portuguÊs situação atual · 2020. 10. 17. · situação atual • os casos confirmados...

Brazil Model updates for October 15, 2020

covid.healthdata.org Institute for Health Metrics and Evaluation

PORTUGUÊS

Durante a segunda semana de outubro, os casos positivos permaneceram estáveis em 26.000 por dia após um declínio gradual ao longo de vários meses. As mortes diárias, agora caindo para 580 por dia, continuaram a tendência de queda que tem sido evidente nas últimas semanas. Além disso, o R efetivo médio caiu abaixo de 1 em todos os estados, indicando uma desaceleração adicional da propagação de COVID-19. Projetamos que o Brasil terá um total de 183.000 mortes até 1 de fevereiro de 2021. No entanto, se o uso de máscaras se generalizar, 9.750 vidas serão salvas até 1 de fevereiro de 2021.

Situação atual

• Os casos confirmados diariamente se estabilizaram e eram cerca de 20.000 em nível nacional em 14 de outubro (Figura 1).

• As mortes diárias continuam diminuindo e estão em 570 nacionalmente (Figura 2a). No entanto, apesar da redução, COVID-19 continua sendo a principal causa de morte no país (Tabela 1).

• Estimativas de mortes cumulativas no Brasil por COVID-19 em 14 de outubro indicam que 50% delas ocorrem em pessoas entre 60 e 84 anos (Figura 2b).

• O R efetivo médio em 1 de Outubro - com base na análise combinada de casos, hospitalizações e óbitos - agora está abaixo de 1 em todos os estados; entretanto, em Algoas e Sergipe, os valores estão muito próximos de 1, entre 0,96 e 0,99 (Figura 3).

• Em 12 de outubro, 16 estados tinham mais de 20,5% da população infectada, com as maiores concentrações nos estados da Amazônia: São Paulo, Goiás, Tocantins e Pernambuco (20,5–25,9%); Ceará, Espírito Santo, Pará, Rondônia, Rio de Janeiro, Amazonas e Acre (26–1,4%); Amapá, Mato Grosso, Sergipe (31,5–36,9%); Distrito Federal (37–42,4%); e Roraima (48–53,4%).

• O número de casos positivos detectados nacionalmente representa 11% da população infectada (Figura 5). • A maioria dos estados tem taxas de mortalidade diárias abaixo de 4 por 1 milhão. A taxa de mortalidade é um

pouco maior em Mato Grosso (4,7 por 1 milhão), Mato Grosso do Sul, (4,6 por 1 milhão,) e no Distrito Federal (4,4 por 1 milhão). As taxas de mortalidade são mais altas em Goiás (7,2 por 1 milhão) e Roraima (6,8 por 1 milhão) (Figura 6).

• Tendências nos principais fatores de transmissão (mobilidade, uso de máscara, teste e sazonalidade) • Poucos estados têm mais de três mandatos em vigor enquanto os estados continuam a reabrir suas economias.

Apenas Amapá, Ceará, Mato Grosso e Rio de Janeiro têm pedido para ficar em casa. Todos os estados, exceto Pernambuco, restringem reuniões, mas apenas Pará e Rio Grande do Norte restringem viagens.

Tendências nos principais fatores de transmissão (mobilidade, uso de máscara, teste e sazonalidade)

• O Rio de Janeiro continua sendo o único estado que possui mandato de máscara (Tabela 2). Desde o início de junho, quando o uso da máscara atingiu o pico de 76% nacionalmente, o uso da máscara diminuiu lentamente, para cerca de 64%. Países semelhantes na América Latina continuam a ter taxas constantemente altas de uso de máscara, em torno de 80% (Figura 9a).

• A mobilidade, embora abaixo da linha de base de janeiro de 2020 (-18%), está entre as mais altas da América Latina, perdendo apenas para a Colômbia (Figura 8a).

• Os testes continuam abaixo da média para a América Latina, com 30 testes por 100.000 habitantes (Figura 10a).

Projeções



• Com as extensões da previsão, agora projetamos um número cumulativo de mortes de 183.000 até 1º de fevereiro. Aumentar o uso de máscaras para 95% poderia salvar 9.750 vidas em comparação com o cenário de linha de base. (Figura 12).

• De 14 de outubro a 1º de fevereiro, projetamos que um total de mais 31,000 vidas serão perdidas (Figura 12). • Projetamos que as mortes diárias diminuirão continuamente até outubro. Em 31 de outubro, esperamos que o

número de mortes diárias seja 498, abaixo dos 580de hoje. Em 1° de fevereiro, prevemos que as mortes diárias cheguem a tão baixo quanto 25 na melhor das hipóteses, ou 101 na pior das hipóteses (Figura 13).

