Showing posts with label Covid-19 statistics and graphs. Show all posts
Showing posts with label Covid-19 statistics and graphs. Show all posts

Wednesday, July 1, 2020

Progress in Covid 19 Deaths




DATA SOURCE = https://ourworldindata.org/grapher/total-daily-covid-deaths



The number of people dying from COVID-19 has declined since the second half of April. Because we now have so many data points, I plotted half months rather than 7-day periods as before.

Note that March and May have an extra day in the second half compared to April and June.

I hope that the recent surge in hospitalizations in some US states do not mean a return to higher death counts. Of course, the symptoms can be severe for some survivors. Nevertheless, the death rate is in decline.



We are seeing far more infections compared to European nations, which are now going to work and open for international travel. Resistance to safety recommendations appears high in some crowded areas like beaches in the US.

The European data suggest what could happen for the US if people would voluntarily follow the scientific guidance about quality masks, safe distances, and hand washing. Avoiding close contact with infected persons appears to work--of course, since we do not know who are asymptomatic carriers, precautions remain.


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Tuesday, May 26, 2020

Declines in weekly US Deaths on latest chart





If the posted data are accurate, we have an evident decrease on weekly deaths for the 7-days ending May 22 2020, which is the far right column. That is two weeks of decline and much lower than the April 18 column.

The bad news is of course that the US has reached 100,000 deaths.


Legend

M21 to A25 and M1 represent the Month and Day. The numbers below the dates are for the 7-days including the date. For example, M1 = 11,989 deaths in the 7-days before and including May 1, 2020. The first M dates are for March, then A for the April dates, then back to May again for M1 and so forth.

The data are beginning to look like a bell curve, but with many states allowing more freedom of movement, it is too early to tell if there will be a rise in a week or two.

The data are from


I use the download data file and create the chart in Excel.


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Tuesday, May 19, 2020

Weekly Progress US Death Rates Decline May15




We have an evident decrease on weekly deaths for the 7-days ending May 15 2020. Downward progress (green columns) resumed after the May 8 increase (yellow column).

Legend

M21 to A25 and M1 represent the Month and Day. The numbers below the dates are for the 7-days including the date. For example, M1 = 11,989 deaths in the 7-days before and including May 1, 2020. The first M dates are for March, then A for the April dates, then back to May again for M1 and so forth.

The data are beginning to look like a bell curve, but with many states allowing more freedom of movement, it is too early to tell if there will be a rise in a week or two.

The data are from


I use the download data file and create the chart in Excel.


Read more about statistics in these two books.


Creating Surveys on AMAZON










Read more about basic statistics in APPLIED STATISTICS: CONCEPTS FOR COUNSELORS at

AMAZON














Connections

Follow this blog

My Page    www.suttong.com

My Books  
 AMAZON     GOOGLE PLAY STORE

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 Geoff W. Sutton

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LinkedIN Geoffrey Sutton  PhD

Publications (many free downloads)
     
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Sunday, May 10, 2020

Covid Weekly Chart of US Deaths update





We have a slight increase on weekly deaths for the 7-days ending May 8 2020. Downward progress (green columns) has halted.


Legend
M21 to A25 and M1 represent the Monty and Day. The numbers below the dates are for the 7-days including the date. For example, M1 = 11,989 deaths in the 7-days before and including May 1, 2020. The first M dates are for March, then A for the April dates.

The data are beginning to look like a bell curve, but with many states allowing more freedom of movement, it is too early to tell if there will be a rise in a week or two.

The data are from


I use the download data file and create the chart in Excel.


Read more about statistics in these two books.


Creating Surveys on AMAZON












Read more about basic statistics in APPLIED STATISTICS: CONCEPTS FOR COUNSELORS at

AMAZON














Connections

Follow this blog

My Page    www.suttong.com

My Books  
 AMAZON     GOOGLE PLAY STORE

FACEBOOK  
 Geoff W. Sutton

TWITTER  @Geoff.W.Sutton

LinkedIN Geoffrey Sutton  PhD

Publications (many free downloads)
     
  Academia   Geoff W Sutton   (PhD)
     
  ResearchGate   Geoffrey W Sutton   (PhD)


Monday, April 27, 2020

Covid19 U S Deaths Weekly Chart



Are we there yet? The most recent weekly data suggest a downward move in the number of people who died in the past week.

In the previous chart, there appeared to be a channel or range between 1800 and 2000 per day with some anomalies. Now that we have more data, it is possible to group the data.

I am avoiding curves and means because it is not clear that there is a curve or that the data are normally distributed. The bar chart offers a clear picture of rapid increase and possibly (and hopefully a decline.

In this chart, I used weekly totals beginning with March 7, 2020 (M = March, A = April).

Important note: The numbers may be revised. This chart is for educational purposes only and not for planning.

Here is my source for the data  https://ourworldindata.org/grapher/total-daily-covid-deaths

Date               Deaths

M21 213
M28 1447
A4 5450
A11 11620
A18 18277
A25 13963







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Tuesday, April 21, 2020

Charting Troubles and Covid-19




How will we know if opening up the country is safe?

