This document provides an overview and analysis of COVID-19 cases and projections worldwide and in several locations. It summarizes daily global and location-specific COVID-19 case and death numbers, and uses statistical modeling to project future case numbers. The document also explains data sources and modeling approaches, and provides figures with trend lines and projections for various geographies.
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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.
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.
Anti ulcer drugs and their Advance pharmacology ||
Anti-ulcer drugs are medications used to prevent and treat ulcers in the stomach and upper part of the small intestine (duodenal ulcers). These ulcers are often caused by an imbalance between stomach acid and the mucosal lining, which protects the stomach lining.
||Scope: Overview of various classes of anti-ulcer drugs, their mechanisms of action, indications, side effects, and clinical considerations.
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.
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.
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
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.
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 goal is to understand the trajectory of COVID. It is not confidential and can be freely shared. The R program code is
available at https://github.com/StevenLShafer/COVID19/. The daily analysis are available at https://1drv.ms/u/s!AuOyHP_aTIy7rowrt2AjGpWm_frnEQ?e=KBcNbh.
You are welcome to use the R code on GitHub for any purpose.
I am attempting to keep the analysis and commentary apolitical. I am now including partisan lean as a metric to help understand the epidemic. I occasionally point
out misrepresentations by government officials. I occasionally point out where government recommendations have placed Americans at increasing risk.
I try to provide a daily update in the morning, except Sundays. My analysis my be delayed by my clinical responsibilities as a Stanford anesthesiologist.
There is a lot of information on the figures. If something isn’t clear, please see the explanation on slide 2.
Data sources:
• USA Case Data: https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_US.csv
• USA Death Data: https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_US.csv
• USA Testing and Hospitalization Data: https://raw.githubusercontent.com/COVID19Tracking/covid-tracking-data/master/data/states_daily_4pm_et.csv
• Global Case Data: https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv
• Global Death Data: https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv
• Global Testing Data: https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/owid-covid-data.csv
• Mobility Data: https://www.gstatic.com/covid19/mobility/Global_Mobility_Report.csv
• Partisan Lean: MIT Election Data and Science Lab: https://doi.org/10.7910/DVN/VOQCHQ/HEIJCQ
• Ensemble Model: https://github.com/reichlab/covid19-forecast-hub/raw/master/data-processed/COVIDhub-ensemble/2020-xx-xx-COVIDhub-ensemble.csv
Models:
1. Future projections of case numbers are based on the Gompertz function (https://en.wikipedia.org/wiki/Gompertz_function): log 𝑐𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝑐𝑎𝑠𝑒𝑠 =
𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑐𝑎𝑠𝑒𝑠 + 𝑚𝑎𝑥𝑖𝑚𝑢𝑚 𝑐𝑎𝑠𝑒𝑠 − 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑐𝑎𝑠𝑒𝑠 1 − 𝑒−𝑘 𝑡 . This is a naïve asymptotic model. k is the rate constant, such that log(2) / k = time to 50%
rise. t is the number of days. Wikipedia The Gompertz function is estimated from the last 3 weeks of data for cumulative cases (red dots in the figures).
Deaths are predicted from a log linear regression of deaths over the past 21 days. For the US, and individual states, I am also including the 98% prediction
interval from the COVID-19 Forecast Hub (https://covid19forecasthub.org/).
2. The rate of change in daily cases and deaths is the slope of delta cases / day over the last 14 days, divided by the average number of cases.
Locations
The locations for the modeling are where Pamela and I have family and friends, locations of interest to friends and colleagues, or countries in the news (e.g.,
China, South Korea, Sweden, Brazil) or with significant economic impact on the United States (e.g., Japan, Canada, Mexico). Locations are easy to add.
Stay safe, well, resilient, and kind.
Steve Shafer
steven.shafer@Stanford.edu
2. 2,586,092
152,804
1
10
100
1,000
10,000
100,000
1,000,000
10,000,000
100,000,000
Actual(points)/Predicted(line)
Phase
Pre-Model
Modeled
Deaths
Tests
USA projection as of 2020-05-27
0
10,000
20,000
30,000
0
2,000
4,000
6,000
Cases/Day
Deaths/Day
Cases: 1,662,302 (32,123) -- Deaths: 98,220 (829) -- Case Mortality: 5.9% -- Daily Change in Cases: -0.5%
Explanation of the Figures
2
Brown dots:
cumulative tests
Red dots: cumulative cases
used to estimate Gompertz
function, presently set to last
3 weeks
Red line: predicted cumulative
cases based on the Gompertz
function estimated from the red
dots
Red number: total cases
on June 30th, based on
the Gompertz function
estimated from the red
dots
Black number: total
Deaths on July 31th,
based on log-linear
regression of the past
21 days
Black line: predicted
cumulative deaths, based
on a log linear regression
of deaths over past 21
days.
