destinations (e.g., hospitals or clinical labs) due to consensus mechanisms,
which also ensures the high quality of data collection. These features of
blockchain would ensure correct data collection and thus support reliable
COVID-19 analysis.

  * •

Also, the incentive mechanisms offered by blockchain can be very useful to
provide solutions for fighting COVID-19, by combining with
crowdsourcing/crowdsensing technologies [13, 14, 15, 16]. Indeed,
crowdsourcing enables to use the crowd to collect information that is needed
for the detection of COVID-19 patients. Besides, these frameworks incentivize
patients and individuals to share information through payments, such as
blockchain coins. For example, an artificial potential field-based incentive
allocation mechanism is proposed in [17] to incentivize IoT witnesses to
pursue the maximum monitoring coverage and crowdsensing deployment in tracking
human activities in the COVID pandemic using a decentralized and
permissionless blockchain protocol.

  * •

Admittedly, the truthfulness of each medical data transaction is hard to
verify since the involved medical entities might lie about the uploaded data.
This make one wonder about the value of blockchain in fighting COVID-19, i.e.,
what is the use of a blockchain if it is full of lies? 111It should be noted
such an issue of fake data has been mitigated in our financial system thanks
to the digital signature mechanism, e.g. verification will be performed for
all of the transactions associated with network banking, online purchasing
using debit/credit cards, etc. Here, we argue that the immutability feature of
blockchain would make the data fabrication significantly more difficult since
the reported data shall be under scrutiny in the future by statistical and
longitudinal data analysis. Hence, the true value of blockchain for combating
COVID-19 is to immensely raise the difficulty and cost for the entities who
dare to cheat and make up data.

In addition, there are two key reasons on why we need to use AI for such a
disease.

  * •

In the epidemic, monitoring the trajectory of virus infection and estimating
the potential outbreak are of paramount importance in the objective of
controlling the COVID-19 disease. AI is able to learn from huge datasets on
relevant factors including the number of cases, deaths, demographics, and
related environments conditions. The learning capability of AI would help
forecast future coronavirus cases and the size of a potential outbreak. It
also helps build prediction models that can be used to estimate the outbreak
in the future, which is very useful for governments in preparing necessary
solutions in response to COVID-19.

  * •

To prevent the COVID-19 disease, modelling the coronavirus structure is very
important to shed lights on which drugs could work against COVID-19. Due to
the massive database collected from ubiquitous sources, developing a data
learning and estimation system to estimate the COVID-19 virus structure is an
urgent need that humans are unable to do without machine support. AI
algorithms based on computer computation can learn data and predict how the
proteins that make up an organism curve or crinkle based on their genomes to
identify the shape of the receptor. This would facilitate the process of
finding suitable drugs against COVID-19.

####  I-A3 Benefits of using blockchain and AI for solving the coronavirus
epidemic

Blockchain and AI are able to provide viable solutions to cope with the
coronavirus epidemic from various aspects. In fact, blockchain has been
applied to other infectious epidemics, such as Ebola [18] where blockchain was
employed to conduct real-time Ebola contact tracing, transmission pattern
surveillance and vaccine delivery. This project also reveals that the
cryptographic feature of blockchain can help prevent unsecure data sharing
between patients and health entities such as healthcare providers. In the
coronavirus outbreak fighting, blockchain can actively simplify the process of
fast-tracking drug trials, and recording and tracking all fundraising
activities and donations, in an immutable fashion, which can support the
management of outbreaks and treatment.

Moreover, AI has been applied to COVID-19-like epidemics. For example, AI
techniques such as neural network architectures based on Long Short Term
Memory (LSTM) were used to predict the influenza-like illness dynamics for
military populations using neural networks and social media [19]. Moreover,
Bayesian ML was employed in [20] to build predictive data learning models for
identifying compounds active against the Ebola virus. These real-world use
cases have demonstrated the potential of AI in supporting the fighting of
infectious epidemics by forecasting the spread of virus and helping the drug
production for treatment of infectious diseases. In the coronavirus epidemic,
AI can also provide solutions in several ways. AI can be used to detect virus
and predict how the virus is going to spread by analysing the combined
information of environmental conditions, access to healthcare, and the way it
is transmitted. Based on that, AI can identify coronavirus within localized
outbreaks of the disease and help reveal the nature of the virus [21]. The
coronavirus can cause severe symptoms such as pneumonia, severe acute
respiratory syndrome, and kidney failure. AI-based algorithms such as genome-
based neural networks already built for personalized treatment can prove very
useful in controlling these adverse events or symptoms caused by a
coronavirus, especially when the impact of the virus depends on immunity and
the genome structure of individuals and no standard treatment can treat all
symptoms effectively at present. For example, ML-based regression models have
been used in [22] to estimate adverse outcome in COVID-19 pneumonia, by using
chest CT scans with reverse-transcription polymerase chain reaction for severe
acute respiratory syndrome coronavirus. More specifically, logistic regression
models were used to evaluate the relationship between clinical parameters and
CT metrics versus patient outcome (intensive care unit admission or death
versus no ICU admission or death). The area under the receiver operating
characteristic curve (AUC) was then computed to determine the adverse effects
which support the clinical decision making for appropriate patient care.
Further, the use of AI may be very helpful in identifying the relationship of
novel coronavirus and related viruses such as SARS [23] to accelerate the
finding of a new vaccine. Finally, AI approaches can automatically build a
model or relationship between treatments documented in healthcare records and
the eventual patient outcomes. These models can quickly identify diagnosis and
treatment choices that assist to guide the process of developing clinical
guidelines for future coronavirus-like epidemics. With these promising
benefits, recently the White House urgently calls for using AI to aid the US
Government’s response to the coronavirus pandemic [24].

###  I-B Comparisons and Our Contributions

Recently, there are several recent research efforts made to survey the use of
blockchain and AI for COVID-19 pandemic. The paper in [25] focuses on
analysing the roles of blockchain to address the crisis of trust during the
COVID-19 pandemic. The authors in [26] explain briefly the opportunities and
limitations of the applications of blockchains in healthcare management,
including COVID-19-related domains. The benefits of using blockchain for
fighting COVID-19 are also explored in [27], with several specific use cases
such as contact tracing, disaster relief, patient information sharing,
e-government, supply chain management, online education, and immigration
management.

Moreover, the roles of AI in fighting COVID-19 pandemic are also investigated
recently. More specifically, the authors in [28] explore the use of
technologies including AI to help mitigate the impact of COVID-19 outbreak.
The work in [29] provides a very brief discussion of the roles of DL for
COVID-19 data analytics, along with a short introduction to blockchain in the
pandemic. Another study in [30] provides an extensive survey on the
contributions of AI in combating COVID-19 from the aspects of disease
detection and diagnosis, virology and pathogenesis, drug and vaccine
development, and epidemic and transmission prediction. Furthermore, the use of
popular AI techniques such as neural systems, classical SVM, and edge
significant learning battling COVID-19 is analyzed in [30]. Meanwhile, a
survey paper in [31] provides a systematic study of deep learning, deep
transfer learning and edge computing to mitigate COVID-19. Other works in [32,
33] also survey the applications of AI techniques in screening, tracking, and
predicting the spread of COVID-19.

TABLE I: Comparison our survey paper with existing works.  Related works |  Topic |  Key contributions |  Limitations
---|---|---|---
[25] |  Blockchain for trust in COVID-19 pandemic |  A discussion of the roles of blockchain to address the crisis of trust during the COVID-19 pandemic. |  The potential of blockchain for fighting COVID-19 such as secure outbreak monitoring, reliable supply chain has not been investigated.
[26] |  Blockchain in healthcare management |  A brief discussion of the opportunities and limitations of the applications of blockchains in healthcare management, including COVID-19-related domains. |  The analysis of blockchain usage for COVID-19 is very limited.
[27] |  Blockchain for COVID-19 |  A survey on the use of blockchain for COVID-19 pandemic via several specific use cases. |  The potential of AI and its connection with blockchain for blockchain has not been discovered.
[28] |  AI for COVID-19 |  A overview of the use of technologies including AI to help mitigate the impact of COVID-19 outbreak. |  The extensive survey on the blockchain for COVID-19 has not been provided.
[29] |  DL for COVID-19 |  A very brief discussion of the roles of DL for COVID-19 data analytics, along with a limited introduction to blockchain in the pandemic. |  The scope of the review of the DL and blockchain in COVID-19 is limited.
[30] |  AI for COVID-19 |  A survey on the contributions of AI in combating COVID-19, from disease detection and diagnosis to transmission prediction. |  The extensive survey on the blockchain for COVID-19 has not been provided.
[31] |  DL and edge computing for COVID-19 |  provides a systematic study of deep learning, deep transfer learning and edge computing to mitigate COVID-19. |  The paper mostly focuses on computing aspects in the network edge for fighting COVID-19.
[32] |  AI for COVID-19 prediction |  A survey of the applications of AI techniques in screening, tracking, and predicting the spread of COVID-19. |  The real benefits of AI for COVID-19 have not been verified.
[33] |  AI techniques for COVID-19 |  A discussion of AI algorithms for COVID-19 data analytics. |  The analysis of the roles of AI for COVID-19 outbreak monitoring and prediction is missing.
Our paper |  Blockchain and AI for COVID-19 |  A comprehensive survey on the applications of blockchain and AI for COVID-19. |  -

Different from these survey works, our paper provides a comprehensive review
on the use of both blockchain and AI for fighting COVID-19 pandemic. The key
purpose of this paper is to provide the readers with an overall picture and
road map of how blockchain and AI can support to solve the coronavirus
epidemic and related healthcare crisis. Therefore, in this paper we present an
extensive survey on the applications and use cases of blockchain and AI
technologies specific to coronavirus (COVID-19) pandemic by using the rapidly
emerging literature and the latest research reports. A case study is then
provided using federated AI for COVID-19 detection. The unique challenges and
future direction are also highlighted. The comparison of the related works and
our paper is summarized in Table I. To this end, our survey provides the
following contributions:

  1. 1.

