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When will the Media and the People in Position of Authority start telling the truth to Americans concerning the risk profile of Covid-19, instead of fomenting panics and hysteria.
It is something else to listen to the media again explaining they are doing a great job when every night all they do is feed anxiety of their audience, including parents and children. Shame on them all...again. Yes, again, because this is the new culture of the past 30 years, where every new knowledge on psychological manipulation quickly finds its way at the top of the Nightly News techniques.submitted by Denys_Picard to u/Denys_Picard
For example, the numbers we hear every night, when they are not visually thrown in our faces. Let's try to bring some pondering to this.
Even some"specialists are being caught explaining the big dangers in the "death rate" of this virus..."for seasonal flu: Its is 0,1% and here for Covid-19 it is 1,5%, even higher in China, up to 3,5%..."
This is the ultimate of Fake news, something comitted out of naivete, from experts whom trust other experts, whom have spoken to a more bigger expert, etc...and sometime out of straight malicious intent.
These media "people" are comparing oranges and apples in the most classical style.
And I will try, so as to not add to the confusion, to take the example of the Seasonal flu to make things clear. From the Weekly U.S. Influenza Surveillance Report on the CDC website we can read:
From: Weekly U.S. Influenza Surveillance Report Week 12 2020 (take note that week 12 extended about 10 days because of the Covid situation). Further, this is week 40 of the whole Season, which starts of Sep 29 2019, and week 12 in 2020.
Now, in the top blue section you can read:
at Least 39 million flu illnesses;
24,000 Deaths from flu.
Illnesses must be understood strictly as an Estimation (it is also referred to as infection, infection rate, infection in the population, illness in the population). This is important, because just after they say Hospitalization, this is almost entirely a measured amount, very little is estimated. Then the Deaths (also often referred to in the literature as the fatalities, and of which some may be estimated and others counted).
Then in the table you see how much total tests where employed up to March 21st (Cumulative). That is 1,2 Million people where tested for the common seasonal flu. Of these, 242,330 tested positive. This results with a 20% positive rate of testing. The number you hear every night blasted into your eyes and ears is this last number, the tested positive cases, which they refer to simply as the "cases".
Now, you must understand that there is a technique the CDC employs to make the estimation of infected people in the population. They use a multiplier, this multiplier is sometime used against the tested positive cases, sometime against the hospitalization level.
An early estimation made by the CDC in 2009 concerning the Swine Flu H1N1 (2009) was a multiplier of 79. Estimates of the Prevalence of Pandemic (H1N1) 2009, United States, April–July 2009
"...23 July 2009, a total of 43,677 laboratory-confirmed cases, 5009 hospitalizations, and 302 deaths had been reported to the Centers for Disease Control and Prevention (CDC)..."
In this case, you multiplied 43,677 confirmed cases against the multiplier 79 = 3,450,000 estimated illnesses.
That is Cases confirmed x Multiplier = Estimated illnesses (or infections) in the general population.
By the end of the crisis, this multiplier had increased quite a bit. Because the last Illness estimation was 61,8 million infections and 240,000 hospitalization. Doing the reverse calculation we get:
61,800,000/240,000 = 257...this was the end multiplier. So, from the July cumulative data of 2009, the multiplier was 79, but by the end of the crisis in may 2010, this multiplier increase about 3 fold to become 257.
An intermediate multiplier from data up to October went as follows:
From the CDC report "2009 H1N1-Related Deaths, Hospitalizations and Cases:Details of Extrapolations and Ranges: United States,Emerging Infections Program (EIP) Data
AS you can read: Calculate cases (which here is the wrong expression and it creates confusion, it should read: Calculate illnesses or infections): Multiply Hospitalization by 221,79.
So right here, is where the CDC often uses the term Cases when they should use Illness or Infections, because in most reports when they use cases, it means diagnosed confirmed cases, not estimation in the population of illness. Fauci himself is often caught in this confusing and misleading language.
You can see for yourself from the same document, they estimated cases by November 2009 to be 14 to 34 millions. Clearly this is the Illness level, not the confirmed cases.
Now, the whole media is stuck up in this hysterics by making you believe that you have 1 chance in 50 of dying or so if you catch the virus...that is not true. In fact, you had, for H1N1 less probability of dying from H1N1 than from the seasonal flu by a ratio of 3 to 1.
Currently, neither the CDC nor the WHO have calculated a Multiplier for Covid-19. The Total rate of Hospitalization is difficult to have. On the 25 of March, Andrew Cuomo, in a press conference live on NPR explained that about 1 in 5 confirmed cases is hospitalized currently. On that day, The larger NY City area had about 25,000 cases confirmed, which mean they had about 5,000 hospitalization.
A paper earlier this month from the CDC Mortality and Morbidity Weekly: Severe Outcomes Among Patients with Coronavirus Disease 2019 (COVID-19) an early report when they were 4229 confirmed cases across the US, had a rate of hospitalization of 58% (2,449 out of 4229).
