Sundhedsudvalget 2021-22
SUU Alm.del Bilag 283
Offentligt
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Impact of travel restrictions on Omicron in Italy
and Finland
Oxera and Edge Health
Prepared for ACI Europe and IATA
26 January 2022
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Summary: what has Omicron taught us about travel restrictions? (I)
•
Italy and Finland introduced pre-departure testing for air passengers in mid- and late-December respectively,
in response to the Omicron variant.
•
This was six to eight weeks after Omicron was first identified, meaning that the variant was likely being
seeded in these countries for a number of weeks before travel restrictions were imposed.
•
As a result, additional travel testing introduced in December was ineffective at preventing the spread of
Omicron.
•
If no travel testing had been introduced at all, Omicron’s spread in Italy and Finland would not have been
impacted.
•
Even if more stringent travel testing requirements had been in place from the beginning of November—i.e.
the day South Africa reported Omicron to the WHO—they would not have had any meaningful impact on
the spread of Omicron in Finland, and would have had a small impact on the spread of Omicron in Italy.
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Summary: what has Omicron taught us about travel restrictions? (II)
•
Now that Omicron is highly prevalent in Italy and Finland, removing all travel testing requirements would not impact
domestic Omicron spread. However, continuing to impose travel restrictions would impose a significant economic
cost on the Italian and Finnish economies.
Italy
Finland
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Table of contents
1.
Impact of
travel restrictions on Omicron in Italy
1.1: Background
–
the Omicron response in Italy
1.2: Travel testing and quarantine policy - what we can learn from the Omicron response in Italy
1.3: Current response to the Omicron variant in Italy
–
weighing the impact of travel and domestic
restrictions going forward
2. Impact of travel restrictions on Omicron in Finland
2.1: Background
–
the Omicron response in Finland
2.2: Travel testing and quarantine policy - what we can learn from the Omicron response in Finland
2.3: Current response to the Omicron variant in Finland
–
weighing the impact of travel and domestic
restrictions going forward
3. Appendix - literature review, methodology and assumptions
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1. Impact of travel restrictions on
Omicron in Italy
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1.1 Background
–
the Omicron
response in Italy
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Testing of air travellers was introduced on 16 December, a few weeks after South Africa reported
Omicron. The variant was likely in circulation internationally for a month prior to being reported,
meaning that it was being seeded for at least six weeks before the travel restrictions were introduced.
Omicron in circulation
internationally
International omicron
detection
Italy travel policies
Italy domestic policies
Note: Date indicates the date the policy came into effect
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1.2 Travel testing and quarantine
policy - what we can learn from the
Omicron response in Italy
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Our model predictions closely match empirical estimates of Omicron
cases in Italy. Both suggest that cases are growing exponentially.
•
Based on the Italian government’s
travel testing policy in November /
December* and estimates of Omicron
prevalence among passengers,** our
modelled cumulative Omicron cases
closely match empirical estimates of
Omicron cases in Italy.***
•
We estimate Omicron cases in Italy
based on recorded cases and
domestic sequencing data.
*The Italian government introduced pre-departure PCR testing on 16
December.
**Based on an average of sequencing data across European countries
(ECDC).
***Based on data from the ECDC.
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Additional travel testing introduced in mid-December was ineffective at
preventing the spread of Omicron in Italy
•
We model Italy’s actual travel testing
policy* (red line) and compare it to
what would have happened had the
government made no changes to travel
testing policy—i.e. no testing or
quarantine (blue line)
•
The modelled trajectories of Omicron
cases in Italy are
virtually
indistinguishable,
suggesting that
introducing further travel restrictions
on 16 December was ineffective.
* The Italian government introduced pre-departure PCR testing
on 16 December.
Note: We assume that domestic policies continue as-is for all of the above scenarios. We have also lagged the position of the
lines for illustrative purposes, however, the raw modelling results in two perfectly overlapping curves.
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Even if travel testing had been in place in November (i.e. the day Omicron was
identified as an issue by the WHO), Omicron’s spread in Italy would have only been
minimally impacted
•
(24
th
Nov, 2021)
If the government had not had any travel
restrictions in place in November/December
(green line), cases would have peaked only
three
days
sooner compared to a scenario where travel
restrictions were put in place on the same day
that Omicron was flagged as an issue to the WHO
on 24 November (blue line).