We introduce a conceptual systematic architecture that integrates blockchain
and AI for fighting COVID-19, aiming to provide the key solutions in response
to coronavirus epidemic.

  2. 2.

We identify a number of specific applications using these technologies for
solving coronavirus-related issues detection. Particularly, we highlight the
potentials of blockchain and AI through a comprehensive analysis and
discussion in different applied scenarios.

  3. 3.

We explore the latest use cases and projects using blockchain and AI for
coronavirus fighting.

  4. 4.

A case study is then provided using federated AI for COVID-19 detection,
aiming to demonstrate the benefits of the discussed technique in the pandemic.

  5. 5.

Based on the extensive survey, we identify possible research challenges and
future directions to encourage scientists and stakeholders to put more efforts
in developing innovative solutions to combat the future coronavirus-like
epidemics.

###  I-C Methods and Materials

A systematic mapping study [34] was selected as the research method for this
survey paper, aiming to provide an overview of the research related to the use
of blockchain and AI for fighting the COVID-19 pandemic, including the
following key steps. First, we recognize the limitations of current healthcare
systems and highlight the motivations on why we should use blockchain and AI
for fighting COVID-19. The second stage is to search for all the relevant
scientific papers on the research topic. We map the papers related to
technical aspects of blockchain and AI for in supporting activities for
COVID-19 pandemic prevention. Here we use the terms such as blockchain,
machine learning, deep learning, COVID-19 as the search strings so that we can
filter and select the most relevant technical papers for our survey. Then we
chose the scientific databases for the searches. We decided to focus on peer-
reviewed, high-quality papers published in conferences, workshops, symposiums,
books and journals related to the research topic. We used four scientific
databases for paper retrieval, including (1) IEEE Xplore, (2) ACM Digital
Library, (3) Springer Link, and (4) ScienceDirect. The third stage is to
screen all related papers based on their titles. Meanwhile, we exclude papers
without high content quality, papers without text availability and papers that
were not written by English. The fourth stage is keywording. We read the
abstract and identified keywords and concepts that reflected the contribution
of the paper. Then, we used the keywords to cluster and form categories for
the mapping of the studies. The final stage is data extraction that gathers
all information needed to survey technical aspects and contributions of
bloclchain and AI in the context of COVID-19.

###  I-D Organization

The structure of this survey is organized as follows. Section II presents the
background of coronavirus epidemic, and two key technologies including
blockchain and AI, along with a conceptual architecture that integrates
blockchain and AI for coronavirus fighting. In Section III, we analyse the
solutions using blockchain for coronavirus fighting. Next, in Section IV, we
present a review on the use of AI for coronavirus epidemic fighting. Section V
summarizes the most popular use cases on blockchain and AI adaption in
response to coronavirus fighting. We present a case study toward using
federated AI for COVID-19 to demonstrate the feasibility of our approach in
Section VI. Then, we point out several important challenges and potential
future directions in Section VII for applying these technologies in COVID-19
epidemic. Finally, Section VIII concludes the paper. A list of key acronyms
and abbreviations used throughout the paper is given in Table II.

TABLE II: List of key acronyms. Acronyms |  Definitions
---|---
COVID-19 |  Coronavirus
WHO |  World Health Organization
CDC |  Centres for Disease Control
IoT |  Internet of Things
AI |  Artificial Intelligence
ML |  Machine Learning
DL |  Deep Learning
FL |  Federated Learning
DNN |  Deep Neural Network
CNN |  Convolutional Neural Network
GAN |  Generative Adversarial Network
CT |  Computed Tomography

##  II  Background

In this section, we provide briefly the coronavirus pandemic, and then
summarize blockchain and AI technologies used for fighting coronavirus. Then
we introduce a systematic architecture for coronavirus fighting.

###  II-A Coronavirus (COVID-19) Pandemic

The first outbreak of the COVID-19 virus epidemic took place in Wuhan, a city
of 11 million, the capital of Hubei Province, China, starting in December 2019
[35], [36]. It is not yet clear which animal the new coronavirus is associated
with, although several data suggests it is similar to those found in bats.
Coronaviruses affect the breathing passages, or respiratory tract. The number
of infected citizens grew exponentially until Chinese authorities declared a
complete lockdown of the affected region, the 60-million-people Hubei
province. Symptoms can be mild like the common cold, including a runny nose
and cough or they can be more severe and can cause difficulty in breathing.
Other common symptoms includes fever, cough, and feeling tired. A few people
become seriously ill but this seems more likely in the elderly and those with
health problems [37]. According to the statistics reported by WHO on 21
September 2020 [38], there have been 30,949,804 confirmed cases of COVID-19,
including 959,116 deaths. The COVID-19 disease has been spreading rapidly
around the world, especially in the US, Southeast Asia and Europe. The
COVID-19 epidemic results in great harm to people’s daily life and country’s
economic development. Governments have locked down cities, restricted
movements of millions and suspended business operations that will slow down
the global economy in the coming years. Undoubtedly, the virus outbreak has
become one of the biggest threats to the global economy and financial markets
[39], [40].

###  II-B Blockchain

Blockchain is mostly known as the technological platform behind Bitcoin [41].
The key concept of a blockchain is decentralization. In fact, its database
does not place in a central location, but is distributed across a network of
participants (e.g., computers). This decentralized concept provides high
robustness and security for database stored on blockchain with no single-point
failure. Importantly, blockchain is visible to each member in the network.
This is enabled by a mechanism called consensus which is a set of rules to
ensure the agreement among all participants on the status of the blockchain
ledger. A concept of how blockchain works is shown in Fig. 2. In general,
blockchains can be classified as either a public (permission-less) or a
private (permissioned) blockchain [42]. A public blockchain is accessible for
everyone and anyone can join and make transactions as well as participate in
the consensus process. The best-known public blockchain applications include
Bitcoin and Ethereum [43]. Private blockchains on the other hand are an
invitation-only network managed by a central entity. A participant has to be
permissioned using a validation mechanism. Each blockchain consists of three
main components, including data block, distributed ledger (database), and
consensus algorithms.

Each data record (e.g., medical data) in the blockchain can be stored in the
form of a transaction. Multiple transactions form a block, and multiple blocks
are linked together to form a blockchain [44]. The header field of each block
contains the hash of the previous block, thus creating an ordered chain. The
unique advantage of blockchain is its ability to ensure the chronological
order of data records such as COVID-19 data. These data records are
permanently stored on the chain in a chronological order. The consensus
mechanism such as Proof of Work (PoW) in the blockchain ensures that the state
of the entire blockchain is consistent and secure without the need for any
third parties. In the distributed ledger, each record contains a unique
cryptographic signature decoupled with a timestamp which makes the ledger
resistant to be modified. In terms of consensus algorithms, the process of
transacting the block over the chain should not be controlled by any entity so
that each block is managed by all participants with equal rights to avoid
security issues, e.g., double-spending attacks. This can be done by a
mechanism called consensus. From the view of blockchain, the consensus process
mainly provides a guarantee for agreement on each data blockchain among
entities. For instance, Bitcoin uses PoW algorithm [44] as a main consensus
scheme for its transaction management. The nodes with high computational
capability can join the mining process and compete with each other to be a
first one to verify the block. In return, the winner can receive a certain
amount of coin as the reward for its mining effort. Along with the advance of
blockchain, there are several new consensus algorithms such as Proof-of-stake
(PoS), Byzantine Faulty Tolerant (BFT) [45].

In the blockchain ecosystem, a smart contract is an innovative technology for
many applications, including healthcare [46]. A smart contract is a
programmable application that runs on a blockchain network. The term ”smart
contract” was defined by Nick Szabo as ”a computerized transaction protocol
that executes the terms of a contract. The general objectives of smart
contract design are to satisfy common contractual conditions (such as payment
terms, liens, confidentiality, and even enforcement), minimize exceptions both
malicious and accidental, and minimize the need for trusted intermediaries”.
These condition scripts are placed on blockchain with a unique address. One
can trigger a smart contract by sending a transaction to it. Due to the
elimination of the involvement of external authorities, smart contracts
potentially provide strong transparency for the involved blockchain network.
Blockchain nodes within the network can execute the smart contract and derive
the same results from the execution, which are reflected and recorded on the
blockchain.

