Content uploaded by Vincenza Gianfredi
Author content
All content in this area was uploaded by Vincenza Gianfredi on May 20, 2020
Content may be subject to copyright.
The spread of COVID-19 in six western metropolitan
regions: a false myth on the excess of mortality in Lombardy
and the defense of the city of Milan
Carlo Signorelli1, Anna Odone1,2, Vincenza Gianfredi1,3, Eleonora Bossi1, Daria Bucci1, Aurea
Oradini-Alacreu1, Beatrice Frascella1, Michele Capraro1, Federica Chiappa1, Lorenzo Blandi4,
Fabio Ciceri2
1School of Medicine, Vita-Salute San Raffaele University, Milan, Italy; 2 IRCCS Ospedale San Raffaele, Milan, Italy; 3 CAPHRI
Care and Public Health Research Institute, Maastricht University, Maastricht, the Netherlands; 4 IRCCS Policlinico San Do-
nato, School of Public Health, University of Pavia, Pavia, Italy
Summary. We analyzed the spread of the COVID-19 epidemic in 6 metropolitan regions with similar demo-
graphic characteristics, daytime commuting population and business activities: the New York metropolitan
area, the Île-de-France region, the Greater London county, Bruxelles-Capital, the Community of Madrid and
the Lombardy region. e highest mortality rates 30-days after the onset of the epidemic were recorded in
New York (81.2 x 100,000) and Madrid (77.1 x 100,000). Lombardy mortality rate is below average (41.4 per
100,000), and it is the only situation in which the capital of the region (Milan) has not been heavily impacted
by the epidemic wave. Our study analyzed the role played by containment measures and the positive contribu-
tion offered by the hospital care system. (www.actabiomedica.it)
Key words: COVID-19, Mortality, Metropolitan regions, Hospital care system
Acta Biomed 2020; Vol. 91, N. 2: 23-30 DOI: 10.23750/abm.v91i2.9600 © Mattioli 1885
Original investigations/Commentaries
Introduction
As history taught us, many airborne transmitted
diseases - causing large epidemics - spread along trade
routes and had the most dramatic effects in large urban
areas in terms of infections, incidence and mortality.
As it happened during the Black Death, a centripetal
trend is even recurring nowadays (1), with the COV-
ID-19 pandemic that, as of the 14th of April 2020, has
surpassed 2 million notified cases (although these data
largely underestimate the real situation) and 120,000
confirmed deaths, affecting large metropolitan areas
for the most part (2). A precise track of the infection
spread, across different geographical areas, is compli-
cated by the globalization that, also, greatly improve
the risks posed by trade routes (3), people gathering
(4) and work activities (5).
erefore, it is not by chance that in industrialized
countries the diffusion of SARS-CoV-2 had a greater
effect in the areas surrounding large urban centers (6):
as London, Paris, New York, Madrid, Bruxelles and
Milan amongst others. All these cities share similar
characteristics and well-established commercial ex-
changes with China, where the virus dissemination
started between the end of 2019 and January 2020.
Preventive actions have changed from the past:
in addition to quarantine, health authorities took ad-
vantage from limitations of mobility (7), lockdown
measures, the establishment of “red zones” (8), con-
tact tracing (6), home fiduciary isolation (9) and the
availability of new technologies (10), together with an
adequate risk communication (11,12). ese measures
acquire a crucial importance considering the current
lack of treatment and vaccinations.
With respect to the past centuries, the healthcare
systems, and in particular hospitals with intensive care
capacity, played an important role in saving lives but
were also an important mean of infection dissemina-
C Signorelli, A Odone, V Gianfredi, et al.
24
tion (13), as it happened with the SARS epidemic
(14), and at the beginning of the COVID-19 epidemic
(15). e infection of patients and healthcare work-
ers in the hospitals of Codogno and Casalpusterlen-
go – which were the first sites in which cases of local
Italian transmission were confirmed (16) – showed
how COVID-19 has a high tendency of diffusion in
healthcare environments (hospitals) and residential
care (nursing homes), that incidentally host individu-
als that are frail and at high risk (of older age and/or
affected by chronic conditions).
is epidemiological study, conducted within the
School of Public Health of Vita-Salute San Raffaele
University, analyzes six geographical settings that in-
clude relevant metropolitan areas, and aims at evalu-
ating the diffusion of COVID-19 and its mortality, to
assess the reaction of healthcare systems, and lastly, to
estimate the dynamics spread of the epidemic and the
efficacy of the healthcare measures implemented.