The peak would have been 8% higher without any
travel testing compared to a scenario where
travel restrictions were introduced on the same
day that Omicron was flagged as an issue to the
WHO in November (blue line).
This is in large part to due the ongoing
vaccination campaigns for children aged 5-12 and
the booster dose campaign.
•
•
Note: We assume that domestic policies continue as-is for all of the above scenarios. We assume that
pre-departure antigen (24h) or PCR (48h) testing policy was put in place either on 24 November, the day
South Africa reported Omicron to the WHO (Policy implemented day of SA announcement) or on the
16
th
of December (Actual policy).
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1.3 Current response to the Omicron
variant
–
weighing the impact of
travel and domestic responses going
forward in Italy
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Now that Omicron is highly prevalent in Italy, removing all travel testing would not impact
domestic Omicron spread. Domestic restrictions would now have a more significant impact
on Omicron cases in Italy than travel testing.
•
Removing all travel testing would not
impact the spread of Omicron in Italy.
•
We consider a scenario where travel
tests were lifted on 1 January 2022.
Peaks are 0.11%-0.23% higher when
travel restrictions are removed.
•
On the other hand, retaining travel testing
could impose a significant cost on the
Italian economy.
•
This is consistent across scenarios where
different domestic restrictions (e.g. limits
on gathering, work from home orders) are
applied on 26 January 2022.
See appendix A.3 for assumptions on the impact of domestic restrictions.
*
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2. Impact of travel restrictions on
Omicron in Finland
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2.1 Background
–
the Omicron
response in Finland
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Testing of air travellers was introduced on 28 December, a month after South Africa reported Omicron.
The variant was likely in circulation internationally for a month prior to being reported, meaning that it
was being seeded for at least eight weeks before the travel restrictions were introduced.
International omicron
detection
Omicron in circulation
internationally
Finland travel policies
Finland domestic
policies
Note: Date indicates the date the policy came into effect
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2.2 Travel testing and quarantine
policy - what we can learn from the
Omicron response in Finland
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Our model predictions closely match empirical estimates of Omicron
cases in Finland. Both suggest that cases are growing exponentially.
•
Based on the Finnish government’s
travel testing policy over the course of
November/December* and estimates
of Omicron prevalence among
passengers,** our modelled
cumulative Omicron cases closely
match empirical estimates of Omicron
cases in Finland.***
•
We estimate Omicron cases in Finland
based on recorded cases and
domestic sequencing data.
*The Finnish government introduced pre-departure PCR testing on 28
December.
**Based on an average of sequenced cases across European countries
(ECDC).
***Based on data from the ECDC.
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Additional travel testing introduced at the end of December was ineffective
at preventing the spread of Omicron in Finland
•
We model Finland’s actual travel testing
policy* (red line) and compare it to what
would have happened had the
government made no changes to travel
testing policy—i.e. no testing or
quarantine (blue line).
•
The modelled trajectories of Omicron
cases in Finland are
indistinguishable,
suggesting that
introducing travel
restrictions on 28 December was
ineffective.
*The Finnish government introduced pre-departure PCR testing
on 28 December.
Note: We assume that domestic policies continue as-is for all of the above scenarios. We have also lagged the position of the
lines for illustrative purposes, however, the raw modelling results in two perfectly overlapping curves.
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Even if travel testing had been in place in November (i.e. the day Omicron was
identified as an issue by the WHO), Omicron’s spread in Finland would not have
been impacted
•
(24
th
Nov, 2021)
If the government had not had any travel
restrictions in place in November/December
(green line), cases would have peaked only
three days
sooner compared to a scenario
where travel restrictions were put in place
on the same day that Omicron was flagged
as an issue to the WHO on 24 November
(blue line).
The peak would have been 2% higher
without any travel testing compared to a
scenario where travel restrictions were put
in place immediately once Omicron was
flagged as an issue to the WHO in
November (blue line).
•
Note: We assume that domestic policies continue as-is for all of the above scenarios. We assume that
pre-departure antigen (48h) or PCR (48h) testing policy was put in place either on 24 November, the day
South Africa reported omicron to the WHO (Policy implemented day of SA announcement) or on 28
December (Actual policy).
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2.3 Current response to the Omicron
variant
–
weighing the impact of
travel and domestic responses going
forward in Finland
21
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Now that Omicron is highly prevalent in Finland, removing all travel testing would not
impact domestic Omicron spread. Domestic restrictions would now have a more significant
impact on Omicron cases in Finland than travel restrictions.