Axis for deaths / day, usually
1/10th of the axis for cases /
day on the left side of the
figure.
Green line: linear regression
over 8 days, used to calculate
percent increase / decrease
(see below)
Daily change in cases,
calculated as the slope of the
green line (above left) /
number of new cases
yesterday.
Case mortality:
cumulative deaths
/ cumulative cases.
Cases / day calculated
from cumulative cases
used to estimate the
Gompertz function
Cases / day calculated
from cumulative cases
not used to estimate the
Gompertz function
Deaths / day,
axis is on the left
Blue line: today
Blue dots: cumulative cases not
used to estimate Gompertz
function
Cumulative cases
(yesterday’s cases)
and cumulative deaths
(yesterday’s deaths)
Axis for cases / day.
Axis for deaths / day
appears to the right.
Geographic
location
Date of analysis,
also shown as
blue vertical line
below
Purple wedge: 98% ensemble
prediction interval from COVID-19
Forecast Hub (USA and US
States only)
6. Comparison of COVID-19 Cases & Deaths
between US & Europe
Cases
Deaths
54,441
41,441
906
371
3
10
30
100
300
1,000
3,000
10,000
30,000
100,000
Date
DailyCasesandDeaths
Location
USA (318MM)
Western Europe (344MM)
Log plot of 7 day average
Comparison of COVID-19 Cases & Deaths between US & Europe
The numbers on the right are yesterday's figures, and will differ a bit from the plotted rolling mean
2020-10-03 Summary: 6
10. Average new cases over past 7 days
Israel
Argentina
Spain
CzechRepublic
Netherlands
France
Belgium
Peru
Iraq
Jordan
Colombia
USA
Brazil
Chile
Paraguay
UnitedKingdom
Libya
Hungary
Slovakia
Denmark
Austria
Ukraine
Honduras
Romania
Tunisia
Ecuador
Portugal
India
Morocco
Russia
Nepal
Sweden
Iran
Canada
Guatemala
Switzerland
Bolivia
Mexico
Poland
DominicanRepublic
Belarus
Italy
Greece
Bulgaria
Venezuela
Kyrgyzstan
SouthAfrica
Germany
Philippines
Finland
Turkey
Myanmar
Indonesia
ElSalvador
Uzbekistan
SaudiArabia
Azerbaijan
Serbia
Mozambique
Bangladesh
Ethiopia
Angola
Malaysia
Tajikistan
Uganda
USA
None
1 in 5,000
1 in 2,500
1 in 1,667
1 in 1,250
1 in 1,000
1 6 11 16 21 26 31 36 41 46 51 56 61 66
Rank
Averagecases/day
Average new cases over past 7 days
Excludes countries with population < 5,000,000
2020-10-03 Summary: 10
13. Average daily deaths over past 7 days
Argentina
Colombia
Israel
Bolivia
Mexico
Brazil
Chile
Spain
Iran
Peru
Paraguay
Ecuador
USA
Honduras
Iraq
Libya
Romania
CzechRepublic
SouthAfrica
Ukraine
France
Hungary
Morocco
Tunisia
Russia
SaudiArabia
Bulgaria
India
Guatemala
Netherlands
Turkey
Jordan
Belgium
UnitedKingdom
Poland
Canada
Philippines
Portugal
Belarus
DominicanRepublic
ElSalvador
Myanmar
Indonesia
Greece
Italy
Austria
Slovakia
Nepal
Venezuela
Angola
Sweden
Switzerland
Kazakhstan
Azerbaijan
Bangladesh
Egypt
Kenya
Algeria
Denmark
Australia
Germany
Syria
Uzbekistan
Serbia
Ethiopia
USA
None
1 in 200,000
1 in 100,000
1 in 66,667
1 in 50,000
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
