Geographical tracking and mapping of coronavirus disease COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic and associated events around the world: How 21st century GIS technologies are supporting the global fight against outbreaks and epidemics

Abstract

In December 2019, a new virus (initially called 'Novel Coronavirus 2019-nCoV' and later renamed to SARS-CoV-2) causing severe acute respiratory syndrome (coronavirus disease COVID-19) emerged in Wuhan, Hubei Province, China, and rapidly spread to other parts of China and other countries around the world, despite China's massive efforts to contain the disease within Hubei. As with the original SARS-CoV epidemic of 2002/2003 and with seasonal influenza, geographic information systems and methods, including, among other application possibilities, online real- or near-real-time mapping of disease cases and of social media reactions to disease spread, predictive risk mapping using population travel data, and tracing and mapping super-spreader trajectories and contacts across space and time, are proving indispensable for timely and effective epidemic monitoring and response. This paper offers pointers to, and describes, a range of practical online/mobile GIS and mapping dashboards and applications for tracking the 2019/2020 coronavirus epidemic and associated events as they unfold around the world. Some of these dashboards and applications are receiving data updates in near-real-time (at the time of writing), and one of them is meant for individual users (in China) to check if the app user has had any close contact with a person confirmed or suspected to have been infected with SARS-CoV-2 in the recent past. We also discuss additional ways GIS can support the fight against infectious disease outbreaks and epidemics.

Full text

KamelBoulosand Geraghty
Int J Health Geogr (2020) 19:8
https://doi.org/10.1186/s12942-020-00202-8
EDITORIAL
Geographical tracking andmapping
ofcoronavirus disease COVID-19/severe
acute respiratory syndrome coronavirus 2
(SARS-CoV-2) epidemic andassociated events
aroundtheworld: how21st century GIS
technologies are supporting theglobal ght
againstoutbreaks andepidemics
Maged N. Kamel Boulos1* and Estella M. Geraghty2
Abstract
In December 2019, a new virus (initially called ‘Novel Coronavirus 2019-nCoV’ and later renamed to SARS-CoV-2) caus-
ing severe acute respiratory syndrome (coronavirus disease COVID-19) emerged in Wuhan, Hubei Province, China,
and rapidly spread to other parts of China and other countries around the world, despite China’s massive efforts to
contain the disease within Hubei. As with the original SARS-CoV epidemic of 2002/2003 and with seasonal influenza,
geographic information systems and methods, including, among other application possibilities, online real-or near-
real-time mapping of disease cases and of social media reactions to disease spread, predictive risk mapping using
population travel data, and tracing and mapping super-spreader trajectories and contacts across space and time,
are proving indispensable for timely and effective epidemic monitoring and response. This paper offers pointers to,
and describes, a range of practical online/mobile GIS and mapping dashboards and applications for tracking the
2019/2020 coronavirus epidemic and associated events as they unfold around the world. Some of these dashboards
and applications are receiving data updates in near-real-time (at the time of writing), and one of them is meant for
individual users (in China) to check if the app user has had any close contact with a person confirmed or suspected
to have been infected with SARS-CoV-2 in the recent past. We also discuss additional ways GIS can support the fight
against infectious disease outbreaks and epidemics.
Keywords: COVID-19, SARS-CoV-2, GIS
© The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and
the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material
in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material
is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the
permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco
mmons .org/licen ses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creat iveco mmons .org/publi cdoma in/
zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Introduction
In December 2019, a new virus (initially called ‘Novel
Coronavirus 2019-nCoV’ and later renamed to SARS-
CoV-2) causing severe acute respiratory syndrome (coro-
navirus disease COVID-19) emerged in Wuhan, Hubei
Province, China [1], and rapidly spread to other parts
of China and other countries around the world, despite
Open Access
International Journal of
Health Geographics
*Correspondence: mnkboulos@mail.sysu.edu.cn
1 School of Information Management, Sun Yat-sen University, East
Campus, Guangzhou 510006, Guangdong, China
Full list of author information is available at the end of the article

