This is my daily update for COVID-19 trends for July 10, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 14, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 8, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 6, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 9, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 20, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 17, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 18, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 11, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 14, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 8, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 6, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 9, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 20, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 17, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 18, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 11, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 15, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 21, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 24, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 22, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 4, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 15, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 21, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 24, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 22, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 4, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 4, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 2, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 3, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 16, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
This is my daily update for COVID-19 trends for July 25, 2020.
Prior analyses and the R program code can be found at https://github.com/StevenLShafer/COVID19
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
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.
- 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
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
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
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!
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
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.
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.
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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
Report Back from SGO 2024: What’s the Latest in Cervical Cancer?bkling
Are you curious about what’s new in cervical cancer research or unsure what the findings mean? Join Dr. Emily Ko, a gynecologic oncologist at Penn Medicine, to learn about the latest updates from the Society of Gynecologic Oncology (SGO) 2024 Annual Meeting on Women’s Cancer. Dr. Ko will discuss what the research presented at the conference means for you and answer your questions about the new developments.
Report Back from SGO 2024: What’s the Latest in Cervical Cancer?
COVID-19 Analysis: July 10, 2020
1. Caveats and Comments
1
Overview:
This is my analysis, not Stanford’s. It is simply a set of regressions in R to understand the trajectory of COVID. It is not confidential and can be freely shared. The R
program code and all previous PowerPoint files are available at https://github.com/StevenLShafer/COVID19/. Please contact me at steven.shafer@Stanford.edu if
you would like to be added or removed from the recipient list. Suggestions are most welcome! You are welcome to use the R code on GitHub for any purpose. If
you create new graphs, please let me know, as I may want to add them to my analyses.
I am attempting to keep the analysis and commentary apolitical. I am now including partisan lean as a metric. This is just more data to understand the COVID
epidemic. I occasionally point out misrepresentations by government officials. Additionally, as the crisis has worsened, I will point out where government
recommendations have placed Americans at increasing risk.
I try to provide a daily update in the morning. However, as an anesthesiologist at Stanford, when I have clinical duties my analysis my be delayed.
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: log 𝑐𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝑐𝑎𝑠𝑒𝑠 = 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑐𝑎𝑠𝑒𝑠 + 𝑚𝑎𝑥𝑖𝑚𝑢𝑚 𝑐𝑎𝑠𝑒𝑠 − 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑐𝑎𝑠𝑒𝑠 1 −
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
cases on Jyly 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)
8. 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 2020-07-10
8
9. 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
9
10. 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 2020-07-10
10
11. 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
11
13. Total deaths per million
HI
AK
MT
WY
WV
OR
ID
UT
ME
VT
KS
AR
OK
TX
TN
ND
SD
KY
WI
NC
NE
CA
MO
SC
WA
NV
FL
AL
VA
IA
NM
OH
MN
GA
AZ
NH
CO
MS
IN
DE
PA
MD
IL
MI
LA
DC
RI
MA
CT
NY
NJ
0
500
1000
1500
0 10 20 30 40 50
State
Totaldeathspermillionpopulation
Total deaths per million as of yesterday
US COVID-19 Death Rates
13
14. Percent Population Tested
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
5
10
15
20
0
5
10
15
20
0
5
10
15
20
0
5
10
15
20
0
5
10
15
20
0
5
10
15
20
0
5
10
15
20
0
5
10
15
20
PercentPopulationTested
Testing as a Percent of State Population
14
15. 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
Numberofdailytestsfrommintomax
Testing trends from min to max
15
16. Percent Positive Tests
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
5
10
15
0
5
10
15
0
5
10
15
0
5
10
15
0
5
10
15
0
5
10
15
0
5
10
15
0
5
10
15
PercentPositive(%)
Percent Positive Tests Over Last 28 Days
16
17. 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
17
18. 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
18
19. Case Mortality vs. Testing
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
TNTX
UT
VT
VA
WA
WVWI
WY
2.5
5.0
7.5
10 15 20
% Tested
%Mortality
Mortality vs. Testing as of 2020-07-10
19
72. 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 2020-07-10
NA = Inadequate data
72
73. Percent Change by Partisan Lean
-10
-5
0
5
10
0 25 50 75 100
Percent Republican
Percentchangeinnewcasesperday
25
50
75
Republican
Counties by 2016 presidential election results
Dark green line is a Friedman's supersmoother
73
74. Percent Change by Population
-10
-5
0
5
10
1,000 10,000 100,000 1,000,000 10,000,000
Population
Percentchangeinnewcasesperday
25
50
75
Republican
Counties by Population
Dark green line is a Friedman's 'super smoother'
74
75. Partisan Lean vs Population and Direction
1,000
10,000
100,000
1,000,000
10,000,000
0 25 50 75 100
Percent Republican
Population
Direction
Increasing > +2%
Increasing between +0.5% and +2%
No Change (-0.5% to +0.5%)
Decreasing between -0.5% and -2%
Decreasing > -2%
Partisan Lean vs Population and Direction
Dark green line is a Friedman's 'super smoother'
75
76. Cases as a Percent of Population
0.001%
0.01%
0.1%
1%
10%
20%
1,000 10,000 100,000 1,000,000 10,000,000
County Population
Totalcases
Total Cases as a Percent of County Population
Slanted lines are counties with small integer numbers of cases, green line: Friedman's 'super smoother'
76
77. Deaths as a Percent of Population
0.0001%
0.001%
0.01%
0.1%
1%
1,000 10,000 100,000 1,000,000 10,000,000
County Population
Totaldeaths
Total Deaths as a Percent of County Population
Slanted lines are counties with small integer numbers of cases, green line: Friedman's 'super smoother'
77
78. Case Mortality vs. Population
0.1%
1%
10%
100%
1,000 10,000 100,000 1,000,000 10,000,000
County Population
Casemortality
Case Mortality vs. County Population
78
155. Case Mortality vs. Testing
LUX
BHR
MLT
DNK
ISL
LTU
RUS
QAT
PRT
ISRAUS
BLR
USA
MDV
GBR
KWT
IRL
ITA
BEL
NZL
LVA
KAZ
EST
CAN
SGP
ESP
CHE
DEU
AUT
SRBCHL
NOR
SWE
SAU
CZE
SVN
FIN
TUR
POL
ROU
SVK
NLD
PAN
GRC
ZAF
HUN
SLV
KOR
MYS
BGR
IRN
MAR
HRV
URY
COL
UKR
CUB
RWA
PRYGHA
ARGTHA
PER
CRI
IND
BOL
PHL
ECU
BRA
PAK
TUN
FJI
BGD
SEN
MEX
NPL
UGA
JPN
TGOKEN
TWN
VNM
ZWE
ETH
IDN
MMR
NGA
0
5
10
15
0 10 20 30 40
% Tested
%CaseMortality
Case Mortality vs. Testing as of 2020-07-10
155