2020-10-03 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 2020-10-03
2020-10-03 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
2020-10-03 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 2020-10-03
2020-10-03 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
2020-10-03 Summary: 18
19. Change in cases vs. change in deaths over
last 14 days
AL
AK
AZ
AR
CA
CO
CT
DE
DC
FL
GA
HI
ID
IL
IN
KS
KY
LA
ME
MD
MA
MI
MSMO
MT
NE
NV
NH
NJ
NM
NY
NC
ND
OH
OK
OR
PA
RI
SC
TN
TX
UT
VT
VA
WA
WV
WI
-6
-3
0
3
6
-6 -3 0 3 6
Change in cases (%/day)
Changeindeaths(%/day)
Change in cases vs. change in deaths over last 14 days as of 2020-10-03
Size is proportional total cases per capita
2020-10-03 Summary: 19
20. Total US COVID-19 Cases
California
Texas
Florida
NewYork
Georgia
Illinois
Arizona
NorthCarolina
NewJersey
Tennessee
Louisiana
Pennsylvania
Ohio
Alabama
Virginia
SouthCarolina
Michigan
Massachusetts
Missouri
Wisconsin
Maryland
Indiana
Minnesota
Mississippi
Iowa
Oklahoma
Washington
Arkansas
Nevada
Utah
Colorado
Kentucky
Kansas
Connecticut
Nebraska
Idaho
Oregon
NewMexico
RhodeIsland
SouthDakota
NorthDakota
Delaware
WestVirginia
DistrictofColumbia
Montana
Hawaii
NewHampshire
Alaska
Wyoming
Maine
Vermont
0
250,000
500,000
750,000
1 6 11 16 21 26 31 36 41 46 51
Rank
Totalcases
Masks
No
Yes
Governor
aa
Democratic
Republican
Total US COVID-19 Cases
p masks as of July 20, 2020: 0.6, p governor: 0.86. NB: association != causation.
2020-10-03 Summary: 20
21. Total US COVID-19 Cases
Louisiana
Mississippi
Florida
Alabama
Georgia
Arizona
NorthDakota
Tennessee
Iowa
SouthCarolina
Arkansas
Texas
SouthDakota
Nevada
Nebraska
Idaho
NewYork
Illinois
RhodeIsland
Utah
NewJersey
Oklahoma
Wisconsin
DistrictofColumbia
Delaware
Missouri
California
Maryland
Kansas
NorthCarolina
Massachusetts
Indiana
Minnesota
Virginia
Connecticut
Kentucky
NewMexico
Michigan
Ohio
Pennsylvania
Montana
Colorado
Washington
Alaska
Wyoming
WestVirginia
Hawaii
Oregon
NewHampshire
Maine
Vermont
None
1 in 100
1 in 50
1 in 33
1 in 25
1 6 11 16 21 26 31 36 41 46 51
Rank
TotalCases
Masks
No
Yes
Governor
aa
Democratic
Republican
Total US COVID-19 Cases
p masks as of July 20, 2020: 0.063, p governor: 0.01. NB: association != causation.
2020-10-03 Summary: 21
22. Average US COVID-19 cases over the past
7 days
NorthDakota
SouthDakota
Wisconsin
Utah
Montana
Iowa
Idaho
Arkansas
Nebraska
Oklahoma
Missouri
Kansas
Alabama
Wyoming
Tennessee
Minnesota
Kentucky
Mississippi
Illinois
Nevada
Alaska
SouthCarolina
Indiana
Texas
Delaware
NorthCarolina
Louisiana
WestVirginia
Florida
NewMexico
Georgia
Colorado
Ohio
Michigan
Maryland
RhodeIsland
Virginia
Massachusetts
California
Pennsylvania
NewJersey
Washington
Hawaii
Oregon
Arizona
Connecticut
NewYork
NewHampshire
DistrictofColumbia
Maine
Vermont
None
1 in 10,000
1 in 5,000
1 in 3,333
1 in 2,500
1 in 2,000
1 in 1,667
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: 0.0024, p governor: 0.0093. NB: association != causation.
