The HIV Pandemic … Extrapolation on U.S. HIV Incidence for 2014 and 2015 … by Alice B. Clagett ..

Revised 07/06/2017; new text is in green font

Werken_van_Barmhartigheid,_Meester_van_Alkmaar_(1504)“The Seven Works of Charity,” by the Master of Alkmaar, 1504, public domain (1)

[Blue font indicates information added after the blog was published. –Alice]

  • A Survey of the 2014 HIV Incidence Data
    • The Deceptive Effect of Data ‘Smoothing’
    • Data for Central and Northwestern United States Cannot Be Extrapolated Because the Data Have Been Suppressed
    • Thoughts on the Data that Have Not Been Suppressed: The East Coast, the Gulf Coast, and the Southwestern United States
    • 20.3% Whitewashing Effect of Data Smoothing for Median 2014 HIV Incidence
    • 62.4% Whitewashing Effect of Data Smoothing for Highest 2014 HIV Incidence
    • Questions Regarding Tabulation of 2014 Data
  • CDC Report on Data for HIV Incidence, 2009-2013
  • The Accelerating Course of Pandemics
  • My Extrapolations for HIV Incidence, 2014 and 2015
  • Guidelines for Community Preparation for the HIV Pandemic

Dear Ones,

It looks to me that United States government figures have been revised on HIV incidence in 2014.


The Deceptive Effect of Data ‘Smoothing’. Take a look at … You’ll find yourself on the State View. This view smoothes out the data on all the counties in a state. Since many of the counties’ data are suppressed, the data on the State View are essentially a way to whitewash the facts; this smoothing technique is often used in statistical analysis to conceal an underlying problem.

Now click on the button “View Counties” … and you will get the county view. In that view, all the areas of the United States that appear to be white are areas where data on HIV incidence for 2014 have been suppressed. On the face of it, it would appear that the great majority of United States counties have declined to report their data. There also exists the possibility that their data have been suppressed at higher levels of government, and this ought to be looked into by the appropriate parties.

Data for Central and Northwestern United States Cannot Be Extrapolated Because the Data Have Been Suppressed. Looking only at the counties for which data have been suppressed, one sees there are essentially no data for the central and northwestern United States. As nearly all the data have been suppressed, this leaves us no room for extrapolation for the central and northwestern United States.

Thoughts on the Data that Have Not Been Suppressed: The East Coast, the Gulf Coast, and the Southwestern United States. A fair amount of counties on the East Coast, the Gulf Coast, and in the Southwestern United States offer data that has not been suppressed. To overcome the concealment effect of the State View smoothing factor, we can extrapolate for each of these areas on the basis of the reporting counties alone. In other words, we won’t allow the ‘zero’ effect of the suppressed data to weight the data so that the pandemic appears to be less advanced than it truly is.

Note that the color keys for the State View and the County View have different HIV incidence values, as follows:

State View        County View              Color
not shown        data suppressed        white with bars *
0-4.8                 1.8-7.2                         cream colored
5.2-9.0              7.3-11.2                       very light green
9.2-12.8            11.3-16.2                     medium bluegreen
13.8-20.4          16.3-27.0                     darker bluegreen
20.7-66.9          27.4-178.0                   darkest bluegreen

20.3% Whitewashing Effect of Data Smoothing for Median 2014 HIV Incidence. As I mentioned above: Since many of the counties’ data are suppressed, the data on the State View are essentially a way to whitewash the facts; this smoothing technique is often used in statistical analysis to conceal an underlying problem.

By comparing the HIV incidence value ranges for the State View to those of the County View in the table above, the extent of this whitewashing becomes clear.

For instance, for the State View, the median value might be calculated as the average of the middle (medium bluegreen) value range. That, then, would be:

(9.2 + 12.8) / 2 = 22 / 2 =
11 as a median 2014 HIV incidence in the State View (the default view of the map, for which data smoothing occurred)

Then for the County View, the median value would be:

(11.3 + 16.2) / 2 = 27.5 / 2 =
13.8 as a median 2014 HIV incidence in the County View (the hidden, but clickable, view of the map, where there was no data smoothing)

The State View whitewashing effect for the median 2014 HIV incidence might be calculated as:

[(13.8 – 11) / 13.8] x 100 =
[2.80 / 13.8] x 100 =
20.3%. This is the whitewashing effect for the median 2014 HIV incidence value.

