A quantitative assessment of epidemiological parameters required to investigate COVID-19 burden

Abstract

Solid estimates describing the clinical course of SARS-CoV-2 infections are still lacking due to under-ascertainment of asymptomatic and mild-disease cases. In this work, we quantify age-specific probabilities of transitions between stages defining the natural history of SARS-CoV-2 infection from 1,965 SARS-CoV-2 positive individuals identified in Italy between March and April 2020 among contacts of confirmed cases. Infected contacts of cases were confirmed via RT-PCR tests as part of contact tracing activities or retrospectively via IgG serological tests and followed-up for symptoms and clinical outcomes. In addition, we provide estimates of time intervals between key events defining the clinical progression of cases as obtained from a larger sample, consisting of 95,371 infections ascertained between February and July 2020. We found that being older than 60 years of age was associated with a 39.9% (95%CI: 36.2-43.6%) likelihood of developing respiratory symptoms or fever ≥37.5 °C after SARS-CoV-2 infection; the 22.3% (95%CI: 19.3-25.6%) of the infections in this age group required hospital care and the 1% (95%CI: 0.4-2.1%) were admitted to an intensive care unit (ICU). The corresponding proportions in individuals younger than 60 years were estimated at 27.9% (95%CI: 25.4-30.4%), 8.8% (95%CI: 7.3-10.5%) and 0.4% (95%CI: 0.1-0.9%), respectively. The infection fatality ratio (IFR) ranged from 0.2% (95%CI: 0.0-0.6%) in individuals younger than 60 years to 12.3% (95%CI: 6.9-19.7%) for those aged 80 years or more; the case fatality ratio (CFR) in these two age classes was 0.6% (95%CI: 0.1-2%) and 19.2% (95%CI: 10.9-30.1%), respectively. The median length of stay in hospital was 10 (IQR: 3-21) days; the length of stay in ICU was 11 (IQR: 6-19) days. The obtained estimates provide insights into the epidemiology of COVID-19 and could be instrumental to refine mathematical modeling work supporting public health decisions.

Full text

Epidemics 37 (2021) 100530
Available online 17 November 2021
1755-4365/© 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
A quantitative assessment of epidemiological parameters required to
investigate COVID-19 burden
Agnese Zardini
a
,
1
, Margherita Galli
a
,
b
,
1
, Marcello Tirani
c
,
d
, Danilo Cereda
c
, Mattia Manica
a
,
Filippo Trentini
a
,
e
, Giorgio Guzzetta
a
, Valentina Marziano
a
, Raffaella Piccarreta
e
,
f
,
Alessia Melegaro
e
,
g
, Marco Ajelli
h
,
2
, Piero Poletti
a
,
*
,
2
, Stefano Merler
a
,
2
a
Bruno Kessler Foundation, Trento, Italy
b
Department of Mathematics, Computer Science and Physics, University of Udine, Udine, Italy
c
Directorate General for Health, Lombardy Region, Milan, Italy
d
Health Protection Agency of the Metropolitan Area of Milan, Milano, Italy
e
Dondena Centre for Research on Social Dynamics and Public Policy, and CovidCrisisLab, Bocconi University, Milan, Italy
f
Department of Decision Sciences, Bocconi University, Milan, Italy
g
Department of Social and Political Sciences, Bocconi University, Milan, Italy
h
Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
ARTICLE INFO
Keywords:
SARS-CoV-2
Risk outcomes
Disease burden
Epidemiological parameters
Contact tracing data
ABSTRACT
Solid estimates describing the clinical course of SARS-CoV-2 infections are still lacking due to under-
ascertainment of asymptomatic and mild-disease cases. In this work, we quantify age-specic probabilities of
transitions between stages dening the natural history of SARS-CoV-2 infection from 1965 SARS-CoV-2 positive
individuals identied in Italy between March and April 2020 among contacts of conrmed cases. Infected
contacts of cases were conrmed via RT-PCR tests as part of contact tracing activities or retrospectively via IgG
serological tests and followed-up for symptoms and clinical outcomes. In addition, we provide estimates of time
intervals between key events dening the clinical progression of cases as obtained from a larger sample, con-
sisting of 95,371 infections ascertained between February and July 2020. We found that being older than 60
years of age was associated with a 39.9% (95%CI: 36.2–43.6%) likelihood of developing respiratory symptoms or
fever ≥37.5 ◦C after SARS-CoV-2 infection; the 22.3% (95%CI: 19.3–25.6%) of the infections in this age group
required hospital care and the 1% (95%CI: 0.4–2.1%) were admitted to an intensive care unit (ICU). The cor-
responding proportions in individuals younger than 60 years were estimated at 27.9% (95%CI: 25.4–30.4%),
8.8% (95%CI: 7.3–10.5%) and 0.4% (95%CI: 0.1–0.9%), respectively. The infection fatality ratio (IFR) ranged
from 0.2% (95%CI: 0.0–0.6%) in individuals younger than 60 years to 12.3% (95%CI: 6.9–19.7%) for those aged
80 years or more; the case fatality ratio (CFR) in these two age classes was 0.6% (95%CI: 0.1–2%) and 19.2%
(95%CI: 10.9–30.1%), respectively. The median length of stay in hospital was 10 (IQR: 3–21) days; the length of
stay in ICU was 11 (IQR: 6–19) days. The obtained estimates provide insights into the epidemiology of COVID-19
and could be instrumental to rene mathematical modeling work supporting public health decisions.
1. Introduction
Mathematical modeling has been one of the cornerstones in the
response to the COVID-19 pandemic (Chinazzi et al., 2020; Ferguson
et al., 2020; Guzzetta et al., 2021; Hellewell et al., 2020; Kucharski et al.,
2020; Marziano et al., 2021; McCombs and Kadelka, 2020; Salje et al.,
2020; Trentini et al., 2021; Vespignani et al., 2020; Wu et al., 2020a). To
provide solid estimates, models need to be properly calibrated based on
empirical evidence (Biggerstaff et al., 2020; He et al., 2020; Ma et al.,
2020; Salje et al., 2020; Wood et al., 2021). While a lot of work has been
done in this direction (Cereda et al., 2021; He et al., 2020; Hilton and
Keeling, 2020; Ma et al., 2020; Park et al., 2020; Peiris et al., 2003;
* Corresponding author.