Blockchain can connect with the Internet of Things (IoT) [47] in the context
of healthcare for biomedical operations. In fact, IoT devices like sensors,
gateways can collect real-time sensory data from patients and submit this data
to a blockchain for sharing and storage in a secure manner. Specially,
blockchain enables decentralized communication between IoT devices and IoT
users (e.g., doctors, patients in healthcare) without the need of any central
authority. Whenever an IoT activity (e.g., sensor data collection) is engaged,
the blockchain nodes such as users in the network can monitor and agree on the
transaction conditions and control the network operations. This process can be
updated on the public ledger which is accessible to all network members while
the role of central authority is eliminated [48]. Up to date, blockchain has
proved its success in healthcare and biomedical applications with promising
performances in terms of healthcare data security [49], secure data management
[50], [51], and transparent medical data storage [52], [53]. Therefore, it is
possible to apply in solving healthcare issues related to coronavirus
epidemic.

Figure 2: The concept of blockchain operation.

###  II-C Artificial Intelligence (AI)

Figure 3: Blockchain and AI for coronavirus fighting.

AI techniques have been used recently as a powerful tool for coronavirus data
analytics, prediction and drug/vaccine discovery [54], [55]. Recent studies
show that AI has been mostly used for solving coronavirus-related issues via
two key approaches: machine learning (ML) and deep learning (DL) [56]. ML is a
subfield of AI. The objective of ML in general is to understand the structure
of data and match that data into models that can be expressed and utilized by
people [57]. ML algorithms instead allow computers to perform training on data
inputs and employ statistical analysis solutions to output values that fall
within a specific range. Based on this, ML can provide building models from
sample data to automate decision-making processes using data inputs. For
example, several preliminary studies in [58] use temperature checking-based
techniques such as facial and body temperature detection with smart helmet
[59] for detecting people with abnormal temperature, aiming to identify
potentially infected persons. For example, recently an AI company in the US
specialising has utilized ML-powered interactive graphs are tracking the virus
migration across China [60], for creating an alert system, whereby users will
be able to receive information about whether an infected individual has
travelled within their vicinity. This solution will help find infected
individuals and provide medical resources to them.

Meanwhile, AI methodologies such as DL have been also exploited to implement
intelligent coronavirus fighting solutions. Conceptually, DL uses multiple
neural network layers in a deep architecture [61]. The neural network use
multiple neurons connected via weighted connections among neural layers. For
example, a basic deep neural network contains an input layer for receiving
data samples, a single hidden layer for training and an output layer for
generating training outcomes. Here, the number of hidden layers reflects the
depth of the deep learning architecture. To generate the desired output,
supervised or unsupervised learning techniques are used with the labeled or
unlabeled data samples, associated with the adjustment of weight values among
perceptrons [62]. DL is known for AI applications such as speech recognition,
computer vision, image processing, object detection for healthcare
applications [63], [64], [65] and thus coronavirus tackling can be a specific
domain that DL can be applied for coronavirus fighting. Many AI companies have
developed DL-enabled solutions to predict coronavirus infection and produce
drugs and vaccines necessary for coronavirus disease [66].

In the open literature, various AI-based approaches have been considered for
COVID-19 detection, diagnosis and prediction. However, in the pandemic,
collecting sufficient data to implement intelligent algorithms becomes more
challenging and the user privacy concerns are growing due to the public data
sharing with datacentres for COVID-19 data analytics. In this context,
federated learning (FL) [67] has emerged as a promising AI solution for
realizing cost-effective COVID-19 analytic-related applications with improved
privacy protection. Conceptually, FL is a distributed AI approach which
enables training of high-quality AI models by averaging local updates
aggregated from multiple hospitals and medical centres without the need for
direct access to the local data. This potentially prevents disclosing
sensitive user information and user preference, and thus mitigates privacy
leakage risks in this pandemic. Moreover, since FL attracts large computation
and dataset resources from a number of COVID-19 data sources to train AI
models, the COVID-19 data training quality, e.g., accuracy, would be
significantly improved which might not be achieved by using centralized AI
approaches with less data and limited computational capabilities.

###  II-D Proposed Blockchain-AI Architecture for Coronavirus Fighting

In this paper, we present an architecture as shown in Fig. 3 that integrates
blockchain and AI for coronavirus fighting. The architecture is conceptually
organized into four layers, including coronavirus data sources, blocchain
functions, AI functions, and stakeholders. The work flow is explained as
follows.

Initially, all the data from clinical labs, hospitals, social media, and many
other sources are consolidated and create raw data that subsequently develops
in scale to big data. In the context of COVID-19, coronavirus data can include
historic infectious cases, information of outbreak areas and coronavirus
database in the formats of time series data, radiology images, X-ray reports
and virus structure sequences that can be collected from various sources such
as CDC, World Health Organization, clinical labs, the public and media. For
example, recently China has built a large database to summarize the daily
numbers of infected cases in time series updated from National Health
commission Updates [68]. The scientific community has also recently shared the
database of chest X-ray or CT images on the open Github website for research
usage [69]. These representative data sources would be valuable resources for
both researchers and governments for the monitoring, analysis, estimation of
COVID-19 epidemic.

The data from these sources needs to be ensured for security during the
coronavirus outbreak tracking and analytics, by using blockchain. Here,
blockchain can offer a number of viable solutions for coronavirus-related
services such as outbreak monitoring, safe day-to-day operations, medical
supply chain, and donation tracing. Secure data collected from the blockchain
network is analysed using intelligent AI-based solutions. By using reliable
prediction and accurate analysis ability on big data collected from
coronavirus sources, AI can provide support for coronavirus fighting via five
main applications, namely outbreak estimation, coronavirus detection,
coronavirus analytics, vaccine/drug development, and prediction of any future
coronavirus-like outbreak. Finally, at the top of the hierarchy comes the
stakeholder layer which includes parties such as governments, healthcare
providers who are benefiting from blockchain-AI solutions. Note that
blockchain can build secure communication networks and protocols to establish
a fast and reliable data exchange with the stakeholders thanks to its
decentralized nature [70]. With this architecture, several blockchain-AI
systems can be developed. For example, blockchain can establish the
communication with hospitals and COVID-19 research centers and transfer
securely COVID-19 data to the cloud computing where AI functions are located
for data processing and computation. Then, analyzed data can be returned via
blockchain so that doctors physicians can do further steps for COVID-19
analytics [71]. The working flowchart of our proposed blockchain-AI
architecture for fighting COVID-19 is summarized in Fig. 4. An illustrative
use case from the proposed architecture is shown in Fig. 5. To be clear, data
collected from hospitals can be shared with the data centre via blockchain. In
the data centre, AI tools can be employed, such as a neural network, for
COVID-19 data analytics, e.g., classification, regression, etc. Blockchain is
also used to establish secure exchange of analyzed outcomes between the data
centre, hospitals and the stakeholders via the decentralized ledger.

Figure 4: The working flowchart of our proposed blockchain-AI architecture for
fighting COVID-19.  Figure 5: A illustrative use case with blockchain and AI
for COVID-19 data analytics.

Note that the blockchain networks in COVID-19-related applications can include
private and public ones. In the case of private blockchains, several solutions
can be adopted to enhance security. For example, by introducing atomic meta-
transactions proposed in [72], blockchain entities can commit to their
obligations during the data exchange (e.g., a private blockchain network
between doctors and patients in a hospital). Particularly, smart contracts can
be used to provide reliable authentication over the blockchain network where
transactions and data exchange events can be verified in a transparent and
reliable manner. In the case of public blockchains, secure data usage can be
also accomplished according to the predefined access permissions of patients
through the smart contracts of blockchain [73]. Another solution is also
proposed in [74] using a public Ethereum blockchain to prevent fake
information, by enabling to trace and track the provenance and history of
digital content to its original source even if the digital content is copied
multiple times. The smart contract utilizes the hashes of the interplanetary
file system (IPFS) used to store digital content and its metadata so that the
content can be credibly traced to a trusted or reputable source. These
proposed solutions are promising to improve the security and performance of
blockchain systems for fighting COVID-19.

In the following sections, we present an extensive survey on the latest
research efforts on using blockchain and AI for coronavirus epidemic.

##  III  Blockchain-based Solutions for Coronavirus Fighting

In this section, we analyse the role of blockchain for coronavirus fighting
via four key solutions, including outbreak monitoring, safe day-to-day
operations, medical supply chain, and donation tracing, as shown in Fig. 3.

