infection levels for us states: peak vs current peak infection rate … · 2020. 7. 23. · eye on...

EYE ON THE MARKET MICHAEL CEMBALEST J .P . MORGAN Last updated 7/23/2020

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1

[2] COVID-19 infection tracking, US hotspots and second waves of infection around the world

This section analyzes COVID infections reported to local, federal and intergovernmental agencies, and are heavily influenced by testing rates and differences in government reporting standards and capabilities. As we explain in Section 5, serology tests suggest that the true number of COVID infections may be 10x-20x higher than the number of reported infections. However, trends in reported infections are still important to monitor, since they influence government policy and the behavior of citizens and companies.

Highest levels of infection, global and US

On data sources. We generally use infection and mortality data from Johns Hopkins, with any exceptions noted in chart sources. While JHU data usually match sources such as covidtracking.com and Worldometers, this is not always the case. Any large differences usually work themselves out over time; even so, such differences are a warning against over-extrapolating any short term trends seen in the data. There are also patterns in some countries in which tests and infections drop over the weekend, only to rise the following week. Other anomalies: countries and US states sometimes make large one-time additions or subtractions to infections or deaths data to reflect over- or underestimations made over the course of the entire pandemic. JHU and other data providers do not amortize such adjustments over time and simply reflect them on the day they are made; we do the same.

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Peak infection rate per mm

Latest infection rate per mm

Infection levels for the largest 50 countries based on GDP, peak vs currentSorted by trailing 7 day average infection rate per mm people

Source: Johns Hopkins University, IMF, JPMAM. Countries shown represent 94% of World GDP. Asterisk denotes countries with infection levels over 225 per mm people. July 22, 2020

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Peak infection rate per mm

Latest infection rate per mm

Infection levels for US states: peak vs currentSorted by trailing 7 day average infection rate per mm people

Source: Johns Hopkins University, IMF, JPMAM. July 22, 2020

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EYE ON THE MARKET MICHAEL CEMBALEST J .P . MORGAN Coronavirus

2

Regional COVID monitors

What does a second infection wave look like? We scanned for countries whose infection rates (a) dropped sharply from prior levels, (b) showed a sustained equilibrium at lower levels, and then (c) rose again. We calibrated our model to pick up different kinds of second waves, so it sometimes flags countries that are not really a concern yet. The first chart shows second waves where the latest infection rates are above 20 per mm people, while the second chart shows second waves below 20 per mm people.

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0 50 100 150 200

Developed regions World Emerging regions

New daily infections per mm people [World]


# of days after total reported cases reaches 100

7 day trailing avg

US

Europe0

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0 50 100 150

New daily infections per mm people



7 day trailing avg

0

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120

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160

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0 50 100 150 200

S America E EuropeM East AfricaEM Asia

New daily infections per mm people [EM World]



7 day trailing avg

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Source: Johns Hopkins University, IMF, JPMAM. July 22, 2020.

25 most infected countriesNew daily infections per mm people, 7 day trailing avg

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ISR AZE SRB ROM BUL CRO

New daily infections per mm people [2nd waves, > 20]



10 day trailing avg

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GHA UZB PARAUS SWI NLDJPN

New daily infections per mm people [2nd waves, < 20]



10 day trailing avg





3

The Mess in the US (see note below for explanation of Hotspot states1)

1 We define infection hotspot states in two ways: the largest one-month increase in new daily infections per mm people, and states with the most persistent, rising infection rates per mm people.

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4/8 4/18 4/28 5/8 5/18 5/28 6/7 6/17 6/27 7/7 7/17

New daily infections (LHS)Cur Hosp (LHS)Daily deaths (RHS)

Hotspots: SC NC UT TX AR FL AL LA NV ID GACurrent hospitalizations and daily infections Daily deaths

Source: COVID Tracking Project, JPMAM. 07/22/2020. 7 day avgs.