• Em uma mudança em relação à semana anterior, agora projetamos que nenhum estado irá superar a taxa de mortalidade diária de 8 por milhão até 1º de fevereiro (Figura 15).

• A maioria dos estados terá mais de 20% da população infectada até 1º de fevereiro, com exceção do Rio Grande do Sul, Santa Catarina, Paraná e Minas Gerais. Roraima vai ultrapassar 50%.

• A taxa de mortalidade diária cairá para menos de 2 por milhão na maior parte do país até 1º de fevereiro; a maioria dos estados experimentará taxas de mortalidade abaixo de 1 por milhão. Roraima e Amapá terão uma taxa de mortalidade diária entre 2% e 2,9%.

• Apresentamos uma comparação de nossas previsões com outros modelos que fornecem atualizações periódicas e arquivam seus resultados. Ao comparar as previsões anteriores com o que aconteceu, nosso modelo tem o menor erro percentual médio para mortes cumulativas. Nos EUA, conforme mostrado na Figura 18, nossas previsões são bastante diferentes. Outros modelos importantes sugerem que a epidemia está chegando ao fim, com queda nas mortes diárias até o final do ano. Encontramos uma forte correlação estatística do R efetivo nos últimos seis meses com o padrão semanal de mortes por pneumonia visto em cada país ou estado. Essa variável de sazonalidade, combinada com nossa previsão de que os mandatos continuarão a diminuir durante o outono, é responsável por nossas previsões marcadamente diferentes.

Atualizações de modelo

Não há grandes atualizações em nosso modelo esta semana. Continuamos buscando evidências sobre se a taxa de mortalidade por infecção (IFR) mudou durante a pandemia. Há uma clara mudança para idades mais jovens nos casos diagnosticados. Essa mudança por si só - devido à dependência da IFR com a idade - reduziria a IFR para todas as idades, mesmo que os tratamentos não tenham melhorado. No entanto, a mudança na distribuição de idade dos casos confirmados pode ser devido ao aumento da capacidade de teste. A análise em andamento de dados sobre tratamentos clínicos individuais e desfechos pode fornecer uma medida mais direta de se a IFR por idade mudou. Se a IFR diminuiu, isso alteraria nossas taxas de mortalidade previstas; até o momento, entretanto, não conseguimos encontrar evidências suficientes para apoiar essa mudança em nosso modelo.



ENGLISH

During the second week of October, positive cases remained stable at 26,000 per day after a gradual decline over several months. Daily deaths, now down to 580 per day, continued the downward trend that has been evident over the past several weeks. Additionally, the mean effective R has dropped below 1 in all states, indicating a further slowing of the spread of COVID-19. We project that Brazil will see a total of 183,000 deaths by February 1, 2021. However, if the use of face masks becomes widespread, 9,750 lives would be saved by February 1, 2021.

Current situation

• Daily confirmed cases have stabilized and are around 20,000 nationally as of October 14 (Figure 1). • Daily deaths continue to decline and are at 570 nationally (Figure 2a). However, despite the decrease, COVID-19

remains the leading cause of death in the nation (Table 1). • Estimates of cumulative deaths in Brazil from COVID-19 as of October 14 indicate that 50% of them occur in

people between the ages of 60 and 84 (Figure 2b). • The mean effective R as of October 1 – based on the combined analysis of cases, hospitalizations, and deaths – is

now below 1 in all states; however, in Alagoas and Sergipe, the values are very close to 1, between 0.96 and 0.99 (Figure 3).

• As of October 12, 16 states had more than 20.5% of the population infected, with the highest concentrations in the western and northern states: São Paulo, Goiás, Tocantins, and Pernambuco (20.5–25.9%); Ceará, Espírito Santo, Pará, Rondônia, Rio de Janeiro, Amazonas, and Acre (26–1.4%); Amapá, Mato Grosso, Sergipe (31.5–36.9%); Federal District (37–42.4%); and Roraima (48–53.4%).

• The number of positive cases detected nationally represents 11% of the infected population (Figure 5). • Most states have daily mortality rates below 4 per 1 million. The death rate is slightly higher in Mato Grosso (4.7

per 1 million), Mato Grosso do Sul, (4.6 per 1 million), and the Federal District (4.4 per 1 million). Mortality rates are highest in Goiás (7.2 per 1 million) and Roraima (6.8 per 1 million) (Figure 6).

