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While the world still struggles against the devastating effects of the COVID-19 pandemic, governments and organisations are discussing how new technologies can be exploited to relieve its impacts and how future pandemics can be avoided or minimised. Among the envisioned solutions, the development of more efficient and widespread smart city initiatives can improve the way critical data is retrieved, processed, stored, and disseminated, potentially improving the detection and mitigation of outbreaks while reducing the execution time when taking critical actions. In fact, some first responses to this pandemic are exploiting different technological solutions that could be ultimately adopted in more integrated city-scale systems, opening many possibilities. Therefore, this study discusses potential solutions and review recent approaches that can be exploited in this complex scenario, describing feasible and promising development trends for the construction of the new expected health-centric smart cities.
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IET Smart Cities
Review Article
COVID-19 pandemic: a review of smart cities
initiatives to face new outbreaks
eISSN 2631-7680
Received on 15th May 2020
Revised 3rd June 2020
Accepted on 8th June 2020
E-First on 30th June 2020
doi: 10.1049/iet-smc.2020.0044
Daniel G. Costa1 , João Paulo J. Peixoto2
1Department of Technology, State University of Feira de Santana (UEFS), Av. Transnordestina, Feira de Santana, Brazil
2Computing Coordination, Federal Institute of Bahia (IFBA), Rua Vereador Romeu Agrário Martins, Valença, Brazil
Abstract: While the world still struggles against the devastating effects of the COVID-19 pandemic, governments and
organisations are discussing how new technologies can be exploited to relieve its impacts and how future pandemics can be
avoided or minimised. Among the envisioned solutions, the development of more efficient and widespread smart city initiatives
can improve the way critical data is retrieved, processed, stored, and disseminated, potentially improving the detection and
mitigation of outbreaks while reducing the execution time when taking critical actions. In fact, some first responses to this
pandemic are exploiting different technological solutions that could be ultimately adopted in more integrated city-scale systems,
opening many possibilities. Therefore, this study discusses potential solutions and review recent approaches that can be
exploited in this complex scenario, describing feasible and promising development trends for the construction of the new
expected health-centric smart cities.
The dramatic events of the COVID-19 pandemic in 2020 are
expected to deeply change the way health information will be
managed in this century. With the rapid and almost uncontrolled
propagation of the SARS-CoV-2 virus, with untraceable cases
suddenly arising inside the borders of countries even far from
known outbreak epicentres, the need for more efficient
management of critical information has become apparent [1]. The
lessons left from this outbreak are pointing to more coordinated
actions from governments and public organisations, requiring new
levels of urban digital integration [2]. In fact, for this complex
envisioned scenario, the smart cities can be leveraged as one of the
best resources to face this and next pandemics [3].
Nowadays, most people live in cities. Since this trend is not
expected to change in this century, large cities will be the home of
most part of mankind. Besides, the expected problems originated
from this urbanisation process, such as pollution, energy efficiency,
mobility, and security [4], new highly-contagious diseases may
emerge in densely populated areas, rapidly propagating. In
addition, international traffic among large cities can strongly
increase the potential of world propagation of new flu-like diseases
[5]. Actually, the negative potential of this urban pattern has been
seen in the COVID-19 pandemic, which rapidly spread to all
continents in just a few weeks. As new highly contagious diseases
may emerge anywhere and anytime, the question to make is what
we must do now to better face a pandemic.
To avoid or even reduce the impacts of this and the next
pandemics, information management is essential. While specialists
argue that fast diagnosis and good sanitation are strong
requirements to face epidemics, the new actors of the digital era
can also play important roles in this fight. Therefore, cities must be
prepared to rapidly detect potential outbreaks of new or already
known diseases, processing sensitive information in a way that fast
decisions can be done. In essence, information can be as vital as
any other element of the healthcare systems, and data provided by
cities are crucial in this aspect.
In general, cities can be perceived as living organisms [6].
Terabytes of data can be daily provided from different sources,
such as lamp posts, buses, climatic stations, police vehicles, traffic
lights, security cameras, automatised hospitals, universities,
museums, and any other ‘element’ that can be connected to a
digital city's macrocosm [7]. This integration of different sources
of data can be one of the greatest transformations in our way of
living in this century, along with the processing possibilities
provided by data science and deep learning algorithms. However,
this potential can be enlarged even more when cities and their
inhabitants are considered as a symbiotic organism: for any city in
the world, its inhabitants spontaneously provide many relevant data
that can tell a lot about what is happening in an urban area. This
final massive integration of a city's cyberspace with social media
and people's smartphones and gadgets can not only pave the way
for the anticipated digital society [8], but it can also be a source for
prevention and mitigation of virus outbreaks.
In 2003, the severe acute respiratory syndrome (SARS) disease
affected many countries with thousands of cases. In that year, the
SARS-CoV virus outbreak was an important alert about fast
contagious in modern urban times, harder affecting cities such as
Beijing, Hong Kong, Taipei, Toronto, and Singapore [9]. After that,
in 2009, the H1N1 outbreak (H1N1pdm09 virus) hit the world very
hard and very quickly, ringing the alarm bell [10]. Once again, this
type of influenza virus took advantage of the modern way of living,
with large globalised cities interconnected in a worldwide
transportation network, infecting more than 1 billion people. Now,
the COVID-19 pandemic hits the globe with a significant death
toll, and the lessons eventually left from this pandemic should not
be neglected. Figs. 1 and 2 show how astonishing was the initial
spread of this virus, urging the world for more effective means to
contain the next pandemics.
The modern way of living in highly-populated cities with
globalised businesses is the perfect scenario for outbreaks of
infectious diseases and the next pandemic is just a matter of time.
Therefore, this study discusses recent developments that can
support current cities to better prepare to face this and the next
pandemics, surveying technologies, and innovative approaches
related to the evolution of the smart cities paradigm. Good
practices are highlighted and promising development trends are
envisioned. Finally, cities that are ahead in this development
process are mentioned, pointing out potential directions to be
IET Smart Cities, 2020, Vol. 2 Iss. 2, pp. 64-73
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2Cities: an emergency-prone environment
Although infectious disease outbreaks can be assumed as global
events, they will spread in cities. The COVID-19 pandemic started
in Wuhan, a large and highly populated city in China that is an
economic and transportation hub. Just a few months later, other
large cities such as Milan (Italy), Madrid (Spain), and New York
(USA) were facing uncontrolled dissemination of the SARS-CoV-2
virus, leading countries to determine lockdown and mandatory
quarantines. Actually, even though coronaviruses will originally
come from human interaction with animals [11, 12], the
urbanisation process has made such interactions a reality in some
cities outskirts, with transportation services connecting them to
downtown areas very quickly. In this complex scenario, new
pandemics will be a constant threat.
Any city is an emergency-prone environment. Fire can be
initiated anywhere and rapidly spread, with potential property
damages and deaths. Heavy rain can result in flooding, devastating
low-lying areas. Chemical gas leakage is also extremely dangerous
in urban areas, requiring prompt action. Whatever the case, there
are many critical events that may happen in large cities, and some
initiatives have been developed to manage such critical situations
[13, 14]. However, can potential outbreaks of infectious diseases to
be managed as city emergencies?
Considering the literature in this subject, an emergency can be
managed considering three different elements [15]:
Detection: emergencies will be detected by the identification of
some pattern that is out of the expected ‘normal’ behaviour in a
city. For example, if the temperature in some area suddenly
rises, it may be an indication of a fire emergency. Concerning
the detection of an infectious disease outbreak, e.g. the
identification of an emergency pattern may be done through the
analysis of the number of requested medical assistance in an
area or the processing of social media for abnormal behaviour
[16, 17].