E-mail address: poletti@fbk.eu (P. Poletti).
1
Equally contributed.
2
Joint senior authors.
Contents lists available at ScienceDirect
Epidemics
journal homepage: www.elsevier.com/locate/epidemics
https://doi.org/10.1016/j.epidem.2021.100530
Received 10 May 2021; Received in revised form 7 October 2021; Accepted 12 November 2021

Epidemics 37 (2021) 100530
2
Riccardo et al., 2020; Zhang et al., 2020), metrics required to estimate
the disease burden are still poorly quantied (Davies et al., 2020; Wu
et al., 2020b). Difculties in deriving these quantities are related to
challenges in dening unbiased denominators (i.e., the infections) for
computing different risk outcomes (e.g., deaths, severe disease, respi-
ratory symptoms) upon infection (Poletti et al., 2020, 2021; Verity et al.,
2020). Indeed, as asymptomatic cases and infected individuals experi-
encing mild symptoms are, in general, more likely to remain undetected,
quantitative estimates of the clinical course of the infection based only
on conrmed cases could result in risk outcomes biased upward (Big-
gerstaff et al., 2020; Poletti et al., 2020, 2021; Verity et al., 2020; Wu
et al., 2020b).
In this work, we provide estimates of the probabilities of transition
across the stages characterizing the clinical progression after SARS-CoV-
2 infection, stratied by age and sex, as well as of the time delays be-
tween key events. To do this, we analyzed a sample of 1965 SARS-CoV-2
positive individuals who were contacts of conrmed cases. These in-
dividuals were identied irrespective of their symptoms as part of
contact tracing activities carried out in Lombardy (Italy) over the period
from March 10 to April 27, 2020. These individuals were daily moni-
tored for symptoms for at least two weeks after exposure to a COVID-19
case and either tested for SARS-CoV-2 via PCR in real time or retro-
spectively via IgG serological assays; their clinical history was also
recorded. In addition to this highly detailed sample, we relied on the
epidemiological records of all the 95,371 SARS-CoV-2 PCR conrmed
infections reported to the surveillance system between February and
July 2020. This allowed us to provide a comprehensive quantitative
assessment of all the main epidemiological parameters essential to
model COVID-19 burden (see Fig. 1), thus laying the foundation for
future COVID-19 modeling efforts.
Estimates on age-specic risk outcomes after SARS-CoV-2 infection
were validated against epidemiological records that have not been used
to derive these quantities, leveraging on data from two serological sur-
veys conducted in Italy (Stefanelli et al., 2021; Italian National Institute
of Statistics ISTAT, 2020) and on the national cumulative incidence
reported up to April 2021 (Istituto Superiore di Sanit`
a,2021).
2. Methods
2.1. Study population
Lombardy represents the earliest and most affected region by the rst
COVID-19 epidemic wave experienced in Italy. Short after the detection
of a rst COVID-19 case on February 20, 2020, a ban of mass gatherings
and the suspension of teaching in schools and universities was applied to
the entire region. The interruption of non-essential productive activities
and strict individual movement restrictions were imposed to the most
affected municipalities. On March 8, 2020, after a rapid increase of
Fig. 1. A Schematic representation of transition probabilities characterizing possible disease outcomes after SARS-CoV-2 infection. These include the symptomatic
ratio (SR), the ratio of critical cases (CR), the case (CFR) and infection fatality ratios (IFR) and similar quantities that could be estimated using ascertained
symptomatic infections (asCR, asCFR) as the set of exposed individuals. B Schematic representation of transition probabilities characterizing the hospital (HR) and
ICU (IR) admission among infected individuals, and of similar quantities that could be estimated using ascertained symptomatic infections (asHR) or hospital patients
(hCFR, hIR) as the set of exposed individuals. C Schematic representation of time to key events dening the temporal clinical progression of cases. D Schematic
representation of the differences in the ascertainment rates associated with SARS-CoV-2 infections and symptomatic cases in the community and among close
contacts of identied cases, with the latter representing individuals who were all tested for SARS-CoV-2 infection and daily monitored for symptoms during their
quarantine or isolation period.
A. Zardini et al.

Epidemics 37 (2021) 100530
3
cases, closure of all non-necessary businesses and industries and limi-
tations of movements except in cases of necessity were extended to the
entire region. A national lockdown was imposed on March 10, 2020.
Suspended economic and social activities were gradually resumed be-
tween April 14 and May 18, 2020.
2.2. Data collection
Data analyzed here consists of the line list of SARS-CoV-2 laboratory
conrmed infections ascertained in Lombardy between February 20 and
July 16, 2020, and regularly updated by the regional public health au-
thorities. Information retrieved from this dataset was complemented
with contact-tracing records collected between March 10 and April 27
and with results of a serological survey targeting case contacts con-
ducted between April 16 and June 15, 2020 (Poletti et al., 2020, 2021).
Data collection, integration, storage, and anonymization was managed
by regional health authorities as part of surveillance activities and
outbreak investigations aimed at controlling and mitigating the
COVID-19 epidemic in Italy.
2.3. Denition of COVID-19 case
From February 21 to February 25, 2020, following the criteria
initially dened by the European Centre for Disease Prevention and
Control (ECDC), suspected COVID-19 cases were identied as:
1. patients with acute respiratory tract infection OR sudden onset of at
least one of the following: cough, fever, shortness of breath AND with
no other aetiology that fully explains the clinical presentation AND at
least one of these other conditions: a history of travel to or residence
in China, OR patients among health care workers who has been
working in an environment where severe acute respiratory infections
of unknown etiology are being cared for;
2. OR patients with any acute respiratory illness AND at least one of
these other conditions: having been in close contact with a conrmed
or probable COVID-19 case in the last 14 days prior to onset of
symptoms, OR having visited or worked in a live animal market in
Wuhan, Hubei Province, China in the last 14 days prior to onset of
symptoms, OR having worked or attended a health care facility in the
last 14 days prior to onset of symptoms where patients with hospital-
associated COVID-19 have been reported.
