COVID-19 Fact Base and Potential Implications for Brazil

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COVID-19 Fact base
and potential implications
for Brazil
June 11th, 2020

2Bain&Co. - COVID-19 Brazil poi ...SAO
Context for these materials
• COVID-19 is a humanitarian crisis and a global challenge. In this interconnected world, we have seen the virus spread
rapidly, and we are still gathering information to understand both its origins and its impact
• We are grateful for the businesses, governments, non-profits, and individuals around the world that are working to
protect those that are sick or in danger of becoming so, and to “flatten the curve”
• There is a lot that is still unknown about COVID-19; these materials are an attempt to shed light on what we know so
far to help businesses make informed decisions
– The top priority for all businesses is to protect the safety and health of employees and customers, for which there are WHO, CDC, and
other national guidelines
– Bain is not an expert on epidemiology and containment policies, however, given our 45+ years of experience advising companies during both
economic booms and busts, we are committed to spreading accurate and timely information to reduce the unknowns for businesses
and business leaders
• Our focus is to enable companies to make rapid and practical decisions; with a global pandemic that is rapidly
spreading and changing, now is not the time for detailed decision making; the need for speed and the right directional
strategy outweighs the need to get intricate details right, and we are committed to helping business leaders navigate the
path forward

COVID-19 is more severe than other major epidemics from the past century due to
its high spread rate amidst greater global interconnectedness
Note: R0 refers to the average number of people infected by one sick person
Source: National Health Commission of the PRC; CDC – Emerging Infection Diseases; WHO; Lit research, Bain Macro Trends Group analysis
E P I D E M I O L O G Y
Cases
Fatality rate
Infectivity (R0)
Swine Flu, 2009-10
~750M-1.4B
COVID-19, 2019-20
~7.3M
Bird Flu, 2014-2017
~1,600
SARS, 2003
~8,000
~1%
Spanish Flu
~10%
SARS
~6.6%
Bird Flu
~40%
Bird Flu
0.03-0.4
Swine Flu
1.3-1.7
Spanish Flu
1.5-1.8
Swine Flu
~0.01-0.08%
~8.9
SARS
3.0
Case count is low, however, it
has only been ~5 months since
the outbreak
Implications
Fatality rate is TBD given
number of unreported cases;
more testing and information
needed but is likely to be on
lower end of spectrum
High spread rate when un-
encumbered by social
distancing, including
community transmissions,
drives global spread
Low HighSeverity
COVID-19
~0.5% COVID-19
~3.8
Deaths
Bird Flu
~600
Spanish Flu
~50-100M
SARS
~776
COVID-19
~413K
Spanish Flu, 1918
~350M-750M
Swine Flu
~152-575K
Death count is relatively high
and still rising as containment
efforts continue
A S O F J U N E 1 0 2 0 2 0

Current status of the COVID-19 outbreak
Global impact of COVID-19
Note: Spain adjusted reporting approach for cases and deaths on May 25th, resulting in a major correction in underlying data
Source: Johns Hopkins University, CDC, WHO, Bain Macro Trends Group analysis
~7.3M
Confirmed
cases
~413K
Confirmed
fatalities
C U R R E N T S T A T U S
Most countries are beginning to see a decline in daily new deaths, while a
few others have seen recent upticks
March April May
A S O F J U N E 0 9 2 0 2 0

The growth rate of infections and deaths has slowed in most countries
Country
Fatalities /
100K
Total deaths in
past 7 days Vs. one month prior Vs. one week prior Vs. peak
7-day peak period
(deaths)
Cases
/ 100K
United Kingdom 62 1,516 44% 65% 23% 04/08 - 04/14 439
Italy 55 513 31% 89% 9% 03/27 - 04/02 378
Spain 54 9 1% 90% 0% 03/28 - 04/03 484
Sweden 46 249 46% 73% 33% 04/18 - 04/24 450
France 43 356 24% 87% 5% 04/03 - 04/09 280
US 34 5,514 47% 76% 36% 04/12 - 04/18 593
Brazil 18 7,207 177% 108% 99% 05/29 - 06/04 350
Mexico 11 4,012 306% 160% 100% Current week 97
Germany 11 173 25% 91% 10% 04/15 - 04/21 233
Iran 10 483 111% 111% 49% 03/31 - 04/06 207
Japan 1 18 13% 32% 11% 04/29 - 05/05 14
India 1 1,921 234% 129% 100% Current week 21
Korea, South 1 3 75% 75% 6% 03/24 - 03/30 23
Singapore 0.4 1 50% 100% 17% 04/27 - 05/03 621
Malaysia 0.4 2 67% N/A – 0 deaths last week 7% 03/29 - 04/04 25
China 0.3 0 0% 0% 0% 04/11 - 04/17 6
Taiwan 0.03 0 0% 0% 0% 03/24 - 03/30 2
Source: Johns Hopkins University, CDC, WHO, Bain Macro Trends Group analysis
C U R R E N T S T A T U S
Change in deaths over past 7 days
A S O F J U N E 0 9 2 0 2 0

COVID deaths in Latin America are now peaking, while APAC, Europe, and North
America have passed the height of the crisis
Mar Apr MayFeb Jun
Note: Increase in Asia Pacific in mid-April is attributable to one-time adjustment in fatalities in Hubei, China on April 17; decreased fatalities in Europe in late May is driven by adjustments in measurement protocols in multiple countries (e.g. Spain, France)
Source: JHU
C U R R E N T S T A T U S A S O F J U N E 0 9 2 0 2 0

A G E N D A
Epidemic evolution and challenges for South America
Perspectives for Brazil
Economic impact & government response
Economic impact
Government response

Most global experiences of successful mobility restrictions presented bell-shaped curves
that peaked ~10-30 days post lockdown and consistently decreased following peak
Lockdown
is lifted
Most countries that went through lockdowns managed to reduce new infections
Note: COVID-19 new cases per day (7 day moving average); Date of lockdown lift is considered when there is a significant lift of schools, stores and/or services
Source: The Business Insider, Oxford Our World in Data Database (04/05)
Czechia
DenmarkNorwayNew Zealand
Lockdown
is lifted
Lockdown
is lifted
Lockdown
is lifted
Netherlands Finland
Lockdown
is lifted
Lockdown
to be lifted
(L+55)
G L O B A L L A N D S C A P EG L O B A L L A N D S C A P E

So, why don’t we see the same curve in South America? Even in countries with more
severe lockdowns, the recovery is taking much longer
New cases per day
(5 days moving average; From 100th case in a day)
Days from 100th new case per dayNote: (1) Considering average mobility from May 23th to May 29th
Source: JHU Data Stream; Google Mobility; Argentinian Department of Health
1
Even though some countries as Chile, Argentina and
Colombia had the spread initially under control, SA countries
are now experiencing a loss of control over case growth
J U N E 0 8 2 0 2 0A S O FG L O B A L L A N D S C A P E

The shape of the curve is tied to R0 control; Countries need to bring R0 below 1 to control
the spread, then it is a matter of controlling R0 according to the health system capacity
R0 >>>1 R0 =~1 R0 < 1 R0 = ~1
# of active cases, # of hospitalizations and health system capacity
Health System Available Capacity
Active cases
Hospitalizations (20-30% of active cases)
Disease picks up
and the spread
is out of control
Countries deploy
measures (testing, tracing,
mobility reduction) to
control the spread
When measures are sufficiently successful
considering the conditions of the country, R0 is
reduced below 1, bringing down the number of
new and active cases.
South American countries have not yet
managed to enter this phase
After reducing the number of cases to a manageable level,
countries start planning on how to ease measures. This
requires active management, as new outbreaks may
appear and measures might need to be re-deployed.
Even after measures are lifted, R0 might stay low due to
technologies in place, new hygiene/protection habits, etc.

Measures taken can
mitigate the spread
Exposure immunity
helps reducing R0
Combination should
prevent HC collapse
The key is to get R0 bellow 1. It goes beyond reducing mobility; cities
demographics, testing, technology and exposure immunity also impact the R0
Specific demographics
lead to “baseline” R0
Different location / stage
leads to diff. strategy
R0 <=1
• Favorable demographics
lead to fewer needs of gov.
measures
1
• Government measures
(such as widespread testing
and screenings) reduce the
need for lockdowns
2
• Exposure immunity
develops as number of
cases increase, reducing
need of other measures
3
Note: Illustrative impact of different factors influencing R0
Source: Lit. research
1 2
3
COVID-19 R0 (as a function of contributing factors)
R0 can be kept at this
level after transmission
control is at low levels
All variables must be taken into consideration when reducing the contamination R0. Different countries are acting in very distinct
ways to approach the problem
At this stage, most
evidence suggests
contaminated patients
develop immunity, but
its length and strength
are still being debated
G L O B A L L A N D S C A P E

New York Brazil
Despite mobility reduction efforts, SA starts from a higher R0 and is not leveraging
any other tool to control the spread (testing, technology, etc.)
Switzerland
~43%
(From 870
cases)
~60-75%
(From ~6k
cases)
~46%
(From 2.5k
cases)
>5>5
Denmark
~39%
(From ~600
cases)
Mitigation
(High use of
tecnology)
Suppression
(Mobility
reduction)
Time to
slowdown
• Technology usage
• # of tests applied vs
confirmed cases
(Mortality %)
• Mobility reduction
during lockdown (%)
• # of weeks from mobility
reduction to case under
control (<50 cases/1M)
LowLow Medium Medium
2:1
(5.4%)
6:1
(7.6%)
14:1
(6.2%)
60:1
(4.9%)
32
Unconstrained
R0
3-81-10 1-42.1
• Population density of
main cities (k hab / km2)
• # of people/household 3.32.5 2.5 2.2
S. Korea
Countries didn’t reach
such contamination levels
~25%
Very High
86:1
(2.3%)
Hong Kong
~30%
Very High
183:1
(0.4%)
4-17
2.5
7
2.8
Favorable DemographicsHigh use of technology S. AmericaStrong suppression
Italy
~70%
(From ~15k
cases)
>5
Low
18:1
(14.4%)
2-2.6
2.6
Exposure
immunity
• Adjusted number of
cases / total pop. (%)
Governmentmeasures
Some east Asian
countries have
demographics that
were not favorable for
containing the virus
However, these countries not only
tested considerably but also utilized
screening technologies from the start
to isolate those contaminated with the
virus
The result is that even without high
levels of mobility reduction, these
countries managed to get the
disease under control very early and
never reached an outbreak level
Source: JHU Coronavirus Data Stream 05/06; Tableau; Worldometers; Google Mobility Database, Bain Analysis.

