current situation trends in key drivers of transmission ... · • the only country with a daily...

WHO EURO Region Model updates for October 2, 2020

covid.healthdata.org Institute for Health Metrics and Evaluation

The WHO European Region continues to record a steady increase in daily case counts. Over the past week, new daily infections have increased to nearly 60,000. Similarly, daily deaths have increased by 150 compared to the third week of September and hospitalizations are on the rise, especially in France, Spain, and the UK. Given the concerning surge in cases and the effect of seasonality, we estimate that deaths will increase to more than 10,500 per day by late December. Last week, COVID-19 proved to be the 7th-leading cause of death in the region. If mask wearing in the region were to increase to 95%, 256,000 lives could be saved.

Current situation

• The exponential surge in daily cases continues in Europe. Daily cases in the last week were close to 60,000, bringing the number close to the mid-April peak (Figure 1).

• Daily deaths have increased to 800 a day up from 675 a day last week (Figure 2). This increase brings COVID-19 up from the 8th to the 7th cause.

• Effective R computed on the basis of cases, hospitalizations and deaths is over 1 in a wide range of countries from Portugal to Greece to Norway to Ireland to the Russian Federation (Figure 3). The fall/winter surge is unfolding throughout the region.

• The only country with a daily death rate over 4 per million is Montenegro (Figure 6).

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

• Three countries continue to have five mandates in place: Azerbaijan, Bulgaria, and Uzbekistan. At the other end of the spectrum, Estonia, Iceland, and Sweden have only one mandate in place. Another 12 countries have only two mandates in place (Figure 7).

• Mobility for the region has remained largely constant just less than 10% below the pre-COVID-19 baseline (Figure 8). Only one country, Ireland, has mobility levels less than 30% below the pre-COVID-19 baseline.

• Mask use in the region has risen slowly since a low in early July such that average use is just over 50%. A North-South gradient still exists with low mask use in Northern and Eastern Europe with the exceptions of Hungary, Romania and Moldova (Figure 9).

• Testing rates continue to steadily increase in the region reaching 170 per 100,000. (Figure 10).

Projections

• In our reference scenario, what we think is most likely to occur, we expect daily deaths to reach approximately 10,500 per day in late December. (Figure 13). Cumulative deaths by January 1 are expected to reach 725,000 by the end of the year.

• If all countries in the region pursued a herd immunity strategy, namely no further action by governments as the winter surge unfolds, the death toll could be nearly double reaching 1.4 million deaths (Figure 12).

• Expanding mask use to the level achieved in Singapore provides an opportunity to reduce the cumulative death toll to 468 by January 1. In other words, expanded mask use can save 256,000 deaths by the end of the year (Figure 12).

• Most countries in the region will need to re-impose mandates in November or December to control the epidemic (Figure 15).

• In Figure 18, we compare our forecasts for the region with other publicly archived models. Two models, the MIT (Delphi) and Los Alamos National Labs models suggest deaths will drop to an extremely low level in November and December. The YYG and USC (SIKJalpha) model have relatively constant numbers until the end of the year. Imperial suggest the largest epidemic with deaths reaching over 12,500 a day by mid-December. The wide range



of mid-term forecasts across these models depends critically on whether seasonality is included in the model and how much the forecast is driven by the trend in cases versus the trend in deaths.

Model updates

Predictive validity

We have conducted an 8-week out-of-sample predictive validity experiment where we have tested a large number of parameterizations of the model for each location to identify what have been in the last 8 weeks likely rates of importation of infections. This rate is governed by a parameter in our SEIIR model. We have used the out-of-sample predictive validity testing to select this parameter for each state.

Social distancing mandates

In our model to date, we have re-imposed social distancing mandates when the daily death rate reaches 8 per million. This re-imposition has been implanted across the pool of 1,000 models we use for each location using the day the mean across draws reaches 8 per million. To improve accuracy and to better reflect uncertainty on when re-imposition of mandates may occur, we have in this iteration of the model re-imposed mandates at the draw level. In other words, in one of the 1,000 models for each location, when a particular model predicts the daily death rate reaches 8 per million, we re-impose on that particular trajectory the mandates at that point. This means that in aggregate, mandate re-imposition is spread across a range of dates. This shift to using draw level mandate re-imposition has a tendency to decrease our predicted cumulative deaths.