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New daily infections (LHS)Cur Hosp (LHS)Daily deaths (RHS)

United States virus trackingCurrent hospitalizations and daily infections Daily deaths


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Positive testing rate (LHS)

Daily # of tests (RHS)

Hotspots: SC NC UT TX AR FL AL LA NV ID GAPositive tests/total tests Total daily tests (000's)


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4/8 4/18 4/28 5/8 5/18 5/28 6/7 6/17 6/27 7/7 7/17

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Positive testing rate (LHS)

Daily # of tests (RHS)

United States virus trackingPositive tests/total tests Total daily tests (000's)


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4/8 4/18 4/28 5/8 5/18 5/28 6/7 6/17 6/27 7/7 7/17

Hotspots: SC NC UT TX AR FL AL LA NV ID GADaily deaths as a % of current hospitalizations


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4/8 4/18 4/28 5/8 5/18 5/28 6/7 6/17 6/27 7/7 7/17

United States virus trackingDaily deaths as a % of current hospitalizations






4

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Hotspots Other States NY/NJ

US new daily infections per mm people



7 day trailing avg

Hot spots: SC NC UT TX AR FL AL LA NV ID GA 0

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1,000

42 62 82 102 122 142


US current hospitalizations per mm people

Source: COVID Tracking Project, IMF, JPMAM. July 22, 2020


7 day trailing avg

Hot spots: SC NC UT TX AR FL AL LA NV ID GA

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US daily deaths per mm people



7 day trailing avg

Hot spots: SC NC UT TX AR FL AL LA NV ID GA 0

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NY/NJ Hotspots Other States

US daily tests per mm people



7 day trailing avg

Hot spots: SC NC UT TX AR FL AL LA NV ID GA

Miami FL

Jacksonville FL

Phoenix AZTampa FLDallas TX

New York NY0

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US new daily infections per mm people [US cities]



7 day trailing avg

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Phoenix AZ Miami FLTampa FL Dallas TXJacksonville FL New York NY

US daily deaths per mm people [US cities]



7 day trailing avg





5

Hotspot states and changes in mobility There are no models we have seen which are accurately able to predict changes in COVID infections or deaths as a function of any weather, time, demographic or behavioral variables; other than age and co-morbidity conditions such as obesity and heart disease, COVID is a very idiosyncratic disease. That said, it is notable that many hotspot states show the lowest peak changes in mobility since the onset of the virus. In other words, on this empirical basis, people in many hotspot states did not modify their social distancing interactions by nearly the same degree as in other states.

State reopening charts

The charts on the following page show 7-day infection trends per mm people with the % of positive tests, current hospitalizations per mm people and current hospital utilization rates. Line colors indicate severity based on our litmus tests that appear on the bottom of the next page. “Reopening date” is defined as the official end of stay-at-home orders; in some states the reopening was restricted to certain sectors, while in others it was more broad-based. The top 24 states shown represent ~83% of US GDP and population.

We used to show hospital bed and ICU bed utilization rates from the CDC, but had to remove them given a change in White House policy on virus data and reporting. The Trump Administration has stripped the CDC of its responsibility and ability to monitor and report hospitalization data. Hospitals have been ordered to bypass the CDC and send all patient information to a central database in Washington that is not open to the public, raising a lot of questions about transparency (and our ongoing ability to produce certain analyses; thank goodness for the independence of US states). This is the same Administration that removed references to climate change and GHG from EPA and Dep’t of Energy websites, removed information about the Affordable Care Act from HHS websites and removed access to Navy safety data at the Dep't of Defense.

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-10%

0%

Hotspot states Other states

Peak declines in mobility data

Source: Google, JPMAM. Jul 10, 2020.

% change from baseline: retail, restaurants and recreation locations, Feb 2020 to present





6

24 largest states: reopening dates, infections and hospitalizations

Tests: Daily infection test: < 25 per mm, 15% decline over prior 2 weeks and positive test rate < 5%; Hospitalization test: < 50/100 per mm, 15% decline. Sources: COVID Tracking Project, Census Bureau, Ballotpedia, JPMAM, 2020.

