This document provides an analysis of COVID-19 cases and projections in various locations around the world. It includes 7 figures showing graphs of actual and predicted COVID-19 cases and deaths for the US, worldwide, and other regions. The document also lists data sources and models used in the analysis. The analysis is intended to help understand the trajectory of the pandemic but also points out risks from government recommendations at times.
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
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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
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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
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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
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)
9. Average new cases over past 7 days
PER
COL
ISR
BRA
USA
ARG
BOL
CHL
IRQ
ZAF
DOM
ESP
ECU
GTM
KGZ
HND
BEL
ROU
SLV
MEX
IND
SAU
LBY
PRY
SRB
PHL
KAZ
NLD
VEN
FRA
RUS
IRN
MAR
SWE
UKR
DNK
CHE
UZB
CZE
BGR
PRT
ZMB
POL
GBR
AUT
BGD
GRC
NPL
AUS
KEN
TUR
DZA
DEU
CAN
AZE
GHA
SEN
BLR
JPN
ETH
IDN
ITA
GIN
SVK
ZWE
USA
None
1 in 20,000
1 in 10,000
1 in 6,667
1 in 5,000
1 in 4,000
1 in 3,333
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
2020-08-15 Summary: 9
12. Average daily deaths over past 7 days
PER
COL
BOL
MEX
ZAF
BRA
ARG
CHL
USA
IRN
IRQ
DOM
GTM
ROU
KAZ
HND
ISR
SLV
ECU
SAU
SRB
KGZ
BGR
IND
RUS
PRY
BEL
AUS
LBY
GBR
ZMB
MAR
UKR
ZWE
AZE
PHL
ESP
PRT
VEN
BLR
HTI
AFG
DZA
POL
EGY
IDN
SEN
SWE
BGD
TUR
AGO
KEN
FRA
CAN
UZB
ETH
MDG
MWI
GRC
NPL
SDN
GHA
NLD
NIC
YEM
USA
None
1 in 200,000
1 in 100,000
1 in 66,667
1 in 50,000
1 in 40,000
1 in 33,333
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
2020-08-15 Summary: 12
13. Case Mortality vs. Testing
LUX
AREBHR
MLT
DNK
ISLISR
RUS
LTU
AUS
USA
QAT
PRT
MDV
GBR
BEL
BLR
IRL
KWT
CAN
KAZ
LVA
SGP
SAU
SRB
DEU
AUT
NZL
ESP
CHLEST
CHE
NOR
FINROU
ITA
CZE
NLD
SVN
GRC
TUR
POL
PAN
ZAF
SVK
BGR
MAR
SLVURY
COL
HUN
MYS
IRN
HRV
KORUKR
CUB
RWA
IND
PRYCRI
ARG
NPL
BOL
PHL
GHA
ECU
BRA
PER
JPNPAK
TUN
FJI
MEX
BGD
SEN
UGA
KEN
TGO
THA
ZWE
CIV
ETH
IDN
TWN
VNM
MMR
NGA
USA
0
5
10
15
0 20 40 60
% Tested
%CaseMortality
Case Mortality vs. Testing as of 2020-08-15
ARE: United Arab Emirates, BHR:Bahrain, MLT: Malta, ISR: Israel, LTU: Lithuania, ISL: Iceland
2020-08-15 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 2020-08-15
2020-08-15 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
2020-08-15 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 2020-08-15
2020-08-15 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
2020-08-15 Summary: 18
19. 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
MDMA
MI
MN
MS
MO MT
NE
NV
NH
NJ
NM
NY
NC
ND
OH
OK
OR
PA
RI
SC
SD
TNTX
UT
VTVA
WA
WV
WI
WY
-6
-3
0
3
6
-4 0 4 8
Change in cases (%/day)
Changeindeaths(%/day)
Change in cases vs change in deaths over last 14 days 2020-08-15
2020-08-15 Summary: 19
20. Total US COVID-19 Cases
CA
FL
TX
NY
GA
IL
AZ
NJ
NC
LA
TN
PA
MA
OH
AL
SC
VA
MI
MD
IN
MS
WA
MO
WI
MN
NV
CO
AR
IA
CT
OK
UT
KY
KS
NE
ID
NM
OR
RI
DE
DC
SD
ND
WV
NH
MT
HI
ME
AK
WY
VT
0
200,000
400,000
600,000
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: 0.38, p governor: 0.72. NB: association != causation.
2020-08-15 Summary: 20
21. Total US COVID-19 Cases
LA
AZ
FL
MS
GA
NY
AL
NJ
SC
NV
RI
TN
TX
DC
MA
AR
DE
MD
IA
IL
CA
NE
ID
UT
CT
NC
VA
OK
IN
KS
SD
MN
NM
WI
ND
MO
MI
PA
CO
OH
WA
KY
AK
WY
OR
MT
NH
WV
HI
ME
VT
None
1 in 200
1 in 100
1 in 67
1 in 50
1 in 40
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: 0.93, p governor: 0.22. NB: association != causation.