Page 2 of 12
KamelBoulosand Geraghty Int J Health Geogr (2020) 19:8
China’s massive efforts to contain the disease within
Hubei.
Compared to the 2002/2003 SARS-CoV and the
2012–2014 MERS-CoV (Middle East Respiratory Syn-
drome-related coronavirus), the COVID-19 coronavirus
spread strikingly fast. While MERS took about two and a
half years to infect 1000 people, and SARS took roughly
4 months, the novel SARS-CoV-2 reached that figure
in just 48days. On 30 January 2020, the World Health
Organization (WHO) declared that the new SARS-CoV-2
coronavirus outbreak constitutes a Public Health Emer-
gency of International Concern (PHEIC) [2].
As with the original SARS-CoV epidemic of 2002/2003
[3] and with seasonal influenza [4, 5], geographic infor-
mation systems (GIS) and methods, including, among
other application possibilities, online real- or near-real-
time mapping of disease cases and of social media reac-
tions to disease spread, predictive risk mapping using
population travel data, and tracing and mapping super-
spreader trajectories and contacts across space and time
(see, as an example, the first diagram in [6]), are proving
indispensable for our timely understanding of the new
disease source, dynamics and epidemiology, and in shap-
ing our effective response to it.
Indeed, health professionals have long considered con-
ventional mapping, and more recently geographic infor-
mation systems (GIS), as critical tools in tracking and
combating contagion. e earliest map visualisation of
the relationship between place and health was in 1694 on
plague containment in Italy [7]. e value of maps as a
communication tool blossomed over the next 225years
in the service of understanding and tracking infectious
diseases, such as yellow fever, cholera and the 1918 influ-
enza pandemic. From the 1960s, when computerised
geographic information systems were born, the possi-
bilities for analysing, visualising and detecting patterns
of disease dramatically increased again. A 2014 review
of the health GIS literature found that 248 out of 865
included papers (28.7%) focused on infectious disease
mapping [8].
Since then we have seen a revolution in applied health
geography through Web-based tools [9, 10]. Now, as we
deploy these tools to protect human lives, we can ingest
big data from their sources and display results in inter-
active and near-real-time dashboards. ese online
dashboards have become a pivotal source of information
during the COVID-19 outbreak.
is paper offers pointers to, and describes, a range
of practical online/mobile GIS and mapping dashboards
and applications for tracking the coronavirus epidemic
and associated events as they unfold around the world.
Some of these dashboards and applications are receiving
data updates in near-real-time (at the time of writing),
and one of them is meant for individual users (in China)
to check if the app user has had any close contact with
a person confirmed or suspected to have been infected
with SARS-CoV-2 in the recent past. We also briefly dis-
cuss additional ways GIS can support the fight against
infectious disease outbreaks and epidemics.
Johns Hopkins University Center forSystems
Science andEngineering dashboard
When disease can travel so quickly, information has to
move even faster. is is where map-based dashboards
become crucial [11]. At the time of this writing in mid-
February 2020, seven coronavirus dashboards are among
the top ten requested applications from Esri ArcGIS
Online service, accumulating over 160 million views.
First published on 22 January 2020 in response to esca-
lating pandemic fears in late January 2020, the Johns
Hopkins University’s Center for Systems Science and
Engineering (JHU CSSE) dashboard leads the pack, gar-
nering 140 million views. Developed by Lauren Gardner
(an epidemiologist) and her team from the JHU CSSE, the
dashboard went viral with hundreds of news articles and
shares on social media (Fig.1) [12]. is intense response
to the JHU CSSE and other dashboards shows how eager
people are to track health threats. Anyone with Inter-
net access can learn, in a few short clicks, a tremendous
amount of information about the COVID-19 virus from
these resources.
e JHU CSSE dashboard’s interactive map locates
and tallies confirmed infections, fatalities and recoveries.
Graphs detail virus progress over time. Viewers can see
the day and time of the most recent data update and data
sources. e dashboard’s five authoritative data sources
include World Health Organization (WHO), US Cent-
ers for Disease Control and Prevention, National Health
Commission of the People’s Republic of China, European
Centre for Disease Prevention and Control, and the Chi-
nese online medical resource DXY.cn. e dashboard
provides links to these sources and others. A blog post
[13] details this work. e corresponding data repository
is accessible as Google sheets in GitHub [14].
Web services allow GIS users to consume and display
disparate data inputs without central hosting or process-
ing to ease data sharing and speed information aggre-
gation. In the first dashboard iteration, from January
22 through January 31, 2020, the Hopkins team manu-
ally updated data twice per day. In February 2020, Esri’s
ArcGIS Living Atlas team assisted them in adopting a
semi-automated living data stream strategy to update
the dashboard. It primarily relies on the DXY.cn data
resource, which updates every 15 min for case reports
at provincial and country levels. However, Lauren Gard-
ner’s blog [13] notes that for countries outside of China,

