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 future projections based on mathematical functions fitted to past data. Locations are selected based on factors like family/friends or economic impact. Updates are typically provided daily with potential delays due to clinical responsibilities.
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.
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
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NYSORA Guideline
2 Case Reports of Gastric Ultrasound
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Prix Galien International 2024 Forum ProgramLevi Shapiro
June 20, 2024, Prix Galien International and Jerusalem Ethics Forum in ROME. Detailed agenda including panels:
- ADVANCES IN CARDIOLOGY: A NEW PARADIGM IS COMING
- WOMEN’S HEALTH: FERTILITY PRESERVATION
- WHAT’S NEW IN THE TREATMENT OF INFECTIOUS,
ONCOLOGICAL AND INFLAMMATORY SKIN DISEASES?
- ARTIFICIAL INTELLIGENCE AND ETHICS
- GENE THERAPY
- BEYOND BORDERS: GLOBAL INITIATIVES FOR DEMOCRATIZING LIFE SCIENCE TECHNOLOGIES AND PROMOTING ACCESS TO HEALTHCARE
- ETHICAL CHALLENGES IN LIFE SCIENCES
- Prix Galien International Awards Ceremony
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
The prostate is an exocrine gland of the male mammalian reproductive system
It is a walnut-sized gland that forms part of the male reproductive system and is located in front of the rectum and just below the urinary bladder
Function is to store and secrete a clear, slightly alkaline fluid that constitutes 10-30% of the volume of the seminal fluid that along with the spermatozoa, constitutes semen
A healthy human prostate measures (4cm-vertical, by 3cm-horizontal, 2cm ant-post ).
It surrounds the urethra just below the urinary bladder. It has anterior, median, posterior and two lateral lobes
It’s work is regulated by androgens which are responsible for male sex characteristics
Generalised disease of the prostate due to hormonal derangement which leads to non malignant enlargement of the gland (increase in the number of epithelial cells and stromal tissue)to cause compression of the urethra leading to symptoms (LUTS
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!
How to Give Better Lectures: Some Tips for Doctors
COVID-19 Update (Summary): September 10, 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)
9. Average new cases over past 7 days
Israel
Argentina
Spain
Peru
Colombia
Brazil
Iraq
Paraguay
France
Libya
USA
Chile
Bolivia
DominicanRepublic
India
CzechRepublic
Honduras
Ukraine
Romania
Netherlands
Morocco
Belgium
Switzerland
Mexico
Austria
Guatemala
Hungary
Venezuela
Portugal
Nepal
UnitedKingdom
SouthAfrica
Russia
Denmark
Iran
SaudiArabia
Philippines
Italy
Turkey
Slovakia
Sweden
Belarus
Greece
Canada
Azerbaijan
Tunisia
Bulgaria
Germany
Kyrgyzstan
ElSalvador
Indonesia
Poland
Bangladesh
Uzbekistan
Ethiopia
Jordan
Serbia
Algeria
Zimbabwe
Zambia
Finland
Kazakhstan
Guinea
Tajikistan
Ghana
USA
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 56 61 66
Rank
Averagecases/day
Average new cases over past 7 days
Excludes countries with population < 5,000,000
2020-09-10 Summary: 9
12. Average daily deaths over past 7 days
Ecuador
Bolivia
Colombia
Argentina
Peru
Mexico
Brazil
Chile
Honduras
Iraq
USA
SouthAfrica
DominicanRepublic
Paraguay
Romania