Methods
For each included metropolitan area, we built a
profile which included administrative, demographic
and social characteristics (the latter was estimated
in terms of daytime commuting population). We re-
treived the number of available hospital beds, with a
focus on intensive care units, and the measures im-
plemented by health authorities to cope with the epi-
demic. With respect to COVID-19, we analyzed the
number of deaths and draw mortality curves, starting
with the day during which the first 3 deaths were de-
clared in each area. Furthermore, we analyzed mortal-
ity rate at the level of regional and metropolitan areas,
to evaluate centripetal trend of the epidemic. Finally,
we analyzed in details the case of Lombardy and in
particular the metropolitan area of Milan due to its
peculiar features, and because its mortality rates has
been considered to be abnormal (17). Additionally,
we examined the containment measures implemented
by the healthcare authorities to reduce the inter-hu-
man transmission. Moreover, we reported the number
of patients admitted, for COVID-19, to two Italian
National Institutes for Scientific Research (IRCCS),
since the beginning of the epidemic to date. Moreover,
we anonymously retrieved their residence.
Results
First, we describe the characteristics of the six
metropolitan areas in terms of demographic data, in-
crease in daytime population, and healthcare:
New York – We analyzed the metropolitan area
of New York City, with five boroughs (Manhattan, e
Bronx, Queens, Brooklyn and Staten Island), a popu-
lation of 8,388,748 and density of 10,715 people/km2
(18). Manhattan is the most densely populated, with
26,821.6 inhabitants/km2 (19), and an increase of day-
time population of 1,499,757 commuters (20). In New
York City there are 23,000 hospital beds (21) (2.74
per 1,000 inhabitants), which increased to 38,400
since to the COVID-19 epidemic (22) (4.57 per 1,000
residents, 67% increase). At the beginning of the epi-
demic, 2,449 intensive care beds were available in New
York City (23), and increased of 62% (total available
3,965) by 10th April 2020 (24). Table 1 shows mortality
data starting from the beginning of the epidemic (15th
March 2020) (25).
Bruxelles – e Bruxelles-Capital Region has a
population of 1,208,542 people and a population den-
Table 1. Cumulative mortality rate (x 100,000) in the six metropolitan areas analyzed
Area Population
x 1,000 Beginning of the
epidemic* Increase of beds in
intensive care units Number of
deaths Cumulative
mortality rate°
New York City 8,623 15th March 67.0% 7,429 81.2
Community of Madrid 6,662 06th March 115.6% 5,136 77.1
Bruxelles-Capital 1,209 11st March 40.0% 587 48.6
Lombardy (Milan) 10,088 23rd February 114.0% 4,178 41.4
Ile-de-France (Paris) 12,278 11st March 109.0% 3,040 26.9
Greater London 9,304 7th March 19.8% 2,193 23.0
*Considered as the day during which the first 3 deaths were recorded; °Considered the 30th day since the beginning of the epidemic
e spread of COVID-19 in six western metropolitan regions 25
sity of 7,489 people/km2 (26). e central area includes
the city of Bruxelles (181,726 inhabitants, and popula-
tion density of 5,570 people/km2) (27), and 324,000
people commute there every day, in addition to the city
residents and the European Commission visitors (28).
Bruxelles-Capital Region has an availability of 6.74
hospital beds per 1,000 population (29). In Belgium,
1,900 intensive care beds increased by 40% during the
epidemic (11th March 2020) (30).