Page 3 of 12
KamelBoulosand Geraghty Int J Health Geogr (2020) 19:8
other data resources were quicker to update than DXY.
cn, so those case counts are manually updated through-
out the day. Current feature layers are freely accessible in
the ArcGIS Living Atlas [15].
Yet, the COVID-19 outbreak has been difficult to
monitor. As Gardner explains, “it is especially challeng-
ing to collect good data at a fine spatial resolution, which
is what most people want to know, and without having
travel data in real time that captures these altered mobil-
ity patterns, it is hard to assess what the geographic risk
profile will look like moving forward” [16].
e JHU CSSE dashboard (at the time of writing) lacks
archiving services for full retrospective map visualisa-
tion of data from previous days. As far as the latter are
concerned, the dashboard only offers timeline charts of
total confirmed cases (grouped into ‘mainland China’
and ‘other locations’) and total recovered cases. But one
is unable to retrieve and display detailed map snapshots
in time (by individual country/region and Chinese prov-
ince), e.g., to see how the coronavirus world distribu-
tion map looked in the past on a specific day, such as 25
January 2020. e dashboard developers are encouraged
to compile and make such interactive daily map snap-
shots permanently accessible online for future reference
after the epidemic has gone, as a service to public health
researchers and professionals worldwide.
The World Health Organization dashboard
e WHO directs and coordinates international health,
combating communicable diseases through surveillance,
preparedness and response, and applying GIS technol-
ogy to this work. On 26 January 2020, the WHO unveiled
its ArcGIS Operations Dashboard for COVID-19, which
also maps and lists coronavirus cases and total number of
deaths by country and Chinese province, with informa-
tional panels about the map and its data resources (Fig.2)
[17].
Prior to 18 February 2020, the WHO and JHU CSSE
dashboards had some interesting differences. Each had a
vastly different total case count as can be seen in Figs.1
and 2 (both taken on 16 February 2020). e WHO dash-
board reflected laboratory-confirmed cases, whereas
JHU CSSE included cases diagnosed using a symptom
array plus chest imaging (accounting for some 18,000
additional reports). However, as of 19 February 2020,
both dashboards are in sync, displaying similar total case
counts.
Fig. 1 Johns Hopkins University CSSE is tracking the spread of SARS-CoV-2 in near real time with a map-centric dashboard (using ArcGIS Online)
that pulls relevant data from the WHO, US CDC (Centers for Disease Control and Prevention), ECDC (European Centre for Disease Prevention and
Control), Chinese Center for Disease Control and Prevention (CCDC), NHC (China’s National Health Commission), and Dingxiangyuan (DXY, China).
Screenshot date: 16 February 2020

Page 4 of 12
KamelBoulosand Geraghty Int J Health Geogr (2020) 19:8
e WHO dashboard includes an epidemic curve up
front, showing cases by date of reporting. Putting the epi
curve visualisation above the cumulative cases graph pro-
vides important information about outbreak progression,
and may decrease fear since we can see a steady decline
in the total number of new cases per day since 4 February
2020 (except for a spike on 13 February 2020, when more
than 15K cases were added after China started to include
‘clinically diagnosed’ cases and not just laboratory-con-
firmed cases in its figures).
A ‘hamburger’ menu at the top right corner of the
WHO dashboard provides links to additional informa-
tion about COVID-19 and leads to an interactive Web
map that puts COVID-19 into context among other
WHO-monitored health emergencies, such as dengue
fever, Rift Valley fever and West Nile fever [18].
e WHO is updating its COVID-19 dashboard auto-
matically using ArcGIS GeoEvent Server to push updates
to a single feature service multiple times per day. e
WHO dashboard optimisation measures include moving
tiled data off its server and into ArcGIS Online tiled ser-
vices to benefit from Esri’s content delivery network. is
allows good map performance at 10–12 levels of zoom.
Both the WHO and JHU CSSE dashboards consider the
importance of mobile devices. Among dashboards built
with Esri’s ArcGIS Operations Dashboard app, nearly 8%
of viewers choose those built for mobile consumption.
Consistent with how people want to receive information,
mobile-optimised dashboards are versatile and accessible
on phones or tablets.
HealthMap: analysing andmapping online
informal sources
Founded in 2006, HealthMap is run by a team of
researchers, epidemiologists and software developers at
Boston Children’s Hospital, USA, and uses online media
sources for real-time surveillance of emerging public
health threats. HealthMap collates outbreak data from a
range of sources, including news media (e.g., via Google
News), social media, validated official alerts (e.g., from
the WHO) and expert-curated accounts [19]. Health-
Map’s interactive map for SARS-CoV-2 available at [20]
offers near-real-time geolocated updates from these
sources to better understand the progression of the pan-
demic (Fig.3).
In the same vein as HealthMap, BlueDot, a Canadian
firm specialising in automated infectious disease surveil-
lance [21], uses machine learning and natural language
processing techniques to sift through news reports in 65
languages, forum and blog posts, airline ticketing data,
animal disease networks, etc., to pick up indications and
news of unusual, unfolding events and possible disease
outbreaks. e firm employs trained epidemiologists to
further analyse outbreak results obtained by automated
Fig. 2 The WHO COVID-19 situation dashboard. Screenshot date: 16 February 2020

