ArticlePDF Available
ResearchGate Logo

This article is featured on the COVID-19 research community page

View COVID-19 community

Abstract and Figures

We analyzed the spread of the COVID-19 epidemic in 6 metropolitan regions with similar demographic 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. The 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 contribution offered by the hospital care system.
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. (
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
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.
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.
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.
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.
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-
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
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
• Identication 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.
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.
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
1. Boerner L, Severgnini B. Il commercio è alleato dei vi-
rus, lo dice la storia. 2020;
dei-virus/. Accessed 13 April 2020.
2. World Health Organization. COVID-19 weekly surveil-
lance report. 2020;
surveillance-report, 2020.
3. Sebastiani G. Il coronavirus ha viaggiato in autostrada?
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.
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-
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.
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.
ti-1.38653880. Accessed 13 April 2020.
18. United States Census. Explore Census Data. 2020; https:// Accessed 13 April 2020.
19. United States Census. New York County (Manhattan Bor-
ough), New York. 2020;
facts/newyorkcountymanhattanboroughnewyork. Accessed
13 April 2020.
20. United States Census. Commuter-Adjusted Population
Estimates: ACS 2006-10. 2010;
Accessed 13 April 2020.
21. New York State Department of Health. Hospitals by Region/
County and Service. 2020;
+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://
rus-outbreak-how-many-hospital-beds/. Accessed 13 April
23. New York State Department of Health. Hospitals by Region/
County and Service. 2020;
24. e City. Coronavirus in New York City. 2020; https://pro- Accessed 13
April 2020.
25. NYC Health. COVID-19: Data. 2020;
site/doh/covid/ Accessed 15 April 2020.
26. European Commission. Population: Demographic situa-
tion, languages and religions. 2019;
graphic-situation-languages-and-religions-7_en. Accessed
13 April 2020.
27. City population. Bruxelles Municipality. 2019; https://
bruxelles/. Accessed 13 April 2020.
28. Brussels Institute for Statistics and Analysis. Key figures for
the Brussels-Capital Region. 2017; http://statistics.brus-
Accessed 13 April 2020.
29. Federal Public Service. e geographical distribution of ac-
credited hospital beds. 2020; https://www.healthybelgium.
pital-beds. Accessed 13 April 2020.
30. Sciensano. COVID-19 - Situation épidémiologique. 2020; Accessed 15 April 2020.
31. Portal del Ayuntamiento de Madrid. Portal web del Ayun-
tamiento de Madrid. 2019;
site/munimadrid#. Accessed 13 April 2020.
C Signorelli, A Odone, V Gianfredi, et al.
32. Instituto Nacional de Estadistica. Demografía y población.
2019; Accessed 13 April 2020.
33. Instituto Nacional de Estadistica. Atlas de la movilidad
residencia-trabajo en Comunidad de Madrid 2017. 2017;
ritorio/atlasmovilidad2017/02_caract_gnral.html. Accessed
13 April 2020.
34. Ministerio de Sanidad de España. Situación de COVID-19
en España. 2020; 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.
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.
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.
id-19-lars-ile-de-france-mobilisee. Accessed 13 April 2020.
41. Gouvernement France. COVID-19 en France. 2020; htt-
nees. Accessed 13 April 2020.
42. World Population Review. London Population 2020. 2020;
population/. Accessed 15 April 2020.
43. NHS England. Bed Availability and Occupancy Data –
Overnight. 2020;
data-overnight/. Accessed 15 April 2020.
44. Financial Times. NHS enlists all English private hospitals
to treat coronaviru. 2020;
9be78-6b7b-11ea-89df-41bea055720b. Accessed 15 April
45. NHS England. New NHS nightingale hospital to fight
coronavirus. 2020;
new-nhs-nightingale-hospital-to-fight-coronavirus/. Ac-
cessed 15 April 2020.
46. NHS England. COVID-19 Daily Deaths. 2020; https://
id-19-daily-deaths/. Accessed 15 April 2020.
47. Istituto Nazionale di Statistica (ISTAT). Popolazione
residente al 1° gennaio. 2019;
aspx?DataSetCode=DCIS_POPRES1. Accessed 13 April
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-
coronavirus. Accessed 15 April 2020.