###  III-A Outbreak Monitoring

Blockchain can provide feasible solutions for monitoring the coronavirus
outbreak. Indeed, the blockchain can realize provide data visualization tools
for deadly coronavirus monitoring. Blockchain can be seen as distributed
database ledgers that can receive multiple updates in near real-time and store
them on blocks linked together in a immutable manner. In the coronavirus
epidemic, blockchain is potential to support the thousands of coronavirus
victims by recording immutably patient symptoms of infection [75]. This is
highly important to monitor the spread of coronavirus because several patients
can deliberately declare wrong their symptoms for avoiding hospital visits,
which can lead to patient quarantine failure. Governments and healthcare
organization can use blockchain to provide real-time data about affected areas
and safe zones for providing fighting efforts [76]. Information of safe zones
such as population, location, current coronavirus outbreak status is recorded
via a chain of blocks, and each block can store an update of the outbreak in a
certain time. Note that such data can be collected from surveillance providers
who use a combination of technologies like AI and geographical information
systems (GIS). Based on that, healthcare service professionals can identify
the free-virus zones from the infected ones to implement quarantine
requirements [77]. Blockchain hence can provide practical approaches to
protect the community against virus spread.

Another major problem in the coronavirus pandemic at this time is the outbreak
of fake news circulated through social media channels and websites. We are
facing an unprecedented crisis of public understanding. In February 2020,
Turkey and North Korea have been under scrutiny for claiming they have no
diagnosed cases, but in fact the actual statistic from the WHO is different
[78], while the accuracy on the case number given by Iran has been called into
question, with lower numbers of infected cases and deaths. Nowadays, people
rely on social media platforms such as Facebook, Twitter to update the
coronavirus pandemic, but these platforms are facilitators and multipliers of
COVID-19-related misinformation. The incorrect information transmission really
increases public confusion about who and what information sources to be
reliable. The outbreak of fake news also generates fear and panic due to
unverified rumours and exaggerated claims, and promotes racist forms of
digital vigilantism and scapegoating [79]. Governments, digital corporations
and public health authorities should find solutions to verify and authenticate
any information and news related to the pandemic which plays an important role
in controlling public panic and facilitating coronavirus management.
Importantly, blockchain is able to provide a consensus on the ordering of the
COVID-19 data records in the blockchain, aiming to achieve an agreement on the
data among distributed entities such as healthcare providers, hospitals, and
governments for better data management.

###  III-B Safe Day-to-day Operations

In the coronavirus crisis, blockchain has emerged as a promising platform that
enables to conduct day-to-day activities in virtual environments to reduce the
risk of virus contraction. In light of the ongoing coronavirus pandemic,
customer services via virtual environments such as digital blockchain networks
would become feasible to mitigate the spread of virus. For example, the UAE’s
Ministry of Community Development has switched to using digital channels via
blockchain for civil services [80]. Instead of visiting government offices and
service centres for paper works, the government services such as the digital
authentication of official certificates and other documents are implemented by
blockchain. The trial results indicate that blockchain can process 2,919
different types of documents, which is even faster than the traditional
working approaches. More importantly, this blockchain-based solution really
helps to reduce the risk of infection from face-to-face contact that
accordingly mitigates the possibility of virus spread in the community.

Furthermore, economic activities can be continued using virtual platforms
based on digital blockchain. In fact, electronic payment among customers and
companies now can be performed via blockchain instead of cash which may be a
source of coronavirus spread [81]. Blockchain is well known to make digital
money such as Bitcoin [82] which proves efficiency in certain digital
transactions such as digital auction. In the coronavirus crisis that people
should not use banknotes as Merchants are encouraging, electronic payment via
blockchain may be an ideal choice. The decentralized concept of blockchain
ensures payments in a secure, transparent, and immutable interface, which is
free from human interaction. Another example is Ant Financial which has been
launching a blockchain-enabled online bid opening system to replace the cash-
based approaches [83]. It allows customers to perform in contactless bidding
from remote locations when they take quarantine measures to combat the
COVID-19 outbreak. This bidding system is operated on a consortium blockchain
platform which ensures that materials and processes of bid openings are
tamper-proof, and the contactless bidding is transparent and trustworthy.

###  III-C Medical Supply Chain

Blockchain has proved extremely useful in supply chain applications such as
goods supply chain, trading supply chain [84], [85], [86], [87]. In this
pandemic crisis, maintaining a continuous supply of medicines and food has
become a challenge for the healthcare sector. The blockchain technology can
help the supply chain companies in achieving a fast flow of supply by tracking
the flow from the origins to the destinations in a reliable manner.

Recently, Alipay, along with the Zhejiang Provincial Health Commission and the
Economy and Information Technology Department, China has launched a
blockchain-based platform that allows users to track the demand and the supply
chains of medical supplies [88]. This consists of the recording and tracking
of coronavirus epidemic fighting materials, such as masks, gloves and other
protective gear. The company claims that blockchain can ensure high
tracebility for the medical supply chain by secure linking among blocks and
transactions and fast data flow thanks to blockchain decentralization. When an
outbreak occurs, a quick reaction and fast supply chain is the most important
weapons authorities have to tackle the problem. By using blockchain, the
supply chain issues can be solved, helping to save thousands of lives and
billions of dollars [89]. In summary, blockchain can offer five key solutions
to support the medical supply chain in the coronavirus crisis:

  * •

Product requirements: provide a solution to update real-time demands and
medical factories for fast response (e.g., supply chain rate adjustment).

  * •

Supply creditability: provide a solution to control the quality of goods from
the factory side, e.g., product specification, supply volumes.

  * •

Transportation tracking: Goods, medical supplies need to be traced to
guarantee transparency in the medical supply chain, which can be done by a
blockchain network with transaction recording and monitoring capabilities.

  * •

Financial payments: blockchain can be used as a payment platform between the
suppliers and the customers (e.g., users in quarantine areas). All digital
payments are recorded on the blockchain with timestamp, signature without
being changed or modified.

  * •

Customs certifications: customer behaviours such as buying, payment actions
can be reflected on the blockchain with digitally signed certificates that
shows the negotiation between the suppliers and the users.

###  III-D Donation Tracing

The potential of blockchain in donation tracing applications has been
investigated in recent works [90], [91]. In the coronavirus crisis, donation
is one of the most important activities to support livings and healthcare
services for infected victims. A critical question raised is how to trace the
donation activities to ensure that donated goods, money are transferred to the
targeted victims. Blockchain can be a feasible solution where the donation
process can be traced through blockchain which issues signature and
certificate to mark each of the donation updates, e.g., when, where, donation
volumes, list of targeted receivers, etc. As an example, blockchain has been
recently applied to trace the donation of food and protective gear like N95
masks to support citizens in the infected area [92]. Blockchain also allows
all parties such as suppliers, healthcare professionals, charity organizations
to monitor the progress of donations. Any updates will be informed to all
related parties to ensure transparency over the donation network. The Italian
Red Cross is now also seeking Bitcoin donations to purchase highly needed
medical equipment to infected areas [93]. Binance Charity, a blockchain-based
donation platform [94], also launches a million-dollars-campaign to seek
donations via blockchain coins that are then exchanged to purchase supplies
for supporting affected countries and regions.

Figure 6: The use case of blockchain for controlling COVID-19.

The Fig. 6 presents a use case on how to integrate blockchain to support
fighting COVID-19. COVID-19 data can be collected from different dataset
sources such as outbreak area information, infected case history, X-ray
reports and patient health records. Blockchain would build a database platform
where COVID-19 data is attached with timestamps for data tracking in an
immutable manner. In the COVID-19 context, many related parties are involved
in a common blockchain ecosystem, such as governments, doctors, and healthcare
organizations. Here, governments may need blockchain for epidemic tracking,
and doctors want to collect reliably data for efficient COVID-19 prediction
and diagnosis associated with the support of AI models (like ML, DL).
Moreover, healthcare organizations can also use blockchain to provide timely
donations or medical supports to the patients. From our observation and
discussions, the use of blockchain has the great potential to support
strategies towards fighting the COVID-19 disease and control effectively the
COVID-19 epidemic.

##  IV  AI-based Solutions for Coronavirus Fighting

In this section, we present a review on the use of AI for coronavirus epidemic
fighting. AI can support by providing five main applications, namely outbreak
estimation, coronavirus detection, coronavirus analytics, vaccine/drug
development, and prediction of future coronavirus-like outbreak, as shown in
Fig. 3.

###  IV-A Estimation of Coronavirus Outbreak Size

AI can help combat COVID-19 spread by its ability to estimate the coronavirus
outbreak size through analysing people’s phone usage patterns. In fact, when
people are sick, dead due to coronavirus infection or taking care of family
members, they tend to change their patterns of phone usage, which can be
detected by AI algorithms run on the datasets provided by wireless operators.
For example, if we can get access to the wireless operators’ data in Wuhan
from November 2018 to March 2020, then we may be able to find out the activity
patterns of cell phones.

