This document provides context and explanations for COVID-19 projections and analyses. It notes that the analysis is conducted independently and aims to be apolitical. Data sources and modeling approaches are described, including using a Gompertz function to model case growth and log-linear regression for deaths. Locations are selected based on factors like family/friends or economic importance. Updates are typically daily, though clinical duties may cause delays.
These lecture slides, by Dr Sidra Arshad, offer a quick overview of physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar leads (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
MANAGEMENT OF ATRIOVENTRICULAR CONDUCTION BLOCK.pdfJim Jacob Roy
Cardiac conduction defects can occur due to various causes.
Atrioventricular conduction blocks ( AV blocks ) are classified into 3 types.
This document describes the acute management of AV block.
Pulmonary Thromboembolism - etilogy, types, medical- Surgical and nursing man...VarunMahajani
Disruption of blood supply to lung alveoli due to blockage of one or more pulmonary blood vessels is called as Pulmonary thromboembolism. In this presentation we will discuss its causes, types and its management in depth.
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
Follow us on: Pinterest
Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
Factory Supply Best Quality Pmk Oil CAS 28578–16–7 PMK Powder in Stockrebeccabio
Factory Supply Best Quality Pmk Oil CAS 28578–16–7 PMK Powder in Stock
Telegram: bmksupplier
signal: +85264872720
threema: TUD4A6YC
You can contact me on Telegram or Threema
Communicate promptly and reply
Free of customs clearance, Double Clearance 100% pass delivery to USA, Canada, Spain, Germany, Netherland, Poland, Italy, Sweden, UK, Czech Republic, Australia, Mexico, Russia, Ukraine, Kazakhstan.Door to door service
Hot Selling Organic intermediates
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
Are There Any Natural Remedies To Treat Syphilis.pdf
COVID-19 Update (Summary): September 27, 2020
1. Caveats and Comments
1
Overview:
This is my analysis, not Stanford’s. My goal is to understand the trajectory of COVID. It is not confidential and can be freely shared. The R program code is
available at https://github.com/StevenLShafer/COVID19/. The daily analysis are available at https://1drv.ms/u/s!AuOyHP_aTIy7rowrt2AjGpWm_frnEQ?e=KBcNbh.
You are welcome to use the R code on GitHub for any purpose.
I am attempting to keep the analysis and commentary apolitical. I am now including partisan lean as a metric to help understand the epidemic. I occasionally point
out misrepresentations by government officials. I occasionally point out where government recommendations have placed Americans at increasing risk.
I try to provide a daily update in the morning, except Sundays. My analysis my be delayed by my clinical responsibilities as a Stanford anesthesiologist.
There is a lot of information on the figures. If something isn’t clear, please see the explanation on slide 2.
Data sources:
• USA Case Data: https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_US.csv
• USA Death Data: https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_US.csv
• USA Testing and Hospitalization Data: https://raw.githubusercontent.com/COVID19Tracking/covid-tracking-data/master/data/states_daily_4pm_et.csv
• Global Case Data: https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv
• Global Death Data: https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv
• Global Testing Data: https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/owid-covid-data.csv
• Mobility Data: https://www.gstatic.com/covid19/mobility/Global_Mobility_Report.csv
• Partisan Lean: MIT Election Data and Science Lab: https://doi.org/10.7910/DVN/VOQCHQ/HEIJCQ
• Ensemble Model: https://github.com/reichlab/covid19-forecast-hub/raw/master/data-processed/COVIDhub-ensemble/2020-xx-xx-COVIDhub-ensemble.csv
Models:
1. Future projections of case numbers are based on the Gompertz function (https://en.wikipedia.org/wiki/Gompertz_function): log 𝑐𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝑐𝑎𝑠𝑒𝑠 =
𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑐𝑎𝑠𝑒𝑠 + 𝑚𝑎𝑥𝑖𝑚𝑢𝑚 𝑐𝑎𝑠𝑒𝑠 − 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑐𝑎𝑠𝑒𝑠 1 − 𝑒−𝑘 𝑡 . This is a naïve asymptotic model. k is the rate constant, such that log(2) / k = time to 50%
rise. t is the number of days. Wikipedia The Gompertz function is estimated from the last 3 weeks of data for cumulative cases (red dots in the figures).