50. Ministero della Salute. Covid-19 - Situazione in Italia.
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
Prof. Carlo Signorelli
Director of the School of Public Health, Vita-Salute San Raf-
faele University, Milan, Italy
... The rapid spread of infection has had a significant impact on the Italian national health system, particularly due to the sudden increase in hospitalisations and the overwhelming demand for intensive care unit (ICU) beds throughout, to date, three epidemic waves [5,6]. Inexperience in handling epidemic situations, particularly of a previously unknown pathogen, and the initial absence of diagnostics, personal protective equipment and poorly updated pandemic preparedness and response structures, substantially aggravated the epidemiological scenario, in particular during the first epidemic wave [7]. The Italian National Health System has been undergoing important changes over the years, including the increasing decentralisation of health care planning and decision making towards regional administrations since the 1990s and the progressive integration of publicly delivered health services with those offered by private health care providers since the global financial crisis [8]. ...
Full-text available
Objectives: To describe the monthly distribution of COVID-19 hospitalisations, deaths and case-fatality rates (CFR) in Lombardy (Italy) throughout 2020. Methods: We analysed de-identified hospitalisation data comprising all COVID-19-related admissions from 1 February 2020 to 31 December 2020. The overall survival (OS) from time of first hospitalisation was estimated using the Kaplan-Meier method. We estimated monthly CFRs and performed Cox regression models to measure the effects of potential predictors on OS. Results: Hospitalisation and death peaks occurred in March and November 2020. Patients aged ≥70 years had an up to 180 times higher risk of dying compared to younger patients [70–80: HR 58.10 (39.14–86.22); 80–90: 106.68 (71.01–160.27); ≥90: 180.96 (118.80–275.64)]. Risk of death was higher in patients with one or more comorbidities [1: HR 1.27 (95% CI 1.20–1.35); 2: 1.44 (1.33–1.55); ≥3: 1.73 (1.58–1.90)] and in those with specific conditions (hypertension, diabetes). Conclusion: Our data sheds light on the Italian pandemic scenario, uncovering mechanisms and gaps at regional health system level and, on a larger scale, adding to the body of knowledge needed to inform effective health service planning, delivery, and preparedness in times of crisis.
... Among all, one of the most impacting diseases -af-ter the challenge posed by SARS-CoV-2 [6,7] -, is the infection due to ZIKV. This is an arthropod-borne virus (arbovirus) of the family Flaviviridae, genus Flavivirus, which was firstly isolated in sentinel rhesus macaque in the Zika forest of Uganda in 1947 [8]. ...
Full-text available
Background: Zika virus (ZIKV) is an arthropod-borne virus transmitted through infected mosquitos. The aim of this Italian nation-wide study was to evaluate general population's knowledge and attitudes towards ZIKV, its transmission, and travel-related preventive measures. Methods: This cross-sectional study was conducted between July and August 2017, through a validated questionnaire. Predictors of knowledge were analysed through multivariate regression. Results: Among 1119 respondents, 20% and 71% knew etiological agent and transmission route of ZIKV infection, respectively. Approximately 43% ignored the preventive measures to be taken after returning from endemic areas. At multivariate analysis, predictors of poor knowledge were age, living in Central or South Italy and Islands, being poorly educated, having never heard of or attended a travel clinic. Conclusions: This study captures an overall poor knowledge of Zika among general public. This research highlights the need of designing and implementing measures to improve travellers' awareness and protection against ZIKV.
... In fact, distrust of institutions is just one of the threats that healthcare providers must be able to address today (14). This is accompanied by an aging population (15), inequality of access to health services (16,17), the continuation of unhealthy lifestyles (18)(19)(20), antimicrobic resistance (21), vaccine hesitancy (22,23) and the risk of epidemics from emerging and re-emerging agents (24)(25)(26)(27). ...
Full-text available
The Author, now a Research Assistant at the Department of Public Health of the University of Milan, Italy, in the period she was a Resident of the School of Public Health of the University of Perugia, had the occasion to interview Prof Maria Antonietta Modolo, one of the most significant pioneers of Health Education and Health Promotion both in Europe and in this Country, and her mentor at that time. Prof Modolo, who recently passed away, in that occasion explained in detail all the goals of these disciplines, and the impact they can show on the life of the population of developed and developing countries, if applied within a robust public health framework.