Conrmed cases were dened as suspect cases testing positive with a
specic real-time reverse transcription polymerase chain reaction (RT-
PCR) assay targeting multiple genes of SARS-CoV-2 (Cereda et al., 2021;
Corman et al., 2020; Cohen and Kessel, 2020). From March 20, 2020
positivity to the nasopharyngeal swab was also granted for assays that
tested a single gene. At any time, ascertained infections were dened as
laboratory conrmed SARS-CoV-2 infections, irrespective of clinical
signs and symptoms. Inconclusive swabs were repeated to reach the
diagnosis.
2.4. Ascertainment of infections among close case contacts
All ascertained SARS-CoV-2 infections were considered as potential
index cases for further spread of SARS-CoV-2. Close contacts of these
individuals were therefore identied through standard interviews of
cases, informed of their possible exposure and quarantined within
24–48 h from a positive test result on the index case.
A close case contact was dened as a person living in the same
household as a COVID-19 conrmed case; a person having had face-to-
face interaction with a COVID-19 conrmed case within 2 m and for
more than 15 min; a person who was in a closed environment (e.g.
classroom, meeting room, hospital waiting room) with a COVID-19
conrmed case at a distance of less than 2 m for more than 15 min; a
healthcare worker or other person providing direct care for a COVID-19
conrmed case, or laboratory workers handling specimens from a
COVID-19 conrmed case without recommended personal protective
equipment (PPE) or with a possible breach of PPE; a contact in an
aircraft sitting within two seats (in any direction) of a COVID-19
conrmed case, travel companions or persons providing care, and
crew members serving in the section of the aircraft where the index case
was seated (passengers seated in the entire section or all passengers on
the aircraft were considered close contacts of a conrmed case when
severity of symptoms or movement of the case indicate more extensive
exposure). Close case contacts were initially considered as contacts
occurred between 14 days before and 14 days after the date of symptom
onset of the index case. After March 20, 2020 the exposure period was
shortened, ranging from 2 days before to 14 days after the symptom
onset of the index case (World Health Organization, 2020). For in-
dividuals unable to sustain the contact tracing interview, close contacts
were identied by their parents, relatives or their emergency contacts.
From February 20 to February 25, 2020 all contacts of conrmed in-
fections were tested with RT-PCR, irrespective of clinical symptoms.
From February 26 onward, the traced contacts were tested with RT-PCR
only in case of symptom onset.
However, on April 16, 2020, regional health authorities initiated an
IgG serological survey of quarantined case contacts without history of
testing against SARS-CoV-2 infection to retrospectively identify all
asymptomatic positive contacts. The test used to detect SARS-CoV-2 IgG
antibodies was the LIAISONR SARS-CoV-2 test (DiaSorin), employing
magnetic beads coated with S1 & S2 antigens. The antigens used in the
tests are expressed in human cells to achieve proper folding, oligomer
formation, and glycosylation, providing material similar to the native
spikes. The S1 and S2 proteins are both targets to neutralizing anti-
bodies. The test provides the detection of neutralizing antibodies with
98.3% specicity and 94.4% sensitivity at 15 days from diagnosis.
Performance analyses validating the accuracy of this serological test can
be found in Bonelli et al. (2020). Serological test results were binary and
communicated to tested participants, who were categorized as sero-
positive if they had developed IgG antibodies.
All case contacts, irrespectively to the presence of a laboratory
diagnosis, were followed up for at least 14 days after exposure to an
index case and required by national regulations to report symptoms to
local public health authorities. Symptomatic cases were dened as
infected subjects showing fever ≥37.5 ◦C or one of the following
symptoms: dry cough, dyspnea, tachypnea, difculty breathing, short-
ness of breath, sore throat, and chest pain or pressure. The denition of
symptoms did not change throughout the period considered in this
study. Clinical manifestations, admission to hospital or intensive care
units and death among both ascertained infections and their close con-
tacts were regularly updated by the regional health surveillance. In our
study, individuals experiencing critical diseases were dened as positive
patients who were either admitted to an intensive care unit or died with
a diagnosis of SARS-CoV-2 infection. Positive subjects who developed a
critical disease are hereafter simply denoted as critical cases. Hospital-
ized patients with a laboratory conrmation of SARS-CoV-2 infection
are denoted as ascertained cases admitted to hospital.
2.5. Sample selection for computing risk outcomes
A large fraction of case contacts remained untested against SARS-
CoV-2 infection, due to difculties in maintaining a high level of
testing during the contact tracing operations and to the relatively low
coverage of IgG serological screening conducted on traced contacts. As
asymptomatic infections ascertained by surveillance systems are likely
under-represented, we selected a subsample of SARS-CoV-2 positive
individuals who were tested irrespectively from their symptoms. In
particular, we considered infections ascertained among case contacts
identied between March 10 and April 27, 2020 and belonging to
clusters whose individuals were all tested and daily followed up for
symptoms. A fraction of these individuals, mainly symptomatic ones,
A. Zardini et al.

Epidemics 37 (2021) 100530
4
was tested by RT-PCR during contact-tracing activities. The remaining
fraction was conrmed via IgG serological assays collected at least one
month after exposure, thus allowing the identication of asymptomatic
infections. This study design allowed us to minimize the risks of bias in
the identication of infections when computing the proportion of SARS-
CoV-2 infections developing symptoms and severe conditions. The
resulting subsample consisted of 1965 positive subjects identied in
2458 clusters of 3947 close contacts. None of these records showed
inconsistent data entries.
2.6. Statistical analysis
The aforementioned subsample of 1965 positive individuals who
were identied as contacts of conrmed cases was analyzed to estimate
the likelihood of developing respiratory symptoms or fever ≥37.5 ◦C
(SR), of being admitted to a hospital (HR) and an ICU (IR), of developing
critical disease (CR) and of dying after SARS-CoV-2 infection (IFR). The
same sample was considered to estimate the case fatality ratio (CFR).
Age and sex specic ratios were computed as crude percentages; 95%
condence intervals were computed by exact binomial tests. Logistic
regression models were used to estimate the corresponding risk ratios
(RRs) using the case age group, sex and month of identication (March
or April) as model covariates. For the regression analysis, the following
age-groups were considered: 0–59 years, 60–74 years, 75 +years.
The entire sample of cases ascertained by regular surveillance ac-
tivities (88,538 symptomatic individuals) was used to investigate tem-
poral changes in the COVID-19 disease burden. In particular, we
computed the age-specic crude percentage of ascertained cases
admitted to hospital (asHR) and the percentage of ICU admissions
among hospitalized cases (hIR) for four epidemic periods: before April,
April, May and after May.