New York Brazil
Switzerland
~43%
(From 870
cases)
~60-75%
(From ~6k
cases)
~46%
(From 2.5k
cases)
>5>5
Denmark
~39%
(From ~600
cases)
Mitigation
(High use of
tecnology)
Suppression
(Mobility
reduction)
Time to
slowdown
confirmed cases
(Mortality %)
during lockdown (%)
32
Unconstrained
R0
3-81-10 1-42.1
S. Korea
~25%
Very High
Hong Kong
~30%
Very High
4-17
2.5
7
2.8
Italy
~70%
(From ~15k
cases)
>5
Low
2-2.6
2.6
Exposure
immunity
Governmentmeasures
Italy and NY are examples
of countries that also did
not have favorable
demographics to contain
the virus
Finally, to contain the spread, strong
mobility reduction measures had to be
taken, and the spread reduction was
slower than the one observed in the
Asian countries.
Due to higher contamination over time,
exposure immunity is starting to be
relevant and help the case spread control
2:1
(5.4%)
6:1
(7.6%)
14:1
(6.2%)
60:1
(4.9%)
86:1
(2.3%)
183:1
(0.4%)
18:1
(14.4%)
Also, those
countries/states didn’t test
considerably and did not
use screening technologies
in order to control the virus

2:1
(5.4%)
6:1
(7.6%)
14:1
(6.2%)
60:1
(4.9%)
86:1
(2.3%)
183:1
(0.4%)
18:1
(14.4%)
New York Brazil
Switzerland
~43%
(From 870
cases)
~60-75%
(From ~6k
cases)
~46%
(From 2.5k
cases)
>5>5
Denmark
~39%
(From ~600
cases)
Mitigation
(High use of
tecnology)
Suppression
(Mobility
reduction)
Time to
slowdown
confirmed cases
(Mortality %)
during lockdown (%)
32
Unconstrained
R0
3-81-10 1-42.1
S. Korea
~25%
Very High
Hong Kong
~30%
Very High
4-17
2.5
7
2.8
Italy
~70%
(From ~15k
cases)
>5
Low
2-2.6
2.6
Exposure
immunity
Governmentmeasures
Those countries tested
considerably but did
not use advanced
screening technologies
As a result, those countries
could be less strict on
mobility reduction
measures, and controlled
the spread faster
On the other hand, some
countries have low densities and
number of people per
household; demographics that
are likely to reduce the spread

2:1
(5.4%)
6:1
(7.6%)
14:1
(6.2%)
60:1
(4.9%)
86:1
(2.3%)
183:1
(0.4%)
18:1
(14.4%)
New York Brazil
Switzerland
~43%
(From 870
cases)
~60-75%
(From ~6k
cases)
~46%
(From 2.5k
cases)
>5>5
Denmark
~39%
(From ~600
cases)
Mitigation
(High use of
tecnology)
Suppression
(Mobility
reduction)
Time to
slowdown
confirmed cases
(Mortality %)
during lockdown (%)
32
Unconstrained
R0
3-81-10 1-42.1
S. Korea
~25%
Very High
Hong Kong
~30%
Very High
4-17
2.5
7
2.8
Italy
~70%
(From ~15k
cases)
>5
Low
2-2.6
2.6
Exposure
immunity
Governmentmeasures
Testing was poor in SA,
especially in Brazil, where
few technologies were
used and only people in
severe states were tested
Differently from NY/Italy, Brazil
opted on a moderate mobility
reduction mainly stimulated
through venue closure, with few
population support.
Cities are still far from developing
exposure immunity, which led to a
slow spread control, that will be
followed by a long recuperation
period with R0 of ~1
South American countries
depart from not favorable
demographics, with dense
cities and high number of
people per household

New York Brazil
~43%
(From 870
cases)
~60-75%
(From ~6k
cases)
~46%
(From 2.5k
cases)
>5>5
Denmark
~39%
(From ~600
cases)
Mitigation
(High use of
tecnology)
Suppression
(Mobility
reduction)
Time to
slowdown
confirmed cases
(Mortality %)
during lockdown (%)
32
Unconstrained
R0
3-81-10 1-42.1
S. Korea
~25%
Very High
Hong Kong
~30%
Very High
4-17
2.5
7
2.8
Italy
~70%
(From ~15k
cases)
>5
Low
2-2.6
2.6
Exposure
immunity
Governmentmeasures
Switzerland
2:1
(5.4%)
6:1
(7.6%)
14:1
(6.2%)
60:1
(4.9%)
86:1
(2.3%)
183:1
(0.4%)
18:1
(14.4%)

Also, not all mobility reduction is the same, demographic and socioeconomic
reasons increase dependence and density of public transportation in South America
Sweden subway, 8h30am
Even with the reduced mobility, South America still
faces issues to keep social distancing
Motor vehicles per capita in SA vs
Europe2
Note: 1Considers number of metro stations per M passengers; 2Considers average of 5 largest economies in each continent; 2Copenhagenize Design Company's Index
Source: Lit. Research
Higher public transportation density
vs Europe1
+90%
-60%
0 / 10
1 / 20
Countries with Most Bicycles per
Capita in SA (8/10 in Europe)
Most Bike-Friendly Cities on the
Planet in SA (17/20 in Europe)3
• European countries usually have alternatives for public
transportation at their disposal. On the other hand, South
American are much more dependent on crowded buses and
subway systems

• Five companies using
AI to identify and
develop drugs for the
treatment of COVID-
19
• VR training for
healthcare workers on
the treatment of
patients with COVID-
19
Asia lead the use of technology to backup mitigation policies; South America is
lagging behind specially in tracing and target quarantine
Source: World Economic Forum “Here’s how Asia is using tech to tackle COVID-19”; New York Times “In Coronavirus Fight, China Gives Citizens a Color Code, With Red Flags”; CNBC “Singapore says it will make its contact tracing tech freely available to
developers”; CNN “Hong Kong managed to contain the virus, now it's worried international travelers will bring it back”; Forbes “Coronavirus Spy Apps: Israel Joins Iran And China Tracking Citizens' Smartphones To Fight COVID-19”; Spectrum “Five
Companies Using AI to Fight Coronavirus”
Healthcare system and capacity
Risks minimized in high vulnerability
settings
• Start-ups are developing
10-minute tests for
Coronavirus using
Internet of Things
• Temperature scanning
solutions to identify
potentially infected
people in crowds,
especially at airports
• Using geolocation data
stored in smartphones of
confirmed cases, to send
notifications for those who
were in close contact
• App to trace its users
movements and send
alerts in case they come in
contact with
someone
infected
• Wearables for workplace-
level contact tracing
• Wristbands and apps that
send notifications for
authorities in case the user
leaves the designated
quarantine area
• In China and Singapore,
drones and robots are
used to deliver food and
medicine to high-risk
areas as
well as to
conduct
disinfection
tasks
• Data to enable
detailed mapping of the
outbreak and hot spots
• Government response
tracker to evaluate
impact of public
measures
• Geolocation data for
governments to see
effects of confinement
measures
• Consolidate dashboards
with health data to give
decision-makers more
accurate visibility
• In China, new
software by
Alipay uses big
date to dictate
quarantines,
allowing user
to enter public
spaces, or not,
according to
their color
coding
Testing Tracing Target
quarantine
Transmission
control
Distancing Protective
gear
• Wristbands that vibrate to
notify the user who breaks
social distancing
Legend
Country with flagship usage of the technology
Measures alto taken by some SA countries

Even in testing, protocols still focusing mostly on symptomatic cases
Strict symptomatic cases Broad symptomatic cases Deep focus on vulnerable Wide testing
• Residents who were on local
transmission zones, traveled
abroad, or had contact with
confirmed cases on the last 14
days
• People need to present two or
more mild symptoms
• Residents who were on local
transmission zones, traveled
abroad, or had contact with
confirmed cases on the last 14
days and present two or more mild
symptoms
• People presenting acute
respiratory condition or
pneumonia who did not visit a
zone with local transmission and
neither had contact with a
confirmed case
• Less requirements for vulnerable
populations to be tested. These
populations are:
– Medical personnel
– Workers from closed institutions
(prisons, nursing homes)
– People who live in vulnerable
neighborhoods
– Close contacts to confirmed cases
• Flexibilities in requirements are:
– Presenting only one symptom
– No need of visiting a zone with local
transmission
• Cases presenting one of COVID’s
symptoms are tested
• All close contacts to confirmed
cases are tracked and tested
• Requires a developed capability
for tracking, using:
– Interviews
– Credit card information
– GPS tracking
+ people to test
Currently testing close
contacts presenting 1
symptom
Argentina introduced 5 changes to
the suspected case definition
Source: Argentina Ministry of Health; Chile Ministry of Health; Brazil Ministry of Health; Austria Ministry of Health; Business Insider; Asia Pacific Foundation of Canada; Europeum Think Tank
Currently testing all
people with only one
symptom
Chile Brazil Austria Australia South
Korea
CzechiaIsrael Switzerland