Page 5 of 12
KamelBoulosand Geraghty Int J Health Geogr (2020) 19:8
means before releasing them to its clients, and is said to
have been among the earliest to break news of COVID-
19 outbreak as it started in China.
HealthMap also offers an ‘outbreaks near me’ feature
that informs individual users about nearby disease trans-
mission risks based on their current location as obtained
from their Web browser/smartphone (Fig.4).
China’s coronavirus ‘close contact detector’
geosocial app andpublic service platform
While government travel restrictions initiate social dis-
tancing, it is now possible for individuals to further the
cause by using a dedicated app that provides a detailed
spatial scale to support informed personal decisions
about self-quarantine and seeking medical treatment.
Co-launched by China’s National Health Commission
and China Electronics Technology Group Corporation,
the ‘close contact detector’ app/platform uses big data
from public authorities about the movement of people
(public transport data covering flights and trains [book-
ing a train seat in China requires the input of ID infor-
mation]), as well as disease case records, to check if the
user has had any close contact with a person confirmed
or suspected to have been infected in the recent past
(Fig.5). e platform can inform the user based on her/
his location and recent movements whether s/he has
within the last 2 weeks (the assumed incubation period
of COVID-19) worked together, shared a classroom, lived
in the same building, or travelled by train (all passengers
and crew members in the same carriage) or plane (cabin
staff and passengers within three rows of an infected per-
son) with a person confirmed or suspected to have the
virus. ‘Close contact detector’ can be accessed via three
of the most popular mobile social and payment apps in
China, namely Alipay, WeChat and QQ [22].
e platform might raise some location data pri-
vacy questions among some audiences, even though it
explicitly asks its users to observe China’s cybersecurity
laws and not abuse private information, and has been
very well received by the public in China. As explained
by Carolyn Bigg, a Hong Kong-based technology lawyer
at the law firm DLA Piper, in a comment to the BBC,
“In China, and across Asia, (individual) data are not
seen as something to be locked down, but as something
that can be used, provided this is done in a transpar-
ent way, with consent where needed. From a Chinese
Fig. 3 Screenshot of HealthMap for Wuhan Coronavirus showing a number of news articles and alerts about the first case of COVID-19 reported in
Africa (Egypt) on 13 February 2020. Screenshot date: 17 February 2020. (HealthMap uses base map data from Google.)

Page 6 of 12
KamelBoulosand Geraghty Int J Health Geogr (2020) 19:8
perspective this is a really useful service for people and a
really powerful tool that shows the power of data being
used for good” [22].
A related and complementary voluntary system was
implemented in Guangzhou Underground (Guang-
dong Province, China), so that if a person is later diag-
nosed with coronavirus, it would be easier to track her/
his transport routines and notify related passengers who
boarded the same metro carriages. Starting on 17 Feb-
ruary 2020, every metro carriage in Guangzhou Under-
ground displays a unique QR code that passengers are
invited to scan once they board the carriage. ey then
need to quickly fill in an online form that appears on their
phone, which includes name, ID No. (optional), gender,
their starting metro station and their destination station.
As each carriage has a unique QR code, if the passenger
walks into another carriage during transportation, s/he
will have to scan the new QR code of that carriage and
(auto) fill in the corresponding form again. If the passen-
ger’s journey involves changing to other metro lines to
reach their final destination, then s/he will have to repeat
the same process for every additional carriage they board
(Fig.6).
WorldPop andEpiRisk predictive global risk
analytics andmaps forSARS‑CoV‑2 based
onpopulation movements outofWuhan andtravel
destinations
Human mobility places scientists at a serious disadvan-
tage in slowing potential epidemics. A person can pick
up a virus in one place and share it to another loca-
tion within hours. Among the jet set, there is potential
to become a super spreader [23], infecting many people
across an expansive geographical area. While vaccine
technology has advanced significantly, it still takes a year
or more to formulate a vaccine—time enough for the
virus to reach every corner of the world.
e last trains and scheduled domestic and interna-
tional flights left Wuhan the morning of 23 January 2020,
putting an end to a surge of outbound Chinese Lunar
New Year travel that had started 3 days earlier (Fig.7).
e WHO commended the move by Chinese authorities
Fig. 4 Screenshot of HealthMap’s ‘outbreaks near me’ taken on 17 February 2020. User location has been correctly detected in the United Kingdom,
and the expanded news box for London, UK, shows a number of news stories about nearby COVID-19 cases in the UK (nine cases as of 17 February
2020). China’s ‘close contact detector’ platform (see below) expands this concept of ‘outbreaks near me’ in a very much more detailed fashion (much
finer location granularity). (HealthMap uses base map data from Google.)

Page 7 of 12
KamelBoulosand Geraghty Int J Health Geogr (2020) 19:8
Fig. 5 Screenshots of the ‘close contact detector’ app/platform and related online location-based inquiry services in China. Functions include close
contact inquiry, including train journey number and plane flight number checking for diagnosed cases, and location information about the activity
spaces of nearby confirmed cases (no individual names are ever displayed in returned results). These screenshots were taken on 17 February 2020