These patterns can include abnormal usage (e.g., frequent calls in early
morning) from sick people, non-calling activities from people died from
coronavirus infection, or new phone appearance in another city from people
sneaked out of the lockdown city. Moreover, during home confinement in the
lockdown period, sleep timing has markedly changed, with people going to bed
and waking up later, and spending more time in bed [95]. During outbreaks of
infection, people are likely to experience fear of falling sick or dying
themselves, feelings of helplessness, and stigma [96]. In addition to that,
the upcoming economic downturn caused by COVID-19 crisis also leads to more
consumption of unhealthy foods as they are cheaper. For example, according to
a recent study [97], from December 2019 through February 2020 as the pandemic
spread, the populations with a higher rate of poverty showed an increased
consumption of unhealthy food.  In these situations, AI can analyse such
different activities which can reveal the patterns of mobile users accordingly
[98]. For instance, ML is able to model and predict the personalized diverse
activities of a user through learning from his phone usage records [99].
Furthermore, DL would be a promising AI technique that relies on deep data
learning and high-performance model prediction, aiming to estimate accurately
the mobile application usage, e.g., abnormal calling behaviours, phone service
inactivity [100]. Therefore, such AI applications can be applied in the
context of coronavirus epidemic to reveal the user patterns so that the
agencies can estimate the size of coronavirus outbreak.

Moreover, a mobile operator can detect and track infected people’s movement to
predict the outbreak using AI analytics based on their cellular network
location data. For example, AI can analyse the user movement patterns to
estimate the locations where crowds are still gathering and specify certain
geographic quarantine areas. Meanwhile, stemming from the fact that people in
several Chinese cities rely on their mobile phone for daily public
transportation, Chinese railway authorities use AI to track the customers’
movement and identify people who were near a passenger infected with COVID-19
[101]. Human mobility patterns can be also estimated by using AI analytics
based on geo-located Twitter social media data records [102]. This solution
can trace the geographical location of infected patients and keep track their
movement to predict the coronavirus spread.

###  IV-B Coronavirus Detection

Several recently emerging solutions using AI for coronavirus detection have
been proposed in the literature. A measure is to detect temperature on human
face so that we can identify potential symptoms of COVID-19 disease. AI can
come up with solutions thanks to its capability in face recognition
applications. The work in [103] presents an improvement in masked face
detection using AI with real-world datasets in response to the coronavirus
epidemic. Through deep learning, the dataset is trained to build an accurate
masked face detection model, which then serves for the masked face recognition
task, aiming to determine whether a person is wearing a mask. Different from
this study, the authors in [104] rely on breathing characteristics to detect
potential people infected with COVID-19. In the coronavirus detection,
accurate identification of the unexpected abnormal respiratory pattern of
people in a remote and unobtrusive manner is highly significant. Deep learning
(DL) has been used for respiratory pattern classification, aiming to to
classify six clinically significant respiratory patterns related to COVID-19
(Eupnea, Tachypnea, Bradypnea, Biots, Cheyne-Stokes and Central-Apnea). The
work in [105] uses image analysis-based techniques for coronavirus detection.
An array of convolutional neural network based models (ResNet50, InceptionV3
and InceptionResNetV2) is leveraged to analyze the chest X-ray radiographs,
aiming for detecting coronavirus pneumonia infected patients. The ResNet50
model can provide the highest classification performance with 98% accuracy
among the other two proposed models, as confirmed from simulations.

To further improve the accuracy of COVID-19 detection, the authors in [106]
leverage a deep convolutional neural network that is able to automatically
extract features starting from the genome sequence of the virus. Experiments
using the 2019nCoVR dataset show a correct classification performance in
detecting SARS-CoV-2, and distinguishing it from other coronavirus strains,
such as MERS-CoV, SARS-CoV. Meanwhile, a research effort in [107] is toward
diagnostic uncertainty verification in the COVID-19 detection. A Bayesian
Convolutional Neural Network (BCNN) is considered to estimate uncertainty in
DL solutions. The objective of this study is to improve the diagnostic
performance of the human-machine combination using a publicly available
COVID-19 chest X-ray dataset. Another interesting work in [108] presents a
scheme for detecting coronavirus disease COVID-19 using on-board smartphone
sensors. The proposed model enables us to read the signal measurements from
smartphone sensors to identify viral pneumonia which can potentially identify
coronavirus-related symptoms. It would be useful for radiologists with a smart
phone in tracking the development of the disease.

###  IV-C Coronavirus Diagnosis and Treatment

In the coronavirus fighting, developing innovative and efficient diagnostic
and treatment methods has great significance, deciding the success of fighting
COVID-19 disease. AI can come up with solutions to help understand and fight
COVID19 via intelligent data analytics [109], [110]. A methodology is the
thoracic CT image analysis to diagnose coronavirus positive patients [111].
Non-contrast thoracic CT has been shown to be an effective tool in diagnosis,
quantification and follow-up of corona-similar diseases. Given a potentially
large number of thoracic CT exams, an analysis system is proposed that uses
both robust 2D and 3D DL models coupled with existing AI models and combining
them with clinical understanding. CT image analysis is also implemented in
[112] to early diagnose COVID-19 patients. The finding results indicate that
the manifestations of computed tomography imaging of COVID-19 had their own
characteristics, which are different from other types of viral pneumonia, such
as Influenza-A viral pneumonia. Using these features, a 3D DL-based a
location-attention classification model is developed using a pulmonary CT
image set for symptom classification, showing a high accuracy with 86.7% for
three groups: COVID-19, Influenza-A viral pneumonia and healthy cases.
Moreover, the work in [113] introduces the AI-based model, called COVIDX-Net
that includes seven different architectures of deep convolutional neural
network models, such as the modified Visual Geometry Group Network (VGG19) and
the second version of Google MobileNet, for COVID-19 diagnosis. Each deep
neural network model is able to analyze the normalized intensities of the
X-ray image to recognize and classify the patient status either negative or
positive COVID-19 case. Based on the current situation where there is a lack
of public COVID-19 datasets, the learning-based scheme is validated on 50
Chest X-ray images with 25 confirmed positive COVID-19 cases, showing good
performances in terms of high accuracy in COVID-19 classification. DL has been
also used for the quantification of COVID-19 infection in CT images, as
reported in [114]. More specifically, a VB-Net neural network is employed to
segment COVID-19 infection regions in CT scans, with a training dataset from
249 COVID- 19 patients, and then the model is validated using 300 new COVID-19
patients. The proposed scheme potentially assists radiologists to refine the
automatic annotation of each infected case.

ML has been also utilized to facilitate coronavirus treatment. The study in
[115] presents an example on using ML to design antibody for supporting
COVID-19 treatment as shown in Fig. 7. In this work, the authors collects 1933
virus antibody sequences from clinical patient IC50 data. The graph
featurization of antibody sequences has been integrated to provide molecular
representation that is then trained by an ML model, such as a neural network,
to generate thousands of potential candidates. Then, several ML classifiers
such as random forest, support vector machine are used to classify these
sequences to select the most optimal sequences and evaluate the stability of
proposed antibodies. This workflow using ML would enable the rapid screening
of potential antibodies that support the treatment of COVID-19 with high
confidence.

Figure 7: An application of machine learning to design antibody against
COVID-19 [115].

The work in [116] also considers a based alignment-free approach for an ultra-
fast, scalable, and accurate classification of whole COVID-19 genomes.
Decision tree coupled with supervised learning has been also used for genome
analysis from a large dataset of over 5000 unique viral genomic sequences. The
authors claim that their scheme can achieve high levels of classification
accuracy and discovers the most relevant relationships among over 5,000 viral
genomes within a few minutes. Additionally, the research in [117] suggests an
ML-based miRNA prediction analysis for the SARS-CoV-2 genome to identify
miRNA-like hairpin which can impact the COVID-19 virus. Learning can help to
realize potential miRNA-based interactions between the viral miRNAs and human
genes as well as human miRNAs and viral genes, which are important to identify
the mechanisms behind the SARS-CoV-2 infections. Furthermore, understanding
personalized therapy is also an efficient approach for COVID-19 treatment
[118]. The remdesivir trials [119] and preliminary data made available from
recovery trial indicate that these therapies (antiviral and steroids,
respectively) may have more benefit for certain patient populations and at
certain timepoints within the illness. Understanding which patient and at what
time would have maximum benefit from which cocktail of therapies would be
clinically useful.

###  IV-D Vaccine/drug Development

In order to ultimately combat the emerging COVID-19 pandemic, it is desired to
develop effective vaccines against deadly disease caused by COVID-19 virus. AI
can come as an attractive tool to support vaccine production. A few rapidly
emerging studies suggest to use AI for this task. For example, the work in
[120] employs Vaxign-ML tool to predict 24 COVID-19 vaccine candidates by
using the S protein and five non-structural proteins that aims investigating
the entire proteome of CoV-2. By applying reverse vaccinology and machine
learning, the authors can predict potential vaccine targets for effective and
safe COVID-19 vaccine development. Another vaccine trial is in [121] which
leverages HLA-binding prediction tools using an peptide stability assay. 777
peptides were assessed that were predicted to be good binders across 11 MHC
allotypes with high prediction binding scores. Preliminary results can make
important contributions to the design of an efficacious vaccine against
COVID-19.