Community of Madrid – e Community of
Madrid has 6,661,949 inhabitants and a population
density of 829.84 people/km2 (31,32). e metropoli-
tan area of Madrid has 3,266,126 inhabitants and a
population density of 5,265 people/km2, in addition
it receives around 345,000 workers daily and 27,000
turists (33). In 2017 the region had 20,458 hospital
beds (3.14 per 1,000 people), among public and pri-
vate structures (34). e 800 intensive care beds of the
region increased to 1,725 to treat patients affected by
COVID-19 (35). To cope with the high number of
patients during the epidemic, the Ifema field hospital
has been built, and it can accommodate 5,000 beds in
9 pavilions (35). On the 1st April 2020 the number of
hospitalized people in the region of Madrid reached
a peak value with 15,227 occupied hospital bed and
1,528 patients in intensive care units (35). On 1st of
April 2020, the Ifema hospital hosted 930 patients, 16
of which in intensive care beds. Table 1 shows mortal-
ity data starting from the beginning of the epidemic
(6th March 2020) (35).
Île-De-France (Paris region) – We analyzed
the region of Île-de-France, with 8 Département, a
total population of 12,278,210 (18% of metropoli-
tan France population) and a population density of
1,022.25 people/km2 (36). It includes the city of Paris,
divided into 20 arrondissement, with 2,148,271 inhab-
itants and a population density of 20,382 people/km2
(37), with a daytime increase in population of 570,000
people commuting from Île -de-France (38). Île-de-
France counts 5.94 hospital beds per 1,000 inhabitants
(2014 update). In 2018 the region had 1,275 intensive
care beds (471 in Paris (39)), that increased by 109%
to 1,390 (40) since the beginning of the epidemic. is
was achieved by increasing the 3,000 intensive care
beds that were already available in the region, which
is sustained by private healthcare hospitals by 23.4%
(39). Table 1 shows mortality data starting from the
beginning of the epidemic (11th March 2020) (41).
Greater London – e county of Greater Lon-
don has 8,899,375 and a population density of 5,671/
km2. Inner London forms the central part of Greater
London with 12 boroughs and the City of London; it
has 3 million residents (42) and a density of popula-
tion of 9,404/km2). e daytime population creases by
a million of commuters daily (42). In Greater London
there are 21,361 NHS hospital beds open overnight,
1,507 of which are reserved for intensive care (43). To
cope with the increase in patients due to the epidemic,
private hospitals signed an agreement to provide more
than 2,000 hospital beds, and 250 between operating
rooms and intensive care beds (44). Furthermore, the
“NHS Nightingale Hospital London” has been tem-
porarily set up in the ExCeL convention center. It
hosts 500 intensive care beds, but it can receive up to
4,000 patients (45). anks to this measure, the total
number of hospital beds increased by 12.9%, and the
number of intensive care beds increased by 49.8%. Ta-
ble 1 shows mortality data starting from the beginning
of the epidemic (7th March 2020) (46).
Lombardy (Milan Region) – e Lombardy
Region, with a population of 10,060,574 people and
a population density of 422 inhabitants per km2. e
central metropolitan area of Milan, which consists of
the city of Milan and other 133 municipalities, with a
total of 3,250,315 inhabitants and a population den-
sity of 2,063 inhabitants per km2 (47), in addition to
1,441,409 people that commute every day. e Lom-
bardy Region consists of several highly populated areas
in proximity to Milan (Bergamo, Brescia, Monza, etc).
e intensive care beds available were 723 at the onset
of the epidemic, which increased to a total of 1,547
beds reserved for patients affected by COVID-19 after
30 days. is resulted in an increment of 113.9%, and
it includes only 10 of the potential 500 additional beds
allocated in the Milano Fiera pavilions. Table 1 shows
mortality data (48) starting from the beginning of the
epidemic (23rd February) (49).
Figure 1 represents the epidemic progress in the
six areas via cumulative daily mortality rate. We de-
cided to put the analytic comparison at day 30 from
the beginning of the outbreak (when 3 deaths were
reported) , due to the different chronological develop-
C Signorelli, A Odone, V Gianfredi, et al.
26
ment in each zone: day 30 is the time at which we
calculated the mortality rates for each area (Table 1).