Deaths are predicted from a log linear regression of deaths over the past 21 days. For the US, and individual states, I am also including the 98% prediction
interval from the COVID-19 Forecast Hub (https://covid19forecasthub.org/).
2. The rate of change in daily cases and deaths is the slope of delta cases / day over the last 14 days, divided by the average number of cases.
Locations
The locations for the modeling are where Pamela and I have family and friends, locations of interest to friends and colleagues, or countries in the news (e.g.,
China, South Korea, Sweden, Brazil) or with significant economic impact on the United States (e.g., Japan, Canada, Mexico). Locations are easy to add.
Stay safe, well, resilient, and kind.
Steve Shafer
steven.shafer@Stanford.edu
2. 2,586,092
152,804
1
10
100
1,000
10,000
100,000
1,000,000
10,000,000
100,000,000
Actual(points)/Predicted(line)
Phase
Pre-Model
Modeled
Deaths
Tests
USA projection as of 2020-05-27
0
10,000
20,000
30,000
0
2,000
4,000
6,000
Cases/Day
Deaths/Day
Cases: 1,662,302 (32,123) -- Deaths: 98,220 (829) -- Case Mortality: 5.9% -- Daily Change in Cases: -0.5%
Explanation of the Figures
2
Brown dots:
cumulative tests
Red dots: cumulative cases
used to estimate Gompertz
function, presently set to last
3 weeks
Red line: predicted cumulative
cases based on the Gompertz
function estimated from the red
dots
Red number: total cases
on June 30th, based on
the Gompertz function
estimated from the red
dots
Black number: total
Deaths on July 31th,
based on log-linear
regression of the past
21 days
Black line: predicted
cumulative deaths, based
on a log linear regression
of deaths over past 21
days.
Axis for deaths / day, usually
1/10th of the axis for cases /
day on the left side of the
figure.
Green line: linear regression
over 8 days, used to calculate
percent increase / decrease
(see below)
Daily change in cases,
calculated as the slope of the
green line (above left) /
number of new cases
yesterday.
Case mortality:
cumulative deaths
/ cumulative cases.
Cases / day calculated
from cumulative cases
used to estimate the
Gompertz function
Cases / day calculated
from cumulative cases
not used to estimate the
Gompertz function
Deaths / day,
axis is on the left
Blue line: today
Blue dots: cumulative cases not
used to estimate Gompertz
function
Cumulative cases
(yesterday’s cases)
and cumulative deaths
(yesterday’s deaths)
Axis for cases / day.
Axis for deaths / day
appears to the right.
Geographic
location
Date of analysis,
also shown as
blue vertical line
below
Purple wedge: 98% ensemble
prediction interval from COVID-19
Forecast Hub (USA and US
States only)
6. Comparison of COVID-19 Cases & Deaths
between US & Europe
Cases
Deaths
45,377
13,193
740
113
3
10
30
100
300
1,000
3,000
10,000
30,000
100,000
Date
DailyCasesandDeaths
Location
USA (318MM)
Western Europe (344MM)
Log plot of 7 day average
Comparison of COVID-19 Cases & Deaths between US & Europe
The numbers on the right are yesterday's figures, and will differ a bit from the plotted rolling mean
2020-09-27 Summary: 6