In summary, South American countries are struggling to have R0 bellow 1 due to a
higher starting point and lack of mitigation policies
Eastern Asia: strong
mitigation/early action
Dense countries/cities:
strong suppression
Less dense Europeans:
favorable demographics
Contamination R0
Days from 100th case
Contamination R0
Contamination R0
Spain
USA (NY)
Italy
Brazil
Colombia
Chile
Argentina
Singapore
Taiwan
Hong Kong
Contamination R0
SA countries: none of the
previous situations
Portugal
Switzerland
Norway
Countries/regions that entered or are entering a recovery phase
Low
unconstrained R0
Mitigation (strong
technology)
Strong
suppression

Exposure immunity could also help slow down the spread, but unlike Northern Italy and
NYC, even most affected Brazilian cities are still in early stages of contamination exposure
Number of adjusted cases1 / Population
(# cases; %)
Note: (1) Adjusted cases per mortality, with death rates of 0,5% to 0,7%
Source: Oxford Our World in Data Database (May 15th); ING; Financial Times; Nordea; Governments; Euronews; World Economic Forum; Brazilian Ministry of Health.
Even Brazilian cities with the highest
levels of contamination are far from
cities such as NYC, which is starting
to show exposure immunity effects
G L O B A L L A N D S C A P E J U N E 1 1 2 0 2 0A S O F

Current landscape and implications for Brazil:
Yet a different progression story
• It is clear by now that South American countries will not follow the same trajectory (the bell shape curve) of most
Asian or European countries
– Even after more than 50 days of restriction measures, the peak hasn’t been reached even under high mobility reduction (e.g. Colombia, Peru
and Argentina)
• Countries adopted different strategies to control the contagiousness level (R0); different combinations of favorable
demographics, the widespread use of technology/testing and harsh suppression measures were needed to take the
R0 below 1 and slowdown the curve
– Although there were some correct measures in places like early mobility restriction and even good testing levels in a few countries (e.g.
Colombia and Chile), this was not enough to compensate that higher starting R0
– Besides, not all mobility is the same, while in Europe people that needed to move was using their cars or even bikes, in South America the
mobility was in crowded buses and for longer distances
– Lastly, South American countries didn’t use technology in their favor – low/average testing levels, no use of tracing and no use of target
quarantine policies
• In summary, to compensate for the higher R0 starting point, South America should have used more weapons to
control the virus
– Brazil, in particular, has always had low testing levels, not enough mobility reduction in the high dense areas and no use of tracing
technologies
What can we expect going forward?

Understanding the current situation in Brazil is hard. Brazil doesn’t test enough,
generating a critical delay in current situation understanding
Brazil is testing ~3:1 vs suggested
higher than 10:1
Death notification is experiencing
delays up to 15 days
Number of tests per confirmed cases
(# tests; confirmed cases)
Note: (1) Top 15 countries with more confirmed cases, excluding Brazil and China. Source: JHU Coronavirus Data Stream; Tableau; Worldometers; Fiocruz
Deaths per SARS related symptoms
(# of k deaths; epidemiological weeks 9 to 23)
Death notification
period is up to 15 days
after death occurrence;
Hospitalizations due to SARS pneumonia
(# of k cases; epidemiological weeks 9 to 23)
Brazil has ~55% of hospitalizations
still being investigated for COVID-19
B R A Z I L I A N P E R S P E C T I V E J U N E 1 1 2 0 2 0A S O F

Three main metrics that can be used to understand the Brazilian situation; despite
some lag, hospitalizations due to SARS is the most reliable figure we have
1. # of confirmed cases: delayed
and highly underestimated
2. # of deaths: more accurate than
cases, but significantly delayed
3. # of hospitalizations due to SARS:
broadest and most reliable metric
• Brazil has one of the worst testing levels
across the globe, testing only ~3 people for
each confirmed case (benchmark countries test
50-200/confirmed case)
• Due to lack of test availability, Brazil is
prioritizing people with severe symptoms
and so, the total number of confirmed cases
is highly underestimated. Besides, the time
between testing and notification is very long,
resulting in a significant delay
• Finally, as availability of tests increase, it is
likely that the number of cases grows because
of more testing, not necessarily because of
more cases
• Severe cases are prioritized for COVID-19
testing which leads to a more accurate number
of total deaths, but there are still important
issues with the indicator:
– Delay on death notification (~15 days)
– Sub notification of deaths causes (E.g. COVID death
being reported as pneumonia)
• Even with more testing, there is still a
significant # of deaths under investigation
for COVID
• Number of hospitalizations due to SARS does
not depend on the efficiency of the testing
system
• Using previous studies that indicate a
hospitalization level of 20-30% for COVID-19, it
is possible to estimate the number of cases
• In our point of view, the # of hospitalizations
due to SARS is the best proxy for the # of
COVID-19 hospitalizations; a comparison with
previous years allows for a reasonably good
estimate of the disease evolution
Source: Stanford; Healthline.

The SARS hospitalizations metric suggests Brazil may have reached new cases
peak, but additional waves are possible
Source: Fiocruz, Ministry of Health
Epidemiological
Week
Hospitalizations due to SARS per epidemiological week
(k# cases; week of the beginning of symptoms)
First wave of
exponential growth
Increase of contamination
due to virus spread to the
countryside and smaller
cities
Contamination
curve
slowdown after
governmental
measures are
applied
J U N E 1 1 2 0 2 0A S O FB R A Z I L I A N P E R S P E C T I V E
Weeks that are
likely still
incomplete on
the reports

The contamination curve for Brazil is, in fact, a combination of the different realities
we see across states
(# cases; week of the beginning of symptoms)
Some states that were heavily impacted by
COVID-19 in a first moment appear to have
reached the slowdown stage of SARS curve
Most of the states appear to be in a stable
level of contamination, having achieved a
plateau of the curve
E.g.: Amazonas curve E.g.: São Paulo curve
Some states that had previously had a very
low level of contamination are now facing
accelerated growth
E.g.: Roraima curve
Source: Fiocruz
J U N E 1 1 2 0 2 0A S O FB R A Z I L I A N P E R S P E C T I V E

Epidemiological status by state on May 25th: As the disease progressed, we expected
most states would trend towards a “control zone”, with R0 ≈ 1 and ICU occupancy ≈ 60-70%
Contamination R0
ICU average occupancy ratio
M A Y 2 5 2 0 2 0A S O FB R A Z I L I A N P E R S P E C T I V E
GrowingSlowingDown
Low Risk Medium Risk High Risk
As states try to balance suppression/mitigation measures to
control the disease with a controlled reopening of economies,
we will likely see this “control zone” as a balancing point, with
stable growth of cases and ICU occupancy under control
Note: ICU occupancy in the state of Minas Gerais on May 25 updated from COVID-19 ICU occupancy to general ICU occupancy, a measure that the state started to adopt in the last few days; R0 calculated as the ratio of average new deaths in the last 7
days to the previous 7 days
Source: Ministry of Health and State Health Departments
State position on May 25th

Evolution by state since May 25th: Many states that were in the medium and high risk
areas indeed improved and progressed towards the “control zone”
Contamination R0
A S O F
States with high ICU occupancy are at high risk of a health care system
collapse and need to keep the R0 (contagiousness level) consistently
below 1 so they can achieve a “sustainable” contamination stage
B R A Z I L I A N P E R S P E C T I V E
GrowingSlowingDown
Among the states that were
considered critical, PA and
CE had the most significant
positive progress
J U N E 1 1 2 0 2 0N O T E X H A U S T I V E

Evolution by state since May 25th: In fact, many states that presented a lower risk also
progressed towards the “control zone”
Contamination R0
A S O F
GrowingSlowingDown
With suppression measures being relaxed,
especially in the “better states”, it is natural to
see states in this area move to the right,
towards the same “control zone”
J U N E 1 1 2 0 2 0N O T E X H A U S T I V E

Evolution by state since May 25th : Unfortunately, a few states had their situation worsened,
demanding a higher level of attention and possible reinforcement of suppression measures
Contamination R0
A S O F
Note: R0 calculated as the ratio of average new deaths in the last 7 days to the previous 7 days
GrowingSlowingDown
J U N E 1 1 2 0 2 0
Most of the states that worsened their risk
rating are in the N/NE regions where social
vulnerability and a weak health infrastructure
makes control more challenging
Last month ES had eased
suppression measures, but
with rapid worsening
returned to adopt more
restrictive measures

Current situation in Brazil: most states are now classified as medium risk and we
expect states to continue trending towards the “control zone” over the following weeks
0.806545 1.272405 99.6777 11.11
Contamination R0
A S O F
Note: R0 calculated as the ratio of average new deaths in the last 7 days to the previous 7 days
GrowingSlowingDown
J U N E 1 1 2 0 2 0
As states try to balance suppression/mitigation measures to
control the disease with a controlled reopening of economies,
we will likely see this “control zone” as a balancing point, with
stable growth of cases and ICU occupancy under control