•
Removing all travel testing in January would
not impact the spread of Omicron in Finland.
•
We consider a scenario where travel
testing was lifted on 1 January 2022.
Peaks are 0.06%-0.07% higher when
travel testing is removed.
•
On the other hand, retaining travel testing
could have a significant cost on the Finnish
economy.
•
This is consistent across scenarios where
different domestic restrictions (e.g. limits on
gathering, work from home orders) are
applied on 26 January 2022.
See appendix A.3 for assumptions on the impact of domestic restrictions.
*
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3. Appendix
–
literature review,
modelling methodology and
assumptions
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A.1 Literature review
–
what we
know about the Omicron variant
24
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Omicron has more mutations in the spike protein than previous variants. While some of
these mutations may be associated with increased infectiousness, others may be
associated with reduced severity.
•
SARS-Cov-2’s
spike protein has an important
role in infectiousness and severity of Covid-19,
as it is how the virus attaches to human cells.*
•
Many mutations in this area may change the
infectiousness, severity, and ability of the
variant to evade immunity.
•
Omicron is thought to be more infectious.
However, mutations in this area may also
contribute to reducing the severity of resulting
illness after infection. **
* https://www.who.int/en/activities/tracking-SARS-CoV-2-variants/
** biorxiv.org/content/10.1101/2021.12.17.473248v2
Note: Graph annotations indicate the month, year, and location where each
variant was first sequenced (WHO).
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Although evidence is still in early stages, laboratory and real-world studies to date indicate
that while Omicron is more infectious and vaccines are less effective at preventing
infections, illnesses resulting from infections may be less severe.
Infectiousness
Vaccine efficacy (i.e. immune
escape)
Severity
date indicate that vaccines are 25-
date indicate that Omicron is 2-3 66% as effective at preventing
•
While studies and emerging data
times more infectious than delta. Omicron compared to Delta
are still in early stages, several
infections (varies by dose).
studies are now pointing to
Omicron infections being milder
•
As studied populations are now
highly vaccinated or have high
•
As studied populations are now than Delta infections. Real-world
Omicron characteristics,
levels of natural immunity, it is
highly vaccinated or have high
data suggests that patients’
relative to Delta (previously
difficult to attribute the increase levels of natural immunity, it is
hospital admission risk decreased
dominant variant in Italy and
in observed infectiousness of
difficult to attribute the increase by 62%.
Finland)
Omicron relative to Delta to innate in observed infectiousness of
infectiousness or to immune
Omicron relative to Delta to innate
•
This effect is demonstrated even
escape/decreases in vaccine
infectiousness or to immune
when variation in vaccination
efficacy. It is likely to be a
escape/decreases in vaccine
status is accounted for.
combination of both.
efficacy. It is likely to be a
combination of both.
•
Data and laboratory studies to
•
Data and laboratory studies to
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A.2: Modelling methodology
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Background on SARS-Cov-2 infection spread dynamics
One measure of how
easily a virus is spread from one person to another is the virus’ reproductive
ratio (called its ‘R’ value). Rt represents the average number of secondary infections that will result
from an initial infection at a given time.
Effective reproduction number is determined by the following:
•
R0, basic reproduction number:
the average number of secondary infections resulting from an
initial infection in a fully susceptible population.
•
Vaccination-induced immunity:
the proportion of the population prevented from being
infected by the virus (either symptomatically or asymptomatically) and hence prevented from
spreading the virus due to being vaccinated.
•
Natural immunity:
the proportion of the population prevented from being infected by the
virus (either symptomatically or asymptomatically) and hence prevented from spreading the
virus due to previous exposure to the virus
•
Behavioural patterns:
different patterns in interactions may hinder the spread of a virus. For
example, reduced social interactions, social distancing and masks will contribute to reducing
the spread.
If Rt > 1, the virus will spread in a population.