Reopening

50%14% 10% 7% 5% 4%

5% 5%5%

7%

8%

7%

0

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0

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4/1 4/11 4/21 5/1 5/11 5/21 5/31 6/10 6/20 6/30 7/10 7/20

<---New infections per mm, with % of positive cases

Active hospitalizations per mm --->

California

Reopening

3% 9%11% 11% 6% 6% 6%

5%7%

8%

13%

14%

16%

15%

0

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0

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3/183/28 4/7 4/174/27 5/7 5/175/27 6/6 6/166/26 7/6 7/16



Texas

Reopening45%

36%

22%

11%

6%3%

2%1% 1% 1% 1%1%

0

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1,000

1,200

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4/8 4/18 4/28 5/8 5/18 5/28 6/7 6/17 6/27 7/7 7/17



New York

Reopening

10%11% 9% 6% 5% 4% 3% 4%

7%

14%

19%

18%19%

0

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0

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3/28 4/7 4/17 4/27 5/7 5/17 5/27 6/6 6/16 6/26 7/6 7/16



Florida

Reopening

22%

17% 14%

10%

6%

5%

3%3%

3%

3%

0

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0

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4/22 5/2 5/12 5/22 6/1 6/11 6/21 7/1 7/11 7/21



Illinois

Reopening

13%

23%

28%

20%

16%

10%

8%

5%4%

5%

5%

6%

0

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4/1 4/11 4/21 5/1 5/11 5/21 5/31 6/10 6/20 6/30 7/10 7/20



Pennsylvania

Reopening

6%

12%17% 12%

10% 6% 7%

4% 4%

5%

6%

5%6%

0

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3/28 4/7 4/17 4/27 5/7 5/17 5/27 6/6 6/16 6/26 7/6 7/16



Ohio

Reopening54% 52%

38%

8%

9%

4%

2%2% 1% 1% 1%

0

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1,000

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4/11 4/21 5/1 5/11 5/21 5/31 6/10 6/20 6/30 7/10 7/20



New Jersey

Reopening

12%23%

28%22% 12% 8%

4% 13% 7% 7%

11%

13%

15%15%

0

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0

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3/183/28 4/7 4/174/27 5/7 5/175/27 6/6 6/166/26 7/6 7/16



Georgia

Reopening

9%

7% 7% 6% 5% 5%6% 6% 3% 6%

6%

6%

5%

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3/29 4/8 4/18 4/28 5/8 5/18 5/28 6/7 6/17 6/27 7/7 7/17



Washington

Reopening

28%

26%

18%

13%

10%

7%

5%3% 2% 2%

2%

0

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600

0

50

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4/11 4/21 5/1 5/11 5/21 5/31 6/10 6/20 6/30 7/10 7/20



Massachusetts

Reopening

7%9% 11%

9% 6%6%

7%

7%

7%

8%7%

7%

0

20

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100

120

0

50

100

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250

4/1 4/11 4/21 5/1 5/11 5/21 5/31 6/10 6/20 6/30 7/10 7/20



North Carolina

Reopening

14%

21%

19%

23%13%

11%

9%

4% 4% 5%

6%6%

0

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0

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4/8 4/18 4/28 5/8 5/18 5/28 6/7 6/17 6/27 7/7 7/17



Virginia

Reopening

74%

40%26%

21% 12%

6%

5%

2%

2%1%

2%

2%

3%3%

0

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250

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0

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3/183/28 4/7 4/174/27 5/7 5/175/27 6/6 6/166/26 7/6 7/16



Michigan

Reopening

18%

25%

17%

22%

17% 11%

8%

6%

5%5%

5%

5%

0

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300

0

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4/8 4/18 4/28 5/8 5/18 5/28 6/7 6/17 6/27 7/7 7/17