Alerting: after an emergency is detected, some alerting
procedure has to be performed. The basic alert procedure is to
deliver warning messages, which could be emails, SMS
messages, or even television broadcasts. Sirens or luminous
signs could also be deployed in some areas for more efficient
alerting, e.g. as deployed when detecting and warning about
tsunamis [18]. For outbreaks prevention, more elaborated alerts
could be performed, e.g. indicating critical places to avoid.
Mitigation: detected and alerted emergencies have to be
eventually mitigated [19]. However, it may be a hard task when
considering infectious diseases. While an obvious response to a
fire emergency will be the dispatch of fire trucks, detected
infected people and potential outbreaks will typically require a
series of actions such as public decontamination, prophylactic
isolation, tracking of potentially infected inhabitants, public
transportation reordering, and many other measures that should
be efficiently coordinated.
The recent events related to the COVID-19 pandemic have raised
many questions about how potential outbreaks can be detected,
alerted, and mitigated. Also, as cities will be in the centre of the
major contagious spread, current and future smart cities should be
designed, implemented, and managed to address disease outbreaks
as highly critical emergencies. Doing so, data provided by the cities
will be the main ally of the governments and healthcare systems
when facing pandemics.
Although alerting and mitigation are separate and independent
procedures [13, 14], we believe that the characteristics of known
and future pandemics will demand a more stringent perception of
emergency management. Hence, we believe that alerting and
mitigation should be performed simultaneously as a single unified
Therefore, considering the literature in this subject, we believe
that disease outbreaks can be processed as urban-related
emergencies. Moreover, we classify the solutions in this area
according to Fig. 3, which presents the processing of potential
outbreaks through three conceptual procedures. In fact, such types
of emergencies have to be detected as soon as possible, providing
at least location (where) and temporal (when) information. In fact,
that information will be crucial when facing an outbreak in its
initial stage. Afterwards, alerting and mitigation procedures are
expected to be performed concomitantly, suppressing the spread of
the detected pathogen.
For the conceptual model depicted in Fig. 3, the first stage is the
detection. After that, assuming that a potential outbreak is an
extremely relevant emergency for any smart city context, both
mitigation and alerting procedures should be performed as soon as
possible, and thus they are equally defined as the second stage.
Doing so, while the alerting procedures will be concerned with
notification of affected people and the government, mitigation
actions such as isolation of the outbreak zone and tracking of
potentially infected people should take place immediately.
Moreover, even though alerting and mitigation are necessarily
separated and independent procedures in a smart city [15], they can
interchange data among themselves (dashed line in Fig. 3),
dynamically updating information to the inhabitants.
Overall, it is expected that cities with a high concentration of
urban poor and deep inequalities are potentially more vulnerable to
new outbreaks. For those cities, the lack of proper sanitation and
the presence of very crowded areas are subject to the rapid
dissemination of airborne diseases. For new pandemics to come,
Fig. 1 Total confirmed cases of COVID-19 per million people on 1
February 2020 – source:
Fig. 2 Total confirmed cases of COVID-19 per million people on 30 May
2020 – source:
Fig. 3 Processing outbreaks of highly-contagious diseases as emergencies
IET Smart Cities, 2020, Vol. 2 Iss. 2, pp. 64-73
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we can also add to this equation the lack of a cyber-physical data
infrastructure to support quick and efficient response to potential
outbreaks. As highly contagious diseases may be assumed as an
actual urban emergency, cities must be ready to face them.
3Monitoring and detecting outbreaks
Initial analysis of the COVID-19 outbreak has demonstrated the
rapid propagation of the virus when initial measures were not
properly adopted. Such lack of rapid response is even more critical
for highly populated areas, as could be seen in the original
epicentre of this pandemic. Actually, initial results in [20] have
shown the rapid propagation in Wuhan, China, and also the
positive impact of quarantines when fighting the propagation. In a
different study [21], results also presented a rapid spread of the
outbreak, with mathematical models being used to predict new
infection cases. Although the impacts of this pandemic will still be
felt for months or even years, the initial studies and reports are
advocating for the need for coordinated identification of new cases.
The adoption of new technologies for different types of
monitoring has been a trend in large cities, but not in a uniform
way [22]. Nowadays, intelligent cameras can be easily spotted in
many cities, supporting the detection of suspicious behaviour and
crowd incidents. With the development of more efficient artificial
intelligence (AI) algorithms, automated crime prevention solutions
have become more common and some cities are already integrating
such data to other databases for stronger results when identifying
criminals and even terrorism. In this context, although face
recognition algorithms have raised privacy issues [23, 24], the
recent COVID-19 pandemic should reignite the need for more
active monitoring in large cities, especially when detecting and
tracking potential individuals that may inadvertently propagate
viruses. However, are smart cameras sufficient to face this type of
The last few years have seen the surge of new data acquisition
and processing paradigms. Actually, the transformations provided
by the cloud computing paradigm are more than one decade ago
have created a wealthy scenario for even deeper changes [25].
Soon, data became the most relevant asset, with AI and data
science algorithms allowing new perceptions about virtually
anything. In parallel, the hardware evolution has culminated with
the development of countless independent and interconnected
devices [26, 27], giving birth to the internet of things era [28].
Ultimately, new technologies and paradigms are supporting more
comprehensive and efficient decisions in critical issues, affecting
business, sports, science, and even how cities detect emergencies.
Therefore, the current technological arsenal can be exploited to
create a new generation of smart cities, which will be able to detect
viruses’ outbreaks more efficiently, potently facilitating their
contention. Obviously, it is not straightforward and many
challenges will still arise as cities become more and more
interconnected. Nevertheless, the negative impacts of the
COVID-19 pandemic may give the required boost for this process.
The ideal detection of potential outbreaks should exploit
retrieved data from different sources. Actually, this overall problem
is not only constrained to the cities, also relating to their local and
global supply chains, travel networks, airports, and
neighbourhoods that may be sources of contagion. In such cases,
sensor stations, public agents systems, social media, and even
individual gadgets should be integrated to provide comprehensive
data. Fig. 4 depicts this overall idea of integrated and distributed
monitoring in smart cities.
Sensor-based monitoring stations can be used to detect patterns
related to flu-like outbreaks. Also, past works have provided
valuable clues of how effective those approaches can be. The work
in [29] employed monitoring stations composed of multiple sensor
units to detect different environmental variables in cities. Doing so,
a more detailed perception of a city is achieved, complementing
other databases. Multi-sensor monitoring units were also
considered in [15], but that work focused on the detection and
alerting of emergencies. With some modifications in sensing and
communication, such types of monitoring approaches based on
heterogeneous sensors can be valuable when detecting patterns
related to outbreaks at early stages.
When employing sensor-based stations, an important
component is the camera. Visual data can be valuable when
detecting and identifying sick people, even in the crowd. For smart
cities being designed, cameras will be indeed a core monitoring
element, as recent works suggest. In [30], a camera-based approach
was proposed to screen febrile passengers at international airports,
detecting people that can potentially spread a contagious disease.
That work combined visible and thermal images and some
algorithms to assess the heart rate, body temperature, and
respiration rate of passengers, potentially reducing the probability
of false alarms. Integrating with other types of sensors for more
comprehensive monitoring, as performed in [31], cameras can be
an important element for the detection of disease-related
emergencies in smart cities.
Still considering the detection of potential outbreaks, social
media can be mined for some types of information. Actually, we
can consider that people are a type of spontaneous sensor, which
asynchronously provide information about their lives. For micro-
blogging services such as Twitter, such a pattern is even more
evident, with people describing their daily events. Since described
negative events such as sickness complains, headaches, fever,
coughing, or things like ‘I heard that some people in a particular
neighbourhood are getting sick’ can be retrieved from social
media, specialised algorithms can extract precious information,
identifying some important patterns [32]. This idea was partially
exploited in [33], which considered the processing of tweets for
Fig. 4 Outbreak should be detected in its early stages. For that, different sources of data may be exploited
66 IET Smart Cities, 2020, Vol. 2 Iss. 2, pp. 64-73
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geo-referenced identification of critical events that could be used
for prioritisation of IoT devices. Similarly, the work in [34] also
processed tweets to identify critical events, but that work is directly
focused on the detection of emergencies. In both works, the target
mined data could be easily adapted to detect patterns related to a
contagious disease outbreak, supporting the overall detection
A different but still promising approach is the individual
monitoring of health conditions using gadgets or wearable sensors.