The same sample of cases was used to investigate the distribution of
patients’ length of stay in hospital and in ICU, and the time interval
between the following key events: from symptom onset to diagnosis,
from symptom onset to hospital and/or ICU admission, from symptom
onset to death, and from hospital to ICU admission. The time at diag-
nosis was dened as the time of testing observed for positive individuals.
As 3855 out of 47,393 inpatients had inconsistent data entries on their
temporal clinical progression after hospital admission, we excluded the
corresponding data records when estimating time to key changes in
patients’ status, such as hospital or ICU admission and discharge. Spe-
cically, we excluded inpatients with a date of hospital admission or of
death preceding the date of symptom onset, patients with a date of ICU
admission or death preceding their hospitalization and patients with a
negative length of stay in ICU or in hospital. Estimates for the hospital
and ICU length of stay and the time between key events are provided for
two epidemic periods, dened by considering the date of peak in the
COVID-19 incidence experienced during the rst epidemic wave in
Lombardy, namely March 16, 2020. Cases were aggregated on the basis
of the initial date of the considered interval. Negative binomial distri-
butions were used to separately t each time interval of interest. A
negative binomial distribution was considered to better reect the data
characteristics: time lags expressed as integer values (delays measured
in days), and a non-negligible proportion of patients with null delays
(events occurring within the same day). Specically, the negative
binomial distribution was preferred over the Poisson, truncated normal,
Gamma, Weibull, and Log-normal distributions, given that these alter-
natives were associated with a lower goodness of t in terms of Akaike
Information Criterion or they requested additional assumptions to t the
available records (e.g., the Gamma distribution is dened for strictly
positive values only).
To assess the robustness of the estimated risk outcomes with respect
to the change in the denition of close contact occurred on March 20,
2020, we investigated how the analyzed metrics would change when
considering infections ascertained after that date only.
Since in our baseline analysis no assumptions were made on the time
from the diagnosis of SARS-CoV-2 to hospital or to ICU admission, we
also explored the effect of excluding patients reporting a delay from
SARS-CoV-2 diagnosis to hospital or ICU admission greater than 30 days.
Specically, we analyzed the impact of this assumption on the estimated
risk outcomes, the time intervals between key events, and the temporal
changes in the probability of being admitted to hospital and ICU.
The statistical analysis was performed with the software R (version
3.6), using the “MASS” package. Fig. 1 provides a schematic represen-
tation of all metrics considered to quantify COVID-19 burden.
2.7. Validation of age-specic risk outcomes
The adopted approach was validated by applying our estimates for
age-specic risk outcomes given SARS-CoV-2 infection to seropreva-
lence data available for Italy and comparing the obtained results with
the age distribution of critical cases and deaths observed in Lombardy
during the rst pandemic wave and throughout Italy up to April 2021.
Combining the estimated risk outcomes with a serological study con-
ducted in a specic period would be inappropriate to estimate the ab-
solute number of patients associated with different outcomes at a
different time. However, the rationale of applying the estimated risk
outcomes to independent seroprevalence data (collected at a different
time) was to test whether the provided estimates could be used to
reproduce the age proles characterizing critical patients and deaths
recorded over different periods.
Specically, we computed the expected age distribution of critical
cases C(a) and deaths D(a) as
C(a) = i(a)CR(a)
∑
a
i(a)CR(a)
and
D(a) = i(a)IFR(a)
∑
a
i(a)IFR(a)
where i(a)is the number of SARS-CoV-2 IgG positive individuals iden-
tied in the age class a through serological surveys, CR(a) and IFR(a)
represent our estimates for the probability of developing critical disease
and the infection fatality ratio for the age class a. i(a)was retrieved from:
1) a serological study conducted at the national level between May 25
and July 15, 2020 (Italian National Institute of Statistics ISTAT, 2020)
and 2) results of an extensive serological screening applied between May
5 and May 15, 2020 to 77% of individuals residing in a high-incidence
area (approximately 8000 residents) located in north-eastern Italy
(Stefanelli et al., 2021). Resulting values for C(a) were compared to the
age distribution of all critical cases recorded in Lombardy between
February 20 and July 16, 2020. Values obtained for D(a) were compared
to the age distribution of cumulative deaths recorded in Lombardy until
July 16, 2020 and that observed at the national level between February
2020 and April 2021. The latter was obtained by using cumulative
notication data stratied by age as provided by the Integrated National
Surveillance System (NSS) (Istituto Superiore di Sanit`
a, 2021). Valida-
tion of risk outcomes was carried by considering the following
age-groups: 0–19, 20–39, 40–59, 60–69, 70 +years.
2.8. Ethical statement
Data collection and analysis were part of outbreak investigations
during a public health emergency. Processing of COVID-19 data is
necessary for reasons of public interest in the area of public health, such
as protecting against serious cross-border threats to health or ensuring
high standards of quality and safety of health care, and therefore
exempted from institutional review board approval (Regulation EU
2016/679 GDPR).
A. Zardini et al.

Epidemics 37 (2021) 100530
5
3. Results
3.1. Sample description
We analyzed a total of 95,371 laboratory conrmed infections
ascertained between February and July 2020. Of these, 88,538 (92.8%,
median age 65 years, IQR: 50–81) reported respiratory symptoms or
fever ≥37.5 ◦C, 47,393 (49.7%, median age 69 years, IQR: 55–80) were
hospitalized, 19,020 (19.9%, median age 79 years, IQR: 70–86) devel-
oped critical disease (i.e., requiring ICU treatment or resulting in a fatal
outcome) and 16,778 (17.6%, median age 81 years, IQR: 73–87) died
with a diagnosis of SARS-CoV-2 (Table 1).
By combining the regional line list of all ascertained infections with
contact-tracing records collected between March 10 and April 27, 2020,
we obtained a subsample of 1965 (median age 53 years, IQR: 32–64)
contacts who resulted positive to SARS-CoV-2. Of these, 630 (32.1%,
median age 57 years, IQR: 42.5–71) developed symptoms, 266 (13.5%,
median age 64 years, IQR: 53.25–76) were hospitalized, 43 (2.2%,
median age 76 years, IQR: 69–81) experienced critical disease condi-
tions, 12 (0.6%, median age 68 years, IQR: 52.5 −72) were admitted to
ICUs, and 35 (1.8%, median age 78 years, IQR: 74.5 – 82.5) resulted in a
fatal outcome; 31 (1.6%, median age 79 years, IQR: 75–84) subjects died
without being admitted to ICU; 4 (0.2%, median age 73.5 years, IQR:
71.25–75) died after an ICU admission (Table 2).