62.4% Whitewashing Effect of Data Smoothing for Highest 2014 HIV Incidence. The highest value for 2014 HIV incidence is 66.9 for the State View (the default view) and 178.0 for the County View (which is the hidden, but clickable, view). For this highest value, then, one might calculate the whitewashing effect as follows:

[(178.0 – 66.9) / 178.0] x 100 =
[111.1 / 178.0] x 100 =
This is the whitewashing effect for the highest 2014 HIV incidence value.

Questions Regarding Tabulation of 2014 Data. Regarding these keys, I have the following questions:

  • Why do the values on the State View differ from those on the County View?
  • Why are there gaps between the data ranges? For instance, shouldn’t the second category on the State View begin where the first category left off, like this: 0-4.8 (first category) and then 4.9-9.0 (second category)? What about the data that would fall in the gaps between the value ranges … have these ‘gap’ data been overlooked?
  • I have a question about the  data for the “darkest bluegreen” category: Why is the data range for the County View (27.4-178.0) so large? Does this indicate that a logarithmic scale is being used? I ask this because it seems likely to me that the use of a logarithmic scale indicates that the people who composed the value ranges are anticipating big increases in the values, or else that such increases have already occurred.
  • Another thought on the data for the “darkest bluegreen” category: For myself, I would like to see a four further breakdowns in this range, so that I might see on the graph how many counties have an incidence in the range of 27.4-50.0, 50.0-100.0, 100.0-150.0, and 150.0-178.0, for instance. That way the future curve of the pandemic might be more accurately predicted.
  • Also, along technical lines, there is a text overlay on the County View that makes the color keys difficult to read. Consequently, I may have misread the value ranges for the County View.


An “HIV Surveillance Report:  Diagnoses of HIV Infection in the United States and Dependent Areas, 2013” is available online: (2)

In this report, there is a table that indicates HIV incidence per 100,000 for the years 2009 through 2013. This is”Table 1a. Diagnoses of HIV infection, by year of diagnosis and selected characteristics, 2009–2013—United States” (3). Here are the summary data:

2009: 15.3
2010: 14.6
2011: 14.1
2012: 14.4
2013: 15.0


The summary data in this table show an average baseline incidence per 100,000 of about 14.7. This baseline effect is similar to the baseline of stock prices, which sets the stage for the subsequent rise in the stock price, as may be seen with the well-known Dow Jones Industrials, NASDAQ, and S&P 500,  stock charts:

Comparison_of_three_stock_indices_after_1975.svg“Comparison of three stock indices after 1975” (4)

As can be seen from the above chart …

  • For the stock market, there was a baseline, or momentum building effect from 1975 to 1985.
  • Then there was the beginning of an acceleration in price from 1985 to 1995.
  • Then there was a big rise in prices from 1995 to the year 2000.

The course of pandemics is similar to the above chart. There is a baseline or momentum building effect, as was the case with the CDC data for 2009 to 2013.

Then follows the beginning of an acceleration of incidence, such as might follow from my extrapolations on HIV incidence in the section of this blog that you are about to read.

After that there is an accelerated phase of the pandemic, during which time it would be good to already have in place the community support measures I propose in the final section of this blog, the Guidelines for Community Preparation.


Extrapolating for the East Coast, the Gulf Coast, and California, on the basis of the reporting counties in the County View of this map: …

On the East Coast, the Gulf Coast, and California, it appears that the majority of the reporting counties fall in the “darker bluegreen” to “darkest bluegreen” categories, whose HIV incidence per 100,000 for 2014 are 16.3-27.0 and 27.4-178, respectively (see above).

An estimated incidence of 27.4 per 100,000, which would be the border line between these two categories, would be a conservative estimate for these regions for 2014.

I calculate the 2015 incidence as double the 2014 incidence, based on my feeling that we’re currently in the moderate acceleration phase of the typical bell curve of an epidemic (as mentioned above, with the stock graph. For more on the bell curve, see “For More Information” below.)