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Basic SEIR modelling review
The entire population is split into groups corresponding to the
S,
E,
I,
and
R
states
•
S
usceptible
•
•
•
R
emoved
Exposed
I
nfected
β
*S*I
k*E
r*I
S
E
I
R
where:
•
β
is the parameter for infectivity
•
r
is the constant per capita recovery
rate
•
k
is the constant per capita
progression from exposed to
infectious rate
Assumptions
•
No one is added to the susceptible group, since we are ignoring births and immigration
•
The only way an individual leaves the susceptible group is by becoming infected
•
A fixed fraction of the infected group recovers (or dies) every day and is immune to the disease
29
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Our modelling approach: SEIR modelling including vaccinations and
imported cases
The entire population is split into groups corresponding to the
S,
E,
I,
and
R
states and others
•
S
usceptible
•
T
+t
β
*S*I
where:
•
β
is the parameter for
infectivity
•
r
is the constant per
capita recovery rate
•
k
is the constant per
capita progression from
exposed to infectious
rate
•
v
is the change in vaccine
induced immunity in the
population
•
t
is daily travel-imported
cases
•
•
R
emoved
•
•
Vaccinated
Travel-related
cases
Exposed
I
nfected
k*E
r*I
S
E
I
R
+v
V
-v
Assumptions
•
No one is added to the susceptible group, since we are ignoring births and immigration
•
The only way an individual leaves the susceptible group is by becoming infected or vaccinated
•
A fixed fraction of the infected group recovers (or dies) every day and is immune to the disease
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Scenarios considered in the modelling
Key question:
What would the impact of different travel
policies have been on the outcome of
Omicron spread in Italy / Finland?
What would the spread and impact of the Omicron variant in Italy / Finland have been under the following
scenarios:
•
•
•
•
Pre-departure antigen (24h) or PCR testing (48h) - Italy
Pre-departure antigen (48h) or PCR testing (48h) - Finland
Actual policy (a combination of all of the above, at different points in time)
No testing or quarantine
Key question:
Now that the Omicron variant is highly
prevalent in Italy / Finland, what would the
relative impact of domestic measures be
compared to further travel restrictions?
What will the the spread and impact of the Omicron variant in Italy / Finland be under the following scenarios:
Italy:
•
Mandatory masks, symptomatic testing
•
Some restrictions on businesses (i.e. green pass/super green pass)
•
Intermediate scenario: Limits on gathering sizes to 10 people, in addition to some restrictions of
businesses
•
Stay at home order: businesses closed, schools and universities closed in conjunction
Finland:
•
Symptomatic testing
•
Mandatory masks and work-from home order
•
Intermediate scenario: Some restrictions to businesses and limits of gathering sizes to 10 people
•
Stay at home order: businesses closed, schools and universities closed in conjunction
Compared with:
•
Continued pre-departure policy or
•
No testing or quarantine January onwards
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A.3: Literature review and modelling
assumptions
DRAFT
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Assumptions: travel volumes and air passenger prevalence
Model input
Median infectious days an air
passenger spends at their
destination
Description
Without quarantine and testing schemes, when a passenger is infected in another country,
they will spend some of their infectious days in their country of departure and some in their
country of arrival. Using a simulation model based on a paper from LSHTM, we estimated that
the median number of infectious days a passenger will spend in their country of arrival is 3.
We use publicly available data on passenger volumes from the Association of Italian airports
(AIGA). We assume that most passengers are completing round trips, so total passenger
volumes are divided by two to get inbound passengers. We project travel volumes by scaling
the latest available values using seasonal scaling factors from 2019 (pre-pandemic). As data
from November 2021 and December 2021 was not yet publicly available, at the time of
writing we use the same assumptions for these months as well.
We use publicly available data on passenger volumes from the Finish National Statistics
Office. We use arriving passenger data. We project travel volumes by scaling the latest
available values using seasonal scaling factors from 2019 (pre-pandemic). As data from
November 2021 and December 2021 was not yet publicly available, at the time of writing we
use the same assumptions for these months as well.
Value
3 days
Source
Oxera and Edge Health (2021) 'Effectiveness of dual-testing
schemes for air passengers'. For LSHTM’s work see: Clifford et al.
(2020), ‘Strategies to reduce the risk of SARS-CoV-2
re-
introduction from international travellers’, 25 July.
Air passenger volumes (Italy)
https://assaeroporti.com/statistiche_202110/
Air passenger volumes (Finland)
https://www.stat.fi/til/ilma/2021/06/ilma_2021_06_2021-07-
28_tie_001_en.html
We estimate the prevalence of incoming air passengers, using UK Government Test-and-
Trace data available up to 13 December. Using tourism data and passengers numbers by
Air passenger Covid-19 prevalence country for Italy and Finland, we adjust the UK values with relative weights to estimate a
country-specific proxy for the prevalence among inbound passengers. We conservatively use
prevalence in mid-October, before the UK government moved to Day 2 antigen testing.
