This document provides an overview and analysis of COVID-19 cases and projections for various locations around the world. It includes caveats about the analysis and data sources. Charts are presented showing trends in cases, deaths, cases per capita, and deaths per capita for various individual countries and regions, with the United States, Western Europe, and worldwide included.
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.
Title: Sense of Smell
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 primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
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.
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
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.
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
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
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.
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
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.
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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
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.
1. Caveats and Comments
1
Overview:
This is my analysis, not Stanford’s. My plots and regressions are intended to understand the trajectory of COVID. It is not confidential and can be freely shared.
The R program code and 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.
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)
8. Worldwide cases per million
CHL
USA
PER
BRA
SAU
ZAF
ESP
RUS
COL
KAZ
GBR
IRN
ITA
ARG
IRQ
MEX
FRA
CAN
NLD
TUR
DEU
ROU
PAK
UKR
BGD
GHA
AFG
IND
POL
EGY
CMR
PHL
DZA
UZB
CIV
AUS
NPL
VEN
MAR
MDG
KEN
SDN
IDN
MYS
KOR
NGA
JPN
ETH
LKA
COD
MOZ
NER
YEM
BFA
CHN
THA
UGA
SYR
TWN
TZA
MMR
VNM
USA
0
5,000
10,000
15,000
20,000
1 6 11 16 21 26 31 36 41 46 51 56 61
Rank
Totalcasestodatepermillion
Worldwide cases per million
ZAF: South Africa, SAU: Saudi Arabia, PER: Peru, COL: Columbia
2020-07-29 Summary: 8
9. Average daily cases per capita over past 7
days
BRA
ZAF
USA
COL
PER
ARG
CHL
KAZ
IRQ
SAU
MEX
ROU
ESP
RUS
IRN
IND
GHA
UZB
UKR
VEN
PHL
AUS
DZA
MDG
BGD
KEN
CAN
FRA
MAR
TUR
POL
GBR
NLD
CIV
ETH
IDN
EGY
CMR
DEU
PAK
JPN
NPL
ITA
NGA
AFG
SDN
MOZ
KOR
SYR
COD
MYS
LKA
YEM
BFA
UGA
NER
CHN
THA
VNM
TWN
MMR
TZA
USA
0
100
200
1 6 11 16 21 26 31 36 41 46 51 56 61
Rank
Averagenewcasespercapita
Average daily cases per capita over past 7 days
ZAF: South Africa, SAU: Saudi Arabia, PER: Peru, COL: Columbia
2020-07-29 Summary: 9
11. Worldwide deaths per million
GBR
ESP
PER
ITA
CHL
FRA
USA
BRA
MEX
NLD
CAN
IRN
COL
ZAF
IRQ
DEU
ROU
SAU
RUS
ARG
TUR
EGY
KAZ
AFG
POL
UKR
DZA
PAK
IND
SDN
CMR
IDN
YEM
PHL
BGD
MAR
AUS
JPN
KEN
GHA
KOR
NGA
VEN
MYS
CIV
NER
MDG
UZB
CHN
BFA
COD
ETH
SYR
NPL
THA
LKA
MOZ
TZA
TWN
MMR
UGA
VNM
USA
0
200
400
600
800
1 6 11 16 21 26 31 36 41 46 51 56 61
Rank
Totaldeathstodatepermillion
Worldwide deaths per million
ZAF: South Africa, SAU: Saudi Arabia, PER: Peru, COL: Columbia
2020-07-29 Summary: 11
12. Average daily deaths per capita over past 7
days
PER
MEX
COL
ZAF
BRA
CHL
IRN
USA
IRQ
ARG
KAZ
GBR
SAU
ROU
RUS
IND
EGY
AFG
UKR
IDN
DZA
AUS
BGD
TUR
CAN
KEN
YEM
MDG
MAR
POL
VEN
PAK
SDN
UZB
PHL
ITA
CMR
ETH
FRA
GHA
SYR
NLD
DEU
NGA
ESP
NPL
CIV
COD
JPN
KOR
UGA
MYS
CHN
BFA
MOZ
MMR
NER
LKA
TWN
TZA
THA
VNM
USA
0
10
20
1 6 11 16 21 26 31 36 41 46 51 56 61
Rank
Averagenewdeathspercapita
Average daily deaths per capita over past 7 days
ZAF: South Africa, SAU: Saudi Arabia, PER: Peru, COL: Columbia
2020-07-29 Summary: 12
13. Case Mortality vs. Testing
LUX
AREBHR
MLT
DNK
ISL
RUS
LTU
ISR
QAT
USAPRT
AUS
MDV
BLR
GBR
IRL
KWT
ITA
BEL
LVA
CAN
SGP
KAZ
NZL
AUT
SRB
DEU
CHE
EST
ESP
SAU
CHLNOR
CZE
FIN
SVN
ROU
POL
TUR
NLD
SVKZAF
PAN
GRC
BGR
SLV
HUN
MAR
KOR
MYS
URY
HRV
IRN
COL
UKR
CUB
RWA
PRY
CRI
BRA
IND
GHA
ARG
NPL
BOL
PHL
PER
ECU
PAK
TUN
FJI
BGD
MEX
SEN
JPN
THAKENTGO
CIV
TWNZWE
IDN
ETH
VNM
MMR
NGA
USA
0
5
10
15
0 20 40 60
% Tested
%CaseMortality
Case Mortality vs. Testing as of 2020-07-29
ARE: United Arab Emirates, BHR:Bahrain, MLT: Malta, ISR: Israel, LTU: Lithuania, ISL: Iceland
2020-07-29 Summary: 13
15. 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 2020-07-29
2020-07-29 Summary: 15
16. 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
2020-07-29 Summary: 16
17. 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 2020-07-29
2020-07-29 Summary: 17
18. 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
2020-07-29 Summary: 18
19. Total US COVID-19 Cases
CA
FL
NY
TX
NJ
IL
GA
AZ
NC
MA
PA
LA
TN
MI
VA
OH
MD
SC
AL
IN
WA
MS
MN
WI
CT
CO
MO
NV
IA
AR
UT
OK
KY
KS
NE
NM
ID
RI
OR
DE
DC
SD
NH
ND
WV
ME
MT
AK
WY
HI
VT
0
100,000
200,000
300,000
400,000
500,000
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: 0.29, p governor: 0.54. NB: association != causation.