3.2. Metrics of COVID-19 burden
Age-specic transition probabilities characterizing the different
outcomes after SARS-CoV-2 infection were estimated by considering
infections occurred among close case contacts identied between March
10 and April 27, 2020. We found that the likelihood of developing res-
piratory symptoms or fever ≥37.5 ◦C after SARS-CoV-2 infection (SR)
was 27.9% (95%CI: 25.4–30.4%) under 60 years of age and 39.9% (95%
CI: 36.2–43.6%) above (see Table 2). We estimated that, in the rst age-
group, 8.8% (95%CI: 7.3–10.5%) of infected individuals required hos-
pital care (HR) and 0.4% (95%CI: 0.1–0.9%) were admitted to ICU (IR);
the corresponding proportions in positive individuals older than 60
years were 22.3% (95%CI: 19.3–25.6%) and 1% (95%CI: 0.4–2.1%),
respectively. A signicantly higher risk of developing critical disease
after infection (CR) was found above 60 years of age when compared to
younger individuals: 5.3% (95%CI: 3.7–7.2%) vs 0.5% (95%CI:
0.2–1.1%). The infection fatality ratio (IFR) ranged between 0.2% (95%
CI: 0.0–0.6%) in subjects younger than 60 years to 12.3% (95%CI:
6.9–19.7%) for those aged 80 years or more. The case fatality ratio (CFR)
in these two age groups was 0.6% (95%CI: 0.1–2%) and 19.2% (95%CI:
10.9–30.1%). Although the case fatality ratio was higher for subjects
older than 80 years compared to cases aged 60–79 years (namely, 9.5%,
95%CI: 5.8–14.4%), a signicantly lower proportion of ICU admissions
was found for the oldest age segment: 1.2% (95%CI: 0.5–2.5%) vs 0%
(95%CI: 0–3.2%). A detailed age-stratication of all these quantities is
provided in Table 2. The strong age dependency in the risk of developing
symptoms and most severe outcomes after SARS-CoV-2 infection was
conrmed by a statistical analysis based on generalized linear models
applied to infected case contacts and accounting for possible con-
founding factors (see Table S1). The regression analysis also highlighted
a signicantly higher risk ratio (RR) of hospital admission (RR: 1.34,
95%CI: 1.07–1.67), critical disease (RR: 2.16, 95%CI: 1.17–3.98), and
death (RR: 2.15, 95%CI: 1.08–4.27) for infected males as compared to
females (Table S1).
Fig. 2 compares the age distributions of critical cases and deaths
observed in Lombardy and in Italy with those resulting when applying
our estimates for risk outcomes after SARS-CoV-2 infection to serolog-
ical data available for the Italian context (Stefanelli et al., 2021; Istituto
Superiore di Sanit`
a, 2021; Italian National Institute of Statistics ISTAT,
2020). These ndings highlight that, although estimates for CR and IFR
were obtained from a relatively small sample of case contacts identied
during the rst pandemic phase (1965 subjects), they can well capture
the age proles characterizing the entire line list of critical patients and
deaths recorded in Lombardy during the rst COVID-19 wave and the
age distribution of all deaths ofcially recorded across the entire Italian
territory until 29 April 2021.
3.3. Temporal changes in the investigated risk metrics
Temporal changes in the risk of being admitted to hospital and ICUs
were explored by analyzing records of the complete list of 88,538
symptomatic cases ascertained between February and July 2020 (see
Table 1 and Table S2 for sample description). The analyzed data includes
inpatients with inconsistencies in dates dening the temporal clinical
progression after hospitalization. Crude ratios computed from ascer-
tained symptomatic cases should be carefully interpreted because of
possible biases due to higher ascertainment rates among more severe
cases. However, the analysis of this large sample highlighted an increase
of admission rates at different levels of intensity of care among the
elderly (Fig. 3). In particular, hospital admission ratios among ascer-
tained symptomatic cases (asHR) aged more than 80 years increased
from the 26.4% (95%CI: 25.5–27.2%) observed between April and May
to 34.7% (95%CI: 30.5–39.1%) afterwards. Similarly, the ICU admission
Table 1
Estimated risk ratios of hospital admission, experiencing critical disease, and fatal outcome among symptomatic cases, disaggregated by age, sex, and period.
Symptomatic cases Hospitalized patients Critical cases Deaths
Count Count Risk ratio (95%CI) Count Risk ratio (95%CI) Count Risk ratio (95%CI)
Age
≥80 24,092 11,849 Reference 9325 Reference 9291 Reference
0–39 11,019 3361 0.54 (0.52–0.56) 158 0.03 (0.03–0.04) 38 0.01 (0.01–0.01)
40–59 25,910 12,037 0.77 (0.75–0.79) 1747 0.12 (0.12–0.13) 733 0.05 (0.05–0.06)
60–69 12,731 8917 1.22 (1.19–1.24) 2642 0.35 (0.33–0.37) 1872 0.24 (0.23–0.26)
70–79 14,784 11,228 1.39 (1.37–1.41) 5147 0.65 (0.63–0.68) 4844 0.62 (0.60–0.64)
Unknown 2 1 1.13 (0.06–1.99) 1 0.86 (0.05–2.40) 0
a
–
Sex
Female 46,234 19,318 Reference 7323 Reference 6804 Reference
Male 42,168 28,061 1.49 (1.47–1.51) 11,682 1.87 (1.82–1.92) 9966 1.78 (1.73–1.84)
Unknown 136 14 0.20 (0.11–0.33) 15 1.28 (0.75–1.97) 8 0.84 (0.38–1.57)
Epidemic period
Before April 56,288 37,391 Reference 14,473 Reference 12,530 Reference
April 21,022 6909 0.52 (0.51–0.54) 3451 0.49 (0.47–0.51) 3239 0.48 (0.46–0.50)
May 6019 1282 0.36 (0.34–0.38) 313 0.20 (0.18–0.22) 276 0.19 (0.16–0.21)
After May 3596 591 0.27 (0.25–0.29) 44 0.06 (0.04–0.08) 39 0.06 (0.04–0.08)
Unknown 1613 1220 1.15 (1.11–1.18) 739 1.56 (1.45–1.66) 694 1.63 (1.51–1.76)
a
RR and 95%CI were not computed for insufciently large sample size.
