current situation trends in key drivers of transmission (mobility, … · 2020. 10. 15. ·...

United States Model updates for October 15, 2020

covid.healthdata.org Institute for Health Metrics and Evaluation

Daily cases have begun to increase and transmission has intensified in the northern half of the United States. We expect deaths to stop declining and begin increasing in the next 1–2 weeks. The winter surge appears to have begun somewhat later than the surge in Europe. Daily deaths will reach over 2,000 a day in January even with many states re-imposing mandates before the end of the year. Expanding mask use remains the best strategy to delay and reduce the magnitude of the surge.

Current situation

• Daily cases now appear to be rising, reaching 45,000 a day, up from 40,000 last week (Figure 1). • Despite the increase in cases, deaths have remained constant at 680 a day (Figure 2). • Effective R computed based on cases, hospitalizations, and deaths is above 1 across most of the northern half of

the continental states, representing a larger range of states than last week (Figure 3). • The daily death rate is over 4 per million in North Dakota, South Dakota, Arkansas, Tennessee, and Florida

(Figure 6). North Dakota presently has one of the highest COVID-19 death rates in the world.

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

• Florida and Indiana removed any business closures; all other mandates remained the same (Figure 7). • Mobility in the last week has remained constant. The vast majority of states, including many with effective R

over 1, have mobility levels within 10% of the pre-COVID-19 baseline (Figure 8). • Mask use has remained constant over the last week. North Dakota and South Dakota, with the highest death

rates in the country, have the lowest mask use (Figure 9). • Diagnostic tests continue to increase slowly (Figure 10).

Projections

• Daily deaths in our reference scenario, the scenario that we think is most likely to occur, will peak in mid-January due to the winter surge at around 2,200 a day (Figure 13).

• Cumulative deaths by February 1 are expected to reach 389,000 (Figure 12). • Increasing mask use from 69%, the current US average, to 95%, the level seen in Singapore, could save 74,000

lives (Figure 12). • We expect in the reference scenario that many Midwestern states will have to re-impose mandates before the

end of this year (Figure 15). • Figure 18 compares our model forecasts to other publicly archived forecasts. While this week our forecasts have

not changed substantially, other models have revised their forecasts up substantially. The USC (SIKJalpha) model now shows a winter surge, but substantially smaller than in our model. The Imperial forecasts have not been revised upward but still show declines through into January in daily deaths. The MIT (Delphi) model has also been revised upward but still suggests a steady decline into mid-December. The Los Alamos National Labs model continues to show a brisk decline in daily deaths through to the end of November.

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

United States Model updates for October 15, 2020

covid.healthdata.org Institute for Health Metrics and Evaluation

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/acknowledgements

http://www.healthdata.org/covid

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

United States of America MODEL UPDATES

COVID-19 Results Briefing: United States of America

Institute for Health Metrics and Evaluation (IHME)

October 15, 2020

This briefing contains summary information on the latest projections from the IHME model on COVID-19 inUnited States of America. 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

United States of America 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 RankingIschemic heart disease 10,724 1COVID-19 4,771 2Tracheal, bronchus, and lung cancer 3,965 3Chronic obstructive pulmonary disease 3,766 4Stroke 3,643 5Alzheimer’s disease and other dementias 2,768 6Chronic kidney disease 2,057 7Colon and rectum cancer 1,616 8Lower respiratory infections 1,575 9Diabetes mellitus 1,495 10

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

0

1,000

2,000

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

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 October 01, 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.

<0.85

0.85−0.88

0.89−0.92

0.93−0.95

0.96−0.99

1−1.03

1.04−1.06

1.07−1.1

1.11−1.14

>=1.15



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

<1

1−3.9

4−6.9

7−9.9

10−13.4

13.5−16.4

16.5−19.4

19.5−22.4

22.5−25.4

>=25.5

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

20

40

Mar Apr May Jun Jul Aug Sep Oct

Per

cent

of i

nfec

tions

det

ecte

d

Republic of Korea Germany Italy United Kingdom United States of America



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


United States of America 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

WyomingWisconsin

West VirginiaWashington

VirginiaVermont

UtahTexas

TennesseeSouth Dakota

South CarolinaRhode IslandPennsylvania

OregonOklahoma

OhioNorth Dakota

North CarolinaNew York

New MexicoNew Jersey

New HampshireNevada

NebraskaMontanaMissouri

MississippiMinnesota

MichiganMassachusetts

MarylandMaine

LouisianaKentucky

KansasIowa

IndianaIllinoisIdaho

HawaiiGeorgiaFlorida

District of ColumbiaDelaware

ConnecticutColoradoCaliforniaArkansas

ArizonaAlaska

Alabama

Mandate in place No mandate



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

WyomingWisconsin

West VirginiaWashington

VirginiaVermont

UtahTexas

TennesseeSouth Dakota

South CarolinaRhode IslandPennsylvania

OregonOklahoma

OhioNorth Dakota

North CarolinaNew York

New MexicoNew Jersey

New HampshireNevada

NebraskaMontanaMissouri

MississippiMinnesota

MichiganMassachusetts

MarylandMaine

LouisianaKentucky

KansasIowa

IndianaIllinoisIdaho

HawaiiGeorgiaFlorida

District of ColumbiaDelaware

ConnecticutColoradoCaliforniaArkansas

ArizonaAlaska

Alabama

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

=<−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

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

<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

100

200

300


Test

per

100

,000

pop

ulat

ion


Figure 10b. COVID-19 diagnostic tests per 100,000 people on October 07, 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%


United States of America 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

100,000

200,000

300,000

400,000

500,000

0

50

100

150

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

2,000

4,000

0.0

0.5

1.0

1.5


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

200,000

400,000

600,000

800,000

0

50

100

150

200


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

<1

1−3.9

4−6.9

7−9.9

10−13.4

13.5−16.4

16.5−19.4

19.5−22.4

22.5−25.4

>=25.5

Figure 17. Daily COVID-19 deaths per million forecasted on February 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.

500

1,000

1,500

2,000

2,500

Nov Dec Jan FebDate

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 557,600 1COVID-19 316,935 2Tracheal, bronchus, and lung cancer 206,200 3Chronic obstructive pulmonary disease 195,800 4Stroke 189,500 5Alzheimer’s disease and other dementias 143,900 6Chronic kidney disease 107,000 7Colon and rectum cancer 84,000 8Lower respiratory infections 81,900 9Diabetes mellitus 77,700 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/

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