To understand the Brazilian situation it is key to analyze the differences between these
risk profile groups on all relevant factors for disease spread and control
Main regions
Starting point:
Unconstrained R0
Mitigation Suppression Healthcare capacity Exposure Immunity
• Capitals in part of
SE, N and NE
– RJ, ES, PE, AP, RR
• Capitals in most of
SE, N, NE and S
– SP, MG, RS, SC,
GO, MT, BA, CE,
MA, RN, SE, PB,
AL, PI, TO, AM,
RO, PA, AC
CW and S
– PR, DF, MS
– Most countryside in
all states
High Risk
Medium Risk
Low Risk
A S O F J U N E 1 1 2 0 2 0

High Risk
Understanding the different risk profile groups:
Starting point - Unconstrained R0
Main regions
Starting point:
Unconstrained R0
SE, N and NE
• Unfavorable
demographics
– Very dense regions
– Poor social and
housing indicators
– High use of public
transport
• Challenging
circumstances that
require stronger
measures to
contain the spread
SE, N, NE and S
– SP, MG, RS, SC,
GO, MT, BA, CE,
MA, RN, SE, PB,
AL, PI, TO, AM,
RO, PA, AC
CW and S
– PR, DF, MS
all states
• Favorable
demographics
– Cities with lower
population
densities, better
social indicators
and less use of
public transport
Medium Risk
Low Risk
B R A Z I L I A N P E R S P E C T I V E U N C O N S T R A I N E D R 0 A S O F J U N E 1 1 2 0 2 0

The urban density of high and medium risk areas is much higher than other regions’
which contributes to a higher unconstrained R0
Source: IBGE
Higher risk areas are much more dense on average, making
it much more challenging to keep contamination at low
levels due to their demographics
Lower density of South and Central-West capitals and countryside cities is
definitely a factor that contributes to a more controlled contamination profile
B R A Z I L I A N P E R S P E C T I V E U N C O N S T R A I N E D R 0
High Risk
Medium Risk
Low risk
Legend

Brazilian social indicators are worrying. Higher # of people per house and sanitation
vulnerability are critical factors especially in the North and Northeast regions
State
5+ residents
per house (%
pop)
+ 3 people per
dorm (% pop)
+ 3 people per
bathroom (% pop)
Absence of
bathroom for
exclusive use at
home (% pop)
Absence of
sewage (% pop)
Absence of drinking
water supply (% pop)
Rio de Janeiro 18% 7% 29% <1% 12% 11%
Ceará 25% 6% 35% 4% 58% 21%
Pará 38% 12% 43% 13% 86% 50%
São Paulo 20% 6% 29% <1% 8% 4%
Rio Grande do Sul 17% 3% 29% <1% 31% 10%
Santa Catarina 17% 2% 26% <1% 44% 16%
Bahia 25% 4% 38% 4% 45% 16%
Amazonas 44% 19% 45% 12% 69% 28%
Minas Gerais 20% 2% 32% 1% 19% 11%
Mato Grosso 23% 5% 31% <1% 66% 20%
Mato Grosso do
Sul
23% 4% 32% <1% 52% 12%
Paraná 19% 2% 30% <1% 31% 10%
B R A Z I L I A N P E R S P E C T I V E U N C O N S T R A I N E D R 0 High threat Mid threat
Source: IBGE
High
RiskMediumRiskLowRisk

High Risk
Mitigation
Main regions
Starting point:
Unconstrained R0
SE, N and NE
• Unfavorable
demographics
– Poor social and
housing indicators
transport
• Challenging
circumstances that
require stronger
measures to
contain the spread
• Very low levels of
testing when
compared to
recommended
levels and
reference countries
(1-5 tests/
confirmed case)
• At these levels,
testing is not a real
tool for disease
control
SE, N, NE and S
– SP, MG, RS, SC,
GO, MT, BA, CE,
MA, RN, SE, PB,
AL, PI, TO, AM,
RO, PA, AC
CW and S
– PR, DF, MS
all states
• Favorable
demographics
population
densities, better
social indicators
and less use of
public transport
• Still on the low end,
but much more
adequate testing
level than higher
risk profiles (~10
tests/confirmed
case)
Medium Risk
Low Risk
B R A Z I L I A N P E R S P E C T I V E M I T I G A T I O N A S O F J U N E 1 1 2 0 2 0

B R A Z I L I A N P E R S P E C T I V E M I T I G A T I O N
Only few states are close to adequate testing level (>10:1); which should represent a
huge challenging for relaxing suppression measures without new outbreaks
Note: (1) Mortality calculated as % of confirmed cases
1
•
High Risk
• •
Medium Risk Low Risk
At current levels, Brazilian testing is not a lever to support the fight against COVID-19 spread in most
States. The vast majority of States only test severe cases, which does not work as a preventive
measure.
Access to reliable data on testing is another major challenge because of: (i) Discrepancy among state
departments and the Ministry of Health; (ii) lack of reporting of private tests in most States and (iii) Long delay
in reporting
A S O F J U N E 1 1 2 0 2 0

High Risk
Suppression
Main regions
Starting point:
Unconstrained R0
SE, N and NE
• Unfavorable
demographics
– Poor social and
housing indicators
transport
• Challenging
circumstances that
require stronger
measures to
contain the spread
testing when
compared to
recommended
levels and
reference countries
(1-5 tests/
confirmed case)
tool for disease
control
• More severe
restrictions
adopted on
average
• Most states with
mobility reduced
by 30-50%
– Below 60-80% level
achieved in Italy,
Spain, NYC
• Some states are
starting to ease
suppression
measure
SE, N, NE and S
– SP, MG, RS, SC,
GO, MT, BA, CE,
MA, RN, SE, PB,
AL, PI, TO, AM,
RO, PA, AC
CW and S
– PR, DF, MS
all states
• Favorable
demographics
population
densities, better
social indicators
and less use of
public transport
but much more
adequate testing
level than higher
risk profiles (~10
tests/confirmed
case)
• Less severe
restrictions
• Mobility reduced
by 15-30%
• Many States re-
laxing restrictions
Medium Risk
Low Risk
B R A Z I L I A N P E R S P E C T I V E S U P P R E S S I O N A S O F J U N E 1 1 2 0 2 0

On average, mobility continues to trend up across all risk profiles and remains far
from levels achieved by countries that applied severe lockdowns
Mobility change from Jan-Feb baseline1
Source: Google Community Mobility Reports
J U N E 0 5 2 0 2 0A S O F
Countries / cities with stricter isolation measures have reached levels of reduced mobility between 60-80% (eg.: Italy, Spain, NYC)
B R A Z I L I A N P E R S P E C T I V E S U P P R E S S I O N
High Risk Medium Risk Low Risk

Some states have already started relaxing suppression measures and reopening the
economy; plans are structured at different levels
Starting
date
State
plan
Region
based
Staged
process
ICU
occupation
Contamination
speed
Mobility
reduction
Mandatory
use of masks
Hygiene
protocols
Occupancy
rules
Already in operation
São Paulo
June 1st
11
In most regions,
commerce and services
activities have returned
with specific operating
rules
Rio Grande
do Sul
May 11
All sectors with different
levels of restrictions,
except schools
Santa
Catarina
April 22
except schools
Minas
Gerais
May 6
Essential services and a
wave of sectors
considered to be of low
risk released (furniture,
sporting goods,
electronics, accessory
activities, etc.)
Mato
Grosso
All sectors, except
schools
Paraná
Late
April
except schools
CriteriaMethodology Protocols
J U N E 1 1 2 0 2 0A S O F
Municipalities responsible for opening decision without disclosure criteria
The state government did not define the closing of commerce and activities, Municipalities are
responsible for adopting the measures they consider appropriate
Source: State Governments, Press Media
The State government did not disclosed a robust plan with clear criteria
B R A Z I L I A N P E R S P E C T I V E S U P P R E S S I O N
MediumRiskLowRisk

High Risk
Healthcare capacity
Main regions
Starting point:
Unconstrained R0
SE, N and NE
• Unfavorable
demographics
– Poor social and
housing indicators
transport
• Challenging
circumstances that
require stronger
measures to
contain the spread
testing when
compared to
recommended
levels and
reference countries
(1-5 tests/
confirmed case)
tool for disease
control
• More severe
restrictions
adopted on
average
mobility reduced
by 30-50%
achieved in Italy,
Spain, NYC
• Some states are
starting to ease
suppression
measure
• COVID-19 ICU
occupancy
consistently at
high levels, with
an imminent
collapse of the
health system
SE, N, NE and S
– SP, MG, RS, SC,
GO, MT, BA, CE,
MA, RN, SE, PB,
AL, PI, TO, AM,
RO, PA, AC
• COVID-19 ICU
occupancy levels
high, but still under
control
• There is margin to
absorb an eventual
increase in cases
CW and S
– PR, DF, MS
all states
• Favorable
demographics
population
densities, better
social indicators
and less use of
public transport
but much more
adequate testing
level than higher
risk profiles (~10
tests/confirmed
case)
• Less severe
restrictions
by 15-40%
• Many States re-
laxing restrictions
• COVID-19 ICU
occupancy
consistently at
low levels
Medium Risk
Low Risk
B R A Z I L I A N P E R S P E C T I V E I C U A S O F J U N E 1 1 2 0 2 0

ICU occupancy across states does not seem to be directly related to the availability
of beds, professionals or ventilators; disease spread is the major factor in play
Ventilators
ICU occupancy
Health
professionals
Source: Ministry of Health - DATASUS, COFEN – Federal Council of Nursing, CFM – Federal Council of Medicine, State Health Departments
Health professionals (per 100k population)
Ventilators (per 100k population)
COVID-19 ICU Occupation (in %) ICUs (per 1M population)
High Medium LowB R A Z I L I C U