Page 8 of 12
KamelBoulosand Geraghty Int J Health Geogr (2020) 19:8
Fig. 6 The Guangzhou Underground COVID-19 tracking and notification service. The actual practical value and ultimate success of such services
deployed for the first time should be documented and confirmed at the end of the outbreak, so that the world community can learn from the
experience
Fig. 7 Left: High-speed rail (in purple; 2016) and domestic flights (2018) into and out of Wuhan. Right: International flights leaving Wuhan (partial
map; 2018). Wuhan, a major regional transit hub, connects directly to dozens of cities in China. Despite strong actions to curb the spread, an
estimated five million people potentially exposed to the virus had already left Wuhan before the city was placed under quarantine, complicating
containment efforts. Understanding travel patterns can help health authorities worldwide establish quarantine stations and passenger screening
programmes at major international airports

Page 9 of 12
KamelBoulosand Geraghty Int J Health Geogr (2020) 19:8
to place Wuhan under quarantine, which was unprec-
edented at scale. By the end of January 2020, Chinese
authorities had enforced further transportation restric-
tions in 15 additional cities.
e WorldPop research group at Southampton Univer-
sity, UK, used historical data and patterns from Baidu’s
location services and international flight itineraries to
better understand population movements out of Wuhan
prior to the city’s lockdown, look at travellers’ volumes
and destinations across China and the world, and com-
pile a predictive global risk map for the likely spread of
SARS-CoV-2 virus from its epicentre in Wuhan. Accord-
ing to WorldPop’s analysis, within mainland China, the
cities of Beijing, Guangzhou, Shanghai and Chongqing
are all identified as high-risk, while the most ‘at-risk’
places worldwide are ailand (1st), Japan (2nd) and
Hong Kong (3rd), followed by the USA (6th), Australia
(10th) and the UK (17th). Detailed results and maps can
be accessed on the WorldPop online portal [24].
It is noteworthy that WorldPop also mapped popula-
tion distributions and mobility patterns (mobile phone
flow maps using mobile telecom data) in West African
countries to support efforts in controlling the 2014 Ebola
virus outbreak [25]. Spatial analysis methods are indeed
powerful for modelling disease spread, detecting patterns
and statistically significant hotspots, and predicting what
will happen next [26].
Similar work has been conducted at Northeastern Uni-
versity, Boston, Massachusetts, USA, to develop predic-
tive models of the COVID-19 epidemic using EpiRisk, a
tool that estimates the probability that infected individu-
als will spread the disease to other parts of the world via
air travel. Epirisk also tracks the effectiveness of travel
bans and is part of the GLEAM (global epidemic and
mobility model) project; see their interactive map of
COVID-19 at [27].
Mapping theworldwide spread ofmisinformation
aboutcoronavirus
During infectious disease outbreaks and epidemics, social
media play an important role in communicating veri-
fied facts and correct prevention tips to the masses, but
also carry the risk of ‘virally’ spreading misinformation,
confusion and fear among the general public [28, 29]. In
the case of COVID-19, false or misleading information,
(such as ‘eating sesame oil or garlic can help prevent and
cure coronavirus’ and a decade-old map showing global
air travel [30]), rumours and panic have been spreading
globally on social media much faster than the virus.
To partially illustrate this phenomenon, Twitter user
Mehdi Moussaïd (@Mehdi_Moussaid), a research sci-
entist at Max Planck Institute for Human Development,
Berlin, Germany, published an animated map of the
world on his account showing the worldwide propagation
of the hashtag#coronavirus on Twitter (in green) and the
actual cases of coronavirus (in red) between 24 and 31
January 2020 (Fig.8) [31]. Of course, not all tweets and
retweets with the hashtag #coronavirus are spreading
misinformation, and many of them originate from legiti-
mate bodies and organisations such as the WHO, but
the map serves as a good illustration of the ‘viral nature’
of Twitter and other social media. A more detailed map
set covering other coronavirus hashtags and classifying
tweets by their truthfulness before mapping them could
offer valuable insights and guidance for social media
companies and health organisations worldwide in their
fight against misinformation.
In fact, it has been said that the WHO is fighting a
parallel pandemic (or ‘infodemic’) of misinformation
besides COVID-19 [32]. e WHO has joined forces
with social media giants such as Facebook, Twitter, You-
Tube (Google) and Pinterest to combat the spread of mis-
information around coronavirus. For example, Pinterest
and YouTube users can now (at the time of writing this
article) see a link prominently displayed that points to
Fig. 8 Screenshot of the tweet by @Mehdi_Moussaid on 3 February
2020 featuring an animated map of the worldwide propagation of
the hashtag #coronavirus on Twitter (in green) and the actual cases of
coronavirus (in red) between 24 and 31 January 2020