Herd immunity

Given considerable public discussion of the role of herd immunity in explaining peaks and subsequent declines in the daily death and case rate, we have explored the implied total death rate for each country based on the infection-fatality rate (IFR) and different assumptions about the level of cumulative infection that will be associated with herd immunity. The natural experiment of the Charles de Gaulle aircraft carrier suggests that up to 70% of individuals can be infected in a situation of near-random mixing. But various theories, including the role of super-spreaders, nonrandom mixing in less dense populations, non-overlapping social networks, and some prior coronavirus immunity, have led to theories that herd immunity may take place at much lower levels of cumulative infection, such as 40% to 60%. Our IFR, based on the analysis of seroprevalence data and herd immunity at 40% cumulative infection, would suggest will eventually see 10,400,000 deaths globally; with herd immunity at 50% cumulative infection, the figure would be 13,100,000 deaths, and at 60% it would be 15,700,000 deaths. Scale-up of a vaccine or improved treatments could substantially reduce these figures. These calculations only serve to suggest that the epidemic is far from complete. In fact, a recent study in Manaus Brazil showed that seroprevalence range between 44% and 66%. The lower estimate of 44% does not account for false-negative cases or antibody waning observed while the upper estimate accounts for both. Therefore, herd immunity is not occurring at low levels of infections and we need to be vigilant until we have an effective and safe vaccine.

Seasonality

Our projections to January 1 take into account the seasonality of COVID-19. The large increase in daily deaths expected in late November and December is driven by continued increases in mobility and declines in mask use, but most importantly by seasonality. We estimate the likely impact of seasonality by examining the trends in the Northern and Southern Hemispheres. For example, Southern Hemisphere countries such as Argentina, Chile, southern Brazil, and South Africa had much larger epidemics than expected on the basis of mobility, testing, and mask use during their winter



months. The statistical association between COVID-19 transmission rates and pneumonia seasonality patterns is strong in our data and is the basis for our estimate of the magnitude of the seasonal increase that is expected.

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/acknowledgements

http://www.healthdata.org/covid

https://www.healthdata.org/covid/contact-us

European Region MODEL UPDATES

COVID-19 Results Briefing: the European Region

Institute for Health Metrics and Evaluation (IHME)

October 02, 2020

This briefing contains summary i nformation on t he l atest projections f rom t he IHME model on COVID-19 in the European Region. The model was run on October 01, 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

European Region CURRENT SITUATION

Current situation

Figure 1. Reported daily COVID-19 cases

0

20,000

40,000

60,000

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 RankingIschemic heart disease 44,253 1Stroke 22,622 2Tracheal, bronchus, and lung cancer 8,918 3Alzheimer’s disease and other dementias 8,022 4Chronic obstructive pulmonary disease 6,719 5Colon and rectum cancer 5,881 6COVID-19 5,637 7Lower respiratory infections 5,254 8Cirrhosis and other chronic liver diseases 4,290 9Hypertensive heart disease 3,949 10

Figure 2a. Reported daily COVID-19 deaths and smoothed trend estimate. Points shown are reporteddeaths, line and ribbon represent estimate with uncertainty.

0

2,000

4,000

6,000

Apr May Jun Jul Aug Sep Oct

Dai

ly d

eath

s



Figure 2b. Estimated cumulative deaths by age group

0

5

10

15

<5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 99Age group

Sha

re o

f cum

ulat

ive

deat

hs, %

Figure 3. Mean effective R on September 17, 2020. The estimate of effective R is based on the combinedanalysis of deaths, case reporting and hospitalizations where available. Current reported cases reflect infections11-13 days prior so estimates of effective R can only be made for the recent past. Effective R less than 1means that transmission should decline all other things being held the same.

<0.81

0.81−0.84

0.85−0.89

0.9−0.94

0.95−0.99

1−1.04

1.05−1.09

1.1−1.14

1.15−1.18

>=1.19



Figure 4. Estimated percent of the population infected with COVID-19 on September 28, 2020

<0.5

0.5−1.9

2−3.4

3.5−5.4

5.5−6.9

7−8.4

8.5−10.4

10.5−11.9

12−13.4

>=13.5

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

Feb Mar Apr May Jun Jul Aug Sep Oct

Per

cent

of i

nfec

tions

det

ecte

d

African Region

Region of the Americas

South−East Asia Region

European Region

Eastern Mediterranean Region

Western Pacific Region



Figure 6. Daily COVID-19 death rate per 1 million on September 28, 2020

<1

1 to 1.9

2 to 2.9

3 to 3.9

4 to 4.9

5 to 5.9

6 to 6.9

7 to 7.9

>=8


European Region 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

UzbekistanUnited Kingdom

UkraineTurkmenistan

TurkeyTajikistan

SwitzerlandSweden

SpainSloveniaSlovakia

SerbiaRussian Federation

RomaniaRepublic of Moldova

PortugalPoland

NorwayNorth Macedonia

NetherlandsMontenegro

MaltaLuxembourg

LithuaniaLatvia

KyrgyzstanKazakhstan

ItalyIsrael

IrelandIceland

HungaryGreece

GermanyGeorgiaFranceFinlandEstonia

DenmarkCzechiaCyprusCroatia

BulgariaBosnia and Herzegovina

BelgiumBelarus

AzerbaijanAustria

ArmeniaAlbania

Mandate in place No mandate



Figure 7. Total number of social distancing mandates (not including mask use)