Since China issued the first warnings about the
COVID-19 outbreak, European and USA health au-
thorities planned new preventive measures in order to
avoid the spread of the virus. ese actions (as health
check- points in airports, quarantine for people arriv-
ing from Hubei region, contact tracing and isolation)
proved to be inefficient in the prevention of a mas-
sive implication of the major metropolitan areas of the
western world. Italy was the first European country
to declare endemic cases, and it was the first country
to report outbreaks in Lombardy and Veneto regions
(50). Table 2 describes the most relevant actions taken
by the Government from February 21st to April 4th
2020. Similar measures were also adopted by the local
Governments of the administrative areas analyzed in
this report. One of the measures implemented was the
increase in the number of hospital beds and intensive
care beds, as the normal hospital capacity was not able
to host the number of COVID-19 cases requiring hos-
pitalization.
As an example, we reported the cases of the Lom-
bardy region and Greater London areas – as these two
entities have similar healthcare systems – in which the
public health authorities arranged agreements with
private hospitals in order to face the increased demand
for healthcare assistance. Figure 2 shows the flatten-
Table 2. Health protection measures against COVID-19 in Lombardy Region, 21 February – 4 April 2020
Date Public Health Measures Authority
21 February 2020 Mandatory supervised quarantine for 14 days for all individuals who have come
into close contact with confirmed cases of disease;
Mandatory communication to the Health Department from anyone who has
entered Italy from high-risk of COVID-19 areas, followed by quarantine and active
surveillance.
Ministry of Health
23 February 2020 Red zones in 11 municipalities in Lombardy Region: adoption of an adequate and
proportionate containment and management measures in areas with >1 person
positive to COVID-19 with unknown source of transmission.
National Government
23 February 2020 Development of a toll-free number for population Lombardy Region
08 March 2020 Lock-down: avoid any movement of people except for motivated by proven work
needs or situations of necessity (health, food and assistance);
National Government
08 March 2020 • Suspension of deferred and non-urgent hospitalization and outpatient activities.
• Reorganization of hospital activities
• Establishment of the Unique Post-Hospital Regional Discharge Center aimed at
managing the patients’ discharge
Lombardy Region
09 March 2020 Public communication campaign on social network #fermiamoloinsieme Lombardy Region
11 March 2020 Suspension of all commercial activities non-indispensable for production. National Government
23 March 2020 • Special Care Continuity Units (USCA) aimed at home management of patients
with COVID-19 who do not require hospitalization
• Identication of accommodation facilities (hotels) for discharged patients with
domestic isolation problems
• Establishment of a telemedicine service for GPs and their patients
Lombardy Region
30 March 2020 Further identification of day-care structure to isolate asymptomatic or low
symptomatic subjects
Lombardy Region
4 April 2020 Use of face mask (or other supply) for the whole population Lombardy Region
Figure 1. Cumulative daily mortality rate in the six areas
e spread of COVID-19 in six western metropolitan regions 27
ing of the epidemic curve as a result of public health
interventions, with the increase of the hospital capac-
ity, variously needed, in all the different areas analyzed.
We decided to investigate further the Lombardy
case, as it was the first to be described in the press and
to be of great scientific interest for its alleged excess of
deaths (45). e crude fatality rate (number of deaths/
number of notified cases) is largely affected by the
number of the tests performed and its results are not
significant, so we analyzed the mortality rate (Table 1).
It showed that, besides the higher number of deaths
and the delay of the start of the epidemic in the dif-
ferent areas, the trend documented in the Lombardy
region is significantly lower than three areas with the
highest mortality (New York, Madrid and Bruxelles).
Lombardy region is slightly higher than Paris and
London, even though it has a wider surface area.
We believe that such trend recorded in the Lom-
bardy region – despite the earlier start of the epidemic
– is due to the fact that the metropolitan area of Milan
has never been heavily hit by the epidemic wave, as
clearly shown considering mortality rates in the prov-
inces of Lombardy ad surrounding regions (Figure 3)
(51). As an example, we took into account the domicile
of 1,058 COVID-19 patients admitted to two Nation-
al Institutes for Scientific Research (IRCCS), situated
just outside the areas in which the two outbreaks took
place, 50 kilometers outside of Milan (Figure 4). e
data shows that most of these patients came from areas
located between the cities involved by the first out-
breaks and the city of Milan. is suggest that these
two hospitals (as well as the others in the metropoli-
tan area of Milan) have not payed a role as multiplier
of SARS-CoV-2 infections as probably happened in
smaller-sized hospitals - as for instance hospitals of
Lodi and Codogno - in the first phase of the epidemic.