Trends in key drivers of transmission (mobility, mask use, testing, and seasonality)

• Rio de Janeiro remains the only state that has a mask mandate (Table 2). Since early June, when mask usage peaked at 76% nationally, mask usage has slowly declined, to about 64%. Peer countries in Latin America continue to have steadily high rates of mask usage, around 80% (Figure 9a).

• Mobility, while below the January 2020 baseline (-18%), is among the highest for Latin America, second only to Colombia (Figure 8a).

• Testing continues to be below average for Latin America, at 30 tests per 100,000 population (Figure 10a).

Projections

• We project a cumulative death toll of 183,000 by February 1. Increasing the use of masks to 95% could save 9,750 lives compared to the baseline scenario (Figure 12).

• From October 14 to February 1, we project that a total of 31,000 more lives will be lost (Figure 12). • We project that daily deaths will decline steadily through October. By October 31, we expect that the number of

daily deaths will be 498, down from 580 today. By February 1, we anticipate daily deaths to reach as low as 25 in the best-case scenario, or 101 in the worst-case scenario (Figure 13).

• In a change from the previous week, we now do not project that any state will surpass a daily mortality rate of 8 per million by February 1 (Figure 15).



• Most states will have more than 20% of the population infected by February 1, with the exceptions of Rio Grande do Sul, Santa Catarina, Paraná, and Minas Gerais. Roraima will exceed 50%.

• The daily mortality rate will fall below 2 per million for most of the country by February 1; most states will experience mortality rates below 1 per million. Roraima and Amapá will experience a daily mortality rate between 2% and 2.9%.

• We offer a comparison of our forecasts with other models that provide periodic updates and archive their results. When comparing past forecasts with what has happened, our model has the lowest average percentage error for cumulative deaths. In the US, as shown in Figure 18, our forecasts are markedly different. Other important models suggest that the epidemic is coming to an end, with a decline in daily deaths through the end of the year. We found a strong statistical correlation of effective R in the last six months with the weekly pattern of pneumonia deaths seen in each country or state. This seasonality variable, combined with our prediction that mandates will continue to decline during the fall, accounts for our markedly different forecasts.

Model updates

There are no major updates to our model this week. We continue to search for evidence on whether the infection-fatality rate (IFR) has changed during the pandemic. There is a clear shift to younger ages in diagnosed cases. This shift alone would – because of the age dependence of the IFR – reduce the all-age IFR even if treatments have not improved. However, the shift in the age distribution of confirmed cases may be due to the scale-up in testing capacity. Analysis underway of data on individual clinical treatments and outcomes may provide a more direct measure of whether the IFR by age has changed. If the IFR has declined, this would alter our forecasted death rates; to date, however, we have not been able to find sufficient evidence to support this change to our model.

IHME wishes to warmly acknowledge the support of these and others who have made our COVID-19 estimation efforts possible. Thank you.

For all COVID-19 resources at IHME, visit http://www.healthdata.org/covid.

Questions? Requests? Feedback? Please contact us at https://www.healthdata.org/covid/contact-us.

http://www.healthdata.org/covid/acknowledgementshttp://www.healthdata.org/covidhttps://www.healthdata.org/covid/contact-us

Brazil MODEL UPDATES

COVID-19 Results Briefing: Brazil

Institute for Health Metrics and Evaluation (IHME)

October 15, 2020

This briefing contains summary i nformation on t he l atest projections f rom t he IHME model on COVID-19 in Brazil. The model was run on October 13, 2020.

Model updates

Updates to the model this week include additional data on deaths, cases, and updates on covariates.

covid19.healthdata.org 1 Institute for Health Metrics and Evaluation

Brazil CURRENT SITUATION

Current situation

Figure 1. Reported daily COVID-19 cases

0

20,000

40,000

60,000

Feb Mar Apr May Jun Jul Aug Sep OctMonth

Cou

nt

Daily cases



Table 1. Ranking of COVID-19 among the leading causes of mortality this week, assuming uniform deathsof non-COVID causes throughout the year

Cause name Weekly deaths RankingCOVID-19 4,114 1Ischemic heart disease 3,293 2Stroke 2,519 3Lower respiratory infections 1,705 4Chronic obstructive pulmonary disease 1,321 5Interpersonal violence 1,267 6Diabetes mellitus 1,257 7Alzheimer’s disease and other dementias 1,050 8Road injuries 856 9Chronic kidney disease 814 10

Figure 2a. Reported daily COVID-19 deaths.