A. Zardini et al.

Epidemics 37 (2021) 100530
6
ratio among patients hospitalized (hIR) in this age group raised from the
0.9% (95%CI: 0.7–1.1%) observed between March and April to 2.3%
(95%CI: 1.2–3.8%) afterwards.
3.4. Time to key events
Time delays from symptom onset to diagnosis and death were
investigated by analyzing all the 88,538 symptomatic infections
ascertained in Lombardy between February and July 2020. The tem-
poral clinical progression of inpatients was investigated by analyzing
43,538 hospitalized cases (Table 3), after having excluded 3855 out of
the 47,393 available inpatient records because of inconsistent dates of
hospital or ICU admission/discharge. We estimated that, overall, the
median delay between symptom onset and diagnosis was 4 (IQR: 1–10)
days. The median time from symptom onset to death was 12 (IQR: 7–21)
days. Hospitalization of cases occurred 5 (IQR: 2–9) days after patients’
Table 2
Estimated crude percentages of symptomatic, hospitalized, ICU admitted, and critical cases among SARS-CoV-2 positive individuals who were identied as contacts of
conrmed cases as well as estimated risk of death among positive individuals (i.e., infections) and symptomatic case (i.e., infected and symptomatic) individuals who
were identied as contacts of conrmed cases. Results are disaggregated by age and sex.
Positives
contacts
Symptomatic cases Critical cases Deaths Hospitalized patients ICU-admitted patients
Count Count Proportion
(95% CI)
Count Proportion
(95% CI)
Count IFR (95% CI) CFR (95% CI) Count Proportion
(95% CI)
Count Proportion
(95% CI)
Age
0–14 219 39 17.8%
(13–23.5%)
0 0% (0–1.7%) 0 0% (0–1.7%) 0% (0–9%) 4 1.8%
(0.5–4.6%)
0 0%
(0–1.7%)
15–19 22 6 27.3%
(10.7–50.2%)
0 0%
(0–15.4%)
0 0%
(0–15.4%)
0% (0–45.9%) 2 9.1%
(1.1–29.2%)
0 0%
(0–15.4%)
20–39 377 99 26.3%
(21.9–31%)
2 0.5%
(0.1–1.9%)
0 0% (0–1%) 0% (0–3.7%) 18 4.8%
(2.9–7.4%)
2 0.5%
(0.1–1.9%)
40–59 662 213 32.2%
(28.6–35.9%)
5 0.8%
(0.2–1.8%)
2 0.3%
(0–1.1%)
0.9%
(0.1–3.4%)
89 13.4%
(10.9–16.3%)
3 0.5%
(0.1–1.3%)
60–69 331 106 32%
(27–37.3%)
5 1.5%
(0.5–3.5%)
3 0.9%
(0.2–2.6%)
2.8%
(0.6–8%)
49 14.8%
(11.2–19.1%)
3 0.9%
(0.2–2.6%)
70–79 240 94 39.2%
(33–45.7%)
17 7.1%
(4.2–11.1%)
16 6.7%
(3.9–10.6%)
17%
(10.1–26.2%)
59 24.6%
(19.3–30.5%)
4 1.7%
(0.5–4.2%)
≥80 114 73 64%
(54.5–72.8%)
14 12.3%
(6.9–19.7%)
14 12.3%
(6.9–19.7%)
19.2%
(10.9–30.1%)
45 39.5%
(30.4–49.1%)
0 0%
(0–3.2%)
Sex
Female 1111 365 32.9%
(30.1–35.7%)
19 1.7%
(1–2.7%)
16 1.4%
(0.8–2.3%)
4.4%
(2.5–7%)
139 12.5%
(10.6–14.6%)
5 0.5%
(0.1–1%)
Male 854 265 31%
(27.9–34.3%)
24 2.8%
(1.8–4.2%)
19 2.2%
(1.3–3.5%)
7.2%
(4.4–11%)
127 14.9%
(12.6–17.4%)
7 0.8%
(0.3–1.7%)
.
.2021
2021
Fig. 2. A Comparison between the age distributions of critical cases as obtained when applying estimated risk outcomes to available serological records with the one
observed in Lombardy during the rst COVID-19 wave. B Comparison between the age distributions of deaths as obtained when applying estimated risk outcomes to
available serological records with the one observed in Lombardy during the rst COVID-19 wave and the one associated to deaths occurred in Italy between February
2020 and April 2021, as reported by the Integrated National Surveillance System (NSS).
A. Zardini et al.

Epidemics 37 (2021) 100530
7
symptom onset; admission to ICU occurred 10 (IQR: 6–15) days after
symptom onset. The median time between hospital and ICU admission
was 3 (IQR: 0–6) days. The median hospital length of stay was 10 (IQR:
3–21) days, while the median length of stay in ICU was 11 (IQR: 6–19)
days. A negative binomial distribution was used to separately t each
time interval of interest (Fig. S1).
When looking at these variables across different ages, we found a
shorter delay between symptom onset and death in individuals older
than 70 years (11–12 days vs 15–16 days at younger ages) and a shorter
length of stay in ICU among patients aged 80 years or more (5 days vs
9–12 days at younger ages). We separately analyzed these quantities for
cases who developed symptoms before and after March 16, 2020, cor-
responding to the peak in the number of hospitalized patients in Lom-
bardy (Fig. S2). We found a marked decrease in the time required to both
diagnose and hospitalize COVID-19 patients after this date (from 7 to 2
days and from 7 to 3 days, respectively, Table 3). It is worth noting that
the lag between the time of the test and the time when the test result
became available remained approximately constant during the entire
period considered (ranging from 2 to 4 days). Detailed estimates ob-
tained on the temporal clinical progression of COVID-19 cases are re-
ported in Table 3.