Possible bias
e six areas analyzed have similar economic
characteristics, healthcare standards and COVID-19
surveillance data collection procedures, which allowed
us to make a reliable comparison of data. e choice
of these areas followed administrative borders and
the availability of the disaggregated mortality data. If
only focusing on the metropolitan area of Milan (3.2
Figure 2. Epidemiological trend and public health measures (“flat-
tening” the curve)
Figure 4. Geographic distribution of COVID cases requiring ho-
spitalization in two major hospitals of Milan (IRCCS)
Figure 3. Cumulative mortality rate per 100,000 population in
the Provinces of four Regions of Northern Italy, last update 17th
April 2020
C Signorelli, A Odone, V Gianfredi, et al.
28
million inhabitants), mortality rates would have been
about 45% lower (data not shown); if a wider area in-
cluding only the provinces surrounding Milan (Mon-
za, Bergamo and Lodi) had been evaluated (area of
5.5 million inhabitants), the mortality rate would have
been comparable to the regional data (data not shown).
Our analysis considered daily COVID-19 mor-
tality rates derived by national surveillance statistics,
which are more reliable than infection notifications
(confirmed cases). Indeed, notified cases data are
largely lower compared to the reality, and highly vari-
able depending on different swab strategies and crite-
ria adopted in different regions (52). Although it can-
not be ruled out that a portion of the deaths caused
by COVID-19 went undiagnosed, we believe that this
possible bias, estimated at 17% (53), is similar in the
other five areas (as suggested by international press re-
ports) thereby it doesn’t greatly affect the final asser-
tion of our comparative study.
Conclusions
We analyzed the COVID-19 epidemic trend in
six areas, comparable from an economic, social and
healthcare perspective, using reliable indicators, such as
the cause of death. New York City (8.4mln. inh.) and
Madrid (6.6mln. inh.) are the two metropolitan areas
mostly affected by the epidemic, while the Lombardy
region (10mln. inh.) – the first western area affected by
the epidemic and, in theory, less prepared – recorded a
high number of deaths (over 10,000) but a mortality
rate lower than three out of the six regions considered,
and a cumulative mortality rate on the 30th day about
50% lower than New York City and the Community
of Madrid. One of the reasons contributing to these
results, as mentioned earlier, could be that the epidemic
has not yet hit the metropolitan area of Milan (3.2mln.
inh.), but only smaller cities including Bergamo (1mil.
inh.). Two factors could have contributed to the posi-
tive “defense” to the metropolitan area with the higher
population density and commercial trades: firstly, the
efficacy and the promptness (54) of containment and
mitigation measures which resulted in increasing phys-
ical distancing and then in a reduction people gather-
ings ; secondly the effectiveness and safety of care pro-
vided by hospitals treating COVID-19 cases (47,48)
(considering that hospitals were an important driving
force of the transmission of this epidemic worldwide).
An additional refers to the general increase of
hospital beds and intensive care beds that was achieved
in a short time in all the examined areas, which allowed
to face the emergency. In particular, the Lombardy Re-
gion (as also done by the Community of Madrid and
Ile-de-France) more than doubled the number of beds,
both ordinary hospital beds and for intensive care.
Finally, the two countries with a public healthcare
system (Italy and UK), which recorded mortality rates
below the average, arranged formal agreements with
the private healthcare system. is aspect might have
given an important contribution to the management of
the emergency. Although emergency treatments were
provided in all considered areas with the same mod-
els (apparently free of charge) in all six areas, the fact
that the two countries with a public healthcare sys-
tem achieved lower mortality rates could be due to the
hospital system efficacy combined with the activity of
territorial services. In conclusion, we can state that a
“Lombardy case” did not occur in terms of a specific
mortality excess; moreover, the rapid adaptation of the
hospital network has been able to cope with a massive
epidemic wave managing, to date, to limit its spread in
the area with the highest population density.
A more complete analysis can be carried out for a
longer follow up (45 or 60 days since the onset of the
epidemic) in which it will be possible to better analyze,
for all the six areas considered (and possibly others),
the medium-term effect of the containment measures,
the actions of primary health care, the ignition of fur-
ther epidemic outbreaks and the overall management
of the epidemic.