10. Average new cases over past 7 days
Israel
Argentina
Spain
CzechRepublic
Peru
France
Belgium
Colombia
Netherlands
Brazil
USA
Iraq
Libya
Paraguay
Chile
Denmark
UnitedKingdom
Hungary
Austria
Ecuador
Ukraine
India
Jordan
Tunisia
Portugal
Morocco
Honduras
Romania
DominicanRepublic
Slovakia
Iran
Guatemala
Russia
Bolivia
Nepal
Switzerland
Mexico
Sweden
Canada
Venezuela
SouthAfrica
Greece
Italy
Poland
Philippines
Belarus
Bulgaria
Turkey
Germany
Uzbekistan
Kyrgyzstan
ElSalvador
Indonesia
SaudiArabia
Finland
Azerbaijan
Myanmar
Bangladesh
Serbia
Mozambique
Angola
Ethiopia
Uganda
Zambia
Tajikistan
USA
None
1 in 5,000
1 in 2,500
1 in 1,667
1 in 1,250
1 in 1,000
1 6 11 16 21 26 31 36 41 46 51 56 61 66
Rank
Averagecases/day
Average new cases over past 7 days
Excludes countries with population < 5,000,000
2020-09-27 Summary: 10
13. Average daily deaths over past 7 days
Argentina
Colombia
Israel
Mexico
Peru
Bolivia
Brazil
Paraguay
Chile
Iran
USA
Honduras
Spain
Iraq
Ecuador
Romania
Libya
SouthAfrica
Guatemala
Ukraine
CzechRepublic
Morocco
SaudiArabia
India
Russia
Turkey
France
Hungary
DominicanRepublic
Netherlands
Tunisia
Bulgaria
Portugal
Philippines
Greece
Belarus
Indonesia
Poland
UnitedKingdom
Belgium
ElSalvador
Austria
Nepal
Venezuela
Italy
Myanmar
Switzerland
Denmark
Jordan
Angola
Kazakhstan
Canada
Sweden
Egypt
Bangladesh
Azerbaijan
Algeria
Australia
Uzbekistan
Kenya
Ethiopia
Syria
Serbia
Slovakia
Germany
USA
None
1 in 500,000
1 in 250,000
1 in 166,667
1 in 125,000
1 in 100,000
1 6 11 16 21 26 31 36 41 46 51 56 61 66
Rank
Averagedeaths/day
Average daily deaths over past 7 days
Excludes countries with population < 5,000,000
2020-09-27 Summary: 13
14. CV for Cases and Deaths
KGZ
BEL
ITA
GBRSWE
CMR
DEUCANSOM
NLDSDN
FINTHA PAK BFA
SLE CHEMWI MLI FRADNKAFG CIVYEM
SRB MYS
SSDCOD
PRT AUTVNM ESPSEN TCDNICEGY NGA TJKAZEGHA GINKAZ USA PERCHLHTI
LKA DZASLVZAF ZWEBRAGTM POLRUS IRNMEX HUNKEN ZMBBLRSAU MDG JPNBGD HND
DOM SVKTURKOR CUBBGR IRQCOL ROU
AUS
IDN CZEGRCETH INDUZB ISRUKRPHL AGOVENPNG TGOBOL SYR ARG MARRWA
ECU NPL LBYUGAPRYMOZ TUNJOR
MMR
0.001
0.010
0.100
1.000
0.010.01 0.10 1.00
Cases CV
DeathsCV
Coefficient of variation for cases and deaths as of 2020-09-27
CV calculated over last 28 days
2020-09-27 Summary: 14
16. Change in New Cases per Day
New cases are:
Increasing > +3%
Increasing between +1% and +3%
No Change (-1% to +1%)
Decreasing between -1% and -3%
Decreasing > -3%
New cases by state as of 2020-09-27
2020-09-27 Summary: 16
17. Cases as a Percent of Peak Cases
HI TX FL
OK LA MS AL GA
AZ NM KS AR TN NC SC DC
CA UT CO NE MO KY WV VA MD DE
OR NV WY SD IA IN OH PA NJ CT RI
WA ID MT ND MN IL MI NY MA
WI VT NH
AK ME
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
PercentofPeak
Daily Cases as a Percent of Peak Cases
2020-09-27 Summary: 17
18. Change in New Deaths per Day
New deaths are:
Increasing > +0.5%
Increasing between +0.1% and +0.5%
No Change (-0.1% to +0.1%)
Decreasing between -0.1% and -0.5%
Decreasing > -0.5%
New deaths by state as of 2020-09-27
2020-09-27 Summary: 18
19. Deaths as a Percent of Peak Deaths