2020-08-15 Summary: 21
22. Average US COVID-19 cases over the past
7 days
GA
FL
MS
ID
TN
LA
NV
AL
TX
CA
AR
SC
ND
MO
OK
KS
IA
HI
IN
IL
KY
NE
AZ
WI
VA
DE
NC
MD
UT
MN
MT
DC
SD
AK
OH
WA
RI
NM
MI
OR
WV
CO
PA
WY
NJ
MA
NY
CT
NH
VT
ME
None
1 in 20,000
1 in 10,000
1 in 6,667
1 in 5,000
1 in 4,000
1 in 3,333
1 in 2,857
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: 0.049, p governor: 0.026. NB: association != causation.
2020-08-15 Summary: 22
23. Total US COVID-19 Deaths
NY
NJ
CA
TX
FL
MA
IL
PA
MI
GA
CT
LA
AZ
OH
MD
IN
VA
NC
SC
MS
AL
CO
WA
MN
MO
TN
NV
WI
RI
IA
KY
NM
OK
DC
DE
AR
NH
KS
OR
NE
UT
ID
WV
SD
ME
ND
MT
VT
HI
WY
AK
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: 0.054, p governor: 0.19. NB: association != causation.
2020-08-15 Summary: 23
24. Total US COVID-19 Deaths
NJ
NY
MA
CT
RI
LA
DC
MS
MI
IL
DE
AZ
MD
PA
IN
GA
SC
FL
AL
TX
NV
NM
CO
OH
NH
MN
IA
CA
VA
WA
MO
NC
AR
TN
NE
KY
WI
SD
OK
ND
ID
KS
UT
ME
VT
OR
WV
MT
WY
AK
HI
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: 0.031, p governor: 0.21. NB: association != causation.
2020-08-15 Summary: 24
25. Average US COVID-19 deaths over the past
7 days
MS
FL
LA
AZ
TX
GA
SC
NV
AL
CA
AR
ID
TN
WV
IA
NC
MA
NM
ND
MT
IN
OH
WA
MD
OK
PA
IL
MN
OR
NE
MO
SD
DC
KY
UT
KS
VA
WI
HI
RI
CO
DE
MI
WY
NJ
CT
NY
NH
AK
ME
VT
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
Rank
Deaths/Day
Masks
No
Yes
Governor
aa
Democratic
Republican
Average US COVID-19 deaths over the past 7 days
p masks: 0.59, p governor: 0.018. NB: association != causation.
2020-08-15 Summary: 25
27. Percent of Positive COVID Tests
AZ
MS
FL
AL
SC
ID
TX
GA
NV
KS
NE
IA
MD
AR
IN
RI
MA
PA
CO
LA
SD
VA
MO
DE
UT
NJ
NC
TN
MN
WA
OK
CA
IL
WI
NY
OH
DC
KY
CT
WY
OR
ND
MI
NH
NM
HI
MT
WV
ME
AK
VT
0.0
5.0
10.0
15.0
20.0
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: 0.18, p governor: 0.015. NB: association != causation.
2020-08-15 Summary: 27
28. 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
2020-08-15 Summary: 28
29. Change in positive tests over past 14 days
HI
MT
WA
ND
NC
TX
MO
ID
OK
NV
AR
MS
KY
OR
FL
KS
TN
WV
AL
UT
SD
WY
WI
MN
IA
IN
SC
CA
NE
AK
GA
VA
OH
CO
LA
AZ
DE
RI
NM
VT
PA
IL
MD
NH
MI
NJ
MA
DC
NY
CT
ME
-1.0
0.0
1.0
2.0
3.0
4.0
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: 0.14, p governor: 0.12. NB: association != causation.
2020-08-15 Summary: 29
30. Current hospitalizations as a percent of peak
since FebruaryAK
HI
KY
MT
ND
WV
ID
MO
KS
AR
MS
AL
OK
NC
TN
GA
OH
UT
VA
WY
NV
SC
NE
OR
CA
IN
SD
WA
FL
LA
NM
IA
TX
WI
MN
AZ
IL
CO
MD
RI
VT
PA
DC
MI
NH
ME
DE
MA
NJ
CT
NY
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: 0.041, p governor: 0.044. NB: association != causation.
2020-08-15 Summary: 30
31. 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
2020-08-15 Summary: 31
32. Change in hospitalizations over past 14
days
HI
SD
WI
ND
MT
WV
RI
NE
IN
KY
MN
MA
IA
IL
AK
OR
MO
VA
KS
ID
WA
MI
AR
MS
NM
NY
PA
DC
OK
CT
AL
NC
OH
TN
GA
SC
UT
NH
MD
CO
LA
NV
WY
CA
VT
FL
NJ
TX
ME
DE
AZ
-2.5
0.0
2.5
5.0
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: 0.63, p governor: 0.79. NB: association != causation.
2020-08-15 Summary: 32
33. Case Mortality vs. Testing
AL
AK
AZ
AR
CA
CO
CT
DE
DC
FL
GA
HI ID
ILIN
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
2.5
5.0
7.5
10 20 30 40
% Tested
%Mortality
Mortality vs. Testing as of 2020-08-15
2020-08-15 Summary: 33