Iran
Israel
Libya
Spain
Guatemala
Bulgaria
Ukraine
SaudiArabia
Morocco
India
ElSalvador
Russia
Australia
Turkey
Belarus
Philippines
Indonesia
Sweden
Kazakhstan
Nepal
Portugal
Poland
Azerbaijan
Venezuela
France
Greece
CzechRepublic
Belgium
Algeria
Bangladesh
Serbia
Egypt
Ethiopia
Austria
Uzbekistan
Tunisia
Zimbabwe
UnitedKingdom
Italy
Syria
Togo
Angola
Netherlands
Haiti
Hungary
Switzerland
Zambia
Cuba
Japan
Kenya
USA
None
1 in 100,000
1 in 50,000
1 in 33,333
1 in 25,000
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
2020-09-10 Summary: 12
14. 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 2020-09-10
2020-09-10 Summary: 14
15. 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
2020-09-10 Summary: 15
16. 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 2020-09-10
2020-09-10 Summary: 16
17. 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
2020-09-10 Summary: 17
18. Change in cases vs change in deaths
AL
AK
AZ
AR
CA
CO
CT
DE
DC
FL
GA
HI
ID
IL
IN
IA
KS
KY
LA
ME
MD
MA
MI
MN
MS
MO
MT
NE
NV
NH
NJ
NM
NY
NC
ND
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VT
VA
WA
WV
WI
WY
-6
-3
0
3
6
-10 0
Change in cases (%/day)
Changeindeaths(%/day)
Change in cases vs change in deaths over last 14 days as of 2020-09-10
2020-09-10 Summary: 18
19. Total US COVID-19 Cases
California
Texas
Florida
NewYork
Georgia
Illinois
Arizona
NewJersey
NorthCarolina
Tennessee
Louisiana
Pennsylvania
Alabama
Ohio
Virginia
Massachusetts
SouthCarolina
Michigan
Maryland
Indiana
Missouri
Mississippi
Wisconsin
Minnesota
Washington
Nevada
Iowa
Arkansas
Oklahoma
Colorado
Utah
Kentucky
Connecticut
Kansas
Nebraska
Idaho
Oregon
NewMexico
RhodeIsland
Delaware
SouthDakota
DistrictofColumbia
NorthDakota
WestVirginia
Hawaii
Montana
NewHampshire
Alaska
Maine
Wyoming
Vermont
0
200,000
400,000
600,000
800,000
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: 0.46, p governor: 0.79. NB: association != causation.
2020-09-10 Summary: 19
20. Total US COVID-19 Cases
Louisiana
Florida
Mississippi
Arizona
Alabama
Georgia
SouthCarolina
Tennessee
Nevada
Texas
Iowa
NewYork
Arkansas
NewJersey
RhodeIsland
DistrictofColumbia
Illinois
Idaho
Nebraska
California
Delaware
Maryland
Massachusetts
NorthDakota
SouthDakota
Utah
NorthCarolina
Oklahoma
Kansas
Missouri
Virginia
Connecticut
Indiana
Minnesota
Wisconsin
NewMexico
Kentucky
Michigan
Pennsylvania
Ohio
Colorado
Washington
Alaska
Montana
Wyoming
Hawaii
Oregon
WestVirginia
NewHampshire
Maine
Vermont
None
1 in 100
1 in 50
1 in 33
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: 0.61, p governor: 0.069. NB: association != causation.
2020-09-10 Summary: 20
21. Average US COVID-19 cases over the past
7 days
NorthDakota
SouthDakota
Iowa
Missouri
Idaho
Oklahoma
Arkansas
Kansas
Tennessee
Mississippi
Alabama
Georgia
Illinois
Louisiana
SouthCarolina
Nebraska
Wisconsin
Kentucky
Hawaii
Texas
Utah
Florida
Indiana
Minnesota
NorthCarolina
Montana
Virginia
Nevada
Alaska
California
Delaware
Maryland
WestVirginia
Ohio
RhodeIsland
Michigan
Arizona
Pennsylvania
Wyoming
DistrictofColumbia
Washington
NewMexico
Colorado
Oregon
NewJersey
NewYork
Connecticut
NewHampshire
Maine
Massachusetts
Vermont
None
1 in 10,000
1 in 5,000
1 in 3,333
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: 0.0018, p governor: 0.0061. NB: association != causation.