High Risk
Exposure immunity
Main regions
Starting point:
Unconstrained R0
SE, N and NE
• Unfavorable
demographics
– Poor social and
housing indicators
transport
• Challenging
circumstances that
require stronger
measures to
contain the spread
testing when
compared to
recommended
levels and
reference countries
(1-5 tests/
confirmed case)
tool for disease
control
• More severe
restrictions
adopted on
average
mobility reduced
by 30-50%
achieved in Italy,
Spain, NYC
• Some states are
starting to ease
suppression
measure
• COVID-19 ICU
occupancy
consistently at
high levels, with
an imminent
collapse of the
health system
• PE and RJ, at risk
for longer time and
have exposure
above 15%; the
other states have
recently worsened
and their levels
are still <10%
SE, N, NE and S
– SP, MG, RS, SC,
GO, MT, BA, CE,
MA, RN, SE, PB,
AL, PI, TO, AM,
RO, PA, AC
• COVID-19 ICU
occupancy levels
control
absorb an eventual
increase in cases
• States that were
strongly impacted
before and
improved have
exposure close to
20%, while other
states are at ~5-
10%
CW and S
– PR, DF, MS
all states
• Favorable
demographics
population
densities, better
social indicators
and less use of
public transport
but much more
adequate testing
level than higher
risk profiles (~10
tests/confirmed
case)
• Less severe
restrictions
by 15-40%
• Many States re-
laxing restrictions
• COVID-19 ICU
occupancy
consistently at
low levels
• Very low exposure
immunity levels
due to the
controlled spread of
the disease
M A Y 2 5 2 0 2 0A S O F
Medium Risk
Low Risk
B R A Z I L I A N P E R S P E C T I V E I M M U N I T Y

Exposure immunity could help lower the spread in states at critical situations, some
capitals are closer to reaching levels where the effect of exposure immunity can be felt
Estimated exposed population (in %)
Note: Total number of cases estimated in function of the mortality rates; Considers only capitals with immunity >=1%
Source: Ministry of Health, Bain Analysis, UFPEL
J U N E 1 1 2 0 2 0A S O FB R A Z I L I A N P E R S P E C T I V E I M M U N I T Y
High Risk Medium RiskLegend Low Risk
To understand how immunity
affects the speed of the
spread, we will deep-dive on
this states

Exposure immunity developed from contact with COVID-19 is among the factors that
influence R0; over time it can take a significant role to control the disease
Exposure immunity
5% 10% 15% 20% 25% 30% 35% 40% 45% 50%
R0*
2.0 1.9 1.8 1.7 1.6 1.5 1.4 1.3 1.2 1.1 1.0
1.9 1.8 1.7 1.6 1.5 1.4 1.3 1.2 1.1 1.0 0.9
1.8 1.7 1.6 1.5 1.4 1.4 1.3 1.2 1.1 1.0 0.9
1.7 1.6 1.5 1.4 1.4 1.3 1.2 1.1 1.0 0.9 0.8
1.6 1.5 1.4 1.4 1.3 1.2 1.1 1.0 1.0 0.9 0.8
1.5 1.4 1.4 1.3 1.2 1.1 1.1 1.0 0.9 0.8 0.7
1.4 1.3 1.3 1.2 1.1 1.1 1.0 0.9 0.8 0.8 0.7
1.3 1.2 1.2 1.1 1.0 1.0 0.9 0.8 0.8 0.7 0.6
1.2 1.1 1.1 1.0 1.0 0.9 0.8 0.8 0.7 0.7 0.6
1.1 1.0 1.0 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.5
1.0 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 0.5 0.5
0.5 0.5 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.2
Note: *R0 without exposure immunity effect
Source: University of Oxford, Brazilian Federal Government, SEIR Model
Overtime, exposure immunity
can reduce R0 to values bellow
one, thus controlling the spread
The R0 is a measure of the speed with which
the disease is spreading. It represents the avg.
number of people an infected individual infects
Exposure immunity refers to the immunity developed by
individuals who had contact with the virus and now
have antibodies to protect them against the disease
Controlled R0 (<=1)Elevated R0 (>=1.5)
B R A Z I L I A N P E R S P E C T I V E I M M U N I T Y A S O F J U N E 1 1 2 0 2 0

Exposure immunity
5% 10% 15% 20% 25% 30% 35% 40% 45% 50%
R0*
2.0 1.9 1.8 1.7 1.6 1.5 1.4 1.3 1.2 1.1 1.0
1.9 1.8 1.7 1.6 1.5 1.4 1.3 1.2 1.1 1.0 0.9
1.8 1.7 1.6 1.5 1.4 1.4 1.3 1.2 1.1 1.0 0.9
1.7 1.6 1.5 1.4 1.4 1.3 1.2 1.1 1.0 0.9 0.8
1.6 1.5 1.4 1.4 1.3 1.2 1.1 1.0 1.0 0.9 0.8
1.5 1.4 1.4 1.3 1.2 1.1 1.1 1.0 0.9 0.8 0.7
1.4 1.3 1.3 1.2 1.1 1.1 1.0 0.9 0.8 0.8 0.7
1.3 1.2 1.2 1.1 1.0 1.0 0.9 0.8 0.8 0.7 0.6
1.2 1.1 1.1 1.0 1.0 0.9 0.8 0.8 0.7 0.7 0.6
1.1 1.0 1.0 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.5
1.0 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 0.5 0.5
0.5 0.5 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.2
~30 days
R0 already has some
impact from
exposure to the virus
Giving enough time, and sustaining behaviors,
more people will have contact with the virus,
increasing overall immunity
Eventually leading to reduction of
R0 to levels equal to or bellow 1
B E L É M E X A M P L EB R A Z I L I A N P E R S P E C T I V E I M M U N I T Y J U N E 1 1 2 0 2 0A S O F
Belém
PA

Exposure immunity
5% 10% 15% 20% 25% 30% 35% 40% 45% 50%
R0*
2.0 1.9 1.8 1.7 1.6 1.5 1.4 1.3 1.2 1.1 1.0
1.9 1.8 1.7 1.6 1.5 1.4 1.3 1.2 1.1 1.0 0.9
1.8 1.7 1.6 1.5 1.4 1.4 1.3 1.2 1.1 1.0 0.9
1.7 1.6 1.5 1.4 1.4 1.3 1.2 1.1 1.0 0.9 0.8
1.6 1.5 1.4 1.4 1.3 1.2 1.1 1.0 1.0 0.9 0.8
1.5 1.4 1.4 1.3 1.2 1.1 1.1 1.0 0.9 0.8 0.7
1.4 1.3 1.3 1.2 1.1 1.1 1.0 0.9 0.8 0.8 0.7
1.3 1.2 1.2 1.1 1.0 1.0 0.9 0.8 0.8 0.7 0.6
1.2 1.1 1.1 1.0 1.0 0.9 0.8 0.8 0.7 0.7 0.6
1.1 1.0 1.0 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.5
1.0 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 0.5 0.5
0.5 0.5 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.2
B E L É M E X A M P L E
Belém
PA
B R A Z I L I A N P E R S P E C T I V E I M M U N I T Y J U N E 1 1 2 0 2 0A S O F
Increasing rigor of actions taken (such
as contact tracing and quarantines)
might accelerate R0 reduction
Relaxing social
distancing before
controlling the spread
might increase time to
reach R0 = 1

Exposure immunity
5% 10% 15% 20% 25% 30% 35% 40% 45% 50%
R0*
2.0 1.9 1.8 1.7 1.6 1.5 1.4 1.3 1.2 1.1 1.0
1.9 1.8 1.7 1.6 1.5 1.4 1.3 1.2 1.1 1.0 0.9
1.8 1.7 1.6 1.5 1.4 1.4 1.3 1.2 1.1 1.0 0.9
1.7 1.6 1.5 1.4 1.4 1.3 1.2 1.1 1.0 0.9 0.8
1.6 1.5 1.4 1.4 1.3 1.2 1.1 1.0 1.0 0.9 0.8
1.5 1.4 1.4 1.3 1.2 1.1 1.1 1.0 0.9 0.8 0.7
1.4 1.3 1.3 1.2 1.1 1.1 1.0 0.9 0.8 0.8 0.7
1.3 1.2 1.2 1.1 1.0 1.0 0.9 0.8 0.8 0.7 0.6
1.2 1.1 1.1 1.0 1.0 0.9 0.8 0.8 0.7 0.7 0.6
1.1 1.0 1.0 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.5
1.0 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 0.5 0.5
0.5 0.5 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.2
Note: Considers state capital data; *R0 without exposure immunity effect
~35 days
The time necessary to develop
sufficient immunity varies
according to starting R0 and
level of exposure to the virus
B R S T A T E S E X A M P L E S
Savador
BA
B R A Z I L I A N P E R S P E C T I V E I M M U N I T Y J U N E 1 1 2 0 2 0A S O F
~30 days
Belém
PA
~35 days
Maceió
AL
São Luís
MA
~40 days
Recife
PE
Vitória
ES
Porto Velho
RO