Italy prevalence: 0.53%
Finland prevalence: 0.70%
https://www.gov.uk/government/publications/weekly-statistics-
for-nhs-test-and-trace-england-2-to-8-december-2021
https://www.bancaditalia.it/pubblicazioni/indagine-turismo-
internazionale/2021-indagine-turismo-
internazionale/statistiche_ITI_18062021.pdf
https://www.finavia.fi/en/about-finavia/about-air-traffic/traffic-
statistics/traffic-statistics-year
The percentage share of Omicron cases are based on the European average from the “SARS-
Percent of positive traveller cases
CoV-2
variants dashboard” disclosed by the European Centre for Disease Prevention and
attributed to Omicron
Control.
https://www.ecdc.europa.eu/en/covid-19/situation-
updates/variants-dashboard
https://www.iss.it/cov19-cosa-fa-iss-varianti
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SARS-Cov-2 and Omicron-specific parameters (1)
Model input
Description
Value
Source
https://www.medrxiv.org/content/10.1101/2021.12.19.21268038v1.full.pdf,
https://assets.publishing.service.gov.uk/government/uploads/system/uploads
/attachment_data/file/1043466/20211222_OS_Daily_Omicron_Overview.pdf,
https://github.com/blab/rt-from-frequency-
dynamics/tree/master/estimates/omicron-countries. Ro of Delta:
https://academic.oup.com/jtm/article/28/7/taab124/6346388
Oxera and Edge Health (2021) 'Effectiveness of dual-testing schemes for air
passengers'. For LSHTM’s work see: Clifford et al. (2020), ‘Strategies to reduce
the risk of SARS-CoV-2 re-introduction
from international travellers’, 25 July.
https://www.eurosurveillance.org/content/10.2807/1560-
7917.ES.2021.26.50.2101147
https://www.ecdc.europa.eu/en/covid-19/situation-updates/variants-
dashboard
http://www.bccdc.ca/Health-Info-Site/Documents/COVID-
19_vaccine/Public_health_statement_deferred_second_dose.pdf
Ro
8, assuming
Initial data suggests that the Rt and secondary attack rates of the that Delta has
Omicron variant is 2-3 times higher than that of the Delta
an Ro of ~3.2
variant. While some of this difference is likely due to differing
(this assumes
immunity for the variants in the population, we conservatively
pre-pandemic
assume that Omicron is 2.5 times more infectious than delta.
mixing
patterns).
We use the median time an individual is infectious calculated
from previous variants.
Preliminary evidence suggests that the time from exposure to
symptoms is shorter for the Omicron variant compared to other
variants.
Days infectious
7.35 days
Incubation period
3 days
The ECDC publishes estimates of the % of sequenced samples
Estimates of Omicron cases
which were determined to be Omicron by EU country. We use
---
compared to Delta cases
this to estimate the curves shown on pages 9 and 18.
While immunity builds up over time after individuals are
Delay between vaccination vaccinated, there is still substantial protection from vaccinations Step function,
and vaccine efficacy
(~60%) on the first day after vaccination. Using a step function
1 week
we are able to approximate this effect.
34
 SUU, Alm.del - 2021-22 - Bilag 283: Rapporter om effekter af rejserestriktioner om omicron in Italien, Finland og UK
2570465_0035.png
SARS-Cov-2 and omicron-specific parameters (2)
Model input
Impact of natural immunity
(for the Alpha variant)
Description
Value
Source
Studies conducted in England suggest that a previous history of
infection reduces the risk of re-infection by 84%. Infections
84% decrease
with previous variants were protective against infection with
https://www.sciencedirect.com/science/article/pii/S0140673621006759?casa_
in risk of
the Alpha variant. Immunity was observed for a minimum of 7
token=d-
infection,
months after initial infection.
Aupl8roEYAAAAA:E_YnW1p75HlEH7DgPN_N_7aCANo7QcSrk93TlvcAS2khOBLt
immune escape
We assume that the immunity for the Delta variant is similar,
6rCwhCpwh8eYPh-bMGIscQ6k
of 16%
and apply scaling based on estimates of the relative efficacy of
vaccines to the Omicron and Delta variants.