... The second sub-study is a retrospective analysis on the impacts of the project in the Lombardy Region, the area first and most heavily hit by the COVID-19 pandemic in Italy (22). We collected data from primary schools in the Lombardy Region which participated in the "Igiene Insieme" project and made school COVID-19 burden data available. ...
Objective: to report on the characteristics and impact of "Igiene Insieme", a school-based national-level educational programme designed and implemented in Italy to promote hygiene and sanitation in schools, stimulate healthy behaviors in teachers, students and their families, and ultimately fight COVID-19. Methods: the project targeted kindergartens and primary schools and included three components: the design and delivery of innovative health education interventions to i) students and ii) teachers, and iii) the provision of sanitation products in schools. Here we describe the intervention, report on the project penetration and evaluate its impact. First, a survey was conducted on a convenience sample of 1,005 teachers to evaluate the project at the national level, then a retrospective analysis was conducted in the Lombardy region comparing SARS-CoV-2 infection incidence rate in schools participating to the project with regional burden data. Results: Over 8,000 Italian schools joined the project, for a total of 32,000 teachers and 1.1 million students. Survey respondents rated the educational interventions and the provision of sanitation products as excellent (66.6% and 82.5%, respectively) and reported the project to have greatly impacted on students' health behaviors. In the Lombardy region, 271 primary schools (11%) joined the project and 140 (52%) provided COVID-19 burden data. Over the study period, SARS-CoV-2 infection incidence rate in schools participating in the project was 14% lower as compared to regional-level data (643 per 100,000 vs. 747 per 100,000). Conclusions: We raise awareness on the importance of promoting health education and infectious diseases primary prevention in schools, and to plan, implement and monitor student-centred interventions during and beyond COVID-19 times.
... After the first case detected in Lombardy Region on 21 February 2020, SARS-CoV-2 infection continued to spread throughout the country. The number of new cases peaked on 21 March 2020 and then decreased progressively due to strict nonpharmacological preventive measures implemented locally and at the national level [5]. Public health interventions to reduce the spread of SARS-CoV-2 infection focused on the stay-at-home policy and social distancing, with general lockdown or restricted mobility, closure of nonessential services including schools, and implementation of protocols such as mask-wearing, as well as regular and correct hand hygiene. ...
Full-text available
The COVID-19 pandemic has affected national healthcare systems worldwide, with around 282 million cumulative confirmed cases reported in over 220 countries and territories as of the end of 2021. The Italian National Health System was heavily affected, with detrimental impacts on preventive service delivery. Routine vaccination services were disrupted across the country during the first months of the pandemic, and both access to and demand for vaccines have decreased during the pandemic. In many cases, parents preferred to postpone scheduled appointments for routine paediatric vaccinations because of stay-at-home orders or fear of COVID-19 infection when accessing care. The objective of the current study was to assess the routine childhood vaccine coverage (VC) rates during the COVID-19 epidemic in Italy. We compared 2020 and 2019 VC by age group and vaccine type. The Italian Ministry of Health collected anonymised and aggregated immunisation national data through the local health authorities (LHAs). Results were considered statistically significant at a two-tailed p-value ≤ 0.05. VC rates for mandatory vaccinations decreased in 2020 compared to 2019 (range of VC rate decrease: −1% to −2.7%), while chicken pox increased (+2.2%) in 7-year-old children. Recommended vaccinations were moderately affected (range of VC rate decrease in 2020 vs. 2019: −1.4% to −8.5%), with the exception of anti-HPV in males, Men ACWY, and anti-rotavirus vaccination (VC increase 2020 vs. 2019: +1.8%, +4.7% and +9.4%, respectively). In the COVID-19 era, the implementation of coherent, transparent, and effective communication campaigns and educational programs on safe childhood vaccinations, together with the increase in the number of healthcare staff employed, is essential to support strategies to reinforce vaccination confidence and behaviour, thus avoiding health threats due to VPD during and beyond COVID-19 times.
... Coupled with their poor ability to identify correct Internet information [22], these behaviors reflect the urgent need to provide a quality Internet health information environment for adolescents. For example, the global COVID-19 pandemic has caused fear among the public about emerging infectious diseases, and as a result, a broad range of misinformation has spread throughout traditional media [23][24][25][26][27][28], affecting both the physical and mental health of the general public [29]. ...