Most cities in the high / medium risk profiles managed to reach exposure levels where
R0<1 or are within 30-50 days from reaching this mark, maintaining other parameters
Note: Total number of cases estimated in function of the mortality rates; Considers only capitals with immunity >=1%
Source: Ministry of Health, Bain Analysis, UFPEL
J U N E 1 1 2 0 2 0A S O FB R A Z I L I A N P E R S P E C T I V E I M M U N I T Y
In addition to current R0, the current presence of
the virus (expressed in cases per number of
people) also influence the time to reach higher
levels of exposure immunity
10%
35d
35d
20%
40%
30d
Exposure immunity in capitals where R0 =<1 will help
control R0 growth as lockdown measures are lifted
40d
30d
40d
30d
40d
High Risk Medium RiskLegend Low Risk
45%
35%
60%
55%
25%
50%
30d
Estimated exposed population (in %)

High Risk
Summary: Groups have significant differences in all relevant variables
Main regions
Starting point:
Unconstrained R0
SE, N and NE
• Unfavorable
demographics
– Poor social and
housing indicators
transport
• Challenging
circumstances that
require stronger
measures to
contain the spread
testing when
compared to
recommended
levels and
reference countries
(1-5 tests/
confirmed case)
tool for disease
control
• More severe
restrictions
adopted on
average
mobility reduced
by 30-50%
achieved in Italy,
Spain, NYC
• Some states are
starting to ease
suppression
measure
• COVID-19 ICU
occupancy
consistently at
high levels, with
an imminent
collapse of the
health system
• PE and RJ, at risk
for longer time and
have exposure
above 15%; the
other states have
recently worsened
and their levels
are still <10%
SE, N, NE and S
– SP, MG, RS, SC,
GO, MT, BA, CE,
MA, RN, SE, PB,
AL, PI, TO, AM,
RO, PA, AC
• COVID-19 ICU
occupancy levels
control
absorb an eventual
increase in cases
• States that were
strongly impacted
before and
improved have
exposure close to
20%, while other
states are at ~5-
10%
CW and S
– PR, DF, MS
all states
• Favorable
demographics
population
densities, better
social indicators
and less use of
public transport
but much more
adequate testing
level than higher
risk profiles (5-20
tests/confirmed
case)
• Less severe
restrictions
by 15-40%
• Many States re-
laxing restrictions
• COVID-19 ICU
occupancy
consistently at
low levels
• Very low exposure
immunity levels
due to the
controlled spread of
the disease
M A Y 2 5 2 0 2 0A S O FB R A Z I L I A N P E R S P E C T I V E
Medium Risk
Low Risk

Note: (1) Testing data updated on June 11th; (2) Google Mobility report data week average (May 29th to June 05th)
Source: Brazilian Ministry of Health bulletin; ICU availability from media press or calculated through weighted average between SUS and Private Brazilian occupations, Google Mobility report, Brasil.io, IBGE, LCA Regional
Data Summary: Groups have significant differences in all relevant variables
State/Region
Cases /
1M pop
Capital urban
density (k hab/km²)
Average household
size (people/house)
Mortality
(%)
Tests per
confirmed cases1
Relative mobility2
(%)
Estimated Capital’s
Exposure Immunity (%)
ICUs /
1M pop
UCI occupation
(%)
Criticality
Level
Amapá 16,969 0.1 3.5 2% 2 -43 5%-7% 53 >90%
High Risk
Roraima 10,056 <0.1 2.9 3% 2 -27 5%-7% 40 75-90%
Espírito Santo 5,744 3.3 2.8 4% 1 -29 6%-8% 210 75-90%
Pernambuco 4,360 7.0 2.9 8% 2 -34 12%-17% 170 >90%
Rio de Janeiro 4,283 6.8 2.8 10% 3 -33 10%-14% 231 75-90%
Amazonas 12,560 4.2 3.3 4% 1 -15 10%-14% 64 50-75%
Medium
Risk
Acre 9,778 <0.1 3.1 3% 2 -33 6%-8% 76 75-90%
Ceará 7,771 8.6 3.0 6% 2 -39 14%-20% 126 75-90%
Maranhão 7,521 1.2 3.4 2% 2 -28 8%-11% 109 75-90%
Pará 7,145 6.9 3.1 6% 1 -26 16%-22% 73 50-75%
Paraíba 5,950 3.4 3.0 2% 3 -43 4%-5% 112 50-75%
Rondônia 5,483 <0.1 2.7 3% 2 -21 5%-7% 178 75-90%
Alagoas 5,424 1.9 3.1 4% 2 -38 6%-8% 119 75-90%
Sergipe 4,577 3.1 3.0 2% 3 -37 3%-4% 142 75-90%
Tocantins 3,935 0.1 2.9 2% 3 -24 <1% 79 50-75%
São Paulo 3,377 11.7 2.8 6% 2 -31 6%-9% 211 50-75%
Rio Grande do Norte 3,273 5.1 3.0 4% 8 -34 3%-4% 128 75-90%
Piauí 2,548 0.6 3.2 3% 2 -44 2%-3% 94 50-75%
Bahia 2,189 10.3 2.8 3% 4 -36 3%-5% 111 50-75%
Santa Catarina 1,737 2.6 2.8 1% 5 -30 <1% 135 50-75%
Mato Grosso 1,305 2.2 2.9 3% 3 -15 <1% 165 50-75%
Rio Grande do Sul 1,192 4.6 2.7 2% 6 -22 <1% 148 50-75%
Goiás 1,006 1.8 2.8 3% 2 -15 <1% 166 75-90%
Minas Gerais 822 7.6 2.8 2% 2 -19 <1% 150 50-75%
Distrito Federal 6,366 0.4 2.9 1% 10 -24 1%-2% 346 50-75%
Low RiskMato Grosso do Sul 924 2.3 2.8 1% 6 -13 <1% 168 <50%
Paraná 680 4.3 2.8 4% 6 -19 <1% 179 <50%
MitigationUnconstrained R0 Suppression Healthcare capacity
J U N E 1 1 2 0 2 0A S O FHigh threat Mid threat
Exposure Immunity

Health System capacity
Health System capacity Health System capacity
Going forward: The upcoming scenario (next few weeks) will vary by state,
depending on lockdown measures, testing and technology usage
Long plateau with a slight
slowdown Curve slowdown in the next weeks
Peak followed by a strong
lockdown
Estimated hospitalizations due to COVID-19
(# cases)
Epidemiological Week
(# cases)
(# cases)
Current hospitalizations Current hospitalizations
• Mobility reduction leads to a strong
peak of contamination and a severe
lockdown I required
• Collapse of the healthcare system
• Maintenance of current government
measures leads to a stabilization of # of
new cases
• Healthcare system is kept under stress
during a significant period
• Contamination starts to slowdown due to
exposure immunity
• Current measures are efficient and
slowdown the curve in the next few weeks
• Mobility reduction measures may be lifted
in this scenario, possibly causing future
case growth
Current hospitalizations
B R A Z I L I A N P E R S P E C T I V E U P C O M I N G S C E N A R I O

Going forward: Combining effects from all States, the most likely scenario for Brazil
is a long plateau of the disease until exposure immunity starts to play a role
(# cases; epidemiological week of the beginning of symptoms) Projections
Note: (1) With SARS Pneumonia symptoms; Source: Brazilian Ministry of Health – Public Health Emergency Operations Center; Bain Estimate.
Healthcare
system capacity
Different stages of COVID-19
spread and government responses
across Brazilian state should
generate a long plateu in the curve
After a significant period, exposure
immunity may help states to
reduce contagiousness levels
B R A Z I L I A N P E R S P E C T I V E U P C O M I N G S C E N A R I O
With many states reopening their
economies, another wave of
growth in the near future is not out
of the equation

Current landscape and implications for Brazil: States appear to be converging to
an equilibrium, but the situation is far from under control
• Be careful with the data: The most common data used (number of new cases) is unreliable for understanding the situation.
Hospitalizations due to SARS is still the most reliable info and suggests a much slower growth already
• The reality is still highly variable across states, but in the past weeks most states progressed in a healthy direction by
reducing contamination levels (the R0) and/or ICU occupancy, moving towards a “control zone”
– States that were classified as high risk and that adopted more restrictive measures managed to evolve to a better situation. Pará and Ceará
are good examples; both states were in a critical condition and reduced considerably the spread of the virus and the ICU occupancy in the
last 15 days
– States that were at low risk (e.g. Paraná and Distrito Federal) started to lift restrictions and re-open parts of their economies and, as a
consequence, increased the occupancy rate of the health system, but remain at a controlled level
• On the other hand, a few states evolved negatively in recent weeks demanding a higher level of attention
– Espírito Santo appear to have prematurely relaxed suppression measures and reached a high ICU occupancy, the state is already returning
to more restrictive measures. Amapá and Roraima (two states in the North of the country where social vulnerability and a weak health
infrastructure makes disease control more challenging) also entered the high risk zone
• It is possible that current exposure levels in some cities are already having some positive effect. Belém and
Fortaleza, for example, achieved ~20% of exposure immunity. Depending on how the disease progresses, other capitals in
the high and medium risk profiles should start to feel positive effects from exposure immunity in 4-6 weeks
• The long plateau continues to be our most likely scenario going forward and should remain a reality at least for
another 4-6 weeks
– It is important to note that this plateau is the combined effect of all states which are going through different stages of the contamination

Restore “new normal”
Heavy
mitigation
Containment/
light mitigation
Deflection
from
pre-crisis
baseline
Blackout
“crisis”
phase
Post-crisis
recovery
Most countries
already past
this point
Preliminary estimate
is 40-50% deflection
This point will
vary by
epidemiological
course and
socio-political
actions
Preliminary
estimate is
5-20%
deflection
Likely dependent
on a permanent
medical intervention
or the attainment of
exposure immunity
Dependent on
timing of (3) and (5):
roughly 0-5%
deflection
1
2
4
5 6
Brownout
“sustain”
phase
3
Economic impact will result from depth and duration of health crisis; The capacity of
countries and regions to control epidemiological factors will shape economic return
Source: Bain Macro Trends Group analysis, April 2020