By considering only positive contacts ascertained after March 20,
2020, when the denition of close contact changed, a lower likelihood of
experiencing critical disease and death for positive individuals older
than 70 years and females was found (see Table S3). Such differences
may be linked to the enhancement of the tracing and treatment pro-
cedures during the rst month of the COVID-19 epidemic, which may
include a faster detection and diagnosis of SARS-CoV-2 infections and
shorter time lags between diagnosis and hospitalization of severe pa-
tients. On the other hand, our estimates did not change when excluding
patients with a delay from SARS-CoV-2 diagnosis to hospital or ICU
admission greater than 30 days (Tables S4 and S5, and Fig. S3).
Fig. 3. A Age-specic case hospital admission ratios among ascertained symptomatic cases (asHR). B Age-specic ICU admission ratios among hospitalized cases
(hIR). Bars of different colors represent crude percentages observed across different epidemic periods; vertical lines represent 95% condence intervals computed by
exact binomial tests. Numbers shown in each panel represent the age-specic number of events observed in the data among exposed COVID-19 cases.
Table 3
Time intervals between key events as estimated from laboratory conrmed infections ascertained in Lombardy between February 20 and July 16, 2020.
Median days (IQR)
Time between symptom
onset and diagnosis of
SARS-CoV-2
Time between
symptom onset and
death
Time between
symptom onset and
hospital admission
Time between
symptom onset and
ICU admission
Time between
hospital and ICU
admission
Hospital
length of
stay
ICU length
of stay
Age
0–39 3 (0–11) 16 (6–27) 4 (1–8) 8 (4–11) 1 (0–5) 4 (0–10) 9 (4–15.75)
40–59 5 (1–11) 15 (9–26) 6 (2–10) 10 (6–13) 2 (0–6) 9 (1–18) 11 (6–19)
60–69 5 (1–10) 16 (9–25) 6 (2–10) 11 (7–15) 3 (0–7) 13 (6–24) 12 (6–20)
70–79 5 (1–9) 12 (7–20) 5 (2–9) 10 (6–16) 4 (1–7) 12 (5–24) 10 (5–18)
≥80 3 (0–8) 11 (6–19) 4 (1–8) 9 (4–23) 2 (0–11.5) 10 (4–24) 5 (3–10.75)
Unknown 13 (9.5–16.5) 0 (0–0) 8 (8–8) 8 (8–8) 0 (0–0) 14 (14–14) 14 (14–14)
Epidemic period
Before 16
March
7 (3–12) 13 (8–21) 7 (3–10) 10 (7–15) 3 (0–6) 10 (3–21) 11 (6–19)
After 16 March 2 (0–7) 11 (6–20) 3 (1–7) 9.5 (5–14) 4 (0–8) 10 (3–23) 11 (6–20)
Unknown 0 (0–0) 0 (0–0) 0 (0–0) 0 (0–0) 0 (0–4) 8 (2–20) 6 (3.75–13)
Overall 4 (1–10) 12 (7–21) 5 (2–9) 10 (6–15) 3 (0–6) 10 (3–21) 11 (6–19)
Estimates obtained by tting a negative binomial distribution to observed data
overdispersion 0.445 1.638 0.831 2.036 0.57 0.746 1.517
mean 9.753 16.105 7.385 12.159 5.309 15.54 14.614
A. Zardini et al.

Epidemics 37 (2021) 100530
8
4. Discussion and conclusions
In this work, we provided a comprehensive assessment of the pa-
rameters regulating COVID-19 burden and natural history. The pro-
posed analysis leveraged data on the infections ascertained in Italy
between February and July 2020 to estimate the time between key
events and the age- and sex- specic stage-to-stage transition probabil-
ities characterizing the clinical progression of COVID-19.
Previous studies have highlighted that a signicant share of SARS-
CoV-2 infections is represented by symptom-free subjects and by in-
dividuals developing mild disease (Emery et al., 2020; Lavezzo et al.,
2020; Poletti et al., 2021; Salje et al., 2020; Wu et al., 2020b). Therefore,
using the number of notied or conrmed COVID-19 cases as an
approximation of the number of infections would likely lead to over-
estimate the risk of disease and severe outcomes, undermining the
comparability and generalizability of the obtained results. An illustra-
tive example of the huge uncertainty caused by this phenomenon is
provided by the high variability around the available estimates of the
proportion of symptomatic infections, ranging from 3% to 87% (Bui-
trago-Garcia et al., 2020; Byambasuren et al., 2020; Emery et al., 2020;
Nikolai et al., 2020; Oran and Topol, 2020; Poletti et al., 2021). To
reduce potential biases in the identication of SARS-CoV-2 infections,
we estimated different risk ratios based on a sample of SARS-CoV-2
positive individuals who were identied as contacts of conrmed
cases and tested irrespectively of their symptoms. A larger sample,
consisting of all notied symptomatic cases, was used only to estimate
the time to key events describing the clinical progression of cases and to
highlight temporal changes in the risk of hospitalization and ICU
admission.
Our results conrmed ndings from other studies on the strong age
gradient in the likelihood of developing symptoms, critical disease, and
death after infection (Onder et al., 2020; Poletti et al., 2020; Salje et al.,
2020; Verity et al., 2020; Yang et al., 2020). The estimated proportion of
symptomatic cases among SARS-CoV-2 infections is within the range of
estimates obtained in previous studies (Buitrago-Garcia et al., 2020;
Nikolai et al., 2020) and particularly close to ndings obtained in Emery
et al. (2020). Our estimated CFR was lower compared to the one ob-
tained in a previous study on other Italian data (Onder et al., 2020), but
slightly higher than those observed in other countries (Fu et al., 2020; Li
et al., 2020; Verity et al., 2020; Yang et al., 2020). The estimated
age-prole of the IFR closely resembles Verity et al. (2020). However,
our aggregate (population-level) estimate of the IFR is generally higher
than those obtained in other studies (O’Driscoll et al., 2021; Perez-Saez
et al., 2020; Salje et al., 2020; Verity et al., 2020). Such difference can be
due to a variety of factors. First, Italy is one of the oldest countries in the
world (average age: 45.7 years (Italian National Institute of Statistics,
2021)). Second, there may be between-country differences in the age
distribution of SARS-CoV-2 infected individuals. Third, there are dif-
ferences in the denition of COVID-19 death. In fact, in Italy, deaths
occurring among SARS-CoV-2 positive subjects are classied as
COVID-19-related deaths regardless of other conditions that might have
caused the observed fatal outcome (Onder et al., 2020). This has
possibly led to overestimate the number of deaths caused by
SARS-CoV-2, especially in the oldest segment of the population. None-
theless, in Italy, a laboratory conrmation for SARS-CoV-2 infection is
required to dene a COVID-19 death. COVID-19 deaths are mainly
represented by cases ascertained before their decease, while only few
cases are ascertained post-mortem. In the subsample of positive case
contacts used to estimate the IFR and CFR, all deaths were conrmed
before their decease.