My bottom line answer is when people stop dying from Covid-19.

But tracking actual deaths can help us know whether we are making progress or not.

We can measure progress against our own baseline. The chart provides an estimate of a baseline, which will need to be corrected when additional deaths are recorded.

Think of the baseline as a channel.

The national US baseline appears to be in the range of 1800 to 2000 with some outliers starting 8 April 2020. I am not using averages because the data are not obviously normalized. I prefer to look at a range of relatively stable values, which can be called a channel. The sharp deviations have to be ignored to get a sense of what is "typical" of a pattern. A new relatively stable range above or below this range should help us determine a new trend. If the numbers are corrected, we will need to revisit the range. I am looking at a move of about 20% either way for evidence of a new range.

Local baselines will be more informative.

I admit, that the suffering for many with Covid-19 looks terrible even when they survive. But there seems to be quite a variation in discomfort of survivors from near death pain and distress to "I didn't know I was infected."

Problem Charts- So Many Ways to Tell a Story

I am fed up with charts showing invalid comparisons, wildly hypothetical projections, and tables incorrectly comparing Covid-19 to other conditions.

But when I tried to grab the raw data and focus on how many of our fellow Americans actually died, I realized that even these data are estimates as you can see from the spikes representing people found on a certain day that had not been counted.

So, after the rapid ramp up, in March we see a rough baseline between 1800 and 2000 deaths per day between 8 April and yesterday, 20 April.

The arithmetic mean is not helpful given the outliers. The data are not normal so far, so forget standard deviations.

The curves referred to by scientists and repeated by news readers are not yet evident. There is a part of the data that curves upward until around April 6 or so.

Death is a lagging indicator as some have said. This makes obvious sense because we do not know if an infected person will survive or not.

Some charts extend lines into the future. That only makes sense if we have accurate past data and if the data we have establish a pattern.

Flatten the curve only makes sense if there is a curve. I would prefer "create a plunge" meaning that there is a plateau and we want to get down near zero in as straight a line as possible.

Some use log charts- I am not a fan when we do not know the trend.

Some charts plot moving averages. That's a good idea--as long as you understand the concept and use the best averaging method. For example, are you going to average 3-day, 5-days, or what?

Testing

Testing is important personally and locally but the national numbers are too iffy because we do not know how many people are tested with what type of test and the level of accuracy of such tests. (Tests are not perfect.)

Testing is most valuable to isolate infected people from loved ones, co-workers, and others. Testing will be even more helpful when there is treatment for the condition.

Testing can help identify "hot spots" needing immediate resources like a nursing home or workplace.

I support testing. But charts of confirmed cases are not useful until an entire population is tested.

And testing with different tests having different levels of accuracy interfere with making other than rough judgments.

Media Folk Science

I'd like to suggest quarantining so many social media posts but that's not going to happen. Best wishes on learning statistics and charts. It really can help you make wise decisions for your personal life as well as in schools and workplaces.

Check the data

Important note: The numbers may be revised. This chart is for educational purposes only and not for planning.

Here is my source for the data  https://ourworldindata.org/grapher/total-daily-covid-deaths

p.s. Data is a plural noun. Say and write "data are" NOT "data is."

1-Apr 909
2-Apr 1059
3-Apr 915
4-Apr 1104
5-Apr 1344
6-Apr 1146
7-Apr 1342
------
8-Apr
------
1906
9-Apr 1922
10-Apr 1873
11-Apr 2087
12-Apr 1831
13-Apr 1500
14-Apr 1541
15-Apr 2408
16-Apr 4928
17-Apr 2299
18-Apr 3770
19-Apr 1856
20-Apr 1772
21-Apr 1857

Added data not in the chart

              22-Apr                    2524
              23-Apr                    1721
              24-Apr                    3179    Median April 8-24 = 1906
              25-Apr                    1054
              26-Apr                    2172
              27-Apr                    1687


Read more about statistics in these two books.


Creating Surveys on AMAZON















Read more about basic statistics in APPLIED STATISTICS: CONCEPTS FOR COUNSELORS at

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My Page    www.suttong.com
  
My Books  AMAZON                       GOOGLE STORE

FACEBOOK   Geoff W. Sutton
TWITTER  @Geoff.W.Sutton

Publications (many free downloads)
 
Academia   Geoff W Sutton   (PhD)     

  ResearchGate   Geoffrey W Sutton   (PhD)






Sunday, March 29, 2020

Death and Treatment Need Statistics 2020 Pandemic



Statistical models are needed to guide government leaders and health service providers concerned with maximizing the number of infected people who survive and providing high quality care to those in need.

All models have multiple assumptions. In the midst of a pandemic such as Covid-19, new data are constantly being processed. Thus, parameters will need to be changed as new data change the models. Multiple outcomes must be considered without biased interpretations favoring either lower or higher estimates.

NYT 13 March 2020 reported by Sheri Fink

This report refers to four scenarios and is early than the ones further down the page.