We estimate this using the relative efficacy (for vaccinated
individuals with 2 or 3 doses) against the Omicron variant
https://www.imperial.ac.uk/media/imperial-college/medicine/mrc-gida/2021-
54%
compared to the Delta variant, using a weighted average of the
12-16-COVID19-Report-48.pdf
Pfizer +Pfizer and AZ + Pfizer combination.
We conservatively assume that a portion of the unvaccinated
population have natural immunity, based on confirmed covid
https://covid19.who.int/info?openIndex=2
cases in Italy and Finland in the 7 months prior to November
https://demo.istat.it/popres/index.php?anno=2021&lingua=ita
Finland: 14%
2021.
https://www.stat.fi/til/vaerak/tau_en.html
Italy: 21%
Using modelling comparing reported cases with the actual
burden of disease, we estimate that that only roughly a third of
cases are reported.
Modelling from Imperial has estimated the relative efficacy of
https://www.imperial.ac.uk/media/imperial-college/medicine/mrc-gida/2021-
vaccinations against the Omicron variant, extrapolating
12-16-COVID19-Report-48.pdf and
laboratory studies to real-world efficacy. We supplement this
https://assets.publishing.service.gov.uk/government/uploads/system/uploads/
See table 1,
with data on real-world efficacy, which is now starting to
attachment_data/file/1043807/technical-briefing-33.pdf for real-world
page 14
become available.
supplementary data.
These estimates are conservative compared to the range of
https://cmmid.github.io/topics/covid19/reports/omicron_england/report_11_
scenarios estimated by other modelling groups (LSHTM).
dec_2021.pdf
Natural immunity for Omicron
compared to Delta variant
Unvaccinated population who
has previously been infected
Estimated relative efficacy of
vaccinations against Omicron
variant compared to the Delta
variant
35
 SUU, Alm.del - 2021-22 - Bilag 283: Rapporter om effekter af rejserestriktioner om omicron in Italien, Finland og UK
2570465_0036.png
Assumptions: travel testing efficacy
Model input
Description
We use the efficacy of pre-departure testing at screening incoming air
passenger infectious days as a model input. We used the estimated
efficacy of antigen and PCR tests taken 48 h pre-departure, taking the
weighted average assuming that 2/3s of passengers will opt for the
cheaper antigen test option.
Value
Source
Pre-departure antigen or pre-
departure PCR, 48 hours before
departure
Oxera and Edge Health (2021), ‘Assessment of the effectiveness
of rapid testing for SARS-CoV-2’.
48%
We use the efficacy of pre-departure testing at screening incoming air
passenger infectious days as a model input. The estimated efficacy of
Pre-departure antigen 24 hours
these two different types of tests taken at different time periods is the
before departure of PCR 48 hours
same. From some countries pre-departure PCR testing is 72 h pre-
pre-departure
departure, however we conservatively assume that PCR testing is 48 h
from all countries.
54%
Oxera and Edge Health (2021), ‘Assessment of the effectiveness
of rapid testing for SARS-CoV-2’.
*We assume a 24 h delay to receive PCR test results. **Only used to model the impact of red-listing countries
36
 SUU, Alm.del - 2021-22 - Bilag 283: Rapporter om effekter af rejserestriktioner om omicron in Italien, Finland og UK
2570465_0037.png
Assumptions: vaccine roll-out
Model input
Description
Value
Historic vaccination rates
We use age-stratified daily vaccination data for Italy and Finland to
estimate age-stratified vaccination uptake. We divide vaccine counts by
population pyramid estimates to obtain vaccination rates.
Source
https://sampo.thl.fi/pivot/prod/en/vaccreg/cov19cov/summary
_cov19covareatime
https://github.com/italia/covid19-opendata-vaccini
See source. https://demo.istat.it/popres/index.php?anno=2021&lingua=ita
https://www.stat.fi/til/vaerak/tau_en.html
Projected vaccination rates
We calculate the average daily vaccinations delivered by age band in the
last week of currently available data to estimate the speed of the
vaccination roll-out. We assume that the number of individuals receiving a
second dose cannot exceed the number of individuals who had received a
first dose 3 months prior. This is based on medical recommendations to
get second doses within 3 months of the previous dose. Equally, we
assume that the number of individuals receiving a third dose (booster)
cannot exceed the number of individuals who had received a second dose.