HI TX FL
OK LA MS AL GA
AZ NM KS AR TN NC SC DC
CA UT CO NE MO KY WV VA MD DE
OR NV WY SD IA IN OH PA NJ CT RI
WA ID MT ND MN IL MI NY MA
WI VT NH
AK ME
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
0
25
50
75
100
PercentofPeak
Daily Deaths as a Percent of Peak Deaths
2020-09-27 Summary: 19
20. Change in cases vs. change in deaths over
last 14 days
AL
AK
AZ
AR
CA
CO
CT
DC
FL
GA
HI
ID
ILIN
IA
KS
KY
LA
ME
MD
MA
MN
MS
MO
MT
NE
NV
NJ
NM
NY
NC
ND
OH
OK
OR
PA
RI
SC
TN TX
UTVT
VA
WA
WI
WY-6
-3
0
3
6
-6 -3 0 3 6
Change in cases (%/day)
Changeindeaths(%/day)
Change in cases vs. change in deaths over last 14 days as of 2020-09-27
Size is proportional total cases per capita
2020-09-27 Summary: 20
21. Total US COVID-19 Cases
California
Texas
Florida
NewYork
Georgia
Illinois
Arizona
NorthCarolina
NewJersey
Tennessee
Louisiana
Pennsylvania
Alabama
Ohio
SouthCarolina
Virginia
Michigan
Massachusetts
Missouri
Maryland
Indiana
Wisconsin
Mississippi
Minnesota
Iowa
Washington
Oklahoma
Arkansas
Nevada
Utah
Colorado
Kentucky
Kansas
Connecticut
Nebraska
Idaho
Oregon
NewMexico
RhodeIsland
SouthDakota
NorthDakota
Delaware
DistrictofColumbia
WestVirginia
Hawaii
Montana
NewHampshire
Alaska
Wyoming
Maine
Vermont
0
250,000
500,000
750,000
1 6 11 16 21 26 31 36 41 46 51
Rank
Totalcases
Masks
No
Yes
Governor
aa
Democratic
Republican
Total US COVID-19 Cases
p masks as of July 20, 2020: 0.59, p governor: 0.9. NB: association != causation.
2020-09-27 Summary: 21
22. Total US COVID-19 Cases
Louisiana
Florida
Mississippi
Alabama
Arizona
Georgia
SouthCarolina
Tennessee
Iowa
Arkansas
NorthDakota
Texas
Nevada
SouthDakota
NewYork
RhodeIsland
NewJersey
Illinois
Idaho
Nebraska
Utah
DistrictofColumbia
Oklahoma
Delaware
California
Maryland
Missouri
NorthCarolina
Wisconsin
Kansas
Massachusetts
Indiana
Virginia
Minnesota
Connecticut
Kentucky
NewMexico
Michigan
Ohio
Pennsylvania
Colorado
Washington
Montana
Alaska
Wyoming
Hawaii
WestVirginia
Oregon
NewHampshire
Maine
Vermont
None
1 in 100
1 in 50
1 in 33
1 in 25
1 6 11 16 21 26 31 36 41 46 51
Rank
TotalCases
Masks
No
Yes
Governor
aa
Democratic
Republican
Total US COVID-19 Cases
p masks as of July 20, 2020: 0.14, p governor: 0.019. NB: association != causation.
2020-09-27 Summary: 22
23. Average US COVID-19 cases over the past
7 days
NorthDakota
SouthDakota
Wisconsin
Utah
Oklahoma
Iowa
Arkansas
Missouri
Montana
Idaho
Alabama
SouthCarolina
Texas
Nebraska
Kansas
Tennessee
NorthCarolina
Mississippi
Minnesota
Kentucky
Illinois
Wyoming
Nevada
Georgia
Indiana
Alaska
Florida
WestVirginia
Colorado
Delaware
Louisiana
Virginia
California
Michigan
RhodeIsland
Maryland
NewMexico
Ohio
Hawaii
Washington
Arizona
Oregon
DistrictofColumbia
Massachusetts
Pennsylvania
NewJersey
NewYork
Connecticut
NewHampshire
Maine
Vermont
None
1 in 10,000
1 in 5,000
1 in 3,333
1 in 2,500
1 in 2,000
1 6 11 16 21 26 31 36 41 46 51
Rank
NewCases/Day
Masks
No
Yes
Governor
aa
Democratic
Republican
Average US COVID-19 cases over the past 7 days
p masks as of July 20, 2020: 0.0027, p governor: 0.0042. NB: association != causation.