Up to now, there is no effective treatment for this COVID-19 epidemic.
However, the viral protease of a coronavirus can be extracted and replicated
for drug discovery. The work in [122] suggests that the potential anti-SARS-
CoV chemotherapies can be potential 2019-nCoV drugs. A machine intelligence-
based generative network complex model is derived to generate a family of
potential 2019-nCoV drugs, inherited from HIV-1 protease [123]. The results
from this work can open up new opportunities to seek new drugs for fighting
COVID-19 in this crisis.

Unlike the previous work, the study in [124] uses DL to develop a drug-target
interaction model called Molecule Transformer-Drug Target Interaction (MT-DTI)
to identify commercially available drugs that can act on viral proteins of
2019- nCoV. They suggest that atazanavir, an antiretroviral medication used
for HIV fighting, can be useful to develop a chemical compound specific for
COVID-19. The efficiency of the discovered drug is under the trial stage
before possibly using on corona-infected patients. Moreover, the work in [125]
designs a data-driven drug repositioning framework, by integrating ML and
statistical analysis approaches to systematically mine large-scale knowledge
graph and transcriptome data. Results from trials show that ML can predict
effective drug candidates against SARS-CoV-2. Meanwhile, drug repositioning is
a possible solution for combating the global coronavirus crisis [126]. A panel
of 49 FDA-approved drugs that have been selected by an assay of SARS-CoV is
monitored to recognize the potential drug candidates against COVID-19
infection. This trial drugs target to be tested on animals for tracking their
antiviral activities before using on human.

###  IV-E Prediction of Future COVID-19 Outbreak

The recent emergence of coronavirus has highlighted the need for prediction of
future outbreak like COVID-19. Epidemic models of COVID to predict and control
the outbreak should be taken into consideration in the coronavirus fighting
activities [127], [128], [129]. Recently, AI has been applied to predict
outbreak like coronavirus. For example, an prediction model using AI to
estimate the size, lengths and ending time of Covid-19 across China [130]. An
auto-encoder is designed for modelling the transmission dynamics of the
epidemic based on dataset collected from WHO sources. Clustering algorithms
may be useful to classify different cities, regions for investigating the
virus transmissions for outbreak prediction. Another model is developed for
outbreak prediction in Mexico, as reported in [131]. The biological features
of the virus and the possible pathophysiological mechanisms of its disease and
a stochastic model based on a Markov chain are used to express the probability
distribution of cases in Mexico states. This also can estimate the number of
cases in Mexico that facilitates the government in the estimation of virus
spread making appropriate actions for future fightings.

From the statistics collected from the COVID-19 outbreak, a research group in
China built a prediction model for future infectious diseases using ML [132].
The key idea is to extend causal inference theory and ML to identify and
quantify the most important factors that cause zoonotic disease and COVID-19
outbreaks. Based on that, the model can generate visual tools to illustrate
the complex causal relationships of animal infectious diseases and their
correlation with COVID-19 diseases. Meanwhile, the authors in [133] model the
non-deterministic data distributions via DL network and fuzzy rule induction
for obtaining better the stochastic insight about the epidemic development.
Based on the model outputs, policy makers can have a better picture about the
coronavirus spread and estimate the possibility of future similar outbreak. DL
has been also tested in [134] to build a virus host prediction model for
identifying the way the virus transmits, with different sources such as bat
coronaviruses, mink viruses and finding the relation with COVID-19. This
research potentially discovers the origins of infection and predicts how
coronavirus transmits for appropriate preventive actions.

###  IV-F Federated AI for Privacy-aware COVID-19 Data Analytics

Many AI-based approaches have demonstrated significant promise in the
intelligent COVID-19 data analytics for detection, diagnosis and prediction.
However, in the pandemic, collecting sufficient data to implement intelligent
algorithms becomes more challenging and the user privacy concerns are growing
due to the public data sharing with datacentres for COVID-19 data analytics
[135]. FL can be an ideal candidate for assisting COVID-19 detection, by its
federation and privacy protection features. In this way, each institution
participates in training their DL model using their local COVID-19 images,
e.g., X-ray and computed tomography (CT) images, and only model parameters
such as gradients are exchanged while there is no need to share actual data
and sensitive user information. For example, a number of hospitals can
cooperatively communicate via the blockchain to run CNN updates locally for
identifying CT scans of COVID- 19 patients [136]. At each hospital, a deep
capsule network is developed to enhance image classification performance,
while FL provides guidance for transmissions of model updates to perform the
model aggregation at a data centre. Simulations from 34,006 CT scan slices
(images) of 89 subjects verify a high COVID-19 X-ray image classification and
low data loss in the FL algorithm running. FL is also used in [137] to provide
privacy-promoting AI solutions for COVID-19 chest X-ray image analytics. The
preliminary experiments have been implemented, where multiple COVID- 19 X-ray
image owners run a CNN-based model for image classification, and then share
the computed parameters with a data centre for mobile averaging while the data
ownership of each user is guaranteed. Four state-of-the-art CNN models, e.g.,
MobileNet, ResNet18, MoblieNet and COVID-Net, are used in the federated
setting for evaluation, where ResNet18 is proven with the best COVID-19
detection performance (98.06%) in federated X-ray image learning settings.

Moreover, a dynamic fusion-based FL method is proposed in [138] for CT scan
image analysis to diagnose COVID-19 infections via two stages, including
client participation and client selection. First, each client such as a
medical institution make a decision to participate in the FL round based on
the performance of the newly trained model. The central server also makes the
decision to select which clients are permitted to update their local gradients
by calculating the updating time. If a client cannot update its gradient
within a predefined time interval, it is excluded from the FL aggregation.
Another FL approach is designed in [139] for COVID-19 screening from Chest
X-ray images by the collaboration of multiple medical institutions. The focus
is on Chest X-ray images classification to identify COVID-19 from non-COVID-19
cases, where the feature extraction and the classification of X-ray images are
performed based on a CNN to detect the COVID-19 disease. At each hospital, a
CNN model is trained by allowing each X-ray image to be put into a
convolutional layer and output the probability of COVID-19 infection, and then
a central server is used to aggregate synchronously with the local
institutions for building a strong classification model for COVID- 19
detection without compromising significantly user privacy which is valuable in
the pandemic [140]. Moreover, FL is also combined with DL to build a deep
collaborative learning solution for detecting COVID-19 lung abnormalities in
CT [141]. The internal datasets are collected from a total of 75 patients
confirmed COVID-19 infection at three local hospitals in Hong Kong for FL
simulations, and then the generalizability is validated on external cohorts
from Mainland China and Germany.

##  V Blockchain and AI Use Cases for Coronavirus Fighting

The use of blockchain and AI for solving coronavirus pandemic has been
attracted much attention from industry and research community with projects
and use case trials. In this section, we highlight the most popular use cases
on blockchain and AI adaption in response to coronavirus fighting.

###  V-A Blockchain Use Cases

####  V-A1  Hashlog

The first project is Hashlog that was created by a Georgia-based health tech
startup Acoer [142]. They has come up with a HashLog blockchain solution to
combat and control the coronavirus from spreading. This can be enabled by the
distributed blockchain ledger technology which ensures logging and data
visualization of the coronavirus outbreak from the public data of US Centres
for Disease Control (CDC) and WHO. Hashlog potentially provides real-time
updates on the transmission of virus by tracking the movement of infected
people which allows the healthcare authorities to make decisions for fighting
further infections.

####  V-A2 Hyperchain

The next blockchain project related to coronavirus is Hyperchain [143], which
has built a donation tracking platform for supporting governments and
healthcare organizations in the donation process to the infected victims in
China. Hyperchain can ensure the transparency of the donation records from the
origins to the destinations without being changed or modified. Moreover, the
Hyperchain is able to connect up to millions of nodes so that more users can
reach donated goods and necessary medical equipment from the factories,
helping to solve the facility shortage issues during the epidemic.

####  V-A3  VeChain

One interesting blockchain use case is VeChain which is a blockchain-based
platform built for monitoring vaccine production in China [144]. All
activities related to vaccine manufacturing from materials, codes to package
are recorded and stored on distributed ledgers. This project also provides a
reliable method to reduce the risk of potential modifications on vaccine
information. VeChain also ensures that vaccine records are immutable and
permanent to achieve high vaccine quality which is highly important in the
healthcare sector, like COVID-19 epidemic.

####  V-A4  PHBC

Another promising blockchain platform is PHBC [145] that is a monitoring
blockchain for the continual and anonymous verification of communities and
workplaces that are free from coronavirus COVID-19 as well as other high-risk
viruses, bacteria, to aid them in staying free of deadly diseases. An
interesting feature of this blockchain platform is the ability of monitoring
the movement of uninfected persons, while restricting their return if they
have gone to the areas known to be infected. PHBC can also automatically
identify zones with and without validated incident reports by integrating the
real-time information collected from virus surveillance providers with AI and
geographical information systems.