Full-text available
Adolescents’ Internet health information usage has rarely been investigated. Adolescents seek all kinds of information from the Internet, including health information, which affects their Health Literacy that eHealth Literacy (eHL). This study is a retrospective observational study, we have total of 500 questionnaires were distributed, 87% of which were recovered, and we explored the channels that adolescents use to search for health information, their ability to identify false information, and factors affecting the type and content of health information queried. Adolescents believe that the Internet is a good means to seek health information because of its instant accessibility, frequent updating, convenience, and lack of time limits. More boys use the Internet to seek health information than girls in junior high schools (p = 0.009). The Internet is an important source of health information for adolescents but contains extensive misinformation that adolescents cannot identify. Additionally, adolescent boys and girls are interested in different health issues. Therefore, the government should implement measures to minimize misinformation on the Internet and create a healthy, educational online environment to promote Adolescents’ eHealth Literacy (eHL).
... Italy is one of the first and most severely affected country in Europe, with its first indigenous case identified on February 21, 2020 (4). As a consequence, in the period February-June 2020 Italy experienced a first wave that severely affected mainly the North of the country (5,6), led to a tight lockdown (7), with regional differences possibly related to genetic, clinical, lifestyle, and environmental factors (8)(9)(10)(11)(12)(13)(14)(15), followed by a decline in the summer period (4,5). ...
Background and aim: In early 2020, SARS-CoV-2 was declared a pandemic by the WHO and Italy was one of the first and most severely affected country in Europe. Despite the global interest about COVID-19 pandemic, several aspects of this infection are still unclear, especially in pediatric population. This study aims to investigate the characteristics of the isolated or quarantined children and adolescents followed by the Public Health Department of the Italian province of Modena during the first wave of COVID-19. Methods: The study population included all non-adult subjects aged 0-18 years who underwent isolation or quarantine during the first wave of SARS-CoV-2 pandemic from February 24 to June 18, 2020 in Modena province, Northern Italy. Results: In Modena province, 1230 children and adolescents were isolated in case of SARS-CoV-2 infection (6.3%), or quarantined due to close contact with confirmed cases (88.7%) or travelling from a high-risk area (5.0%). Among 349 individuals who underwent swab testing, 294 (84.2%) reported close contact with an infected cohabiting relative and 158 (45.3%) were symptomatic. Among all tested subjects, 78 (22.4%) resulted positive, with a higher proportion of symptomatic subjects compared with the SARS-CoV-2-negative (78.2% vs. 35.8%). Fever was mostly present in SARS-CoV-2-positive children (48.7% vs. 12.6%). Both anosmia (58.3% vs. 41.7%) and dysgeusia (54.5% vs. 45.5%) had only slightly higher frequency in SARS-CoV-2-positive. Conclusions: These findings allow to expand the knowledge regarding characteristics of non-adult subjects isolated or quarantined during the first wave of SARS-CoV-2 pandemic. (
Full-text available
COVID-19 is the most recent respiratory pandemic to necessitate better knowledge about city planning and design. The complex connections between cities and pandemics, however challenge traditional approaches to reviewing literature. In this article we adopted a rapid review methodology. We review the historical literature on respiratory pandemics and their documented connections to urban planning and design (both broadly defined as being concerned with cities as complex systems). Our systematic search across multidisciplinary databases returned a total of 1323 sources, with 92 articles included in the final review. Findings showed that the literature represents the multi-scalar nature of cities and pandemics – pandemics are global phenomena spread through an interconnected world, but require regional, city, local and individual responses. We characterise the literature under ten themes: scale (global to local); built environment; governance; modelling; non-pharmaceutical interventions; socioeconomic factors; system preparedness; system responses; underserved and vulnerable populations; and future-proofing urban planning and design. We conclude that the historical literature captures how city planning and design intersects with a public health response to respiratory pandemics. Our thematic framework provides parameters for future research and policy responses to the varied connections between cities and respiratory pandemics.