If people are being seamlessly monitored about their body
temperature, heart rate, sugar levels, or any other variable, a smart
city macro-system could retrieve that information and store the
historical health behaviour of their inhabitants, quickly detecting
sick people. Although such an approach could raise privacy
concerns, the COVID-19 pandemic has shown how some
‘invasives’ solutions can be highly effective when trying to reduce
the virus spread, as could be seen in China. Then, as a promising
approach, the work in [35] developed a smart shirt that was able to
retrieve physiological data of any individual. Employing a group of
different sensors, the sensed data is transmitted to a smartphone
and then to a cloud-based system. Doing so, information about the
health of all monitored people can be known. Other promising
solutions for monitoring based on wearable-sensors have emerged
in the last few years, giving clues of how active health monitoring
can be performed [36].
All presented solutions can be exploited when composing
integrated systems for active monitoring and detection of potential
outbreaks. However, the reach of such detection resources could be
enlarged even more, especially during initial stages of an already
detected outbreak emergency. For example, in China, the
Government of Wuhan employed drones to monitor people and
reduce the virus spread: people not using masks were detected
using drones’ cameras and immediately alerted. Moreover, drones
could be used to detect if people are not respecting established
quarantine curfews, supporting the action of police agents, as could
be seen in some cities during the COVID-19 pandemic. In this
sense, thermal cameras could also be used in drones to potentially
identify people with fever, proactively detecting infected people.
When smart cities are properly designed, all potential sources of
data are valuable for early and continued detection of an outbreak.
The designing and management of smart cities should then
consider the maximisation of the number and types of data sources,
which obviously will demand a robust networking structure and
massive data storage [37]. Therefore, the construction of such
cyberspace should be one of the main goals for the development of
cities in the next decades.
4Processing massive data
With the availability of huge amounts of data, from different
sources and with different characteristics, the development of
computational solutions to process all retrieved data is as important
as the implementation of monitoring and detection approaches [38,
39]. Actually, smart cities may provide massive amounts of data
every single second, uninterruptedly, demanding highly efficient
algorithms to transform such data in useful information [40]. For
the smart cities that should be created or adapted to handle this and
the next pandemics, recent developments are giving some clues of
how these goals might be achieved.
When considering historical data about people and cities,
statistical information is of paramount importance [41]. Recently,
big data and data science disciplines have become hot research and
development topics, being largely exploited by companies for
better marketing campaigns and business intelligence [42]. In the
last few years, the development of algorithms for data processing
following those paradigms has also opened opportunities for the
maturation of smart cities projects, with promising results in areas
such as healthcare assistance and public governance [43]. In fact,
the lack of efficient governance, poor planning, and decentralised
healthcare assistance can undermine eventual pandemic responses,
with terrible consequences. Therefore, the development of models
to understand how virus outbreaks emerge and how transmissions
evolve is an important trend for the construction of smart cities.
AI algorithms can also be a valuable tool when predicting and
mitigating a virus outbreak. In [20], a neural network was defined
to predict confirmed cases of people infected by the SARS-CoV-2
virus. In that work, different variables were considered as input for
the city of Wuhan, notably temperature, its population density,
relative humidity, and wind speed. Then, the proposed model was
used to predict infections for 30 days. Machine learning techniques
may also be exploited, as proposed in [44]. In that work, potential
outbreaks were predicted considering different factors, such as the
number of reported cases, the type of pathogen (and how
contagious it is), environment information mined from the Web
(provided by the press) and social media (provided by the
individuals). Then, the combined processing of all that data could
give important indications of how and where new outbreaks of
infectious diseases might arise. In fact, these initial results
concerning the COVID-19 pandemic are some of the promising
initiatives that have been employed to address this crisis, but new
reports are still coming about data-centric developments around the
world, continuously, indicating research and development trends
for the next few years.
In general, AI will be a powerful new weapon against infectious
diseases, especially when models and algorithms are defined to
predict new outbreaks. New developments in machine/deep
learning are already bringing promising results when tracking how
diseases spread and the COVID-19 outbreak has provided valuable
data to train and assess the effectiveness of neural networks.
Actually, population screening and assessment of infection risks
are some of the expected ‘services’ of AI algorithms, but other
relevant information may be produced by such algorithms, with
research in this area still gaining a lot of attention [45].
Another not so obvious potential use of AI when facing a
pandemic is the detection of fake news on social media. The
misinformation resulted from false messages posted accidentally or
not on social media may be too dangerous for any contention and
mitigation plan in a city, and thus algorithms may be used to
identify and denounce such posts [46, 47]. In short, the detection,
avoidance, and mitigation of fake news should be an active service
of any healthcare-oriented smart city.
Finally, the effectiveness of AI-based prediction and detection
approaches will depend on the ‘quality’ of the available data, and
previous outbreaks (H1N1, SARS-Cov, Ebola etc.) can be valuable
when training computation solutions based on AI [48]. Therefore,
we believe that the development of highly-integrated smart cities,
with lots of different data sources, will be the most important factor
when facing a pandemic.
5Alerting and mitigation
The particular characteristics of worldwide pandemics have shown
how fast and dramatic they can evolve and infect people. Thus, the
response to them must be equality fast [2]. After an outbreak is
detected, which can be handled as an urban emergency, alerting
and mitigation should be performed as soon as possible. Ideally,
they should be coordinated for enhanced impact, although this can
be complex and tricky sometimes. Nevertheless, there are some
initiatives that can be exploited for this goal, as discussed in this
When an outbreak of an infectious disease is detected, different
resources should be employed to mitigate its impacts. Actually,
cities will need to have well-defined plans to mitigate an outbreak,
as soon as possible, potentially reducing the number of deaths and
also the number of newly infected people [49]. For the COVID-19
pandemic, its rapid worldwide dissemination is an important clue
that the current resources may not be sufficient to face such an
adverse scenario [50]. This is, in fact, a fight against the clock,
demanding serious resources from the cities.
There are different alerting and mitigation approaches when
responding to a detected outbreak, as can been seen in academic
works and practical developments. Among such possibilities, we
analysed promising solutions and selected five basic procedures
that should be implemented when creating smart cites, discussed as
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Automatised hospitals and healthcare systems: this is, in fact,
the most obvious approach when considering the mitigation of a
pandemic. Nonetheless, it is not the easiest. When facing initial
stages of an outbreak and eventually an uncontrolled pandemic,
hospitals should be managed considering the diversity of data
provided by a smart city [51, 52]. The number of available
hospital beds, medical staff, and medicine has to be managed in
advance, processing statistical data, or even employing AI
algorithms. The presence of a hospital in the area of a detected
outbreak should also be exploited to redirect new patients not
related to that pathogen to different hospitals, avoiding new
infections and overwhelming of the healthcare system,
potentially reducing deaths. In fact, the absence of such
measures required multiple quarantine decrees in entire cities
and countries during the COVID-19 pandemic, aimed at the
reduction of the number of concurrent patients at critical
conditions [53]. As discussed in recent works [54–56],
intelligent healthcare systems have to be a central element of
future smart cities.
Smart transportation: the way people move in large cities is
determinant to the spread of any contagious disease. Hence,
smart transportation can also be an ally when preventing or
mitigating the effects of an outbreak [57]. Processing data
provided by the city, affected areas can be rapidly isolated,
limiting the movement of people from and to that area. Public
transportation can also display warning messages and
instructions on how to prevent from being infected, as has been
seen in many large cities during the COVID-19 outbreak [58].