2020-10-03 Summary: 22
23. Total US COVID-19 Deaths
NewYork
Texas
NewJersey
California
Florida
Massachusetts
Illinois
Pennsylvania
Michigan
Georgia
Arizona
Louisiana
Ohio
Connecticut
Maryland
Indiana
NorthCarolina
SouthCarolina
Virginia
Mississippi
Alabama
Tennessee
Missouri
Washington
Minnesota
Colorado
Nevada
Arkansas
Iowa
Wisconsin
Kentucky
RhodeIsland
Oklahoma
NewMexico
Kansas
Delaware
DistrictofColumbia
Oregon
Nebraska
Idaho
Utah
NewHampshire
WestVirginia
NorthDakota
SouthDakota
Montana
Hawaii
Maine
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: 0.075, p governor: 0.33. NB: association != causation.
2020-10-03 Summary: 23
24. Total US COVID-19 Deaths
NewJersey
NewYork
Massachusetts
Connecticut
Louisiana
RhodeIsland
Mississippi
DistrictofColumbia
Arizona
Michigan
Illinois
Florida
Georgia
SouthCarolina
Delaware
Maryland
Pennsylvania
Texas
Indiana
Nevada
Alabama
Arkansas
Iowa
NewMexico
Ohio
California
Virginia
Minnesota
Tennessee
Colorado
Missouri
NorthDakota
NorthCarolina
NewHampshire
Washington
Kentucky
SouthDakota
Idaho
Oklahoma
Nebraska
Kansas
Wisconsin
WestVirginia
Montana
Utah
Oregon
Maine
Hawaii
Vermont
Wyoming
Alaska
None
1 in 2,000
1 in 1,000
1 in 667
1 in 500
1 6 11 16 21 26 31 36 41 46 51
Rank
TotalDeaths
Masks
No
Yes
Governor
aa
Democratic
Republican
Total US COVID-19 Deaths
p masks as of July 20, 2020: 0.04, p governor: 0.41. NB: association != causation.
2020-10-03 Summary: 24
25. Average US COVID-19 deaths over the past
7 days
NorthDakota
Arkansas
Mississippi
Florida
Missouri
SouthDakota
Tennessee
Georgia
Louisiana
SouthCarolina
Texas
NorthCarolina
Iowa
Kansas
Montana
Massachusetts
WestVirginia
Arizona
Illinois
California
Ohio
Indiana
Nebraska
Virginia
Oklahoma
Wisconsin
Alaska
Nevada
Kentucky
NewMexico
Minnesota
Hawaii
Delaware
RhodeIsland
Idaho
Utah
Michigan
Alabama
DistrictofColumbia
Pennsylvania
Washington
Maryland
Oregon
NewJersey
NewYork
Wyoming
Colorado
Connecticut
NewHampshire
Maine
Vermont
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 as of July 20, 2020: 0.02, p governor: 0.01. NB: association != causation.
2020-10-03 Summary: 25
26. 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
2020-10-03 Summary: 26
27. Change in daily tests over past 14 days
Alaska
Oregon
Colorado
Minnesota
SouthCarolina
Utah
NorthCarolina
Idaho
Oklahoma
Maine
Wisconsin
NewMexico
Kentucky
WestVirginia
Connecticut
Wyoming
Louisiana
Illinois
Ohio
Michigan
Arkansas
Texas
Montana
Virginia
Missouri
California
NorthDakota
NewYork
NewHampshire
Kansas
Washington
Nebraska
Tennessee
SouthDakota
NewJersey
Mississippi
RhodeIsland
Iowa
Nevada
Massachusetts
DistrictofColumbia
Delaware
Indiana
Pennsylvania
Georgia
Alabama
Florida
Vermont
Maryland
Arizona
Hawaii
-5.0
-2.5
0.0
2.5
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: 0.71, p governor: 0.17. NB: association != causation.
2020-10-03 Summary: 27
29. Percent of Positive COVID Tests
Arizona
Idaho
Mississippi
Alabama
Florida
SouthDakota
Texas
Kansas
Nevada
Iowa
SouthCarolina
Georgia
Nebraska
Missouri
NorthDakota
Utah
Indiana
Wisconsin
Arkansas
Pennsylvania
Maryland
Colorado
Oklahoma
RhodeIsland
Delaware
Louisiana
Virginia
Minnesota
NorthCarolina
Tennessee
Wyoming
Massachusetts
NewJersey
California
Illinois
Kentucky
Oregon
Ohio
Washington
Hawaii
NewYork
Montana
DistrictofColumbia
Michigan
Connecticut
NewMexico
NewHampshire
WestVirginia
Alaska
Maine
Vermont
0.0
5.0
10.0
15.0
1 6 11 16 21 26 31 36 41 46 51
Rank
PercentofPositiveTests
Masks
No
Yes
Governor
aa
Democratic
Republican
Percent of Positive COVID Tests
p masks as of July 20, 2020: 0.0034, p governor: 0.0018. NB: association != causation.