2020-09-27 Summary: 23
24. Total US COVID-19 Deaths
NewYork
NewJersey
Texas
California
Florida
Massachusetts
Illinois
Pennsylvania
Michigan
Georgia
Arizona
Louisiana
Ohio
Connecticut
Maryland
Indiana
NorthCarolina
SouthCarolina
Virginia
Mississippi
Alabama
Tennessee
Washington
Minnesota
Missouri
Colorado
Nevada
Iowa
Arkansas
Wisconsin
Kentucky
RhodeIsland
Oklahoma
NewMexico
Kansas
Delaware
DistrictofColumbia
Oregon
Nebraska
Idaho
Utah
NewHampshire
WestVirginia
NorthDakota
SouthDakota
Montana
Maine
Hawaii
Vermont
Alaska
Wyoming
0
10,000
20,000
30,000
1 6 11 16 21 26 31 36 41 46 51
Rank
TotalDeaths
Masks
No
Yes
Governor
aa
Democratic
Republican
Total US COVID-19 Deaths
p masks as of July 20, 2020: 0.072, p governor: 0.3. NB: association != causation.
2020-09-27 Summary: 24
25. Total US COVID-19 Deaths
NewJersey
NewYork
Massachusetts
Connecticut
Louisiana
RhodeIsland
Mississippi
DistrictofColumbia
Arizona
Michigan
Illinois
Florida
Georgia
Delaware
Maryland
SouthCarolina
Pennsylvania
Texas
Indiana
Nevada
Alabama
Arkansas
Iowa
NewMexico
Ohio
California
Virginia
Minnesota
Colorado
Tennessee
Missouri
NorthCarolina
NewHampshire
NorthDakota
Washington
Kentucky
Idaho
Oklahoma
SouthDakota
Nebraska
Wisconsin
Kansas
WestVirginia
Montana
Utah
Oregon
Maine
Vermont
Hawaii
Wyoming
Alaska
None
1 in 2,000
1 in 1,000
1 in 667
1 in 500
1 6 11 16 21 26 31 36 41 46 51
Rank
TotalDeaths
Masks
No
Yes
Governor
aa
Democratic
Republican
Total US COVID-19 Deaths
p masks as of July 20, 2020: 0.035, p governor: 0.38. NB: association != causation.
2020-09-27 Summary: 25
26. Average US COVID-19 deaths over the past
7 days
NorthDakota
Mississippi
Missouri
Arkansas
Florida
Georgia
SouthCarolina
Texas
Tennessee
SouthDakota
Arizona
Montana
Virginia
NorthCarolina
Louisiana
WestVirginia
Nevada
RhodeIsland
Massachusetts
California
Oklahoma
Alabama
Iowa
Illinois
Kansas
Nebraska
NewMexico
Delaware
Pennsylvania
Idaho
Indiana
Kentucky
Ohio
Minnesota
Alaska
Maryland
Michigan
Washington
Hawaii
Wisconsin
DistrictofColumbia
Colorado
Oregon
NewJersey
Utah
Connecticut
NewYork
Maine
NewHampshire
Vermont
Wyoming
None
1 in 500,000
1 in 250,000
1 in 166,667
1 in 125,000
1 6 11 16 21 26 31 36 41 46 51
Rank
Deaths/Day
Masks
No
Yes
Governor
aa
Democratic
Republican
Average US COVID-19 deaths over the past 7 days
p masks as of July 20, 2020: 0.27, p governor: 0.014. NB: association != causation.