When the cyber-physical integration level of the cities goes
deeper, other ideas may emerge in the short time. For example,
driveless autonomous cars could be used to transport infected
people or health workers, reducing the probability of new
contagious. Obviously, the correct positioning of such
automated services requires a comprehensive perception of the
entire urban space, which can only be achieved when smart
cities are properly built..
Response teams: after an outbreak is detected, the city must act
very quickly. Public decontamination, prophylactic isolation,
and tracking of potential infected people are some examples of
required responses, which will typically involve public health
workers, transit agents, police, and even special response teams
that may be created to deal with this particular scenario [59].
Moreover, some new technologies and development platforms
may also play an important role in this mitigation process,
complementing ‘traditional’ action measures [26]. In China, the
epicentre of the COVID-19 pandemic, drones were used as
essential tools, guiding and alerting people about quarantine
restrictions. Moreover, drones also had some other innovative
uses, as the delivery of supplies and the disinfection of certain
areas by releasing chemical products. In Hong Kong, robots
were reported disinfecting public transports, reducing the risk of
new infections when humans are designated to clean potentially
infected areas. Actually, such initiatives could be provided as an
automatic contention service of the smart city macrocosm,
which would dynamically allocate mitigation services with very
short delays.
Research and innovation: when a new disease is discovered, the
correct pathogen and its DNA/RNA information should be
discovered as soon as possible, allowing clinical tests and
experimentation for better treatment of infected people [12]. In
this context, scientists will struggle to create new medicines and
vaccines to face that pathogen, trying to save lives and
potentially reduce its propagation. In fact, scientific labs and
universities should receive all types of information from the city
cyberspace, supporting a better perception of how the studied
pathogen is spreading in different conditions: e.g. the weather,
sanitation conditions, and population density information of a
recently detected outbreak can give clues of how to face this
new pathogen. Moreover, the information should also be
provided by the scientific units towards the cities’ services, e.g.
allowing the rapid alerting of people and authorities about how
to prevent from contagious.
Alerting and notification messages: when emergencies are
detected, people must be alerted. Although the contents of the
alert messages may be different according to the recipient
(inhabitants, government staff, public agents, healthcare workers
etc.), the important part is that alert messages should reach the
greatest number of people [60]. Also, there may be different
ways to implement it, employing since broadcast information to
personal smartphones until the delivery of massive alerts on
digital outdoors. In fact, the level of digital integration of all
actors in a smart city will dictate the probability of warning
messages to reach most people.
Fig. 5 presents the expected mitigation procedures that should be
automatically coordinated to achieve the most efficient responses
in all phases of a pandemic. The highest is the level of integration
among them, higher is the effectiveness of the city when facing
detected infectious diseases outbreaks.
6Current and promising smart cities initiatives
The emergence of epidemic diseases is expected to significantly
transform the way we live and interact with the world. Besides the
death toll of this pandemic, which is still unknown but may be
huge [61], negative impacts on the economy and the consequent
social problems may be too severe. While the COVID-19
pandemic still leaves its mark creating one of the biggest economic
crises of modern history, with a significant recession period ahead,
the world starts to wonder how to avoid the next pandemic, which
may be even worse. The answer to this question may be on the
development of more robust, proactive, and integrated smart cities.
Large urban centres are naturally vulnerable to infectious
diseases. In general, cities that are more collaborative and
integrated are better prepared to manage pandemics than those that
are not. In this sense, this section discusses what some large cities
are already doing to become smarter and how such actions can be
leveraged when facing the next pandemics.
6.1 Wuhan
Widely known as the source of the COVID-19 pandemic, Wuhan is
a ‘Tier II’ city in China (cities having from 3 to 15 million
residents) [62]. Wuhan's Smart City planning started back in 2010,
in a conference held by its Science and Technology Bureau,
finishing their construction blueprint in July 2011. According to the
authors in [63], this is the most ‘perfect’ system in China, paying
high attention to smart health.
The Wuhan's Smart Health approach is intended to connect
various healthcare systems and databases to enhance
communication among patients, doctors, and other healthcare
professionals. Automated systems are capable of exchanging
Fig. 5 Most common alerting/mitigation procedures that should be
implemented in smart cities
68 IET Smart Cities, 2020, Vol. 2 Iss. 2, pp. 64-73
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patients’ health records to ensure that correct information reaches
proper healthcare professionals, reducing mistakes (e.g. nurses
getting wrong data).
Besides Wuhan's Smart Health, the Chinese government has
been using technologies to tell who must get into quarantine. In a
partnership with Alipay, they are providing a colour QR code to
citizens that install Alipay app on their smartphone. The QR code
may have three colours: green, meaning the user can move around;
yellow, meaning the user must have a 7-day quarantine; and red,
meaning the user must have a 14-day quarantine [64]. The app gets
users' information provided by the healthcare system and it also
tracks their pharmacy purchases and movements to verify if they
have had contact with infected people. Although this approach can
help determine who must self-isolate, some people are reporting
that the app sends personal data, as user localisation to police
enforcement, which may raise some privacy concerns.
6.2 Singapore
Since 2003, Singapore has learned a lot from the SARS outbreak.
After that, they created a task force to fight new outbreaks and
minimise their effects. Largely considered as the smartest city in
the world [65], Singapore has created a programme called Smart
Nation that makes extensive use of smart city technologies to
improve the perceived life quality in the city. One of the products
of Smart Nation is the Contact Tracing App [66]. A person can
install it in a smartphone to detect nearby people who are also
using this app and store information about people at user's
proximity. This information can then be shared with the Ministry of
Health and help to identify citizens who were in contact with
infected individuals, potentially enhancing the mitigation actions
(who to test, interviews etc.).
Another initiative of Singapore was to create a national level
WhatsApp one-way messaging group to feed people with
COVID-19 information and fight the outbreak. Also, web sites to
find masks and healthcare facilities, as well as gaming apps to
educate against panic buying, have favoured Singapore's response
to the COVID-19 pandemic [67].
6.3 London
London is doing well when it comes to smart city initiatives. The
Mayor's Office promotes digital healthcare through London's Smart
City technologies, linking data in the National Health Service
through its DigitalHealth.London program [68]. Additionally,
London is making use of data provided by wearable devices, data
analytics, and AI to better understand its citizens’ conditions.
DigitalHealth.London is aimed at bringing digital innovation to
its healthcare system. Some of its initiatives are digital outpatient
services, digital therapeutics, and an innovation hub, among others
[69]. With this programme, London is making use of technology
and smart city concepts to enhance its health system.
Regarding tourism, a businesswoman in London who owns a
tour company has found a way to continue working despite the
lockdown. She is making use of Google Maps and her own photos
to create virtual tours for those who want to visit London sites, but
cannot go out [70]. Although it is not properly a London smart city
initiative, it is something that could be considered as a tool to
relieve stress during quarantine.
6.4 Seoul
Seoul is another good example of a smart city. Actually, South
Korea is an early adopter of the smart city concept at a national
level [71]. In Seoul, the programmes address various issues such as
mobility and energy efficiency (commonly supported in initial
smart cities implementations), as well as e-governance and public
spaces reorganisation.
Seoul and South Korea as a whole have also implemented a
Digital Health programme. The South Korea Digital Health key
sectors include big data, AI, block chain, telemedicine, and
consumer health electronics [72]. All this digital health
implementation has helped South Korea fight the COVID-19
outbreak. The Ministry of the Interior and Safety has developed a
self-quarantine app that keeps track of its users' location to make
sure they are not breaking the quarantine. Also, another app called
‘Corona 100m’ alerts when the user comes within 100 m of an area
visited by an infected person. The Corona map website keeps a
history of confirmed patients’ movements so its users can know
where infected people were [73].