Maryland

Reopening

9%

21%

23%

24%

19%

14%

11%7%

5%

4%

5%6%

7%

0

20

40

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120

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200

0

20

40

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100

120

3/213/314/104/204/305/105/205/30 6/9 6/196/29 7/9 7/19



Colorado

Reopening

7%3% 6%

13%

15%13%

10%

6%

3%

2%

3%4%

4%

0

20

40

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100

120

0

20

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100

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140

3/213/314/104/204/305/105/205/30 6/9 6/196/29 7/9 7/19



Minnesota

Reopening

15%

22% 16%

22%16%

10%

8%7% 6%

4%

6%

8%

8%

0

50

100

150

200

250

0

20

40

60

80

100

120

140

3/28 4/7 4/17 4/27 5/7 5/17 5/27 6/6 6/16 6/26 7/6 7/16



Indiana

Reopening

10%10% 7% 7%

5%3% 4%

5% 5%

7%

9%

8%

9%

0

50

100

150

200

250

0

50

100

150

200

250

300

350

400

3/213/314/104/204/305/105/205/30 6/9 6/196/29 7/9 7/19



Tennessee

Reopening

10% 9% 12% 14% 9% 7% 8%

11%

18%

23%

25%

23%

25%

0

50

100

150

200

250

300

350

400

450

500

0

100

200

300

400

500

600

3/28 4/7 4/17 4/27 5/7 5/17 5/27 6/6 6/16 6/26 7/6 7/16



Arizona

Reopening

7%

7%

12%11%

11%

10% 7%

5%

3%3%

4%

6%

7%8%

0

20

40

60

80

100

120

0

20

40

60

80

100

120

140

160

180

3/183/28 4/7 4/174/27 5/7 5/175/27 6/6 6/166/26 7/6 7/16



Wisconsin

Reopening

7%

11%14% 9%

8%

3%36%

3% 4%

6% 5%

6%

9%

0

20

40

60

80

100

120

140

160

180

0

20

40

60

80

100

120

140

160

3/28 4/7 4/17 4/27 5/7 5/17 5/27 6/6 6/16 6/26 7/6 7/16



Missouri

Reopening

37%

39%

18% 12%8%

4%2%

1% 1% 1% 1%0

100

200

300

400

500

600

0

50

100

150

200

250

300

350

4/13 4/23 5/3 5/13 5/23 6/2 6/12 6/22 7/2 7/12 7/22



Connecticut

Reopening

30%

9%

17% 6%6%

5%5%

5%7%

9%

10%

10%

0

50

100

150

200

250

300

350

400

450

500

0

50

100

150

200

250

300

350

400

450

500

4/8 4/18 4/28 5/8 5/18 5/28 6/7 6/17 6/27 7/7 7/17



Louisiana





7

Distribution of infections to date by age for select states

The table below illustrates the distribution of infections by age group for the 10 largest states by GDP for which frequently updated data is available. Many states do not publish a breakdown of infections by age on their state health pages. While this is not an exhaustive list, the table shows some level of consistency in the distribution of infections by age. Since the “Under 50” group represents the largest population, it is unsurprising that this age group is facing the greatest proportion of total infections. The table on the right shows the number of infections adjusted for population bracket size.

Testing rates for select countries

Distribution of COVID infections by age group

Under 50 50-64 65+

California 69% 19% 12%

Texas 63% 23% 14%

Florida 65% 20% 14%

Virginia 66% 20% 15%

Ohio 60% 20% 20%

Washington 65% 19% 16%

Massachusetts 50% 22% 28%

Michigan 51% 24% 26%

Colorado 64% 20% 17%

Wisconsin 70% 18% 13%

Source: State department of health. July 22, 2020.

Under 50 50-64 65+

California 10,864 11,043 8,166

Texas 922 1,372 1,109

Florida 19,607 18,201 11,860

Virginia 9,539 9,244 8,562

Ohio 6,405 6,752 7,549

Washington 6,175 6,151 6,387

Massachusetts 13,116 17,974 27,558

Michigan 6,173 8,695 10,912

Colorado 6,940 7,717 8,264

Wisconsin 8,661 6,687 5,521

Source: State department of health. July 22, 2020.

Cumulative COVID infections per mm in each age group

USITA

KOR0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

30 80 130

Positive tests: Positive test rate



7 day trailing avg





8

Infections in the Developed World (all charts scaled to 200 per mm people)

0

20

40

60

80

100

120

140

160

180

200

0 50 100 150

ESP BEL AUTSWI FRA NLDDEU ITA

New daily infections per mm people [W. Europe]



7 day trailing avg

0

20

40

60

80

100

120

140

160

180

200

0 50 100 150

SWE DEN ICE FIN NOR

New daily infections per mm people [Scandinavia]



7 day trailing avg

0

20

40

60

80

100

120

140

160

180

200

0 50 100 150

US AUS CAN UK IRE NZL

New daily infections per mm people [Anglosaxons]



7 day trailing avg

0

20

40

60

80

100

120

140

160

180

200

0 50 100 150

CZE POL SVN SLK GRC HUN

New daily infections per mm people [Dev E Europe]