2020-09-27 Summary: 26
27. Daily testing trends
HI TX FL
OK LA MS AL GA
AZ NM KS AR TN NC SC DC
CA UT CO NE MO KY WV VA MD DE
OR NV WY SD IA IN OH PA NJ CT RI
WA ID MT ND MN IL MI NY MA
WI VT NH
AK ME
min
max
min
max
min
max
min
max
min
max
min
max
min
max
min
max
Dailytestingfrommintomax
Daily testing trends from min to max
Line = Friedman's supersmoother
2020-09-27 Summary: 27
28. Change in daily tests over past 14 days
Utah
SouthCarolina
Wisconsin
Colorado
Minnesota
NewMexico
Idaho
NorthCarolina
Florida
SouthDakota
Oklahoma
Ohio
Kentucky
WestVirginia
Illinois
Wyoming
Michigan
Arizona
Nevada
Montana
Missouri
DistrictofColumbia
NorthDakota
Virginia
Arkansas
Connecticut
Maine
NewYork
Texas
Washington
NewHampshire
Tennessee
Iowa
Kansas
Mississippi
Nebraska
NewJersey
Massachusetts
Louisiana
Indiana
California
Vermont
Pennsylvania
Oregon
Delaware
RhodeIsland
Alaska
Maryland
Georgia
Alabama
Hawaii
-6.0
-3.0
0.0
3.0
1 6 11 16 21 26 31 36 41 46 51
Rank
Changeindailytests(%/day)
Masks
No
Yes
Governor
aa
Democratic
Republican
Change in daily tests over past 14 days
p masks as of July 20, 2020: 0.26, p governor: 0.76. NB: association != causation.
2020-09-27 Summary: 28
30. Percent of Positive COVID Tests
Arizona
Alabama
Florida
Mississippi
Idaho
Texas
SouthCarolina
Nevada
SouthDakota
Kansas
Iowa
Georgia
Nebraska
Missouri
Utah
Indiana
NorthDakota
Arkansas
Wisconsin
Pennsylvania
Colorado
Maryland
RhodeIsland
Louisiana
Virginia
Oklahoma
Delaware
NorthCarolina
Minnesota
Tennessee
Massachusetts
NewJersey
California
Wyoming
Illinois
Kentucky
Ohio
Oregon
Washington
NewYork
Hawaii
DistrictofColumbia
Michigan
Connecticut
Montana
NewMexico
NewHampshire
WestVirginia
Alaska
Maine
Vermont
0.0
5.0
10.0
15.0
1 6 11 16 21 26 31 36 41 46 51
Rank
PercentofPositiveTests
Masks
No
Yes
Governor
aa
Democratic
Republican
Percent of Positive COVID Tests
p masks as of July 20, 2020: 0.0064, p governor: 0.0023. NB: association != causation.
2020-09-27 Summary: 30
31. Positive fraction trends
HI TX FL
OK LA MS AL GA
AZ NM KS AR TN NC SC DC
CA UT CO NE MO KY WV VA MD DE
OR NV WY SD IA IN OH PA NJ CT RI
WA ID MT ND MN IL MI NY MA
WI VT NH
AK ME
min
max
min
max
min
max
min
max
min
max
min
max
min
max
min
max
Fractionpositivefrommintomax
Positive fraction trends from min to max
2020-09-27 Summary: 31
32. Change in positive tests over past 14 days
NorthDakota
Wisconsin
Wyoming
Montana
SouthDakota
Utah
WestVirginia
Iowa
Missouri
Kansas
Alaska
Idaho
Oklahoma
Nebraska
Minnesota
Oregon
Arkansas
Indiana
Alabama
Nevada
Colorado
Delaware
Florida
Texas
Hawaii
NorthCarolina
Pennsylvania
Washington
RhodeIsland
Tennessee
NewMexico
Maryland
Vermont
Georgia
Michigan
Virginia
Illinois
Louisiana
NewHampshire
California
SouthCarolina
Arizona
Massachusetts
Ohio
NewJersey
Connecticut
NewYork
DistrictofColumbia
Mississippi
Kentucky
Maine
-1.0
0.0
1.0
1 6 11 16 21 26 31 36 41 46 51
Rank
Changeinpositivetests(%/day)
Masks
No
Yes
Governor
aa
Democratic
Republican
Change in positive tests over past 14 days
p masks as of July 20, 2020: 0.017, p governor: 0.027. NB: association != causation.