As the speed of vaccination roll-out is dose-specific, to prevent a violation
of the assumption above in later stages of the projection, the speed of roll-
out of for a dose is set to the speed of the dose of the lower tier where
required. We do not assume that anyone under the age of 12 for Finland
or 5 for Italy will be vaccinated as they are ineligible for vaccination at the
time of writing.
--
https://sampo.thl.fi/pivot/prod/en/vaccreg/cov19cov/summary
_cov19covareatime
https://github.com/italia/covid19-opendata-vaccini
https://demo.istat.it/popres/index.php?anno=2021&lingua=ita
https://www.stat.fi/til/vaerak/tau_en.html
37
 SUU, Alm.del - 2021-22 - Bilag 283: Rapporter om effekter af rejserestriktioner om omicron in Italien, Finland og UK
2570465_0038.png
Assumptions: impact of domestic social distancing measures on
infection spread (Italy)
Model input
Impact of mandatory masks,
symptomatic testing
Description
The reduction in Rt resulting from non-pharmaceutical
interventions.
Value
-17.9%
Source
https://www.medrxiv.org/content/10.1101/2020.05.28.20116129v4.full.pdf,
http://epidemicforecasting.org/containment-calculator
https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-020-01872-
8/figures/5
Impact of some businesses being
suspended/restricted
(approximation of green pass and
super green pass)
The reduction in Rt resulting from non-pharmaceutical
interventions. This is additive with impact of
mandatory masks, symptomatic testing, limits of
gathering sizes to 1000.
-46.3%
https://www.medrxiv.org/content/10.1101/2020.05.28.20116129
v4.full.pdf, http://epidemicforecasting.org/containment-calculator
The reduction in Rt resulting from non-pharmaceutical
Intermediate restrictions: Impact of
interventions. All interventions are additive (i.e. in
limits on gathering sizes to 10
addition to interventions mentioned in previous
people
scenarios).
-61.0%
https://www.medrxiv.org/content/10.1101/2020.05.28.20116129
v4.full.pdf, http://epidemicforecasting.org/containment-calculator
Impact of stay at home order,
businesses closed, schools and
universities closed in conjunction
The reduction in Rt resulting from non-pharmaceutical
interventions. All interventions are additive (i.e. in
addition to interventions mentioned in previous
scenarios).
-82.2%
https://www.medrxiv.org/content/10.1101/2020.05.28.20116129
v4.full.pdf, http://epidemicforecasting.org/containment-calculator
38
 SUU, Alm.del - 2021-22 - Bilag 283: Rapporter om effekter af rejserestriktioner om omicron in Italien, Finland og UK
2570465_0039.png
Assumptions: impact of domestic social distancing measures on
infection spread (Finland)
Model input
Impact of symptomatic testing
Description
The reduction in Rt resulting from non-pharmaceutical interventions.
Value
-9.6%
Source
https://www.medrxiv.org/content/10.1101/2020.05.28.
20116129v4.full.pdf,
http://epidemicforecasting.org/containment-calculator
https://bmcmedicine.biomedcentral.com/articles/10.11
86/s12916-020-01872-8/figures/5
https://bmcmedicine.biomedcentral.com/articles/10.11
86/s12916-020-01872-8#Abs1
The reduction in Rt resulting from non-pharmaceutical interventions.
Impact of work-from-home orders and mask
All interventions are additive (i.e. in addition to interventions
orders
mentioned in previous scenarios).
-32.0%
The reduction in Rt resulting from non-pharmaceutical interventions.
Intermediate restrictions: Some restrictions
All interventions are additive (i.e. addition to interventions mentioned
to businesses and limits of gathering sizes to
in previous scenarios).
10 people
-61.0%
https://www.medrxiv.org/content/10.1101/2020.05.28.
20116129v4.full.pdf,
http://epidemicforecasting.org/containment-calculator
Impact of stay at home order, businesses
closed, schools and universities closed in
conjunction
The reduction in Rt resulting from non-pharmaceutical interventions.
All interventions are additive (i.e. addition to interventions mentioned
in previous scenarios).
-82.2%
https://www.medrxiv.org/content/10.1101/2020.05.28.
20116129v4.full.pdf,
http://epidemicforecasting.org/containment-calculator
39