7 day trailing avg

0

20

40

60

80

100

120

140

160

180

200

0 50 100 150

SGP HK JPN KOR TAI

New daily infections per mm people [Dev Asia]



7 day trailing avg





9

Infections in the Developing World (all charts scaled to 200 per mm)

0

20

40

60

80

100

120

140

160

180

200

0 50 100 150 200

S America E EuropeM East AfricaEM Asia

New daily infections per mm people [EM World]



7 day trailing avg

0

20

40

60

80

100

120

140

160

180

200

0 50 100 150

BRA COL PERCHL ARG ECUMEX VEN

New daily infections per mm people [Latin America]



7 day trailing avg

0

20

40

60

80

100

120

140

160

180

200

0 50 100 150

PHL IDN MAL THA VNM MYA

New daily infections per mm people [Emerg Asia]



7 day trailing avg

SAU

RUSIRN

TUR0

20

40

60

80

100

120

140

160

180

200

0 50 100 150

New daily infections per mm people [Autocracies]



7 day trailing avg

0

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0 50 100 150

IND UKR BAN PAK

New daily infections per mm people [Other EM]



7 day trailing avg

ISR

IRQ

ALGEGYMOR0

20

40

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0 50 100 150

New daily infections per mm people [ME/NAfr]



7 day trailing avg

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0 50 100

SAF GHA KENETH NGA MOZANG TZA

New daily infections per mm people [Africa]



7 day trailing avg





10

How did Asia do it?

Differences in infection rates reflect in part the degree of government policy, testing, quarantine, and voluntary social distancing by individuals and companies.

South Korea contact tracing tools include: checking a person’s use of medical facilities and pharmacies, and for what reason; global positioning system (GPS) tracking of their movements; credit card transaction logs as another way of figuring out where they went and who they were with; close-circuit television (CCTV) records to see whether people are wearing masks or coughing

Singapore has drive-thru testing stations, and its Infectious Diseases Act provides legal power to enforce contact tracing and quarantine and to prosecute those who do not comply. Singapore launched a new Bluetooth app that tracks users contacts with other app users, and is one of the tools used for enforcement

Hong Kong imposes a 14-day mandatory quarantine and medical surveillance period on persons entering from mainland China, Korea, Japan, France, Germany, Spain, Italy, etc; electronic wristbands connected to a smartphone app are placed on arriving passengers to ensure quarantine adherence

Taiwan uses QR code scanning and online reporting of travel history and health symptoms to classify traveler infectious risks, and tracks people through mobile phones to ensure quarantine compliance. Taiwan also seeks out patients with severe respiratory symptoms (based on info from a national health database) so that they can be tested for COVID-19, and uses machine learning and artificial intelligence techniques to identify possible carriers using info from health and immigration databases

China works with mobile carriers to gather GPS information on infected people, and works with Tencent and Alibaba on the development of “virus passport” smartphone apps

To further illustrate the impact of these policy choices, we looked at infection rates alongside a measure of “collectivism vs individualism” first developed by Geert Hofstede in the 1970’s. There is no single variable that perfectly explains differences in COVID infection rates across regions; I believe this one is an indispensable part of the story. A standard cluster analysis shows a high degree of significance when thinking about COVID within a collectivism/individualism dimension. The Singapore exception: the vast majority of its cases have occurred in overcrowded dormitories that house more than 300,000 of Singapore's 1 million foreign workers.

CN

HK

JPKO

SG

TW

AU

AT

BE

NL CA

DK

EE FI

FR

DE

UK

HU

IE

IL

IT

LV

LU

MT

NZ

NO

ES

SE

CH

US

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

11,000

12,000

13,000

14,000

15,000

COVID-19 infections rates vs a measure of societal collectivism / individualismTotal infections to date per million people

Source: Johns Hopkins University, IMF, G. Hofstede Cultural Dimensions (2015), JPMAM. Diamonds represent cluster centroids. July 22, 2020

collectivism individualismHofstede dimension (VSM 2015)

cluster significance coefficient 88%Dev. Asia/ChinaGPS tracking; credit card receipt logs; close circuit television monitoring; mandatory quarantine, electronic wristbands and saliva testing of incoming travelers; mandatory smartphone "virus passport" apps





11

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