0

500

1,000

1,500

Feb Mar Apr May Jun Jul Aug Sep Oct

Dai

ly d

eath

s



Figure 2b. Estimated cumulative deaths by age group

0

5

10


Figure 4. Estimated percent of the population infected with COVID-19 on October 12, 2020

=50.5

Figure 5. Percent of COVID-19 infections detected. This is estimated as the ratio of reported COVID-19cases to estimated COVID-19 infections based on the SEIR disease transmission model.

0

5

10

15

20

25

Mar Apr May Jun Jul Aug Sep Oct

Per

cent

of i

nfec

tions

det

ecte

d

Argentina United States of America Colombia Mexico Brazil



Figure 6. Daily COVID-19 death rate per 1 million on October 12, 2020

=8


Brazil CRITICAL DRIVERS

Critical drivers

Table 2. Current mandate implementation

All

gath

erin

gs r

estr

icte

d

All

none

ssen

tial b

usin

esse

s cl

osed

Any

bus

ines

ses

rest

ricte

d

Mas

k us

e

Sch

ool c

losu

re

Sta

y ho

me

orde

r

Trav

el li

mits

Tocantins

São Paulo

Sergipe

Santa Catarina

Roraima

Rondônia

Rio de Janeiro

Rio Grande do Sul

Rio Grande do Norte

Piaui

Pernambuco

Pará

Paraíba

Paraná

Minas Gerais

Mato Grosso do Sul

Mato Grosso

Maranhão

Goiás

Espírito Santo

Distrito Federal

Ceará

Bahia

Amazonas

Amapá

Alagoas

Acre

Mandate in place No mandate



Figure 7. Total number of social distancing mandates (including mask use)

Tocantins

São Paulo

Sergipe

Santa Catarina

Roraima

Rondônia

Rio de Janeiro

Rio Grande do Sul

Rio Grande do Norte

Piaui

Pernambuco

Pará

Paraíba

Paraná

Minas Gerais

Mato Grosso do Sul

Mato Grosso

Maranhão

Goiás

Espírito Santo

Distrito Federal

Ceará

Bahia

Amazonas

Amapá

Alagoas

Acre

Feb Mar Apr May Jun Jul Aug Sep Oct

# of mandates

0

1

2

3

4

5

6

7

Mandate imposition timing



Figure 8a. Trend in mobility as measured through smartphone app use compared to January 2020 baseline

−80

−60

−40

−20

0

Jan Feb Mar Apr May Jun Jul Aug Sep Oct

Per

cent

red

uctio

n fr

om a

vera

ge m

obili

ty


Figure 8b. Mobility level as measured through smartphone app use compared to January 2020 baseline(percent) on October 12, 2020

=−10



Figure 9a. Trend in the proportion of the population reporting always wearing a mask when leaving home

0

25

50

75


Per

cent

of p

opul

atio

n


Figure 9b. Proportion of the population reporting always wearing a mask when leaving home on October12, 2020

=85



Figure 10a. Trend in COVID-19 diagnostic tests per 100,000 people

0

100

200


Test

per

100

,000

pop

ulat

ion


Figure 10b. COVID-19 diagnostic tests per 100,000 people on October 01, 2020

=500



Figure 11. Increase in the risk of death due to pneumonia on February 1 compared to August 1

=80%


Brazil PROJECTIONS AND SCENARIOS

Projections and scenarios

We produce three scenarios when projecting COVID-19. The reference scenario is our forecast of what wethink is most likely to happen. We assume that if the daily mortality rate from COVID-19 reaches 8 permillion, social distancing (SD) mandates will be re-imposed. The mandate easing scenario is what wouldhappen if governments continue to ease social distancing mandates with no re-imposition. The universal maskmandate scenario is what would happen if mask use increased immediately to 95% and social distancingmandates were re-imposed at 8 deaths per million.

Figure 12. Cumulative COVID-19 deaths until February 01, 2021 for three scenarios.

0

50,000

100,000

150,000

0

20

40

60

80

Feb Apr Jun Aug Oct Dec Feb

Cum

ulat

ive

deat

hsC

umulative deaths per 100,000

Continued SD mandate easing

Reference scenario

Universal mask use

Fig 13. Daily COVID-19 deaths until February 01, 2021 for three scenarios.

0

300

600

900

0.0

0.1

0.2

0.3

0.4

0.5


Dai

ly d

eath

sD

aily deaths per 100,000


Reference scenario

Universal mask use



Fig 14. Daily COVID-19 infections until February 01, 2021 for three scenarios.