6.5 New York
New York has been in the first place in IESE Cities in Motion
Index for three consecutive years until 2019 when the first place
was taken by London. However, according to IESE Business
School, New York is still leading in the economy dimension [74].
In the health dimension, the city is favoured by the New York
eHealth Collaborative (NYeC) approach, a non-profit organisation
that works in partnership with the New York State Department of
Health, creating a special network called State-wide Health
Information Network for New York (SHIN-NY) [75].
The SHIN-NY network is aimed at connecting state's regional
networks to exchange data quickly. Participants can make use of
this network to retrieve patients' records, receive alerts about their
patients, and share clinical data, among other services [75]. This
initiative is making use of technology to connect the whole State of
New York and bring digital health to its citizens. In outbreaks times
or when any epidemics threaten a region's health, fast access to
records can be of great importance to fight the disease spreading
6.6 Initiatives and future developments
It is noticeable that many cities are using smart city concepts and
digital technology to embrace healthcare, even before the
COVID-19 pandemic [54, 77]. From simple websites providing
useful information to its citizens to complex data exchange
networks aiding health professionals in their daily work, cities are
undergoing a silent but important transformation. Although the
results are promising, however, cities still have a lot to do when
concerning pandemics.
In general, we can say that Asia seems to be ahead in the
COVID-19 fighting, deploying smartphone applications
specifically for this purpose, while cities in other continents rely on
their current digital health initiative to defeat the disease.
Nevertheless, the challenges are still great and new developments
are still required.
Table 1 lists ten cities that have implemented promising smart
cities initiatives, summarising some of their adopted procedures
and solutions. Their results and lessons should be considered when
Table 1Some current initiatives when implementing smart
cities to face a pandemic
City Continent Population Initiatives
Berlin Europe 3.7M ambient-assisted living; web-
based services [78]
Helsinki Europe 0.6M Helsinki smart region [79]
London Europe 8.9M DigitalHealth.London
programme (digital health
New York North
8.6M NYeC; SHIN-NY [75]
Seoul Asia 9.7M big data, AI, blockchain; self-
quarantine app; corona 100 m
app; Coronamap
Shanghai Asia 24.2M 5G-powered robots [80]
Singapore Asia 5.6M contact tracing app [66];
WhatsApp group; websites;
educational games
Sydney Oceania 5.2M my health record [81]; secure
messaging [82]
Wuhan Asia 11M records exchange; Alipay QR
code for quarantine [64];
smartphone tracking
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implementing the expected transformations in urban life to face the
next pandemics.
The COVID-19 has brought new challenges that have
demanded fast responses to mitigate the dramatic effects of this
pandemic. In this context, there is no ‘silver bullet’ and each city
has applied technological solutions that better fit to the encountered
challenges [83]. Nevertheless, studying employed solutions can be
crucial when preparing to face the next pandemics.
Actually, although the public initiative is mostly responsible for
digital health and smart city programmes, private companies can
also join the battle and help cities to defeat COVID-19 and the next
pandemics. In Belgium, e.g. telecom operators are combining their
datasets to determine high-risk areas, offering real-time monitoring
for proactive authorities response [84]. In China, big companies
such as Alibaba are also playing an important role in fighting the
pandemic, managing large amounts of data. Also, other initiatives
supported by private companies are expected to be implemented in
many countries.
Finally, digital health programmes are very important in smart
cities to get prepared for infectious disease outbreaks. Actually,
cities that had previously implemented digital health solutions are
doing better fighting COVID-19. The cities listed in Table 1 are
making use of their digital health solutions to cease the outbreak
and give their citizens access to good information about the
disease. However, the negative impacts of the COVID-19
pandemic should push additional pressure on them, demanding
new technologies and solutions in the near future.
7Brazilian case when facing the COVID-19
Brazil is the largest country in South America with over 210
million people, having an important political and economic
influence in that region. Although it is natural to associate this
importance with the surge of new cases of COVID-19, mostly due
to many international daily flights and the existence of economic
hubs such as São Paulo, the rapid spread of the SARS-CoV-2 virus
through many Brazilian cities drew the attention of the world, with
serious concerns about the death toll after this pandemic. Actually,
the lack of unified actions from the Brazilian governments, in both
national and regional levels, has been pointed out as one of the
main reasons for the high-infection rate, but other factors such as
poor sanitation and high population density also contribute to the
achieved numbers.
The dramatic situation of Brazil, which had its first confirmed
case on 26 February, has been aggravated by the absence of
coordinated actions for the tracking of the infection and the
definition of measures to avoid its spread. This combination of
factors could be seen in the first months of this pandemic. Fig. 6
presents the increasing number of infected people per million
inhabitants, compared to other countries with early cases of
Regarding South America, COVID-19 cases in Brazil are also
rapidly increasing, as can be seen in Fig. 7.
In both graphics, Brazil is presenting an ascending trend for the
COVID-19, but the real situation can be even worse [85]. Owing to
under-reporting resulted from the low number of performed tests,
as can be seen in Fig. 8, the actual number of cases in Brazil may
be seven times higher [86]. As an example of this concern,
although Chile and Peru are having more cases than Brazil per
million inhabitants, they make many more tests for COVID-19.
Actually, cases of the new COVID-19 disease dramatically
grew in a short period of time and many more cases are expected in
2020. However, although this scenario is terrible, many initiatives
are being developed by universities and research centres in Brazil,
trying to reduce the spread of the virus. Such initiatives are already
having some good results and they can help Brazil to better face
this and next pandemics. Some of those developments are
presented in this section.
Some cities in Brazil are making use of smart cities initiatives
to fight COVID-19. One of them, Curitiba, was the first large
Brazilian city to use remote medical appointments through online
video calls to aid COVID-19 suspected people [87]. Their robust
infrastructure was essential in this task, avoiding unnecessary
crowds in hospitals and the complementary healthcare system,
potentially reducing the risk of new transmissions. This
infrastructure is then considered as a positive case for other large
cities in Brazil.
Considering the largest city in Brazil, São Paulo, many
initiatives are being developed exploiting different strategies. In
that city, some smartphone-based and web solutions were
developed, mostly focused on the management of health
information, the reduction of agglomerations, and the tracking of
quarantine restrictions. As examples of those initiatives, Meu
Corujão [88] and Busca Saúde [89] are some of the early solutions
designed to face the COVID-19 pandemic. People can use the web-
based Busca Saúde to search for health centres and hospitals, while
Meu Corujão gives citizens exam results online, without the need
of going to a health centre or hospital, reducing crowding.
Following this trend to better manage health information, we can
also cite Aqui Tem Remédio [90] as a positive initiative.
Additionally, the São Paulo State government developed the
mobile phone tracking SIMI (Sistema de Monitoramento
Inteligente – Smart Monitoring System), a dynamic monitoring
Fig. 6 Brazil when compared with other countries with earlier cases of
COVID-19 – source:
Fig. 7 Brazil when compared with other countries in South America
Fig. 8 Number of COVID-19 tests per thousand people on 30 May 2020 –
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system to check how many people are obeying the quarantine
restrictions, using for that cellphone networks data [91].
The under-reporting of COVID-19 cases is one of the biggest
challenges that Brazil is facing in the initial months of the
pandemic. Since this is a known fact, some initiatives have
leveraged big data techniques as an alternative to better understand
the real situation over the country. In São Paulo, a statistical tool
called ‘Nowcasting’ was created to mitigate the delay of the
notification systems [92]. In other cities of the State, live maps
dynamically show the detected COVID-19 cases [93], allowing
people to check where the virus is spreading with higher rates and
also supporting the authorities to better implement an isolation
agenda. In fact, the lack of coordinated governmental actions in
many cities in Brazil has fostered the development of maps and
open databases, creating parallel solutions [94]. Also, such actions
are being strongly supported by universities and research centres.