2020-09-27 Summary: 32
33. Change in tests vs. change in positive tests
over last 14 days
AL
AK
AZ
AR
CA
CO
CT
DE
DC
FL
GA
HI
ID
IL
IN
IA
KYME
MN
MS
MT
NV
NH
NJ
NM
NC
ND
OH
OK
OR
RI
SC
SD
TN
UT
VT
WV
WI
WY
-1
0
1
-5.0 -2.5 0.0 2.5 5.0
Change in tests (%/day)
Changeinpositivetests(%/day)
Change in tests vs. change in positive tests over last 14 days as of 2020-09-27
Size is proportional daily deaths per capita over the past 7 days
2020-09-27 Summary: 33
34. Current hospitalizations as a percent of peak
since FebruaryMissouri
Nebraska
NorthDakota
SouthDakota
WestVirginia
Wisconsin
Wyoming
Alaska
Oklahoma
Kansas
Arkansas
Montana
Iowa
Utah
Kentucky
NorthCarolina
Oregon
Idaho
Virginia
Georgia
Tennessee
Hawaii
Indiana
Minnesota
Ohio
Mississippi
Alabama
SouthCarolina
Washington
Nevada
California
NewMexico
Illinois
Texas
RhodeIsland
Louisiana
Colorado
Maine
Florida
DistrictofColumbia
Maryland
Delaware
Pennsylvania
Arizona
Michigan
NewHampshire
Massachusetts
NewJersey
Connecticut
Vermont
NewYork
0
30
60
90
1 6 11 16 21 26 31 36 41 46 51
Rank
Hospitalizations(%ofpeak)
Masks
No
Yes
Governor
aa
Democratic
Republican
Current hospitalizations as a percent of peak since February
p masks as of July 20, 2020: 0.0027, p governor: 0.04. NB: association != causation.
2020-09-27 Summary: 34
35. Hospitalizations trends
HI TX FL
OK LA MS AL GA
AZ NM KS AR TN NC SC DC
CA UT CO NE MO KY WV VA MD DE
OR NV WY SD IA IN OH PA NJ CT RI
WA ID MT ND MN IL MI NY MA
WI VT NH
AK ME
min
max
min
max
min
max
min
max
min
max
min
max
min
max
min
max
Hospitalizationsfrommintomax
Hospitalizations trends from min to max
2020-09-27 Summary: 35
36. Change in hospitalizations over past 14
days
NewHampshire
SouthDakota
Wisconsin
Wyoming
NorthDakota
Oregon
Minnesota
Connecticut
Kansas
Nebraska
Utah
Arkansas
Oklahoma
Maine
Massachusetts
RhodeIsland
WestVirginia
Alaska
NewYork
Missouri
DistrictofColumbia
Iowa
NewMexico
Colorado
Illinois
NorthCarolina
Washington
Mississippi
SouthCarolina
Indiana
NewJersey
Alabama
Kentucky
Idaho
Montana
Texas
Delaware
Maryland
Virginia
Arizona
Pennsylvania
Nevada
Ohio
Georgia
Tennessee
Michigan
California
Louisiana
Florida
Hawaii
Vermont
-8.0
-4.0
0.0
4.0
1 6 11 16 21 26 31 36 41 46 51
Rank
Changeinhospitalizations(%/day)
Masks
No
Yes
Governor
aa
Democratic
Republican
Change in hospitalizations over past 14 days
p masks as of July 20, 2020: 0.25, p governor: 0.42. NB: association != causation.
2020-09-27 Summary: 36
37. CV for Cases and Deaths
IN IL
CA MAIA NCMD FL COTX WISC MS NMLA NV MNOKKY GA WA
TN AZ
WVPA MI OR MTDC
HI
NJRIAL UTID
KS
AR CTNYOH DE SDMO NEVA
NDME
AKNH
WY
0.010.01
0.10
1.00
0.010.01 0.10 1.00
Cases CV
DeathsCV
Coefficient of variation for cases and deaths as of 2020-09-27
CV calculated over last 28 days
2020-09-27 Summary: 37
38. Change in New Cases per Day
Direction
Increasing > +2%
Increasing between +0.5% and +2%
No Change (-0.5% to +0.5%)
Decreasing between -0.5% and -2%
Decreasing > -2%
NA
Trends by county as of 2020-09-27
NA = Inadequate data
2020-09-27 Summary: 38