Introduction: Unexpectedly, Italy was the first Western country to face COVID-19 outbreak, but promptly it was the first one to adopt stringent procedures to stem the spread of infection. The objective of this study was to describe the epidemiological situation and comorbidities in Italy, in addition to containment measures and health system and social protection strengthening ones applied in this country. Methods: Available population data were collected, managed, and analysed from the daily reports on COVID-19 published every day, from 1 February to 8 June 2020. Results: Lombardia, a northern region of Italy, is considered the epicentre for the wave of the infection with the first diagnosed case, but in a few weeks other regions were involved (with Piemonte, Emilia-Romagna, and Veneto covering more than 70% of the Italian total cases). In the European context, after 3 months of containing measures of the sanitary emergency, Italy is the fourth country for the number of total positive cases (with 235,278 total case as at 8 June 2020), after Russia, the United Kingdom, and Spain, whereas it is the second for the number of deaths (with 33,964 deaths as at 8 June 2020), only after the United Kingdom. Regarding incidence, the curve of daily new cases shows an increasing trend up to 22 March 2020 with 6557 new daily cases and then a decreasing trend up to 280 as at 8 June. This turnaround can be explained by the application of national lockdown starting from 9 March and by the following 14 days of incubation of infection. Profiles of subjects at major risk of poor prognosis and death for COVID-19 are elderly (mean age of 80 years) and with three or more comorbidities. These characteristics can partially explain the high lethality rate for coronavirus observed in Italy, which is the European country with the highest share of elderly. In addition, other possible explanations of this high lethality are differences in testing policies among countries that influence the number of asymptomatic or pauci-symptomatic patients diagnosed as coronavirus positive, together with differences in definition and in the way of recording deaths for coronavirus. In the absence of a vaccine, severe nonpharmaceutical interventions (NPIs), including national lockdown, quarantine, social distancing, and use of facial masks, have been applied with success to reduce the virus spread and the burden on the National Health System. In addition to these stringent containment measures to fight the pandemic, other policies have been adopted searching to ensure economic sustainability, social safety, and stability. Conclusion: Italy was the first Western country with a wide spread of COVID-19, but it was the first one to introduce containment restrictions, tightening them week by week and subjecting the 60 million people living in the country to unprecedented limitations. Many measurements have been adopted by the government, such as lockdown during the early stages of infection and subsequent social distancing and wearing face masks in public areas. Italians were compliant with all the measures ordered by the government and their discipline reflected in the COVID numbers: the curve of daily new cases after a peak at the end of March now shows a consistent decreasing trend up. In this phase of current reduction of virus diffusion, it is crucial to accommodate the need to continue protecting citizens from the risk of infection with the undeferrable, although gradual, restart of the economic and social system. This new scenario requires an active collaboration among all the actors: statutory bodies, employers, civil society, and the third sector.
Full-text available
52° Congresso NazionaleSocietà Italiana di Igiene, Medicina Preventiva e Sanità Pubblica (SItI)Perugia 16-19 ottobre 2019 Within the workshop: Le potenzialità delle azioni comunicative per la promozione e la tutela della salute pubblica
Full-text available
The spread of coronavirus disease 2019 (COVID-19) in Italy prompted drastic measures for transmission containment. We examine the effects of these interventions, based on modeling of the unfolding epidemic. We test modeling options of the spatially explicit type, suggested by the wave of infections spreading from the initial foci to the rest of Italy. We estimate parameters of a metacommunity Susceptible–Exposed–Infected–Recovered (SEIR)-like transmission model that includes a network of 107 provinces connected by mobility at high resolution, and the critical contribution of presymptomatic and asymptomatic transmission. We estimate a generalized reproduction number ( R0 = 3.60 [3.49 to 3.84]), the spectral radius of a suitable next-generation matrix that measures the potential spread in the absence of containment interventions. The model includes the implementation of progressive restrictions after the first case confirmed in Italy (February 21, 2020) and runs until March 25, 2020. We account for uncertainty in epidemiological reporting, and time dependence of human mobility matrices and awareness-dependent exposure probabilities. We draw scenarios of different containment measures and their impact. Results suggest that the sequence of restrictions posed to mobility and human-to-human interactions have reduced transmission by 45% (42 to 49%). Averted hospitalizations are measured by running scenarios obtained by selectively relaxing the imposed restrictions and total about 200,000 individuals (as of March 25, 2020). Although a number of assumptions need to be reexamined, like age structure in social mixing patterns and in the distribution of mobility, hospitalization, and fatality, we conclude that verifiable evidence exists to support the planning of emergency measures.