In a combined effort of the State University of Feira de Santana
and other universities in Bahia, a Northeast state in Brazil, the
‘Portal GeoCovid-19’ combines data of all the country's confirmed
cases and deaths, generating charts and projections [95]. That open
web-based portal can then support public authorities to have a
better understanding of the disease situation across the country.
Another effort was designed by the Federal University of Rio
Grande do Norte, also in the Northeast region. The designed
system performs sociodemographic analysis about COVID-19
effects on people's lives, associating data related to violence,
unemployment, poverty, education, among others [96].
Actually, Brazil is struggling to face this pandemic and it is still
too early to say what its impacts will be. By the end of May, Brazil
was already the second country worldwide with the greatest
number of confirmed cases, even with under-reporting.
Nevertheless, many promising initiatives are emerging, mostly
exploiting big data analyses for better understating of the
contagious rates and quarantine monitoring. Owing to the
economic and political importance of Brazil, as well as its
challenges as an under-developed country, its success and failure
cases may give important clues to how the world may prepare for
the next pandemics, and what mistakes should be avoided.
The COVID-19 pandemic that was declared on 11 March 2020 has
affected countries on all continents, dramatically impacting our
lives. Although the final death toll and economic impacts are not
yet known, with bad news still coming from this pandemic, it is
certain that the mankind will have to deal with new outbreaks.
Therefore, we should be prepared to face the next pandemic in a
better possible way.
A crucial element to predict, detect, and mitigate a pandemic is
‘data’. As discussed in this study, data can be retrieved from
different sources and the increase of data sources should be
pursued by the cities. Actually, the required actions to create smart
cities can come from different areas, but governments should play
the leading role in this process, particularly with the definition of
laws and budgets for it. Hence, when the effects of the COVID-19
pandemic are relieved, the world should start to prepare for the
next pandemic.
The surveyed works indicated promising solutions to be
adopted by cities, but there is no golden rule. In fact, each city has
to consider its particularities when implementing the most
appropriate technologies and systems. The discussed cities
provided some clues on how to do that, but each urban area will
pose particular challenges that must be properly addressed.
Nevertheless, the reward may be compensatory.
As our last comments, pandemics are real and scientists around
the globe expect that they will be more frequent and potentially
more deadly. The preparedness for them is urgent and governments
must take it seriously. Nevertheless, the construction of more
efficient smart cities can significantly support better responses to
outbreaks, which may be crucial when saving lives.
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... The other two types of hazards, Urban and Health, are both causes of human-induced disasters. We propose to subdivide them into two different groups due to the expected relevance that outbreaks surveillance and detection systems should assume in the development of smart cities [40]- [42]. This way, Urban hazards will be related to the way we live in cities, with increasing overpopulated areas and crowded mobility systems, resulting in hazards related to traffic accidents, house firing, gas explosion, building collapsing, terrorist attacks, violent protests, robbery, etc. ...
... Roughly speaking, the spread of a modern infectious disease can be perceived as a urban emergency [40]. This way, detection, alerting, and mitigation actions can be performed to handle an infectious disease outbreak or even a pandemic. ...
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The rapid urbanization process in the last century has deeply changed the way we live and interact with each other. As most people now live in urban areas, cities are experiencing growing demands for more efficient and sustainable public services that may improve the perceived quality of life, specially with the anticipated impacts of climatic changes. In this already complex scenario with increasingly overcrowded urban areas, different types of emergency situations may happen anywhere and anytime, with unpredictable costs in human lives and economic losses. In order to cope with unexpected and potentially dangerous emergencies, smart cities initiatives have been developed in different cities, addressing multiple aspects of emergencies detection, alerting, and mitigation. In this context, this article surveys recent smart city solutions for crisis management, proposing definitions for emergencies-oriented systems and classifying them according to the employed technologies and provided services. Additionally, recent developments in the domains of Internet of Things, Artificial Intelligence and Big Data are also highlighted when associated to the management of urban emergencies, potentially paving the way for new developments while classifying and organizing them according to different criteria. Finally, open research challenges will be identified, indicating promising trends and research directions for the coming years.
... The digital technology which had already been leveraged by China helped to accelerate, and optimize health care service even before the outbreak. Similarly, digital health technology has been deployed to address the most urgent needs during the pandemic to track the immediate outbreak response and later mitigate the impact [15]. Today several affected countries severely and truly followed Chinese model for the best use of technology to fight against COVID'19 pandemic to save their population. ...
The pandemic of Covid-19, an epidemic outbreak created many challenges and increased the demand for medical equipments, medicines, and related accessories and most of them had to be imported from abroad. The advanced information technology (Industry 4.0) was considered imperative to track and monitor the spread of the SARS-2 Virus that is Covid-19. A detailed review of literature is done to understand the challenges and the remedial action taken so far during the Covid-19 epidemic outbreak had been gone through using appropriate search engines and databases like Google-search, Science Direct, Scopus, Research Gate, and relevant blogs. The case reports were also considered in this study. We have found ten significant challenges (barriers) and identified several useful technology of industry 4.0 to control and manage the Covid-19 pandemic. This research paper is an attempt to examine and discuss the application of 4.0 technologies in containing the pandemic outbreak. Ten challenges were identified and those could be overcome by promptly applying appropriate technologies of industry 4.0 to control the spread of virus. These technologies help to educate and communicate the public and make them aware of the hazardous attack of Covid-19 virus when properly used.
... In today's world, the medical profession is equipped with cutting-edge technology that is utilized to treat a wide range of life-threatening ailments. Damaged tissue, for example, can be replicated using nanotechnology (Costa & Peixoto, 2020). Artificial Stimulated Cells, 3D printed Artificial Organs, Smart Inhalers, Precision Medicines, Health Wearables, and other recent medical advances include Robotic Surgeries, Wireless Brain Sensors, and more. ...
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The Industry 5.0 system is analogous to a system in which machines provide services and communicate information with products in real-time. It offers numerous capabilities that aid in any system's automation, monitoring, and diagnostics. It is a process for transitioning from dominating machine production to digital manufacturing. The Industry 5.0 standard must be thoroughly understood to achieve a successful transformation, and a detailed road map must be developed and implemented. Several approaches and conversations are now underway to produce roadmaps, some of which are discussed in this paper. Evaluating Industry 5.0 components and features is critical for defining the fundamental pillars of a concrete future manufacturing environment as well as a specific ranking position in India. This study aims to describe, examine, and compare some of the most promising existing methodologies for calculating national Industry 5.0 readiness.
... The digital technology which had already been leveraged by China helped to accelerate, and optimize health care service even before the outbreak. Similarly, digital health technology has been deployed to address the most urgent needs during the pandemic to track the immediate outbreak response and later mitigate the impact [15]. Today several affected countries severely and truly followed Chinese model for the best use of technology to fight against COVID'19 pandemic to save their population. ...
The pandemic of Covid-19, an epidemic outbreak created many challenges and increased the demand for medical equipments, medicines, and related accessories and most of them had to be imported from abroad. The advanced information technology (Industry 4.0) was considered imperative to track and monitor the spread of the SARS-2 Virus that is Covid-19. A detailed review of literature is done to understand the challenges and the remedial action taken so far during the Covid-19 epidemic outbreak had been gone through using appropriate search engines and databases like Google-search, Science Direct, Scopus, Research Gate, and relevant blogs. The case reports were also considered in this study. We have found ten significant challenges (barriers) and identified several useful technology of industry 4.0 to control and manage the Covid-19 pandemic. This research paper is an attempt to examine and discuss the application of 4.0 technologies in containing the pandemic outbreak. Ten challenges were identified and those could be overcome by promptly applying appropriate technologies of industry 4.0 to control the spread of virus. These technologies help to educate and communicate the public and make them aware of the hazardous attack of Covid-19 virus when properly used.