2020-09-10 Summary: 21
22. Total US COVID-19 Deaths
NewYork
NewJersey
California
Texas
Florida
Massachusetts
Illinois
Pennsylvania
Michigan
Georgia
Arizona
Louisiana
Connecticut
Ohio
Maryland
Indiana
NorthCarolina
SouthCarolina
Virginia
Mississippi
Alabama
Washington
Colorado
Tennessee
Minnesota
Missouri
Nevada
Iowa
Wisconsin
RhodeIsland
Kentucky
Arkansas
Oklahoma
NewMexico
DistrictofColumbia
Delaware
Kansas
Oregon
NewHampshire
Utah
Nebraska
Idaho
WestVirginia
SouthDakota
NorthDakota
Maine
Montana
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: 0.062, p governor: 0.24. NB: association != causation.
2020-09-10 Summary: 22
23. Total US COVID-19 Deaths
NewJersey
NewYork
Massachusetts
Connecticut
Louisiana
RhodeIsland
Mississippi
DistrictofColumbia
Arizona
Michigan
Illinois
Maryland
Delaware
Pennsylvania
Georgia
SouthCarolina
Florida
Indiana
Texas
Alabama
Nevada
NewMexico
Iowa
Ohio
California
Colorado
Minnesota
NewHampshire
Virginia
Arkansas
Tennessee
NorthCarolina
Missouri
Washington
Idaho
Kentucky
Oklahoma
Nebraska
NorthDakota
Wisconsin
SouthDakota
Kansas
WestVirginia
Utah
Oregon
Montana
Maine
Vermont
Wyoming
Hawaii
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: 0.031, p governor: 0.3. NB: association != causation.
2020-09-10 Summary: 23
24. Average US COVID-19 deaths over the past
7 days
Mississippi
Arkansas
Georgia
SouthCarolina
Texas
Florida
Louisiana
Nevada
Idaho
Arizona
Iowa
Missouri
Tennessee
California
NorthCarolina
Alabama
WestVirginia
Montana
NorthDakota
Kansas
Kentucky
Oklahoma
Illinois
Indiana
NewMexico
Hawaii
Michigan
Massachusetts
Nebraska
DistrictofColumbia
RhodeIsland
Virginia
Wisconsin
Maryland
Pennsylvania
Minnesota
Wyoming
Oregon
SouthDakota
NewJersey
Washington
Ohio
Utah
Colorado
Delaware
Alaska
NewYork
Connecticut
Maine
NewHampshire
Vermont
None
1 in 1,000,000
1 in 500,000
1 in 333,333
1 in 250,000
1 in 200,000
1 in 166,667
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: 0.48, p governor: 0.025. NB: association != causation.
2020-09-10 Summary: 24
25. 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
2020-09-10 Summary: 25
26. Change in daily tests over past 14 days
Maine
Minnesota
Hawaii
Wyoming
Illinois
Washington
Connecticut
Oklahoma
RhodeIsland
Arkansas
Kentucky
NorthCarolina
Kansas
NorthDakota
NewJersey
Montana
NewYork
Mississippi
Iowa
California
SouthDakota
Nebraska
Missouri
NewHampshire
Ohio
DistrictofColumbia
Michigan
Texas
Utah
Georgia
Pennsylvania
Tennessee
Colorado
NewMexico
Delaware
SouthCarolina
Louisiana
Virginia
Maryland
Alabama
Alaska
Wisconsin
Indiana
Arizona
Oregon
Idaho
Florida
Massachusetts
Nevada
Vermont
WestVirginia
-4.0
-2.0
0.0
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: 0.52, p governor: 0.027. NB: association != causation.