Full-text available
The ongoing COVID-19 outbreak expanded rapidly throughout China. Major behavioral, clinical, and state interventions have been undertaken to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, affected COVID-19 spread in China. We use real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation on transmission in cities across China and ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was explained well by human mobility data. Following the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases were still indicative of local chains of transmission outside Wuhan. This study shows that the drastic control measures implemented in China substantially mitigated the spread of COVID-19.
Full-text available
Background In December, 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel coronavirus, emerged in Wuhan, China. Since then, the city of Wuhan has taken unprecedented measures in response to the outbreak, including extended school and workplace closures. We aimed to estimate the effects of physical distancing measures on the progression of the COVID-19 epidemic, hoping to provide some insights for the rest of the world. Methods To examine how changes in population mixing have affected outbreak progression in Wuhan, we used synthetic location-specific contact patterns in Wuhan and adapted these in the presence of school closures, extended workplace closures, and a reduction in mixing in the general community. Using these matrices and the latest estimates of the epidemiological parameters of the Wuhan outbreak, we simulated the ongoing trajectory of an outbreak in Wuhan using an age-structured susceptible-exposed-infected-removed (SEIR) model for several physical distancing measures. We fitted the latest estimates of epidemic parameters from a transmission model to data on local and internationally exported cases from Wuhan in an age-structured epidemic framework and investigated the age distribution of cases. We also simulated lifting of the control measures by allowing people to return to work in a phased-in way and looked at the effects of returning to work at different stages of the underlying outbreak (at the beginning of March or April). Findings Our projections show that physical distancing measures were most effective if the staggered return to work was at the beginning of April; this reduced the median number of infections by more than 92% (IQR 66–97) and 24% (13–90) in mid-2020 and end-2020, respectively. There are benefits to sustaining these measures until April in terms of delaying and reducing the height of the peak, median epidemic size at end-2020, and affording health-care systems more time to expand and respond. However, the modelled effects of physical distancing measures vary by the duration of infectiousness and the role school children have in the epidemic. Interpretation Restrictions on activities in Wuhan, if maintained until April, would probably help to delay the epidemic peak. Our projections suggest that premature and sudden lifting of interventions could lead to an earlier secondary peak, which could be flattened by relaxing the interventions gradually. However, there are limitations to our analysis, including large uncertainties around estimates of R0 and the duration of infectiousness. Funding Bill & Melinda Gates Foundation, National Institute for Health Research, Wellcome Trust, and Health Data Research UK.
Full-text available
Coronavirus disease 2019 (COVID-19) poses an occupational health risk to healthcare workers. Several thousand healthcare workers have already been infected, mainly in China. Preventing intra-hospital transmission of the communicable disease is therefore a priority. Based on the Systems Engineering Initiative for Patient Safety model, the strategies and measures to protect healthcare workers in an acute tertiary hospital are described along the domains of work task, technologies and tools, work environmental factors, and organizational conditions. The principle of zero occupational infection remains an achievable goal that all healthcare systems need to strive for in the face of a potential pandemic.
The Corona Virus 19 (COVID 19) epidemic is an infectious disease which was declared as a pandemic and hit all the Countries, all over the world, from the beginning of the year 2020. In Italy the epidemic is particular serious with 169.325 confirmed cases and 21.551 deaths on 20.04.2020. To stop the contagion on March 8 and up to May 3, the Italian Government decided a lockdown for all the Country, the authors suggest how to manage the reopening and restarting of all the activities avoiding a restart of the epidemic.
Since the beginning of the COVID-19 epidemic in Italy, the Italian Government implemented several restrictive measures to contain the spread of the infection. Data shows that, among these measures, the lockdown implemented as of 9 March had a positive impact, in particular the central and southern regions of Italy, while other actions appeared to be less effective. When the true prevalence of a disease is unknown, it is possible estimate it, based on mortality data and the assumptive case-fatality rate of the disease. Given these assumptions, the estimated period-prevalence of COVID-19 in Italy varies from 0.35% in Sicity to 13.3% in Lombardy.