... • To support distant monitoring and diagnostic [82][83][84]; particularly in an era like the current COVID-19 pandemic one [85][86][87][88]; • To deal with vehicle accident detection, reporting [80]; • To collect road loads for computing a continuously updated (medical) emergency evacuation path [80,83,89]; ...
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Social Media and the Internet of Things are nowadays full and strong components of day-to-day life worldwide. Both allow communicating with others 24 hours a day, 7 days a week without distance limitations. During the last decade, on-site citizens have shared disaster-related first reports on social media. Official institutions are using the same framework for delivering up-to-date and follow-up directives. Moreover, monitoring health risks, patients, and systems behavior in real-time over the Internet-of-Things allows detecting different levels of anomalies that might lead to critical events that need to be managed as an emergency. Emergency and disaster medicines deal with broad and complex medical, surgical, mental health, epidemiological, managerial, and communicational issues. Social Media platforms and the Internet of Things are technologies that increase cyber-physical interactions between individuals, machines, and their environment. The generated data over time are massive and are supporting the emergency or disaster mitigation process. This chapter deals with, in the first section, the social media platforms, and the Internet of Things. Then, at a second one, the concepts of emergency, disaster medicine and management are discussed. In the following two sections, we discuss applications and usages of social media and IoT technologies for improving the management (preparedness, response, recovery, mitigation) of emergencies and disasters as fundamental keys and pillars for efficiently handling the managerial information flow in emergency and disaster contexts.
... Nonetheless, to be effective against pandemics, smart cities must change into more collaborative, robust, proactive, and integrated environments [25]. Despite this issue, the implementation and promotion of new governance models have not received adequate attention [107] and many smart cities were as much unprepared for the COVID-19 pandemic as other cities without a smart-city platform [41]. ...
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The COVID-19 pandemic has introduced important challenges into public management models in all levels of public administrations, with special focus on the local government level due to both the impact of pandemics in the local space and the impact of public policies on the quality of life of the citizenry. The experience in facing this COVID-19 pandemic show the existence of conflicting interests not only regarding the inconsistency of the information disclosure, but also in the public policies taken to tackle the pandemic, which has produced ineffective measures against this epidemiological context. The emergence of new technologies has the potential to redesign more robust governance models using Open Government mechanisms to be more effective in the fight again health pandemic and disaster management, achieving more resilient cities. Putting the focus on this issue, this paper debates about the lessons learned from the management of the COVID-19 pandemic and raises some research questions to be solved by future research. Concretely, this paper advocates the need for the introduction of new aligned and collaborative governance models implementing emerging technologies embedded in Open Government projects as efficient mechanisms to achieve more collaborative and resilient smart cities.
Accurate prediction of Public Transport (PT) mobility is important for intelligent transportation. Nowadays, mobility data have become increasingly available with the General Transit Feed Specification (GTFS) being the format for PT agencies to disseminate such data. Estimated Time of Arrival (ETA) of PT is crucial for the public, as well as the PT agency for logistics, route-optimization, maintenance, etc. However, prediction of PT-ETA is a challenging task, due to the complex and non-stationary urban traffic. This work introduces a novel data-driven approach for predicting PT-ETA based on RBF neural networks, using a modified version of the successful PSO-NSFM algorithm for training. Additionally, a novel pre-processing pipeline (CR-GTFS) is designed for cleansing and reconstructing the GTFS data. The combination of PSO-NSFM and CR-GTFS introduces a complete framework for predicting PT-ETA accurately with real-world data feeds. Experiments on GTFS data verify the proposed approach, outperforming state-of-the-art in prediction accuracy and computational times.
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(A&EI) as a novel subfield became obvious. As in all areas of Biomedical Informatics, A&EI must deal with issues such as relevant data collection, the management of data extracted from accident sites, health records or sensors, wearables and apps, and appropriate data processing, with the dual purpose of preventing harm and decision support. This book is an introduction to the research and application domain of A&EI, and is the product of three years’ work by the Working Group in A&EI of the International Medical Informatics Association (IMIA). The book presents ten chapters organized in four sections. The first section explores the framework for achieving an emergency-informatics health information infrastructure; the second focuses on the gathering of critical clinical data related to the building up of a smart environment for A&EI; the third introduces state-of-the-art technologies for integration into virtual emergency registries; and the final part considers the delicate issues of patient safety raised by the introduction of surveillance technologies into clinical care, along with other issues presenting challenges to the domain of A&EI for the future. The book is an important contribution to the field of A&EI, and will be of interest to healthcare professionals, informaticians, and all those who want a better understanding of the domain of Accident and Emergency Informatics.
The 21st century has seen an enormous growth in emergency medical services (EMS) information technology systems, with corresponding accumulation of large volumes of data. Despite this growth, integration efforts between EMS-based systems and electronic health records, and public-sector databases have been limited due to inconsistent data structure, data missingness, and policy and regulatory obstacles. Efforts to integrate EMS systems have benefited from the evolving science of entity resolution and record linkage. In this chapter, we present the history and fundamentals of record linkage techniques, an overview of past uses of this technology in EMS, and a look into the future of record linkage techniques for integrating EMS data systems including the use of machine learning-based techniques.
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Vietnam, with a geographical proximity and a high volume of trade with China, was the first country to record an outbreak of the new Coronavirus disease (COVID-19), caused by the Severe Acute Respiratory Syndrome Coronavirus 2 or SARS-CoV-2. While the country was expected to have a high risk of transmission, as of April 4, 2020—in comparison to attempts to contain the disease around the world—responses from Vietnam are being seen as prompt and effective in protecting the interests of its citizens, with 239 confirmed cases and no fatalities. This study analyzes the situation in terms of Vietnam’s policy response, social media and science journalism. A self-made web crawl engine was used to scan and collect official media news related to COVID-19 between the beginning of January and April 4, yielding a comprehensive dataset of 14,952 news items. The findings shed light on how Vietnam—despite being under-resourced—has demonstrated political readiness to combat the emerging pandemic since the earliest days. Timely communication on any developments of the outbreak from the government and the media, combined with up-to-date research on the new virus by the Vietnamese science community, have altogether provided reliable sources of information. By emphasizing the need for immediate and genuine cooperation between government, civil society and private individuals, the case study offers valuable lessons for other nations concerning not only the concurrent fight against the COVID-19 pandemic but also the overall responses to a public health crisis.
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In the wake of community coronavirus disease 2019 (COVID-19) transmission in the United States, there is a growing public health concern regarding the adequacy of resources to treat infected cases. Hospital beds, intensive care units (ICUs), and ventilators are vital for the treatment of patients with severe illness. To project the timing of the outbreak peak and the number of ICU beds required at peak, we simulated a COVID-19 outbreak parameterized with the US population demographics. In scenario analyses, we varied the delay from symptom onset to self-isolation, the proportion of symptomatic individuals practicing self-isolation, and the basic reproduction number R0 . Without self-isolation, when R0 = 2.5, treatment of critically ill individuals at the outbreak peak would require 3.8 times more ICU beds than exist in the United States. Self-isolation by 20% of cases 24 h after symptom onset would delay and flatten the outbreak trajectory, reducing the number of ICU beds needed at the peak by 48.4% (interquartile range 46.4–50.3%), although still exceeding existing capacity. When R0 = 2, twice as many ICU beds would be required at the peak of outbreak in the absence of self-isolation. In this scenario, the proportional impact of self-isolation within 24 h on reducing the peak number of ICU beds is substantially higher at 73.5% (interquartile range 71.4–75.3%). Our estimates underscore the inadequacy of critical care capacity to handle the burgeoning outbreak. Policies that encourage self-isolation, such as paid sick leave, may delay the epidemic peak, giving a window of time that could facilitate emergency mobilization to expand hospital capacity.