Page 10 of 12
KamelBoulosand Geraghty Int J Health Geogr (2020) 19:8
an official WHO page about COVID-19 whenever they
search for, or browse/watch, material about coronavirus
on these platforms.
Other ways GIS technologies can help incombat
infectious disease outbreaks andepidemics
During the COVID-19 outbreak, map-centric dash-
boards, such as the ones by Johns Hopkins CSSE [12],
the WHO [17] and Early Alert Inc. [33], have gone viral
themselves, informing both the public and health profes-
sionals. But dashboards are just the beginning of how GIS
and location technologies can support the fight against
infectious diseases. Following are a few more examples.
Outbreak source
John Snow (1813–1858) was able to trace the source of a
cholera outbreak in Soho, London, in 1854, thanks to his
well-known manual spatial analysis exercise using hand-
drawn paper maps of cholera cases and water pumps/
water companies supplying them with water. Today, more
advanced computerised spatial analyses integrating phy-
loepidemiological methods are used to identify the likely
sources of new outbreaks; e.g., see the map and discus-
sion of the likely source of SARS-CoV-2 in [34].
Public events
An important factor affecting epidemics such as COVID-
19 is the calendar. During the Ebola and MERS scares of
2014, many people considered cancelling their participa-
tion in the Hajj pilgrimage to Mecca made by over two
million Muslims every year. Equipped with days-old data
and rumours, many faithful proceeded with their pil-
grimage, putting themselves at risk of contracting poten-
tially deadly viruses and further spreading disease when
they returned home.
In the coronavirus outbreak, Chinese New Year cele-
brations posed a threat as the themes of togetherness and
reunion trigger the largest human migration in the world.
e Chinese government extended the Lunar New Year
holiday to reduce mass gatherings (that were to happen
upon return to work and schools), a public health inter-
vention called social distancing. Travellers worldwide
were subsequently restricted from entering China. With
access to current information, authorities in Beijing,
Macao and Hong Kong cancelled many major festivities.
Dashboards and Web maps that bring together location
and time-sensitive events in relationship to a spreading
disease give travellers and officials the potential to reduce
exposure.
Site selection
Facing treatment facility shortages in Wuhan, gov-
ernment officials commissioned in late January the
emergency construction of two new hospitals, which
together provide an additional 2600 beds. Construction
teams finished the first hospital on 2 February 2020, just
10 days after breaking ground [35]. e second facil-
ity received its first patients on 6 February 2020. Site
selection, whether for emergency treatment units or
permanent infrastructure, is a common and high-value
application of GIS technology.
Supply chain
During public health emergencies of international con-
cern, we often see shortages of medicines and supplies.
Shortages can have significant consequences such as
hoarding and price gauging by suppliers or distributors.
Sometimes, the impacted places are also manufacturing
centres for the supplies, leading to a production decline.
Digital supply chain maps prove foundational to planning
and ensuring geographical diversity in suppliers as well as
aligning needs with distribution.
Resource locators
Residents of affected areas can use publicly available
applications to locate crucial aid and resources. Apps and
maps can display information and navigation to hospi-
tals with available beds, clinics offering medical aid along
with current wait times, grocery stores and pharmacies
that are open, places to purchase personal protective
equipment, and more. In heavily impacted cities, this
information could critically improve outcomes and save
lives.
Drones
In China, unmanned aerial vehicles (UAV) are transport-
ing crucial medical supplies and patient lab samples. In
highly impacted areas, drones reduce human contact
with lab samples and free up ground transport assets and
personnel [36]. Drones are also being used for broad dis-
infectant operations in China [37]. Integrated drone and
GIS technologies can help target and speed efforts in
places they are needed most.
Conclusions
Modern GIS technologies centre around web-based
tools, improved data sharing and real-time information
to support critical decision-making. Dashboards exem-
plify those ideals and have been extremely popular in
sharing and understanding the spread of SARS-CoV-2
coronavirus. Communication through map-based dash-
boards offers accessible information to people around the
world eager to protect themselves and their communi-
ties. is tool type improves data transparency and helps
authorities disseminate information.