"Between 160 million and 214 million people in the United States could be infected over the course of the epidemic, according to a projection that encompasses the range of the four scenarios. That could last months or even over a year, with infections concentrated in shorter periods, staggered across time in different communities, experts said. As many as 200,000 to 1.7 million people could die."

HealthData.org 25 March 2020 research paper submitted for peer review.

This document can be downloaded for careful study. The authors detail their assumptions and provide charts and tables indicating projected deaths and needs for various resources such as ICU beds and ventilators. The data are for the US as a whole as well as for each state. Figure 9 projects the cumulative deaths plotted by month and the range from low to high is wide. They estimate 81 thousand deaths over the next four months. Again, the estimate is subject to a wide margin of statistical error. For most states, peak hospital needs are projected to occur in April (See Table 1).

Dr. Fauci March 29 2020 also in Slate

According to Taylor Hatmaker writing for Techcrunch, Dr. Fauci estimated between 100,000 to 200,000 deaths from Covid-19.

Bases for Comparison
For the USA

  Number of deaths  = 2,913,503 (population in 2018 = 327.2 million)
  Life expectancy = 78.6 years (This will vary with various parameters)
  Influenza and Pneumonia deaths = 55,672 
         Note: 7 conditions cause more deaths according to the CDC 

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Wednesday, March 25, 2020

Watch those Covid 19 curves


Some highly intelligent scientists have used available data to plot trends related to the spread of Covid-19 such as number of identified infections, recoveries, and deaths. Others plotted ideas rather than data such as flattening the curve. Some rely on "common sense" and others rely on data.

Given my experience in teaching graduate research methods and statistics and talking with physicians and psychologists about statistics, I think it worth reminding any readers of this post that a lot of very intelligent people have difficulty with statistical models. There are many PhDs younger than I who know more methods of sophisticated data analyses than I learned during my PhD or since; however, not everyone stops to consider their assumptions when modeling data.

That's my point here--we need to review and challenge the assumptions about the data, the way the data are plotted, and how people interpret those data.

Missing data are very important to developing an accurate model. 
Unfortunately, in the early stages of a pandemic, there are a lot of missing data. Missing data can skew distributions. And it is very hard to identify outliers until sufficient data are available. People using different testing methods have different delay times in getting results. Some nations have more test kits in service than others.

Validity of data is crucial to developing a valid model. 
We have data from various nations, but we do not have evidence of verification of those data. Nor do we know if we are dealing with sufficient samples to make judgments about the population of an entire nation. Data from a small country can yield very different ratios than data from a large country. We need to know what percentage of a population are in the tested sample.

Generalization of data from one nation to another can be misleading.
In any research study, scientists usually caution readers about the limitations of their research. The current "real-time" reports of data translated into graphs showing different trends for different nations do not necessarily consider all the relevant variables that ensure trends from China may be applicable to future trends in the UK or the USA. So, in addition to the above problems, we cannot assume that the trend lines will be the same.

Different methods of plotting can lead to different conclusions.
I've seen plots of raw data and transformed data. Remember all that high school math about logarithms? Do you recall exponential growth curves? Plotting raw data is a good idea. Transforming those data requires that the assumptions for transformation make sense. It's one thing to present data in different ways as one seeks to understand the spread of a virus (or any other behavior), but it is quite another thing to make decisions for the public based on such assumptions.

Drawing conclusions from early data can lead to bad policy. 
It is generally wise to be conservative in drawing conclusions about data--especially when we do not know enough to truly plot future trends. I do not claim to know the best way to monitor the extent of the human tragedy due to Covid-19 virus effects. My inclination is to skeptically monitor the available data and various models, and follow conservative guidelines for the sake of my health and the health of others.

We might think about who has the most to gain from advice to do this or that in response to health and related economic policies during the time of the current pandemic and at other times as well. People usually promote ideas that are in their best interest (self-serving bias) and they view data and draw conclusions influenced by a confirmation bias (we ignore data that contradicts our assumptions).

In the case of Covid-19 policy, we might ask the following questions.

  1. Who will win or lose a future election based on the number of people who get infected or die?
  2. Who will gain or lose large sums of money based on the number of people who get infected or die?
  3. Who will win or lose a future election based on how well the economy is doing?
  4. Who will win or lose a future election based on the number of people who are unemployed?
  5. Who will gain or lose large sums of money based on the length of time people are under severe restrictions?
  6. Who will benefit from claiming shortages of various items considered "essential" or "nonessential"
  7. Who will benefit from having their employment labeled as "essential" or "nonessential?"
  8. How do decisions made during a crisis based on incomplete data reveal prejudice or even hate? For example, are the elderly worth less than the young? Are foreigners worth less than citizens? Are the lives of the wealthy worth more than the lives of the poor?
  9. What data tell us how policies will deferentially affect vulnerable populations? For example, I have hypothesized that requiring some people to remain at home might increase child abuse and domestic violence. Others have wondered about increased suicide in response to some healthcare policies focused heavily on Covid19.

You can probably generate more questions based on your profession and /or knowledge base.

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