###  V-B AI Use Cases

Various AI companies are also making a play towards efficient detection and
diagnostic of diseases caused by COVID-19 virus. The important use cases and
projects will be highlighted as follows.

####  V-B1 Bluedot

Bluedot is an AI project developed by a Toronto-based startup that aims to
detect coronavirus in the epidemic [146]. Bluedot use AI algorithms to build
prediction models for virus ditection. It even identified the coronavirus
outbreak in Wuhan hours before the local authorities had diagnosed the first
cases of the virus. Bluedot collects information from social media, government
documents, data of healthcare organizations to build intelligence enabled by
natural language processing (NLP) and machine learning. It is able to track
outbreaks of over 100 different diseases, every 15 minutes around the clock.

####  V-B2 Infervision

Another emerging AI use case in response to coronavirus pandemic is
Infervision [147] that aims to help clinicians detect and monitor the disease
efficiently. Infervision has been used at the center of the epidemic outbreak
at Tongji Hospital in Wuhan, China. Infervision AI has improved the CT
diagnosis speed, which is highly important to accelerate pneumonia diagnosis
for early detection of suspected patients. This AI solution has proved very
useful in supporting healthcare services in the emergency situations, like
coronavirus outbreak.

####  V-B3 AlphaFold system, Google DeepMind

Very recently, Good DeepMind has introduced an AI tool called AlphaFold [148]
which enables the structure predictions of several under-studied proteins
associated with SARS-CoV-2 virus that causes the coronavirus outbreak. This AI
platform is empowered by a DL system and an ML-based free modelling technique
to help it estimate protein structures when no similar structures of protein
are available. Up to date, AlphaFold is under the trial stages and experiments
have yet not been verified.

####  V-B4 NVIDIA’s AI

Last month, a team of physicians at the Zhongnan Hospital, Wuhan China, used
GPU-accelerated AI software called NVIDIA’s AI, which was primarily used to
detect cancer in lung CTs, to identify the visual signs of the COVID-19 virus
[149]. Data training was performed by using over 2,000 CT images from several
of the first coronavirus patients in China, aiming to build AI models for
detecting signs of pneumonia caused by coronavirus. This AI solution assist
doctors in the virus recognition for fast diagnosis and treatment.

####  V-B5 Federated AI

Recently, a real-world federated AI project using FL for COVID-19 region
segmentation in chest CT with the participation of medical institutions from
China, Italy, and Japan is presented in [150]. Specifically, a multi-national
database consisting of 1704 scans from these three countries is collected for
setting up the FL framework and compare with the traditional local training
approach using 945 scans with the support of expert radiologists. Given the
strict regulatory policy on data privacy, FL is proven as a promising solution
for countries to collaborate in the COVID-19 segmentation and detection
without worrying the user information leakage and lacks of dataset.

###  V-C Combining Blockchain and AI for Combating COVID-19

Recently, several efforts have been made to combine blockchain and AI
technologies to build innovative solutions against the COVID-19 epidemic. For
example, a research group in Spain leverages these two technologies to develop
a blockchain and AI-based app that is able to predict the evolution of the
COVID-19 pandemic [151]. Blockchain has been used to ensure that people comply
with the social distancing rules by creating digital identities and licenses
that keep track of the daily activities of a person. Meanwhile, a deep
learning architecture empowered by neural networks and rules-based symbolic
systems is designed to build an estimation model by using the datasets of
genetic profiles, medical records, and medical treatments. This AI-based model
would help government officials to make appropriate decisions regarding social
distancing and quarantine measures.

Moreover, the Immuno-oncology company that specialises in RNA therapeutics
Mateon is recently cooperated with the startup Meridian IT to build a new drug
manufacturing system for COVID-19 disease, by combining both blockchain and AI
technologies [152]. Due to regulatory concerns, much of current drug
manufacturing faces many challenges with complex legacy processes and the
manufacturing operations are largely human-involved. These working procedures
are less effective and potentially remains production errors due to human
involvement. The new application is proposed by exploiting AI-based neural
networks to automate the operating procedure for rapid drug production.
Blockchain has been also used to ensure that the production line complies with
the FDA regulations (the law set of Food, Drug, and Cosmetic Act policies).
The combination of blockchain and AI thus would bring drug manufacturing to an
advanced stage with a faster, more accurate, and more reliable working
procedure that would enable timely drug supply for COVID-19 prevention.

##  VI A Case Study

This section presents a case study towards the application of AI for COVID-19
detection, by developing a federated AI approach using FL for COVID-19
classification. The details of our AI approach with FL will be provided as
follows.

Figure 8: Performance of discriminator loss.

###  VI-A System Model

We consider a system model for the federated AI-based COVID-19 detection,
including a set of medical institutions (e.g., hospitals) and a cloud server.
Each institution participates in the FL process using its own COVID-19 X-ray
image dataset, e.g., X-ray images, to build a global GAN with the cloud,
aiming to generate high-quality synthetic COVID-19 X-ray images for improving
the overall COVID-19 detection. Specifically, at each institution we design a
GAN consisting of two components, namely a generator and a discriminator based
on CNNs which alternatively train via a min-max game [153]. To implement the
FL process, each institution participates in training its local GAN model by
updating the parameters of the discriminator and the generator in each global
round and exchanges them with the cloud server for parameter aggregation. For
every global epoch, each institution collaboratively trains its discriminator
and generator. First, the generator produces minibatches of fake samples from
the noise probability distribution. Also, the discriminator samples
minibatches of real data from the actual image distribution. Then, each
institution simultaneously updates the discriminator and generator by
ascending its stochastic gradients to update its own weights. After local
training, all institutions transmit the learned updates to the cloud server
for model averaging, while actual COVID-19 X-ray images are kept at local
institutions which thus ensures data privacy. Then, the cloud server sends the
new global update back to all participating institutions to prepare for the
next round of FL training [154]. The learning process is iterated until the
global loss function converges with a desired accuracy, where the synthetic
COVID-19 X-ray images are generated with high-quality with respect to actual
images.

###  VI-B Simulation Results

TABLE III: Comparison of performance results for COVID-19 detection. Classes | Standalone scheme without GAN | Standalone scheme with GAN | FL scheme without GAN | Our advanced FL scheme
---|---|---|---|---
Precision | Sensitivity | F1-score | Precision | Sensitivity | F1-score | Precision | Sensitivity | F1-score | Precision | Sensitivity | F1-score
COVID-19 | 0.891 | 0.865 | 0.921 | 0.914 | 0.872 | 0.927 | 0.950 | 0.973 | 0.986 | 0.983 | 0.993 | 0.995
Normal | 0.820 | 0.996 | 0.833 | 0.928 | 1 | 0.980 | 0.936 | 0.981 | 0.944 | 0.946 | 1 | 0.962
Pneumonia | 0.822 | 0.448 | 0.601 | 0.912 | 0.964 | 0.963 | 0.942 | 0.796 | 0.884 | 0.964 | 0.854 | 0.918

We report simulation results obtained when training a COVID-19 dataset [23] of
620 X-ray images in three classes: COVID-19, normal, and pneumonia in an FL
system with five institutions. By using the proposed FL model, we generate
1500 synthetic X-ray images which are then combined with an actual dataset for
COVID-19 classification. We use a CNN-based classifier with three
convolutional layers and Adam optimizer. We evaluate our approach and compare
with the state-of-the-art schemes, including the standalone scheme (training
dataset from an institution without federation), the standalone scheme with
GAN [39], the FL scheme without GAN [23], and the centralized scheme (all
datasets are transmitted to the cloud for classification). For reliable
evaluation, the reported results are averaged from five runs of numerical
simulations.

In Fig. 8, we compare the discriminator loss of our advanced FL scheme and the
standalone scheme with GAN [39], which only trains the GAN model using its
dataset without the federation. It is observed that the standalone scheme is
unable to achieve an optimal performance due to the lack of access to the full
dataset which thus limits the quality of generated synthetic data. Meanwhile,
our advanced FL scheme with GAN can learn over the entire data span from
distributed institutions which achieves better minimum loss and helps extract
better image features for efficient data augmentation.

We also evaluate the performance of COVID-19 detection via common quality
metrics including precision, sensitivity, and F1-score. As illustrated in
Table III, our advanced FL scheme outperforms all other state-of-the-art
schemes in terms of these three metrics. For instance, for the COVID-19 class,
the precision of our scheme is the best (0.983), while the standalone scheme
without GAN, the GAN-based approach, and the FL scheme achieve lower
performances, with 0.891, 0.914, and 0.950, respectively. Our scheme is also
much better in sensitivity and F1-score performances. The intuition behind
these observations is that our advanced FL scheme allows the cooperation of
distributed institutions to enrich the global GAN model with a better COVID-19
X-ray image feature learning and a complete global COVID-19 X-ray image
construction. As a result, our scheme can generate better realistic X-ray
images that leads to higher detection rates.