Here are my extrapolations for 2014 and 2015:

2014: 27.4
2015: 57.8


  • Through schools, churches, social clubs such as Rotary and Lions Club, and in our immediate families, let us talk together about the current situation, and devise good strategies to meet the needs of our communities.
  • Let us make home means of testing for HIV readily available across demographics. I note that HIV tests are available in drugs stores such as Rite Aid for about $40.00. I suggest this alternative because of the social stigma connected with the HIV and AIDS ‘labels’.
  • It seems likely that statistics for women and children are greatly underestimated, as these groups until now have been considered as not being at risk. As this pandemic has begun to accelerate, such assumptions are not tenable. Women and children ought also be advised of risk, of testing procedures, and of treatment methods and prognoses, both traditional and alternative.
  • It seems likely, as the epidemic progresses, that traditional medical facilities will be overstretched. Let us look into the existence of, and efficacy of alternative medical techniques such as homeopathy, healing tinctures and crystals, and energy rebalancing techniques, as well as alternative ways to bolster the immune system, such as healing foods and yoga.
  • Let us look to our schools and churches as possible places where care may be provided if hospitals should be overstretched. Making plans for such an eventuality, even though it most likely will not occur, will ease the minds of everyone in our communities, I feel.
  • With a better understanding of the course of epidemics (see “For More Information,” below) it becomes clear that the survival of human beings with resistance to the infecting agent is the key to the survival of the human race. Thus it becomes important to keep our HIV-infected populace … including HIV-infected newborns … alive and well cared for. Among them will be the resistant strain of humankind that lives on into the future and preserves our species, as was the case with the deadly influenza epidemic of the early 1900s.
  • Let us look with an open heart to everyone whose immune system has been weakened by the HIV virus. Let us understand that this is no longer a disease affecting only men who have intercourse with men, or people who use intravenous recreational drugs. Straight families will be equally affected; men, women and children across the board, all over America. Let us lift the HIV social stigma, so that we can talk with each other about the times of tribulation we are now facing.
  • Let us stand with Christ in these times. Let us look to the work of feeding the hungry, giving drink to the thirsty, clothing the naked, sheltering the homeless, visiting those in prison, and burying the dead. (5)
  • Let us stand as people of faith, hand in hand with the people of all faiths. Let us do what we can for all peoples everywhere, as this is a time of tribulation that crosses all borders and affects all nations, all races, and all cultures. Let us stand together, with and for each other, in the highest love. Let us persevere through faith, in compassionate love, and in hope of a better tomorrow for all humankind.

In love, light and joy, Alice B. Clagett
I Am of the Stars


“Overview of the HIV Pandemic, and of Data Suppression by the Centers for Disease Control,” by Alice B. Clagett *, 7/10/2016, .. 

“Overview of Surgical Risk, HIV, AIDS, Hepatitis, and Hard Drugs,” by Alice B. Clagett *, 17 October 2016, ..


(1) at the Museum Boijmans Van Beuningen, from,_Meester_van_Alkmaar_(1504).jpg … public domain


(2) This report was prepared by the National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention, Division of HIV/AIDS Prevention, CDC. Since the CDC (Centers for Disease Control) is a public domain website, and the CDC is a federal agency, I’m assuming their work is in the public domain.

“A work of the United States government, as defined by the United States copyright law, is “a work prepared by an officer or employee” of the federal government“as part of that person’s official duties.”[1] In general, under section 105 of the Copyright Act,[2] such works are not entitled to domestic copyright protection under U.S. law and are therefore in the public domain.” — Wikipedia, … Text [of Wikipedia] is available under the Creative Commons Attribution-ShareAlike License; additional terms may apply.


(3) ‘Table 1a. Diagnoses of HIV infection, by year of diagnosis and selected characteristics, 2009–2013—United States:



(4) from Wikimedia, … public domain


(5) For more on the Corporal Works of Mercy, see


Except where otherwise noted, “I Am of the Stars: Awakening with Planet Earth” by Alice B. Clagett is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.


HIV, AIDS, community health, Christianity, grassroots action, social issues, HIV pandemic, Corporal Works of Mercy, data suppression, tribulation, compassion, End Times,

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