2020-10-03 Summary: 29
30. 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
2020-10-03 Summary: 30
31. Change in positive tests over past 14 days
NorthDakota
Montana
Wyoming
Wisconsin
SouthDakota
Alaska
Utah
Idaho
Missouri
Iowa
Kansas
WestVirginia
Hawaii
Nebraska
Indiana
Oklahoma
Minnesota
Mississippi
Oregon
Nevada
NorthCarolina
Delaware
NewMexico
Alabama
Florida
Pennsylvania
Colorado
Tennessee
Maryland
Washington
Michigan
Vermont
Georgia
RhodeIsland
Illinois
Louisiana
Massachusetts
Virginia
Arizona
Texas
California
NewJersey
Arkansas
Ohio
NewYork
DistrictofColumbia
SouthCarolina
Connecticut
NewHampshire
Maine
Kentucky
-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: 0.0044, p governor: 0.06. NB: association != causation.
2020-10-03 Summary: 31
32. Change in tests vs. change in positive tests
over last 14 days
AL
AK
AZ
AR
CA
CO
CT
DE
DC
GA
HI
ID
IL
IN
IA KS
KY
ME
MD
MA
MI
MN
MS
MT
NH
NJ
NM
NY
NC
ND
OH
OK OR
PA
RI
SC
SD
TN
UT
VT
WV
WI
-1
0
1
-5.0 -2.5 0.0 2.5 5.0
Change in tests (%/day)
Changeinpositivetests(%/day)
Change in tests vs. change in positive tests over last 14 days as of 2020-10-03
Size is proportional daily deaths per capita over the past 7 days
2020-10-03 Summary: 32
33. Current hospitalizations as a percent of peak
since FebruaryIowa
Missouri
Montana
Nebraska
NorthDakota
Oklahoma
SouthDakota
WestVirginia
Wisconsin
Wyoming
Arkansas
Alaska
Kansas
Utah
Kentucky
NorthCarolina
Indiana
Oregon
Tennessee
Minnesota
Ohio
Idaho
Virginia
Georgia
Alabama
Hawaii
Washington
Mississippi
SouthCarolina
Nevada
NewMexico
California
Illinois
RhodeIsland
Texas
Colorado
Louisiana
Delaware
DistrictofColumbia
Florida
Maine
Maryland
Pennsylvania
Michigan
Arizona
NewHampshire
Massachusetts
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: 0.0041, p governor: 0.055. NB: association != causation.
2020-10-03 Summary: 33
34. 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
2020-10-03 Summary: 34
35. Change in hospitalizations over past 14
days
NewHampshire
Wisconsin
Wyoming
Montana
SouthDakota
Connecticut
Iowa
NorthDakota
Minnesota
Utah
NewYork
Nebraska
Massachusetts
Indiana
RhodeIsland
NewMexico
Delaware
Arkansas
Pennsylvania
Oklahoma
Oregon
Missouri
NewJersey
Colorado
Ohio
Michigan
Kansas
Tennessee
Illinois
DistrictofColumbia
Kentucky
WestVirginia
Arizona
NorthCarolina
Washington
Alaska
Maine
Maryland
Texas
Alabama
Idaho
Nevada
SouthCarolina
Virginia
Georgia
Mississippi
California
Florida
Louisiana
Vermont
Hawaii
-2.0
0.0
2.0
4.0
1 6 11 16 21 26 31 36 41 46 51
Rank
Changeinhospitalizations(%/day)
Masks
No
Yes
Governor
aa
Democratic
Republican
Change in hospitalizations over past 14 days
p masks as of July 20, 2020: 0.39, p governor: 0.98. NB: association != causation.
2020-10-03 Summary: 35
36. 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 2020-10-03
NA = Inadequate data
2020-10-03 Summary: 36