Conflict of interest: Each author declares that he or she has no
commercial associations (e.g. consultancies, stock ownership, equity
interest, patent/licensing arrangement etc.) that might pose a con-
flict of interest in connection with the submitted article
Funding: is paper is a preliminary activity among the EU Project
n. 101003562 “ree Rapid Diagnostic tests (Point-of-Care) for
COVID-19 Coronavirus, improving epidemic preparedness, and
foster public health and socio-economic benefits - CORONADX”
(Task 7.1) supported by the Europeam Commission (Horizon
2020, H2020-SC1-PHE-CORONAVIRUS-2020).
e spread of COVID-19 in six western metropolitan regions 29
References
1. Boerner L, Severgnini B. Il commercio è alleato dei vi-
rus, lo dice la storia. 2020; https://www.lavoce.info/ar-
chives/64259/lo-dice-la-storia-il-commercio-e-alleato-
dei-virus/. Accessed 13 April 2020.
2. World Health Organization. COVID-19 weekly surveil-
lance report. 2020; http://www.euro.who.int/en/health-
topics/health-emergencies/coronavirus-covid-19/weekly-
surveillance-report, 2020.
3. Sebastiani G. Il coronavirus ha viaggiato in autostrada?
2020; https://www.scienzainrete.it/articolo/coronavirus-
ha-viaggiato-autostrada/giovanni-sebastiani/2020-04-09.
Accessed 13 April 2020.
4. Gatto M, Bertuzzo E, Mari L, et al. Spread dynamics of the
covid-19 epidemic in Italy: effects of emergency contain-
ment measures. PNAS. 2020;in press.
5. Prem K, Liu Y, Russell TW, et al. e effect of control
strategies to reduce social mixing on outcomes of the COV-
ID-19 epidemic in Wuhan, China: a modelling study. Lan-
cet Public Health. 2020.
6. Wilder-Smith A, Chiew CJ, Lee VJ. Can we contain the
COVID-19 outbreak with the same measures as for SARS?
Lancet Infect Dis. 2020.
7. Kraemer MUG, Yang CH, Gutierrez B, et al. e effect of
human mobility and control measures on the COVID-19
epidemic in China. Science. 2020;in press.
8. Signorelli C, Scognamiglio T, Odone A. COVID-19 in
Italy: impact of containment measures and prevalence es-
timates of infection in the general population. Acta Biomed.
2020;91(3-S):175-179.
9. Tang B, Wang X, Li Q, et al. Estimation of the Transmis-
sion Risk of the 2019-nCoV and Its Implication for Public
Health Interventions. J Clin Med. 2020;9(2).
10. Mayor S. Covid-19: Researchers launch app to track spread
of symptoms in the UK. BMJ. 2020;368:m1263.
11. Gianfredi V, Odone A, Fiacchini D, Rosselli R, Battista T,
Signorelli C. Trust and reputation management, branding,
social media management nelle organizzazioni sanitarie:
sfide e opportunità per la comunità igienistica italiana. J
Prev Med Hyg. 2019;60(3):E108-E109.
12. Gianfredi V, Grisci C, Nucci D, Parisi V, Moretti M. [Com-
munication in health.]. Recenti Prog Med. 2018;109(7):374-
383.
13. Gan WH, Lim JW, Koh D. Preventing intra-hospital infec-
tion and transmission of COVID-19 in healthcare workers.
Saf Health Work. 2020;in press.
14. Lau JT, Fung KS, Wong TW, et al. SARS transmission
among hospital workers in Hong Kong. Emerg Infect Dis.
2004;10(2):280-286.
15. European Centre for Disease prevention and Control. Coro-
navirus disease 2019 (COVID-19) pandemic: increased trans-
mission in the EU/EEA and the UK – seventh update. Stock-
holm25 March 2020.
16. Cereda D, Tirani M, Rovida F, et al. e early phase of the
COVID-19 outbreak in Lombardy, Italy. Arvix. 2020;in press.
17. UCSC. Coronavirus, le stime dei ricercatori: “In Italia al-
meno 2800 morti non dichiarati”. 2020; https://www.
lastampa.it/topnews/primo-piano/2020/03/30/news/le-
stime-dei-ricercatori-almeno-2800-morti-non-dichiara-
ti-1.38653880. Accessed 13 April 2020.