Heavy mitigation at “Blackout” phase brought substantial impacts on the Brazilian
economy during April
Significant economic indicator
shifted from in April
~42% of total industry capacity
was vacant in April
Services demand also felt on
multiple Items
14%
0,3%
44%
Demand for electricity1
Inflation in April2
(lowest in 22y)
Movement on road tolls3
Source: 1Bruegel, ONS, CCEE; 2Brasil Econ6omico; 3Poder Econônmico; 4FGV/Ibre; 5Instituto Aço Brasil; 6Anfavea; 7Auto Esporte; 8FCA; 9Ambev
-
-
-
Installed Capacity Utilization Level4
(in %)
Car sales7
Car review at dealerships8
Beverage sales9
76%
90%
27%
-
-
-
Car production6Steel consump.5
99%- -50%
E C O N O M I C I M P A C T L O S S E S I N A P R I L V S M A R C H

“Sustain” and “new normal” phases should also bring challenges to the local
economy, especially driven by a step demand decline
E C O N O M I C I M P A C T
Note: Question – “How much do you expect Coronavirus will impact your household income?”; Low income considers households with total income lower than R$2,078 per month; Mid income considers households with total income between R$2,078 –
R$10,309; High income considers households with total income above R$10,309
Source: Bain survey, (N=2,131)
Expected household financial impact
(in % of respondents)
Expected household financial impact by income level
(in % of respondents)

Manufacture
Transport. &
Storage
Commerce
COVID-19 crisis will hit all sectors at different levels; while agriculture still has a
positive GPD forecast vs 2019, most industries & services will be severely hit
Legend
Note: 1Baseline projection on Feb/20; 2Baseline projection on May/20; 32018 growth vs 2020 Post-COVID projection
Source: LCA GDP Growth projections; WTTC; FGV
2020 GDP growth projection before COVID-191
2020 GDP growth projection after COVID-192
ConstructionTourism3
Agriculture
Industries Services
Real Estate
Financial
Services
Public. Adm
High Impact Medium Impact Low Impact
Energy &
Water
Mineral
Extraction
AgricultureTaxes
Other
Services
Information
Services
A S O F M A Y 1 4 T H

Northeast & South economies expected to be more severely hit in 2020, mostly due to
tourism and industrial activities; North and Central-West to face slightly lower losses
• ~13% of the economy based on mineral extractives
and energy, with the modest drop and profitability
advantage
• ~10% of the economy based on agriculture will help
mitigate local services loss
High epidemiological impact
Low economic impact (5,0% vs 5,6% BR)
North
• ~10% of total GDP based on Tourism,
sector that will suffer a major blow
• ~42% of total state revenue based on
fiscal transfers that will be maintained by
the federal government using aid measures
• ~27% of GDP based on agribusiness; strong soy harvest and high USD
will keep 2020 performance similar to last years
• High public spending and FS shares will also mitigate the drop in the region
High economic impact (7,2% vs 5,6% BR)
Central-West
Northeast
Southeast
Low epidemiological impact
Low economic impact (5,1% vs 5,6% BR)
Average to high economic impact (5,2% vs 5,6% BR)
• ~13% share of manufacture in the economy
will lead GDP fall in the region
• ~16% participation of FS and professional
services in the economy will avoid an even
more delicate situation for SE
Epidemiological impact
High Medium Low
South
Medium epidemiological impact
High economic impact
(5,9% vs 5,6% BR)
• ~16% of the economy based on manufacture and higher concentration
of severely hit goods such as furniture, textiles, clothing and footwear
• While agriculture share is similar to other regions, weather will lead to
severe harvest losses in 2020 (47% loss of soybean harvest in RS)
Epidemiological
risk in early May

Manufacture fall will be felt on Southeast, while service performance will help
maintain economic activity in the region
Manufacture and services are
more prominent in the Southeast Sector Loss1 Regional impact
Region GDP per sector
(in R$ B)
Note: 1Green means lower loss than BR average, yellow similar loss and red higher loss; 2Sales drop between April 15th and 29th
Source: IBGE; LCA Projections; F360
-5.2%
+1.8%
-4.5%
E C O N O M I C I M P A C T S O U T H E A S T
-8.7%
A S O F M A Y 1 4 T HB A C K U P
Share vs
other regions
• High tax dependence means fiscal losses might
affect the region more severely
• Federal aids for the financial sector, low
interests and increase digitalization lead sector
to stability in 2020
• Due to social distancing measures, commerce
in Southeast is the most affected in the country,
with a 71% drop on retail sales2
• Lower dependence in the region from public
spending means higher GDP fall, but lower
exposition in case of insolvency
• Larger share of manufacture in SE drives GDP
to fall. Automotive production, one of the most
affected businesses, fell by 99% in April
• Although with a small share n the economy,
agriculture is set to perform well in the SE

Economy in the South is expected to be expressively hit given it’s dependence and
performance on manufacturing and loss harvest due to weather
Manufacture and commerce are
more prominent in the South Sector Loss1 Regional impact
(in R$ B)
Note: 1Green means lower loss than BR average, yellow similar loss and red higher loss
-5.9%
-1.9%
-4.5%
-11.1%
Share vs
other regions
• High tax dependence means fiscal losses might
affect the region more severely
• Commerce in South follows a similar trend to
the rest of the country
• Lower dependence in the region from public
spending means higher GDP fall, but lower
exposition in case of insolvency
• Furniture, textile, clothing and footwear
industries are relevant in the South and are due
to fall significantly
• Agriculture in South region might not perform as
well as in the rest of the country due to severe
weather impacting harvest (~47% soybean
harvest lost in RS)
E C O N O M I C I M P A C T S O U T H

Economy in Northeast will fell the step decrease in tourism due to social distancing;
not even federal transfers and agriculture performance should avoid critical losses
Services, specially tourism, are
prominent in NE Sector Loss1 Regional impact
(in R$ B)
-7.2%
+1.3%
-7.6%
-8.6%
Share vs
other regions
• Tax collection in the region should fall in line
with overall GDP loss
• All tourism in NE represents ~10% of region
GPD and its fall should be felt on many service
activities
• In April commerce in Northeast felt similarly to
Brazilian trend, but the impact on tourism might
deteriorate the sector
• Federal transfers, that in 2019 represented 42%
of gov. revenue should be maintained,
stabilizing public expenditure
• Northeast has an overall low dependence on the
industry, potentially reducing GDP fall in 2020
• Agriculture in NE should slightly attenuate
economic impact felt on the region
E C O N O M I C I M P A C T N O R T H E A S T

Central-West region should expect a lower drop on GDP, mostly due to agriculture,
financial services and public spending stability from 2019 levels
Agriculture, public spending and
FS are more prominent in the CO Sector Loss1 Regional impact
(in R$ B)
-5.1%
+0.6%
-5.6%
-7.2%
Share vs
other regions
• Tax collection in the region should fall in line
with overall GDP loss
• Federal aids for the financial sector, low
interests and increase digitalization lead sector
to stability in 2020
• Stable revenue form agriculture is likely to
reduce commerce loss in the region, specially
for agribusiness items
• High dependence in the region from public
spending means reduced GDP fall, but
increased exposition in case of insolvency
• Manufacturing is particularly low in Central-
West, mitigating GDP loss
• Agriculture is expected to show similar overall
performance from 2019, with +3% growth in
crops and -8% fall on livestock
E C O N O M I C I M P A C T C E N T R A L - W E S T

North region should have fewer economics impacts due to COVID-19, mostly due to
its better performance in services, extractive & energy industry
Agriculture, energy and public
spending are more prominent in N Sector Loss1 Regional impact
(in R$ B)
-5.0%
-2.6%
-4.7%
-5.8%
Share vs
other regions
• Fiscal losses might not affect the region as
severely as the rest of the country
• High dependence in the region from public
spending means reduced GDP fall, but
increased exposition in case of insolvency
• Energy consumption felt less in Brazil if
compared to other countries due to COVI-19,
but default increase is an issue
• Financially attractive mining sites in Pará (vs
MG) might increase extractive industry
performance in the North
• Agriculture in the North might perform under the
rest of the country because it is 35% livestock,
with a -8% growth projection due to lower
demand on animal protein during crisis
E C O N O M I C I M P A C T N O R T H

Overall, agriculture has as stable landscape, result of +3% growth in vegetables and
-8% drop in livestock; commodities price increases are only partially fulfilled
~70% of total agriculture in Brazil
comes from 5 items
Despite price decrease intl., exch.
rate fluctuation offset R$ value1
However, drop on demand is
keeping prices and volumes
Agriculture production value
(in R$ B)
• Domestic: Drop in protein demand &
increase on production costs (crops)
• Export: 70% growth in May vs 2019
• Domestic: Shows signs of retraction
• Export: 97% drop on exports, due to large
offer from the US
• Domestic: -40% ethanol sales in April
• Export: Record export of sugar 20/21
Note: 1On international markets, considers average exchange rate for 2019, and May 12th 2020 exchange for 2020; Considers 2019 avg. value vs 2020 projected value
Source: CENSO Agro, World Bank, ACSP, Morningstar; Economics FNP, UNICA, Reuters, Agrifatto
• Domestic: Drop in anima protein demand
• Export: Slowdown by the end of April
E C O N O M I C I M P A C T A G R I C U L T U R E
• Domestic: Record production (44,5 Mt)
• Export: 4% volume growth vs 2019 (p)
+44%
Price in USD Price in R$
-5%
+42%
-3%
+38%
-6%
+40%
-7%
+71%
+14%
-18%-
Sugar
Ethanol Sugar exports represent 65% of total production.
Ethanol is 95% directed to domestic market