2020-09-10 Summary: 26
28. Percent of Positive COVID Tests
Arizona
Mississippi
Florida
Alabama
Idaho
SouthCarolina
Texas
Nevada
Georgia
Kansas
Iowa
SouthDakota
Nebraska
Missouri
Indiana
Arkansas
Maryland
Pennsylvania
Utah
Colorado
Louisiana
Virginia
RhodeIsland
NorthCarolina
Delaware
Tennessee
Oklahoma
Minnesota
Wisconsin
NorthDakota
Massachusetts
NewJersey
Kentucky
California
Illinois
Ohio
Washington
NewYork
Wyoming
Oregon
DistrictofColumbia
Hawaii
Connecticut
Michigan
NewHampshire
NewMexico
Montana
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: 0.025, p governor: 0.0042. NB: association != causation.
2020-09-10 Summary: 28
29. 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
2020-09-10 Summary: 29
30. Change in positive tests over past 14 days
NorthDakota
SouthDakota
WestVirginia
Montana
Alabama
Missouri
Kansas
Kentucky
Oklahoma
Iowa
Hawaii
Wisconsin
Minnesota
Nebraska
Utah
Arkansas
Idaho
Wyoming
Tennessee
Nevada
Indiana
Oregon
Mississippi
Florida
Virginia
SouthCarolina
Alaska
Pennsylvania
Georgia
NewMexico
Louisiana
Maryland
Colorado
Texas
NorthCarolina
Delaware
Ohio
Michigan
California
Illinois
Arizona
NewHampshire
Washington
RhodeIsland
NewJersey
DistrictofColumbia
NewYork
Vermont
Connecticut
Maine
Massachusetts
-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: 0.0089, p governor: 0.027. NB: association != causation.
2020-09-10 Summary: 30
31. Change in tests vs change in positive tests
AL
AK
AZ
AR
CA
CO
CT
DE
DC
FL
GA
HI
ID
IL
IN
IA
KS
KY
LA
ME
MD
MA
MI
MN
MS
MO
MT
NE
NV
NH
NJ
NM
NY
NC
ND
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VT
VA
WA
WV
WI
WY
-1
0
1
-4 -2 0
Change in tests (%/day)
Changeinpositivetests(%/day)
Change in tests vs change in positive tests last 14 days as of 2020-09-10
Size of the state font reflects the number of deaths over the past 7 days.
2020-09-10 Summary: 31
32. Current hospitalizations as a percent of peak
since FebruaryKansas
Montana
NorthDakota
WestVirginia
Hawaii
Missouri
Nebraska
Alaska
Wyoming
SouthDakota
Kentucky
Iowa
Arkansas
Oklahoma
NorthCarolina
Georgia
Virginia
Tennessee
Idaho
Ohio
Mississippi
Utah
Indiana
Alabama
Oregon
Nevada
Washington
California
Minnesota
SouthCarolina
Wisconsin
Louisiana
Texas
NewMexico
Illinois
Florida
RhodeIsland
Colorado
Maryland
DistrictofColumbia
Delaware
Arizona
Pennsylvania
Michigan
Maine
Massachusetts
NewHampshire
NewJersey
Vermont
Connecticut
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: 0.017, p governor: 0.063. NB: association != causation.
2020-09-10 Summary: 32
33. 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
2020-09-10 Summary: 33
34. Change in hospitalizations over past 14
days
Montana
SouthDakota
Delaware
Kansas
Nebraska
Iowa
DistrictofColumbia
WestVirginia
Hawaii
NorthDakota
Utah
Connecticut
Missouri
Massachusetts
Illinois
RhodeIsland
Pennsylvania
Michigan
NewMexico
NewJersey
Virginia
Wyoming
Tennessee
Maine
Kentucky
Arkansas
Alaska
Georgia
Wisconsin
Oregon
NewYork
NorthCarolina
NewHampshire
Indiana
Minnesota
Ohio
Colorado
Maryland
Oklahoma
Idaho
Mississippi
Washington
Louisiana
Alabama
California
SouthCarolina
Texas
Nevada
Arizona
Florida
Vermont
-10.0
-5.0
0.0
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: 0.5, p governor: 0.29. NB: association != causation.
2020-09-10 Summary: 34
35. 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 2020-09-10
NA = Inadequate data
2020-09-10 Summary: 35