18. United States Census. Explore Census Data. 2020; https://
data.census.gov/cedsci/. Accessed 13 April 2020.
19. United States Census. New York County (Manhattan Bor-
ough), New York. 2020; https://www.census.gov/quick-
facts/newyorkcountymanhattanboroughnewyork. Accessed
13 April 2020.
20. United States Census. Commuter-Adjusted Population
Estimates: ACS 2006-10. 2010; https://www.census.gov/
library/working-papers/2010/demo/mckenzie-01.html.
Accessed 13 April 2020.
21. New York State Department of Health. Hospitals by Region/
County and Service. 2020; https://profiles.health.ny.gov/
hospital/county_or_region/region:new+york+metro+-
+new+york+city. Accessed 13 April 2020.
22. Melby C, Gu J, Rojanasakul M. Mapping New York City
Hospital Beds as Coronavirus Cases Surge. 2020; https://
www.bloomberg.com/graphics/2020-new-york-coronavi-
rus-outbreak-how-many-hospital-beds/. Accessed 13 April
2020.
23. New York State Department of Health. Hospitals by Region/
County and Service. 2020; https://profiles.health.ny.gov/
hospital/county_or_region/region:new+york+metro+-
+new+york+city.
24. e City. Coronavirus in New York City. 2020; https://pro-
jects.thecity.nyc/2020_03_covid-19-tracker/. Accessed 13
April 2020.
25. NYC Health. COVID-19: Data. 2020; https://www1.nyc.gov/
site/doh/covid/covid-19-data.page. Accessed 15 April 2020.
26. European Commission. Population: Demographic situa-
tion, languages and religions. 2019; https://eacea.ec.europa.
eu/national-policies/eurydice/content/population-demo-
graphic-situation-languages-and-religions-7_en. Accessed
13 April 2020.
27. City population. Bruxelles Municipality. 2019; https://
www.citypopulation.de/en/belgium/bruxelles/_/21004__
bruxelles/. Accessed 13 April 2020.
28. Brussels Institute for Statistics and Analysis. Key figures for
the Brussels-Capital Region. 2017; http://statistics.brus-
sels/figures/key-figures-for-the-region#.XpScEcgzZPZ.
Accessed 13 April 2020.
29. Federal Public Service. e geographical distribution of ac-
credited hospital beds. 2020; https://www.healthybelgium.
be/en/key-data-in-healthcare/general-hospitals/organisa-
tion-of-the-hospital-landscape/categorisation-of-hospital-
activities/the-geographical-distribution-of-accredited-hos-
pital-beds. Accessed 13 April 2020.
30. Sciensano. COVID-19 - Situation épidémiologique. 2020;
https://epistat.wiv-isp.be/covid/. Accessed 15 April 2020.
31. Portal del Ayuntamiento de Madrid. Portal web del Ayun-
tamiento de Madrid. 2019; https://www.madrid.es/portal/
site/munimadrid#. Accessed 13 April 2020.
C Signorelli, A Odone, V Gianfredi, et al.
30
32. Instituto Nacional de Estadistica. Demografía y población.
2019; https://www.ine.es/. Accessed 13 April 2020.
33. Instituto Nacional de Estadistica. Atlas de la movilidad
residencia-trabajo en Comunidad de Madrid 2017. 2017;
https://www.madrid.org/iestadis/fijas/estructu/general/ter-
ritorio/atlasmovilidad2017/02_caract_gnral.html. Accessed
13 April 2020.
34. Ministerio de Sanidad de España. Situación de COVID-19
en España. 2020; https://covid19.isciii.es/. Accessed 13
April 2020.
35. Ministerio de Sanidad de España. Situación de COVID-19
en España. 2020.
36. Institut National de la statistique et des études éeconom-
iques. e National Institute of Statistics and Economic
Studies collects, analyses and disseminates information on
the French economy and society. 2019; https://www.insee.
fr/en/accueil. Accessed 13 April 2020.