Page 11 of 12
KamelBoulosand Geraghty Int J Health Geogr (2020) 19:8
Certainly, dashboards have taken centre stage in
COVID-19 outbreak awareness. But we hope that readers
consider how a comprehensive GIS platform can support
the entire process of infectious disease surveillance, pre-
paredness and response, because as one epidemiologist
put it, outbreaks like this “should be expected to happen
more frequently moving forward” [16]. In other words,
it is not a question of if another outbreak will occur,
but when and where. Viruses like SARS-CoV-2 know no
country or continent boundaries.
Acknowledgements
Not applicable.
Disclaimer
Reference in the manuscript to any specific commercial product, process or
service by trade name, trademark, manufacturer or otherwise does not neces-
sarily constitute or imply its endorsement, recommendation or favouring
by the authors or the entities they are affiliated to, and shall not be used for
commercial advertising or product endorsement purposes.
Authors’ contributions
MNKB conceived the editorial’s scope, and invited EG to contribute her
material to it. MNKB and EG contributed equally to the text and editing of the
manuscript. Both authors read and approved the final manuscript.
Funding
Not applicable.
Availability of data and materials
Data sharing is not applicable to this article, as no datasets were generated or
analysed for the current paper.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
MNKB is Editor-in-Chief of International Journal of Health Geographics. EG is
Chief Medical Officer of Esri, whose ArcGIS products have been used in some
of the dashboard examples described in this article.
Author details
1 School of Information Management, Sun Yat-sen University, East Campus,
Guangzhou 510006, Guangdong, China. 2 Esri (Environmental Systems
Research Institute), 380 New York St, Redlands, CA 92373, USA.
Received: 25 February 2020 Accepted: 26 February 2020
References
1. Chinese Center for Disease Control and Prevention (CCDC). The Epide-
miological Characteristics of an Outbreak of 2019 Novel Coronavirus
Diseases (COVID-19)–China. 2020. http://weekl y.china cdc.cn/en/artic le/
id/e5394 6e2-c6c4-41e9-9a9b-fea8d b1a8f 5117 Feb 2020.
2. World Health Organization. Statement on the second meeting of the
International Health Regulations Emergency Committee regarding the
outbreak of novel coronavirus (2019-nCoV), Geneva, Switzerland, 30
January 2020. 2005. https ://www.who.int/news-room/detai l/30-01-2020-
state ment-on-the-secon d-meeti ng-of-the-inter natio nal-healt h-regul
ation s-(2005)-emerg ency-commi ttee-regar ding-the-outbr eak-of-novel
-coron aviru s-(2019-ncov).
3. Kamel Boulos MN. Descriptive review of geographic mapping of severe
acute respiratory syndrome (SARS) on the internet. Int J Health Geogr.
2004;3(1):2. https ://doi.org/10.1186/1476-072X-3-2.
4. U.S. Centers for Disease Control and Prevention (CDC). 2019–2020 U.S. Flu
Season: Preliminary Burden Estimates. https ://www.cdc.gov/flu/about /
burde n/preli minar y-in-seaso n-estim ates.htm.
5. Public Health England. Weekly national flu reports: 2019 to 2020 season.
https ://www.gov.uk/gover nment /stati stics /weekl y-natio nal-flu-repor
ts-2019-to-2020-seaso n.
6. Braden CR, Dowell SF, Jernigan DB, Hughes JM. Progress in global
surveillance and response capacity 10 years after severe acute respiratory
syndrome. Emerg Infect Dis. 2013 Jun;19(6):864–869. https ://wwwnc .cdc.
gov/eid/artic le/19/6/13-0192-f1.
7. Koch T. Plague: Bari, Naples 1690–1692. In: Koch T, editor. Cartographies
of disease: maps, mapping and medicine. Redlands: Esri Press; 2005. p.
19–24.
8. Lyseen AK, Nøhr C, Sørensen EM, Gudes O, Geraghty EM, Shaw NT,
Bivona-Tellez C, IMIA Health GIS Working Group. A review and framework
for categorizing current research and development in health related
geographical information systems (GIS) studies. Yearb Med Inform.
2014;23(01):110–24. https ://doi.org/10.15265 /IY-2014-0008.
9. Kamel Boulos MN. Chapter 49: Principles and techniques of interactive
Web cartography and Internet GIS. In: M Madden, Editor. Manual of
Geographic Information Systems. Bethesda, Maryland: ASPRS—American
Society for Photogrammetry and Remote Sensing, 2009, p. 935–974
(ISBN: 1-57083-086-X).
10. Kamel Boulos MN (Collection Editor). Web GIS in practice–IJHG Article
collection (2004–2011). https ://www.biome dcent ral.com/colle ction
s/1476-072X-Gis.
11. Esri China (Hong Kong). List of Novel Coronavirus Dashboards. https
://story maps.arcgi s.com/stori es/a1746 ada9b ff48c 09ef7 6e5a7 88b59 10
and https ://go.esri.com/coron aviru s. Accessed 02 Mar 2020.
12. Johns Hopkins. CSSE Coronavirus COVID-19 Global Cases (dashboard).
https ://gisan ddata .maps.arcgi s.com/apps/opsda shboa rd/index .html#/
bda75 94740 fd402 99423 467b4 8e9ec f6.
13. Gardner L. Mapping 2019-nCoV (23 January 2020; updated 11 February
2020). https ://syste ms.jhu.edu/resea rch/publi c-healt h/ncov/.
14. Johns Hopkins. CSSE GitHub–CSSEGISandData/COVID-19: Novel Corona-
virus (COVID-19) Cases (data repository). https ://githu b.com/CSSEG ISand
Data/COVID -19.
15. Johns Hopkins CSSE. Corona Virus 2019-nCoV Cases (The Living Atlas).
https ://livin gatla s.arcgi s.com/en/brows e/#d=2&q=%22Cor ona%20Vir
us%20201 9%20nCo V%20Cas es%22.
16. Esri’s StoryMaps team. Mapping the Wuhan coronavirus outbreak. https
://story maps.arcgi s.com/stori es/4fdc0 d03d3 a34aa 485de 1fb0d 2650e e0.
Accessed 18 Feb 2020.
17. World Health Organization. Novel coronavirus (COVID-19) situation (pub-
lic dashboard). http://healt hcybe rmap.org/WHO_COVID 19/.
18. World Health Organization. WHO Health Emergency Dashboard. https ://
extra net.who.int/publi cemer gency .
19. Hossain N, Househ M. Using healthmap to analyse middle east respira-
tory syndrome (MERS) data. Stud Health Technol Inform. 2016;226:213–6.
20. HealthMap. Novel Coronavirus 2019-nCoV (interactive map). https ://healt
hmap.org/wuhan /.
21. BlueDot Products–AI-driven infectious disease surveillance. https ://blued
ot.globa l/produ cts/.
22. BBC News. China launches coronavirus ‘close contact detector’ app. https
://www.bbc.co.uk/news/techn ology -51439 401. Accessed 11 Feb 2020.
23. Gallagher J. Coronavirus super-spreaders: Why are they important? BBC
News https ://www.bbc.co.uk/news/healt h-51447 143. Accessed 10 Feb
2020.
24. World Pop. Preliminary risk analysis of 2019 novel coronavirus spread
within and beyond China (25 January 2020). https ://www.world pop.org/
event s/china . Accessed 25 Jan 2020.
25. WorldPop. Ebola. https ://www.world pop.org/regio n/ebola .
26. Gardner L. Update January 31, 2020: Modeling the Spreading Risk of
2019-nCoV. Johns Hopkins University. https ://syste ms.jhu.edu/resea rch/
publi c-healt h/ncov-model -2/.
27. EpiRisk interactive COVID-19 map. https ://tinyu rl.com/EpiRi sk.