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The ongoing outbreak of the novel coronavirus disease (COVID-19) that occurred in China is rapidly spreading globally. China’s bond and strict containment measures have been proved (in practice) to significantly reduce the spread of the epidemic. This was obtained through the use of emergency control measures in the epidemic areas and the integration of resources from multiple systems, including business, community, technology, education, and transportation, across the country. In order to better understand how China has managed to reduce the public health and economic impacts of the COVID-19 epidemic, this editorial systematically reviews the specific measures for infection prevention and control of the disease. The best practices for COVID-19 eradication in China provide evidence-based strategies that could be replicated in other countries.
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Over the last two decades, the world experienced three outbreaks of coronaviruses with elevated morbidity rates. Currently, the global community is facing emerging virus SARS-CoV-2 belonging to Betacoronavirus, which appears to be more transmissible but less deadly than SARS-CoV. The current study aimed to track the evolutionary ancestors and different evolutionary strategies that were genetically adapted by SARS-CoV-2. Our whole-genome analysis revealed that SARS-CoV-2 was the descendant of Bat SARS/SARS-like CoVs and bats served as a natural reservoir. SARS-CoV-2 used mutations and recombination as crucial strategies in different genomic regions including the envelop, membrane, nucleocapsid, and spike glycoproteins to become a novel infectious agent. We confirmed that mutations in different genomic regions of SARS-CoV-2 have specific influence on virus reproductive adaptability, allowing for genotype adjustment and adaptations in rapidly changing environments. Moreover, for the first time we identified nine putative recombination patterns in SARS-CoV-2, which encompass spike glycoprotein, RdRp, helicase and ORF3a. Six recombination regions were spotted in the S gene and are undoubtedly important for evolutionary survival, meanwhile this permitted the virus to modify superficial antigenicity to find a way from immune reconnaissance in animals and adapt to a human host. With these combined natural selected strategies, SARS-CoV-2 emerged as a novel virus in human society.
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Nowadays, sustainable development is considered a key concept and solution in creating a promising and prosperous future for human societies. Nevertheless, there are some predicted and unpredicted problems that epidemic diseases are real and complex problems. Hence, in this research work, a serious challenge in the sustainable development process was investigated using the classification of confirmed cases of COVID-19 (new version of Coronavirus) as one of the epidemic diseases. Hence, binary classification modeling was used by the group method of data handling (GMDH) type of neural network as one of the artificial intelligence methods. For this purpose, the Hubei province in China was selected as a case study to construct the proposed model, and some important factors, namely maximum, minimum, and average daily temperature, the density of a city, relative humidity, and wind speed, were considered as the input dataset, and the number of confirmed cases was selected as the output dataset for 30 days. The proposed binary classification model provides higher performance capacity in predicting the confirmed cases. In addition, regression analysis has been done and the trend of confirmed cases compared with the fluctuations of daily weather parameters (wind, humidity, and average temperature). The results demonstrated that the relative humidity and maximum daily temperature had the highest impact on the confirmed cases. The relative humidity in the main case study, with an average of 77.9%, affected positively, and maximum daily temperature, with an average of 15.4 • C, affected negatively, the confirmed cases.
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In December 2019, a cluster of fatal pneumonia cases presented in Wuhan, China. They were caused by a previously unknown coronavirus. All patients had been associated with the Wuhan Wholefood market, where seafood and live animals are sold. The virus spread rapidly and public health authorities in China initiated a containment effort. However, by that time, travelers had carried the virus to many countries, sparking memories of the previous coronavirus epidemics, severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), and causing widespread media attention and panic. Based on clinical criteria and available serological and molecular information, the new disease was called coronavirus disease of 2019 (COVID-19), and the novel coronavirus was called SARS Coronavirus-2 (SARS-CoV-2), emphasizing its close relationship to the 2002 SARS virus (SARS-CoV). The scientific community raced to uncover the origin of the virus, understand the pathogenesis of the disease, develop treatment options, define the risk factors, and work on vaccine development. Here we present a summary of current knowledge regarding the novel coronavirus and the disease it causes.
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Over the past century, society has achieved great gains in medicine, public health, and health-care infrastructure, particularly in the areas of vaccines, antibiotics, sanitation, intensive care and medical technology. Still, despite these developments, infectious diseases are emerging at unprecedented rates around the globe. Large urban centers are particularly vulnerable to communicable disease events, and must have well-prepared response systems, including on the front-line level. In November 2018, the United States’ largest municipal health-care delivery system, New York City Health + Hospitals, hosted a half-day executive-level pandemic response workshop, which sought to illustrate the complexity of preparing for, responding to, and recovering from modern-day infectious diseases impacting urban environments. Attendees were subjected to a condensed, plausible, pandemic influenza scenario and asked to simulate the high-level strategic decisions made by leaders by internal (eg, Chief Medical Officer, Chief Nursing Officer, and Legal Affairs) and external (eg, city, state, and federal public health and emergency management entities) partners across an integrated system of acute, postacute, and ambulatory sites, challenging players to question their assumptions about managing the consequences of a highly pathogenic pandemic.
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Background An outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to 95 333 confirmed cases as of March 5, 2020. Understanding the early transmission dynamics of the infection and evaluating the effectiveness of control measures is crucial for assessing the potential for sustained transmission to occur in new areas. Combining a mathematical model of severe SARS-CoV-2 transmission with four datasets from within and outside Wuhan, we estimated how transmission in Wuhan varied between December, 2019, and February, 2020. We used these estimates to assess the potential for sustained human-to-human transmission to occur in locations outside Wuhan if cases were introduced. Methods We combined a stochastic transmission model with data on cases of coronavirus disease 2019 (COVID-19) in Wuhan and international cases that originated in Wuhan to estimate how transmission had varied over time during January, 2020, and February, 2020. Based on these estimates, we then calculated the probability that newly introduced cases might generate outbreaks in other areas. To estimate the early dynamics of transmission in Wuhan, we fitted a stochastic transmission dynamic model to multiple publicly available datasets on cases in Wuhan and internationally exported cases from Wuhan. The four datasets we fitted to were: daily number of new internationally exported cases (or lack thereof), by date of onset, as of Jan 26, 2020; daily number of new cases in Wuhan with no market exposure, by date of onset, between Dec 1, 2019, and Jan 1, 2020; daily number of new cases in China, by date of onset, between Dec 29, 2019, and Jan 23, 2020; and proportion of infected passengers on evacuation flights between Jan 29, 2020, and Feb 4, 2020. We used an additional two datasets for comparison with model outputs: daily number of new exported cases from Wuhan (or lack thereof) in countries with high connectivity to Wuhan (ie, top 20 most at-risk countries), by date of confirmation, as of Feb 10, 2020; and data on new confirmed cases reported in Wuhan between Jan 16, 2020, and Feb 11, 2020. Findings We estimated that the median daily reproduction number (Rt) in Wuhan declined from 2·35 (95% CI 1·15–4·77) 1 week before travel restrictions were introduced on Jan 23, 2020, to 1·05 (0·41–2·39) 1 week after. Based on our estimates of Rt, assuming SARS-like variation, we calculated that in locations with similar transmission potential to Wuhan in early January, once there are at least four independently introduced cases, there is a more than 50% chance the infection will establish within that population. Interpretation Our results show that COVID-19 transmission probably declined in Wuhan during late January, 2020, coinciding with the introduction of travel control measures. As more cases arrive in international locations with similar transmission potential to Wuhan before these control measures, it is likely many chains of transmission will fail to establish initially, but might lead to new outbreaks eventually. Funding Wellcome Trust, Health Data Research UK, Bill & Melinda Gates Foundation, and National Institute for Health Research.