37. Institut National de la statistique et des études éeconom-
iques. e National Institute of Statistics and Economic
Studies collects, analyses and disseminates information on
the French economy and society. 2019; https://www.insee.
fr/en/accueil.
38. Institut National de la statistique et des études éeconom-
iques. Les mobilités dans le Bassin parisien à trois âges de
la vie : faire ses études, aller travailler, prendre sa retraite.
2019; https://www.insee.fr/fr/statistiques/4258068?sommai
re=4258113. Accessed 13 April 2020.
39. Ministère des Solidarités et de la Santé. Nombre de lits
de réanimation, de soins intensifs et de soins continus en
France, fin 2013 et 2018. 2020. Accessed 13 April 2020.
40. Agence régional de santé Ile-de-France (Siège). Coronavi-
rus (Covid-19) : La lutte contre l’épidémie en Île-de-France.
2020; https://www.iledefrance.ars.sante.fr/coronavirus-cov-
id-19-lars-ile-de-france-mobilisee. Accessed 13 April 2020.
41. Gouvernement France. COVID-19 en France. 2020; htt-
ps://www.gouvernement.fr/info-coronavirus/carte-et-don-
nees. Accessed 13 April 2020.
42. World Population Review. London Population 2020. 2020;
https://worldpopulationreview.com/world-cities/london-
population/. Accessed 15 April 2020.
43. NHS England. Bed Availability and Occupancy Data –
Overnight. 2020; https://www.england.nhs.uk/statistics/
statistical-work-areas/bed-availability-and-occupancy/bed-
data-overnight/. Accessed 15 April 2020.
44. Financial Times. NHS enlists all English private hospitals
to treat coronaviru. 2020; https://www.ft.com/content/c9a-
9be78-6b7b-11ea-89df-41bea055720b. Accessed 15 April
2020.
45. NHS England. New NHS nightingale hospital to fight
coronavirus. 2020; https://www.england.nhs.uk/2020/03/
new-nhs-nightingale-hospital-to-fight-coronavirus/. Ac-
cessed 15 April 2020.
46. NHS England. COVID-19 Daily Deaths. 2020; https://
www.england.nhs.uk/statistics/statistical-work-areas/cov-
id-19-daily-deaths/. Accessed 15 April 2020.
47. Istituto Nazionale di Statistica (ISTAT). Popolazione
residente al 1° gennaio. 2019; http://dati.istat.it/Index.
aspx?DataSetCode=DCIS_POPRES1. Accessed 13 April
2020.
48. Odone A, Delmonte D, Scognamiglio T, Signorelli C.
COVID-19 deaths in Lombardy. Lancet Public Health.
2020;published April 24, 2020.
49. Dipartimento della Protezione Civile. COVID-19 Italia-
Monitoraggio della situazione. 2020; http://www.protezi-
onecivile.gov.it/attivita-rischi/rischio-sanitario/emergenze/
coronavirus. Accessed 15 April 2020.
50. Ministero della Salute. Covid-19 - Situazione in Italia.
2020; http://www.salute.gov.it/portale/nuovocoronavirus/
dettaglioContenutiNuovoCoronavirus.jsp?area=nuovoCoro
navirus&id=5351&lingua=italiano&menu=vuoto. Accessed
13 April 2020.
51. Emilia-Romagna Region. Epidemia COVID-19. Aggiorna-
mento del 17 aprile 2020 sui dati del giorno precedente. 2020.
52. Romanò L, Pariani E, Biganzoli E, Castaldi S. e end of
lockdown what next ? Acta Biomedica. 2020;91(2).
53. Rajgor DD, Lee MH, Archuleta S, Bagdasarian N, Quek
SC. e many estimates of the COVID-19 case fatality rate.
Lancet Infect Dis. 2020.
54. Gianfredi V, Balzarini F, Gola M, et al. Leadership in Public
Health: Opportunities for Young Generations Within Sci-
entific Associations and the Experience of the “Academy of
Young Leaders”. Front Public Health. 2019;7:378.
Received: 17 April, 2020
Accepted: 23 April 2020
Correspondence:
Prof. Carlo Signorelli
Director of the School of Public Health, Vita-Salute San Raf-
faele University, Milan, Italy
E-mail: signorelli.carlo@hsr.it