Page 12 of 12
KamelBoulosand Geraghty Int J Health Geogr (2020) 19:8
•
fast, convenient online submission
•
thorough peer review by experienced researchers in your field
•
rapid publication on acceptance
•
support for research data, including large and complex data types
•
gold Open Access which fosters wider collaboration and increased citations
maximum visibility for your research: over 100M website views per year
•
At BMC, research is always in progress.
Learn more biomedcentral.com/submissions
Ready to submit your research
? Choose BMC and benefit from:
28. Fung IC, Fu KW, Chan CH, Chan BS, Cheung CN, Abraham T, Tse ZT. Social
media’s initial reaction to information and misinformation on Ebola,
August 2014: facts and rumors. Public Health Rep. 2016;131(3):461–73.
29. Liang H, Fung IC, Tse ZTH, Yin J, Chan CH, Pechta LE, Smith BJ, Marquez-
Lameda RD, Meltzer MI, Lubell KM, Fu KW. How did Ebola information
spread on twitter: broadcasting or viral spreading? BMC Public Health.
2019;19(1):438. https ://doi.org/10.1186/s1288 9-019-6747-8.
30. Reality Check team. Coronavirus: How a misleading map went global.
BBC News, 19 February 2020. https ://www.bbc.co.uk/news/world -51504
512. Accessed 19 Feb 2020.
31. Moussaid M. Tweet by @Mehdi_Moussaid on 3 February 2020. https ://
twitt er.com/Mehdi _Mouss aid/statu s/12243 58536 08956 7233.
32. Richtel M. W.H.O. Fights a Pandemic Besides Coronavirus: an ‘Info-
demic’. The New York Times, 6 February 2020. https ://www.nytim
es.com/2020/02/06/healt h/coron aviru s-misin forma tion-socia l-media
.html.
33. Early Alert Inc. Coronavirus 2020 (dashboard). https ://early -alert .maps.
arcgi s.com/apps/opsda shboa rd/index .html#/20bfb f89c8 e74c0 494c9
0b1ae 0fa7b 78.
34. Yu W-B, Tang G-D, Zhang L, Corlett RT. Decoding the evolution and trans-
missions of novel pneumonia coronavirus using whole genomic data
[ChinaXiv:202002.00033]. Updated 21 Feb 2020. http://www.china xiv.org/
abs/20200 2.00033 . Accessed 19 Feb 2020.
35. BBC News. Coronavirus:10 days of hospital building in 60 seconds (2
February 2020). https ://www.bbc.co.uk/news/av/world -asia-china -51348
297/coron aviru s10-days-of-hospi tal-build ing-in-60-secon ds.
36. Huber M. Drones Enlisted To Fight Corona Virus in China. AIN Online, 7
February 2020. https ://www.ainon line.com/aviat ion-news/gener al-aviat
ion/2020-02-07/drone s-enlis ted-fight -coron a-virus -china .
37. Brickwood B. XAG introduces drone disinfection operation to fight the
coronavirus outbreak. Health Europa. https ://www.healt heuro pa.eu/xag-
intro duces -drone -disin fecti on-opera tion-to-fight -the-coron aviru s-outbr
eak/97265 /. Accessed 5 Feb 2020.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in pub-
lished maps and institutional affiliations.