Industry GDP will suffer more severely than other sectors, mostly due to
manufacturing and construction retractions projected for 2020
Industry GDP is composed of 4 categories,
with strong prevalence of Manufacture
Industry GDP per sector
(in R$ B)
E C O N O M I C I M P A C T I N D U S T R Y
Category analysis
Source: IBGE; LCA Projections
-0.4%
-3.1%
-9.8%
-12.2%
• Domestic and international demand to drop will frustrate
growth projections of main commodities: Oil & Gas and Iron
• International disputes affect more Oil & Gas while exchange
rate and repressed demand since Brumadinho will help
maintain prices and volume of Iron Ore
• Energy demand fell less in Brazil compared to other
countries but erosion of consumer revenue might pose
• Unemployment and deterioration of customer confidence
are set to reduce the construction GDP, frustrating positive
cheerful estimates after 6 years of low performance
• Gov. stimulus towards mortgages lightly relief the sector
• Manufacture is expected to severely hit, with drop ranging
between group/essentiality of goods produced
• Auto, furniture, textile, clothing and footwear should expect
a more significant fall
• Agriculture related items should be fall at a more discreet
pace due to agro stability
Projected GDP
loss 2020 vs 2019

COVID-19 affected services in a number of ways, worst hit categories felt special
impact of social distancing and eroding buying power & confidence of customers
Services GDP per sector
(in R$ B)
E C O N O M I C I M P A C T S E R V I C E S
Six categories of services account for 75%
of the sector
Projected GDP
loss 2020 vs 2019
-38.9%
+0.2%
-1.2%
-6.6%
+0.4%
• Although Hotel & Food are a small share of services,
tourism GDP is more relevant, ~10% in selected areas.
• Tourism demand will drop sharply, specially from foreigners
• 2020 was seen as a very positive year for Real Estate, but
erosion of customer income and confidence tuned the tide
(~2/3 of the clients plan to delay purchases on 6+M)
• Gov. stimulus towards mortgages partially relief the sector
• Monetary measures taken by the BC to reduce interest &
digitalization trends help stabilize the financial sector
• While higher work from home flexibility of skilled professional
also avoid further losses
• Social distancing measures will directly affect commerce,
specially on areas with high epidemiological impact
• Share of essential goods and on-line sales attenuate the
impact on the sector
• Public administration expenditure should remain constant
throughout the COVID-19 crisis due to employment stability
and fiscal aids by the federal government
• Some sectors, such as health, will have increased budgets
• There is a risk that if the fiscal situation of the government
deteriorate they might become insolvent and skip paymentsSource: IBGE; LCA Projections
Category analysis
Information & communication also play a significant on service volume: should suffer
moderate losses on 2020 (default payments might pose a challenge to the sector)

Lockdown measures
Store traffic fell sharply across all states, but has since recovered partially in Midwest
and South regions
Notes: * Include places such as restaurants, cafes, shopping centers, theme parks, museums, libraries, and movie theaters. Source: Google, Bain analysis
Store traffic index at 1-7/May - retail & recreation*
(Base 100 = median of same week day before the crisis)
Store traffic index weekly evolution - retail & recreation
(Base 100 = median of same week day before the crisis)
51
44
49
61
38
39
33
50
47
36
54
55
52
68
42
57
59
35 - 44
45 – 54
44
32
64
39
44
40
33
34
40
39
Brazil avg. 44
Ceará
São Paulo
Mato Grosso
do Sul< 35
> 54 50-54
Brazil
Ceará
São Paulo
Mato Grosso
do Sul
Rio Grande do Sul
Rio Grande
do Sul
E C O N O M I C I M P A C T S E R V I C E S

Drop on tax collection might impose further challenges for states as current federal
aid package should not be sufficient to cover income losses
State tax collection felt ~17% in
April; May projection is even worst
Nominal drop1 in state taxes
expected to reach R$ 54B in 2020
Federal aid program is not
sufficient to cover drop
State Tax Collection fall (R$B)
RJ 10.1 + 3.2 (Royalties)
SP 9.7
MG 7.5
RS 2.3
PA 2.0
BA 1.9
PA 1.8
ES 1.7
CE 1.5
Others 12.4
Total R$ 54B
Projected tax collection fall per state (in %)
R$ 30B
-
R$ 54B
Without further federal aid state
economies might deteriorate even
further, resulting in increase on debt
and/or insolvency
-R$ 24 B
Federal Government
fiscal aid for states
Total state tax loss
State governments
income losses
Legend
>20%
>15%
<15%
Note: 1State projections of total collection impact in 2020
Source: State Governments, Lit. research
Does not consider
city and federal
collection losses
A S O F M A Y 1 4 T HE C O N O M I C I M P A C T T A X C O L L E C T I O N

Countries have employed, at different degrees, both monetary and fiscal policy
measures to deal with the economic consequences of the COVID-19 outbreak
Note: [1] Including loans and guarantees; [2] Monetary policies taken by European Union
Source: Investopedia, Quartz, Lit. research, BCRA, Federal Reserve Bank of New York, Bain Macro Trends Group Analysis
Country Degree of action
Fiscal stimulus Rate cut Quantitative Easing1
Economic stimulus to kick-start
growth, such as loans, tax
reduction, increasing spending
Interest rate at which banks and
credit unions lend resources to
each other
Central bank purchases of longer-
term securities in order to increase
the money supply and encourage
lending and investment
USA
USD 6,700B
1.5%
(1.63% to 0.13%)
USD 2,700B
Repo ops. and bond purchases
Italy
USD 900B
No cut2 USD 930B2
Bond purchasesGermany
USD 840B
Brazil
USD 222B
1.25%
(4.25% to 3.0%)
USD 230B
Liquidity injections
Chile
USD 38B
0.5%
(1.0% to 0.5%)
USD 4B
Bond purchases
Argentina
USD 13B
3.0%
(31.0% to 28.0%)
USD 6B
Liquidity injections
China
USD 740B
0.1%
(4.15% to 4.05%)
USD 175B
Repo operations
More action
Less action
Monetary policiesFiscal policies
M A Y 2 8 2 0 2 0A S O FE C O N O M I C I M P A C T G O V E R N M E N T R E S P O N S E

Governments are heavily investing in fiscal measures; stimulus proposals appear to
correlate with the severity of COVID-19’s impact
Fiscal measures taken by country to fight COVID-19
(as % of GDP)
Largest aid package ever
announced in history
Note: Currency conversion on march 31st; 2019 GDP in USD, 1Not at scale
Source: Trading Economics; Morningstar
Mortality
(deaths per million people)
3141
5481
3761
Measures to increasing liquidity in
the system, with suspension of loan
and mortgage repayments
M A Y 2 8 2 0 2 0A S O FE C O N O M I C I M P A C T F I S C A L M E A S U R E S
1031
1241121

Often, government loans and grants to businesses have a range of requirements to
help guarantee jobs and payroll
Note: Separate direct payments to employed, furloughed, or laid off workers not included above
Source: Lit. research, Bain Macro Trends Group Analysis
Payroll
guarantee to
employers Guidance
Brazil
• Companies will be able to reduce workload and payment proportionally, in predetermined percentages: 25%, 50% or 70% (up to 100%
for companies with revenue under R$4.8M/year) for wages up to R$3,135 (35% for over R$3,135)
• A percentage equal to the payment reduction of the unemployment insurance (R$1.045 or R$1,1813) will be payed by the government
Argentina
• Anses paying salaries during April: 100% for companies up to 25 workers; 75% for those from 26-60; and 50% for those from 61-100
• Decree that prohibits laying off workers for 60 days
United
States
• Small business loan funds used for payroll, rent, interest and utilities to be forgiven
• Mid-sized businesses (500-10,000 employees) to make a good-faith certification that funds will be used to retain 90% of the workforce
at full compensation and benefits through September 30, 2020
• Airlines and “businesses critical to national security” receiving aid required to maintain “employment levels as of March 24, 2020 to the
extent practicable, until Sep 30, 2020”; no such stipulation for other large businesses
United
Kingdom
• Up to 80% of wage bills covered for workers retained on payroll but not working
Italy • Companies are prohibited from laying off workers for the next two months without “justified objective reasons”
France
• Full reimbursement of salaries up to ~€6,900/month – paired with a requirement to keep workers’ jobs open and pay 70% of gross
salary and 100% for minimum wage workers – for companies having to reduce or suspend activities (“partial unemployment”)
Germany
• 60% of pre-crisis pay in compensation to workers sent home or made to work part-time (as part of existing Kurzarbeit scheme, a way for
firms to retain workers during a downturn); scheme expanded so companies can now register when just 10% of their workforce is
impacted, down from 33%
Japan
• ~$80 per day in reimbursement to employers through March 31st for workers taking leave to stay home with children due to
nationwide school closures
Full wages (up to a limit) covered
No explicit coverage
E C O N O M I C I M P A C T F I S C A L M E A S U R E S
B A C K U P

COVID-19 Fact Base and Potential Implications for Brazil

COVID-19 Fact Base and Potential Implications for Brazil

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