##  VII  Challenges and Future Directions

The application of blockchain and AI technologies is promising to help deal
with the coronavirus epidemic. However, the provided throughout survey also
reveals several critical challenges and open issues that should be considered
carefully when applying these technologies in the context of healthcare sector
for epidemic. We first highlight the research challenges, then we point out
several potential future directions in this field.

###  VII-A Challenges

We here analyse the challenges from four main aspects: regulatory
consideration, people’s privacy preservation, security of blockchain and AI
ecosystem, and the lack of unified databases.

####  VII-A1 Regulatory Consideration

The use of blockchain and AI in the healthcare sector like coronavirus
fighting should be considered carefully with regulatory laws. While the
features of blockchain and AI can bring benefits, they also poses a legal and
regulatory challenge if there is no party that is responsible and can be held
accountable. For example, in the blockchain network, it will be important to
consider what law might apply to transactions and what appropriate risk
management should be put into place [155]. Regarding AI, it might be easier to
create several forms of legal schemes and internal governance models that will
dictate the governing law for AI operations in healthcare. Specially, we also
consider legal issues about content, personal information running on
blockchain and AI platforms, such as problems with copyright infringement and
defamation.

####  VII-A2 People’s Privacy Preservation

In the coronavirus tracking applications, how to protect people’s privacy is
highly important. The governments can use mobile location data to help track
the outbreak spread, but this solution must ensure the privacy of user data,
especially sensitive information, such as home address, banking details,
shopping records, etc. The governmental agencies may impose privacy laws on
the user tracking mobile apps to ensure the safety and security to the public
[156]. Besides, nowadays many healthcare organizations and institutions are
collecting data from their patients via electronic healthcare records that
helps monitor the COVID-19 disease symptoms and serve treatment [157]. In such
healthcare activities, the conflict between data collection and user privacy
is inevitable that needs to solved by laws and reinforcement from the related
authorities.

####  VII-A3  Security of Blockchain and AI Ecosystem

Blockchain is widely regarded as secure monitoring platform to ensure safety
and privacy healthcare applications, such as COVID-19 tracking. However,
recent research reports have revealed inherent security weaknesses in
blockchain which are related to medical and healthcare systems [44]. Data
threats or adversaries can enter the blockchain software to hold the control
of the blockchain, which can lead to serious consequences, like modifications
of medical transaction or patient data information, raising privacy concerns
[158]. Security is also a critical concern of AI systems for healthcare
applications in the context of COVID-19 crisis. Data collected from patients,
clinical labs and hospitals can be modified by data threats. More importantly,
malicious attacks can inject false data or adversarial sample inputs which
makes AI learning invalid, while the AI model can be tampered with [159].
Therefore, security issues related to blockchain and AI deployment when
solving the healthcare issues in the epidemic should be given high priority
during the design and development stages.

####  VII-A4 Lack of Unified Databases

A critical challenge in the coronavirus fighting is the lack of unified
database related coronavirus epidemic such as infected cases, affected areas,
and medical supply status. Most of the current coronavirus-related databases
come from individual resources, e.g., social media [160], patient collection
[161], [162], but they are not sufficient for large-scale AI operations that
potentially creates greater impacts on fighting COVID-19, compared to the
current results. Countries are now reluctant to share database, that make
international healthcare organizations like WHO challenging to evaluate the
epidemic for fighting effectively the virus spread. In fact, WHO cannot
provide appropriate public health guidance without disaggregated data and
detailed local epidemic information.

####  VII-A5 Implementation Challenges

Another challenge comes from the implementation of blockchain and AI platforms
for COVID-19 prevention. In fact, the operation of blockchain requires
powerful hardware and storage for running blockchains [163]. Therefore,
developing hardware specific to blockchain is vitally important to accelerate
mining by a combined usage of computing elements within machines in blockchain
networks, including private and public platforms such as Ethereum for COVID-19
tracing [164]. Another critical issue is the extensive energy consumption and
high network latency caused by running consensus processes such as PoW in the
blockchain. This may hinder the applications of blockchain in distributed
healthcare networks with resource-constrained IoT devices. Another problem is
the limited throughput of blockchain systems. For example, Bitcoin can only
process a maximum of four transactions/second, and the throughput of Ethereum
achieved is about 20 transactions/second, while Visa can process up to 1667
transactions/second [44]. Therefore, to make the applications of blockchain in
COVID-19-related systems reliable, developing lightweight and throughput-
efficient blockchain platforms are highly essential [165].

Moreover, the unprecedented increase of data volumes associated with advances
of analytic techniques empowered from AI has promoted big data analysis, big
data searching and big data mining activities for fighting COVID-19 [166]. The
provision of big data platforms and their unified integration to the current
COVID-19-related application of analytics, monitoring and research tools is
thus highly needed.

###  VII-B Future Directions

We discuss several of the future research directions on blockchain and AI
adoption for fighting COVID-19 pandemic.

####  VII-B1  Performance Improvement of Blockchain

Blockchain platforms should be optimized to achieve better performances from
different technical perspectives, such as scalability and network efficiency
in terms of resource consumption, throughput, and network latency, aiming to
make blockchain an ideal choice for emergency healthcare applications like the
COVID-19 epidemic. For example, scalable and lightweight blockchain designs in
healthcare are necessary to optimize data verification and transaction
communication for ultralow-latency information broadcasting [167]. In this
regard, low-latency mining mechanisms with optimized block verification can be
considered to mitigate delays in block processing for fast data sharing
services that is necessary for efficient COVID-19 data analytics. Another
feasible solution for blockchain performance enhancement is to minimize the
size of blockchain by establishing local and private blockchain networks, one
of these is responsible to monitor the outbreak in a certain area for fast
response. To realize this, building customized ledgers that can placed on
local servers in the outbreak area can help reduce latency in block
broadcasting in the blockchain operations [168].

####  VII-B2 Security Issues of Blockchain

Despite its great potential, blockchain still exists security issues, such as
51% attack in the block mining and double spending attacks on data storage on
blockchain. Therefore, innovative solutions should be considered for improving
the security of blockchain. For example, a mining pool strategy is proposed in
[169] to enhance the efficiency of the mining process, by solving security
bottlenecks such as 51% vulnerability with reduced block and transaction
propagation delays. In this regard, defense mechanisms is integrated for
fighting against data threats in the consensus process in the blockchain.
Moreover, to mitigate double spending attacks in blockchain, a solution with
recipient-oriented transaction processing is introduced in [170], aiming to
validate the transaction before appending to the block. This is enabled by
using the concept of stealth address and master-node, where both transaction
senders and recipients actively join the verification for each transaction in
the propagation process. This allows for preventing double spending attacks
using the verification time of the recipient and blocking time of the
transaction.

####  VII-B3 Improved AI Algorithm for Better Analytic Accuracy

The efficiency of intelligent healthcare data analytics, e.g., virus
information analysis, mainly depends on the AI algorithms. Developing AI
architectures specialized in medical applications might be the key for
empowering future intelligent data analytics with the ability to handle
multimedia healthcare data. Adaptive AI models should be developed in response
to COVID-19-like emergency healthcare, such as AI in predictive modeling, AI
in patient monitoring, and AI in emergency department operations [171].

####  VII-B4 Combination with Other Technologies

To achieve better efficiency in solving epidemic-related issues, blockchain
and AI can be incorporated with other technologies to build a comprehensive
healthcare system. For instance, Alibaba has integrated AI with cloud
computing for supporting coronavirus data analytics [172]. The resourceful
storage and high computation capability are the key features that cloud can
provide to facilitate AI analytics. More interesting, China has recently
exploited the mobility of drones [173] to improve the provisions of medical
supplies [174]. In fact, using drones would be the fastest way to deliver
essential facilities to the victims in the quarantine areas where all modes of
transport are interrupted. Besides, drones also help to reinforce the
contactless monitoring of the outbreak. In the near future, these promising
technologies can be integrated to build highly advanced medical system for
coping with the coronavirus-like epidemics.

##  VIII Conclusions

In this paper, we have presented an state-of-art survey on the utilization of
blockchain and AI technologies to combat the coronavirus (COVID-19) epidemic.
We have first introduced a conceptual architecture which integrates blockchain
and AI towards the fighting of coronavirus crisis. Specially, we have
extensively discussed the key roles of blockchain for solving the pandemic via
five important solutions, including outbreak tracking, user privacy
protection, safe day-to-day operations, medical supply chain, and donation
tracking. Moreover, the potential of AI for coping with the COVID-19 crisis
has been also analysed though five main application domains, namely outbreak
estimation, coronavirus detection, coronavirus analytics, vaccine/drug
development, and prediction of future coronavirus-like outbreak. The important
use cases and projects using blockchain and AI towards fighting COVID-19 have
been also highlighted, followed by a case study. Finally, we have pointed out
several potential challenges and future directions. We believe our timely
survey will shed valuable light on the research of the blockchain and AI for
fighting COVID-19 as well as motivate the interested researchers and
stateholders to put more efforts into using these promising technologies to
combat future coronavirus-like epidemics.

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