0

100,000

200,000

300,000

0

50

100


Dai

ly in

fect

ions

Daily infections per 100,000


Reference scenario

Universal mask use



Fig 15. Month of assumed mandate re-implementation. (Month when daily death rate passes 8 per million,when reference scenario model assumes mandates will be re-imposed.)

October

November

December

JanuaryNo mandates before Feb 1



Figure 16. Forecasted percent infected with COVID-19 on February 01, 2021

=50.5

Figure 17. Daily COVID-19 deaths per million forecasted on February 01, 2021 in the reference scenario

=8



Figure 18. Comparison of reference model projections with other COVID modeling groups. For thiscomparison, we are including projections of daily COVID-19 deaths from other modeling groups when avail-able: Delphi from the Massachussets Institute of Technology (Delphi; https://www.covidanalytics.io/home),Imperial College London (Imperial; https://www.covidsim.org), The Los Alamos National Laboratory(LANL; https://covid-19.bsvgateway.org/), the SI-KJalpha model from the University of Southern Cal-ifornia (SIKJalpha; https://github.com/scc-usc/ReCOVER-COVID-19), and Youyang Gu (YYG; https://covid19-projections.com/). Daily deaths from other modeling groups are smoothed to remove inconsistencieswith rounding. Regional values are aggregates from availble locations in that region.

0

200

400

600

Nov Dec Jan FebDate

Dai

ly d

eath

s

Models

IHME

Delphi

Imperial

LANL

SIKJalpha

YYG


https://www.covidanalytics.io/homehttps://www.covidsim.orghttps://covid-19.bsvgateway.org/https://github.com/scc-usc/ReCOVER-COVID-19https://covid19-projections.com/https://covid19-projections.com/


Table 3. Ranking of COVID-19 among the leading causes of mortality in the full year 2020. Deaths fromCOVID-19 are projections of cumulative deaths on Jan 1, 2021 from the reference scenario. Deaths fromother causes are from the Global Burden of Disease study 2019 (rounded to the nearest 100).

Cause name Annual deaths RankingCOVID-19 179,459 1Ischemic heart disease 171,200 2Stroke 131,000 3Lower respiratory infections 88,600 4Chronic obstructive pulmonary disease 68,700 5Interpersonal violence 65,900 6Diabetes mellitus 65,400 7Alzheimer’s disease and other dementias 54,600 8Road injuries 44,500 9Chronic kidney disease 42,300 10

Mask data source: Premise; Facebook Global symptom survey (This research is based on survey resultsfrom University of Maryland Social Data Science Center) and the Facebook United States symptom survey(in collaboration with Carnegie Mellon University); Kaiser Family Foundation; YouGov COVID-19 BehaviourTracker survey.

A note of thanks:

We would like to extend a special thanks to the Pan American Health Organization (PAHO) for keydata sources; our partners and collaborators in Argentina, Brazil, Bolivia, Chile, Colombia, Cuba, theDominican Republic, Ecuador, Egypt, Honduras, Israel, Japan, Malaysia, Mexico, Moldova, Panama, Peru,the Philippines, Russia, Serbia, South Korea, Turkey, and Ukraine for their support and expert advice; andto the tireless data collection and collation efforts of individuals and institutions throughout the world.

In addition, we wish to express our gratitude for efforts to collect social distancing policy information inLatin America to University of Miami Institute for Advanced Study of the Americas (Felicia Knaul, MichaelTouchton), with data published here: http://observcovid.miami.edu/; Fundación Mexicana para la Salud(Héctor Arreola-Ornelas) with support from the GDS Services International: Tómatelo a Pecho A.C.; andCentro de Investigaciones en Ciencias de la Salud, Universidad Anáhuac (Héctor Arreola-Ornelas); Lab onResearch, Ethics, Aging and Community-Health at Tufts University (REACH Lab) and the University ofMiami Institute for Advanced Study of the Americas (Thalia Porteny).

Further, IHME is grateful to the Microsoft AI for Health program for their support in hosting our COVID-19data visualizations on the Azure Cloud. We would like to also extend a warm thank you to the many otherswho have made our COVID-19 estimation efforts possible.


http://observcovid.miami.edu/

COVID-19 Results Briefing: BrazilInstitute for Health Metrics and Evaluation (IHME)October 16, 2020

Model updatesCurrent situationCritical driversProjections and scenarios

portuguÊs situação atual · 2020. 10. 17. · situação atual • os casos confirmados...

Documents