The proportion of hospitalized patients among positive individuals
older than 60 years was almost double than that observed in France
(Salje et al., 2020). On the other hand, the proportion of ICU admissions
and deaths among hospitalized cases was markedly lower in our sample
(4.5% and 12.4% vs 19% and 18.1%, respectively), and we found a
strong temporal decreasing pattern in the risk of hospital admission
among ascertained symptomatic cases. This suggests that hospitalization
criteria might have been highly heterogeneous across different countries
and may also greatly vary over time.
The estimated time from symptom onset to laboratory diagnosis well
compares with estimates obtained from Belgian patients (Faes et al.,
2020). Although in line with previous ndings from Belgium (Faes et al.,
2020), the time from symptom onset to hospital admission we found is
markedly shorter than those observed in France, in China, and in the US
(Bhatraju et al., 2020; Salje et al., 2020; Zhou et al., 2020). This may be
the consequence of the higher proportion of severe cases observed in
Italy compared to other countries, which strictly relates to the older
age-structure characterizing this country. This hypothesis is partially
supported by the shorter hospital length of stay and by the longer length
of stay in ICU we found, compared to estimates from China (Guan et al.,
2020; Zhou et al., 2020).
The relatively low ICU admission ratio we observed among the
elderly was already highlighted in previous studies (Grasselli et al.,
2020; Salje et al., 2020). However, our ndings clearly show that hos-
pitalized patients aged 80 years or older faced the highest risk of fatal
outcome, but also the lowest likelihood of being admitted to ICU. The
increased ratio of ICU admission among inpatients we found for this age
group after April 2020 suggests that elective ICU admission has been
initially adopted in Lombardy due to saturation of healthcare resources.
The lower delay between symptom onset and admission to hospital
observed after March 16, 2020, and the progressive temporal increase in
the likelihood of hospital admission among older patients strongly
suggest that reducing the pressure on the regional healthcare system
markedly improved its capacity to rapidly identify and treat severe pa-
tients (Trentini et al., 2021) (see Fig. S2).
Our estimates of the risk ratios of hospital and ICU admissions after
infection should be interpreted cautiously. In fact, rather than being
purely biological features, such quantities strongly depend on the
available healthcare resources, on the temporal changes in the number
of patients seeking care, and on the protocols adopted to face a brisk
upsurge of COVID-19 cases. Consequently, using these estimates to
investigate the healthcare burden over different phases of the pandemic
could produce misleading results. Additionally, due to temporal changes
in the ascertainment rates of infections, we were not able to quantify the
reduced risk of severe outcomes determined by timely detection, diag-
nosis and treatment of cases, nor to evaluate the role played by the
progressive enhancement in the treatment procedures in reducing the
risk of disease. A further limitation affecting our study is the lack of data
to disentangle the role played by patients’ comorbidities in shaping the
risk of severe diseases. Finally, it is important to stress that estimates
reported here are associated with the historical and dominant variant of
the virus that circulated during 2020, in the absence of vaccination. As
such, estimated metrics may not apply to new emerging SARS-CoV-2
variants (Davies et al., 2021; Kiem et al., 2020; Volz et al., 2021) and
may not reect the risk of developing COVID-19 disease among in-
fections occurring among vaccinated individuals.
Although disease parameters may be specic for the time and place
of the data collection (northern Italy’s rst COVID-19 wave), we showed
that estimated risk outcomes after SARS-CoV-2 infection well compare
with data associated with broader time periods and geographical
locations.
Metrics dening the natural history of SARS-CoV-2 infection were
estimated from positive individuals who belonged to clusters of con-
tacts, who were all tested and daily followed up for symptoms and for
severe outcomes. A fraction of these individuals, mainly consisting of
symptomatic ones, was tested via RT-PCR during contact-tracing activ-
ities. The remaining case contacts were retrospectively tested via IgG
serological assays collected at least one month after exposure, thus
allowing the identication of asymptomatic infections as well. Despite
the heterogeneous testing procedure, we believe that the strengths of
this study design rely on: (1) the minimization of the risks of bias in the
identication of infections (contacts were identied and tested
A. Zardini et al.

Epidemics 37 (2021) 100530
9
independently of their clinical signs), and (2) the daily follow-up of the
infections for symptoms and critical disease in the weeks following the
exposure to a conrmed infection. Therefore, the analyzed sample does
not suffer the usual limitations of surveillance data (i.e., underestima-
tion of asymptomatic individuals) and of serological data (i.e., lack of
longitudinal records about the clinical history of study participants).
Despite the aforementioned limitations, the provided metrics can be
instrumental to rene model estimates. In particular, our ndings could
be used to assist the design and evaluation of forthcoming vaccination
efforts and the development of appropriate strategies to control the
COVID-19 pandemic until a sufciently large proportion of the popu-
lation has become immune.
Funding
AZ, MM, FT, VM, GG, PP and SM acknowledge funding from EU grant
874850 MOOD (catalogued as MOOD 016). AM acknowledges funding
support from the Fondazione Romeo and Enrica Invernizzi to the Boc-
coni Covid Crisis Lab. The funders had no role in study design, data
collection and analysis, decision to publish, or preparation of the
manuscript.
CRediT authorship contribution statement
PP, MA, SM conceived and designed the study. AZ and MG performed
the analysis. MT and DC collected data. AZ, MG, MT, DC, FT, RP and PP
collated linked epidemiological data. MT, DC veried all data. AZ, MG
and PP wrote the rst draft. All authors contributed to data interpreta-
tion, critical revision of the manuscript and approved the nal version of
the manuscript.
Competing Interest
MA has received research funding from Seqirus. The funding is not
related to COVID-19. All other authors declare no competing interest.
Appendix A. Supporting information
Supplementary data associated with this article can be found in the
online version at doi:10.1016/j.epidem.2021.100530.
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