UzbekistanUnited Kingdom

UkraineTurkmenistan

TurkeyTajikistan

SwitzerlandSweden

SpainSloveniaSlovakia

SerbiaRussian Federation

RomaniaRepublic of Moldova

PortugalPoland

NorwayNorth Macedonia

NetherlandsMontenegro

MaltaLuxembourg

LithuaniaLatvia

KyrgyzstanKazakhstan

ItalyIsrael

IrelandIceland

HungaryGreece

GermanyGeorgiaFranceFinlandEstonia

DenmarkCzechiaCyprusCroatia

BulgariaBosnia and Herzegovina

BelgiumBelarus

AzerbaijanAustria

ArmeniaAlbania

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

−50

−25

0

Jan Feb Mar Apr May Jun Jul Aug Sep Oct

Per

cent

red

uctio

n fr

om a

vera

ge m

obili

ty

African Region



European Region



Figure 8b. Mobility level as measured through smartphone app use compared to January 2020 baseline(percent) on September 28, 2020

=<−50

−49 to −45

−44 to −40

−39 to −35

−34 to −30

−29 to −25

−24 to −20

−19 to −15

−14 to −10

>−10



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

0

20

40

60


Per

cent

of p

opul

atio

n

African Region



European Region



Figure 9b. Proportion of the population reporting always wearing a mask when leaving home on September28, 2020

<30%

30 to 34%

35 to 39%

40 to 44%

45 to 49%

50 to 54%

55 to 59%

60 to 64%

65 to 69%

>=70



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

0

50

100

150


Test

per

100

,000

pop

ulat

ion

African Region



European Region



Figure 10b. COVID-19 diagnostic tests per 100,000 people on September 25, 2020

<5

5 to 9.9

10 to 24.9

25 to 49

50 to 149

150 to 249

250 to 349

350 to 449

450 to 499

>=500



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

<−80%

−80 to −61%

−60 to −41%

−40 to −21%

−20 to −1%

0 to 19%

20 to 39%

40 to 59%

60 to 79%

>=80%


European Region 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 January 01, 2021 for three scenarios.

0

500,000

1,000,000

0

50

100

150

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan

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 January 01, 2021 for three scenarios.

0

10,000

20,000

30,000

40,000

0

1

2

3

4

5


Dai

ly d

eath

sD

aily deaths per 100,000


Reference scenario

Universal mask use



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

0

2,000,000

4,000,000

6,000,000

0

200

400

600


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.)

September

October

November

DecemberNo mandates before Jan 1



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

<0.5

0.5−1.9

2−3.4

3.5−5.4

5.5−6.9

7−8.4

8.5−10.4

10.5−11.9

12−13.4

>=13.5

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

<1

1 to 1.9

2 to 2.9

3 to 3.9

4 to 4.9

5 to 5.9

6 to 6.9

7 to 7.9

>=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

5,000

10,000

Oct Nov Dec JanDate

Dai

ly d

eath

s

Models

IHME

Delphi

Imperial

LANL

SIKJalpha

YYG


https://www.covidanalytics.io/home

https://www.covidsim.org

https://covid-19.bsvgateway.org/

https://github.com/scc-usc/ReCOVER-COVID-19

https://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 RankingIschemic heart disease 2,301,100 1Stroke 1,176,300 2COVID-19 734,809 3Tracheal, bronchus, and lung cancer 463,800 4Alzheimer’s disease and other dementias 417,200 5Chronic obstructive pulmonary disease 349,400 6Colon and rectum cancer 305,800 7Lower respiratory infections 273,200 8Cirrhosis and other chronic liver diseases 223,100 9Hypertensive heart disease 205,400 10

Mask data source: Premise; Facebook Global symptom survey (This research is based on survey resultsfrom University of Maryland Social Data Science Center); Kaiser Family Foundation; YouGov COVID-19Behaviour Tracker 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/

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