2020-07-29 Summary: 19
20. Total US COVID-19 Cases per Million
LA
AZ
NY
FL
NJ
MS
RI
DC
MA
AL
SC
GA
DE
NV
TN
MD
TX
IL
CT
IA
AR
NE
UT
CA
NC
ID
VA
SD
IN
NM
MN
KS
PA
MI
WI
OK
ND
CO
OH
MO
WA
KY
NH
WY
OR
AK
WV
MT
ME
VT
HI
0
5,000
10,000
15,000
20,000
25,000
1 6 11 16 21 26 31 36 41 46 51
Rank
TotalCasesperMillion
Masks
No
Yes
Governor
aa
Democratic
Republican
Total US COVID-19 Cases per Million
p masks: 0.54, p governor: 0.51. NB: association != causation.
2020-07-29 Summary: 20
21. Average US COVID-19 cases over the past
7 days
FL
LA
MS
AZ
TN
AL
NV
SC
GA
TX
ID
OK
AR
CA
MO
NC
UT
ND
WI
NM
NE
KS
IA
MD
AK
KY
VA
IN
MN
OH
DE
IL
DC
WA
CO
MT
RI
WY
SD
OR
PA
WV
MI
NJ
MA
CT
NY
HI
NH
ME
VT
0
100
200
300
400
500
1 6 11 16 21 26 31 36 41 46 51
Rank
DailyCasesperMillion
Masks
No
Yes
Governor
aa
Democratic
Republican
Average US COVID-19 cases over the past 7 days
p masks: 0.022, p governor: 0.012. NB: association != causation.
2020-07-29 Summary: 21
22. Total US COVID-19 Deaths
NY
NJ
CA
MA
IL
PA
MI
FL
TX
CT
LA
GA
MD
AZ
OH
IN
VA
NC
CO
MN
SC
WA
MS
AL
MO
RI
TN
WI
IA
NV
KY
NM
DC
DE
OK
AR
NH
KS
NE
OR
UT
ID
SD
ME
WV
ND
VT
MT
HI
WY
AK
0
10,000
20,000
30,000
1 6 11 16 21 26 31 36 41 46 51
Rank
TotalDeathsperMillion
Masks
No
Yes
Governor
aa
Democratic
Republican
Total US COVID-19 Deaths
p masks: 0.039, p governor: 0.1. NB: association != causation.
2020-07-29 Summary: 22
23. Total US COVID-19 Deaths per Million
NJ
NY
MA
CT
RI
DC
LA
MI
IL
DE
MD
PA
MS
AZ
IN
GA
CO
AL
SC
NH
NM
OH
MN
FL
IA
NV
VA
CA
TX
WA
MO
NC
NE
KY
WI
TN
AR
SD
ND
OK
KS
ME
VT
ID
UT
OR
WV
MT
WY
AK
HI
0
500
1,000
1,500
1 6 11 16 21 26 31 36 41 46 51
Rank
TotalDeathsperMillion
Masks
No
Yes
Governor
aa
Democratic
Republican
Total US COVID-19 Deaths per Million
p masks: 0.027, p governor: 0.096. NB: association != causation.
2020-07-29 Summary: 23
24. Average US COVID-19 deaths over the past
7 days
FL
LA
MS
AZ
TN
AL
NV
SC
GA
TX
ID
OK
AR
CA
MO
NC
UT
ND
WI
NM
NE
KS
IA
MD
AK
KY
VA
IN
MN
OH
DE
IL
DC
WA
CO
MT
RI
WY
SD
OR
PA
WV
MI
NJ
MA
CT
NY
HI
NH
ME
VT
0
100
200
300
400
500
1 6 11 16 21 26 31 36 41 46 51
Rank
DailydeathsperMillion
Masks
No
Yes
Governor
aa
Democratic
Republican
Average US COVID-19 deaths over the past 7 days
p masks: 0.022, p governor: 0.012. NB: association != causation.
2020-07-29 Summary: 24
26. 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
2020-07-29 Summary: 26
27. 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
2020-07-29 Summary: 27
28. 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
MSMO
MT
NE
NV
NH
NJ
NM
NY
NC ND
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VT
VA
WA
WVWI
WY
2.5
5.0
7.5
10 15 20 25 30
% Tested
%Mortality
Mortality vs. Testing as of 2020-07-29
2020-07-29 Summary: 28