Some independent papers have calculated tentative Multipliers. It's the case of the paper which was used for the structure of the article in the NYTimes of March 20th "Coronavirus Could Overwhelm U.S. Without Urgent Action, Estimates Say". The paper is entitled "Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2)" The problem with the NYTimes is that they used color saturation excessively to terrorize people, like they do with Hurricane maps, when they want the people to listen. They don't say how the authors of the paper contributed to its use by the NYTimes. The NYTimes explains that they used their database of Covid pandemics from US cases.But the 500,000 new cases per day appears an exageration...But the scientific paper is excellent, it uses a complex statistical mathematics modeling which is explained over 30 pages Supplemental data.
There might be some short comings, like there always is in these kinds of work, firstly because many parameters must be estimated. And further, for example, some of the tools they use make the assumptions of Gaussian distributions, when progressively it is understood that epidemics, like the weather and networking are non-linear distributions. For example, they use what is called an Ensemble Adjustment Kalman Filter. A model developed in2008, as an adjustment or refinement on the original 1950 Ensemble Kalman Filter. But in climate science,which obeys to laws where the 4 parameters of a distribution are mobile, the Gaussian has only 2 of these, the Mean and the Variance, while the 2 others Kurtosis and Asymmetry are fixed at 2 and 0 respectively.
Parameters of the Standard Normal Distribution are (2,0,1,0), Kurtosis (alpha α ), Skewness (Beta β), Standard Deviation (Sigma σ ) and Mean (Mu µ) respectively.
From the wikipedia Stable Distribution page: https://en.wikipedia.org/wiki/Stable_distribution
The Stable Distribution has always been the parent of most probability distributions, the normal distribution just being a special case of it. The Stable distribution s very complex, to me also, and many attempts have been made over the decades to make integration in many linear models so they perform with better likelihood. Recently, an academic paper has created a more pragmatic approach to it : "...this paper attempts to present a uniform analytical approximation for the stable distribution based on matching power series expansions. For this solution, the trans-stable function is defined as an auxiliary function to evaluate the stable distribution." Asymptotic Expansions of the Stable distribution.
For a graphic represention of the fluidity of the Stable Distribution in regard to the sensitivity of a change in its papameters, you can go to John P Nolan's page.
Recentely, to better adapt the Kalman Filter, climate science has created the Local Ensemble Adjusted Kalman Filter which makes use of of outlayers instead of doing data softening. Network Theory also has made adaptation to take into account the non-linearity of dynamics modeling. Epidemiology is a close relative of both Climatology and Network theory.
Non-Gaussian statistics in global atmospheric dynamics: a study with a 10 240-member ensemble Kalman filter using an intermediate atmospheric general circulation model :
"This study also discusses how many ensemble members are necessary to represent a non-Gaussian PDF (probability distribution function) without contamination due to the sampling error, as higher-order non-Gaussian statistics are generally more vulnerable to the sampling error due to a limited ensemble size."
From the result of their paper, you can infer a multiplier, they used the Wuhan Proving data of Confirmed cases, which stood on the 24 of January at 370. From there, they estimated that 13,118 people were infected in the population. So 13,118/370 = 35. Would this be a reliable multiplier to use against the confirmed cases in the US...it is very difficult to say.
Another original statistical approach is by Tian Hao,Infection Dynamics of Coronavirus Disease 2019 (Covid-19) Modeled with the Integration of the Eyring's Rate Process Theory and Free Volume Concept
I won't explain his approach, but is a short cut approach when too many parameters must be evaluated. His multiplier is 10.
Using the Spreadsheet available at the CDC on the REED's model for Multiplicator, I obtained a multiplier of 110, when confirmed cases were 19,240 in the US. My feeling was that this was a bit high, I thought, from reading quite a bit that would be between 40 and 90. But I made some assumptions myself for lack of information.
But these multiplier may give you an idea of what is the real fatality risk of Covid 19.
For example, with a multiplier of 10, today March 29, with 125,000 cases in the US and about 2000 fatalities, you first calculate the Case Fatality Rate (CFR): 2,000/125,000 = 1,6%.
Then you calculate the Infection Mortality Rate, dividing the CFR by the multiplier:
1,6% / 35 = 0,046%
1,6% / 10 = 0,16%
So, you sit between a bit more than the Seasonal Flu, to half of it.
But these numbers may be misleading because a pandemic is something unique for which there is no empirical data, unlike the seasonal flu. Further, because of the massive rapid distribution, a different time of reactions of authorities, the different social structures where the virus may settle...Chronic events statistics, such as those of the seasonal flu, and those of exceptional event, may not really compare and may misinform. What is important to understand, is that the number of cases you see in the media for Covid-19 do reflect the amount of people infected in the population. So the case mortality rate doesn't reflect your probability of death if you catch the SARS-CoV2 virus.
The WHO explained this in detail in the Situation Report 46 (note here they use the term Crude, from the British, instead of Case: but they o use Infection Mortality Rate):
"Mortality for COVID-19 appears higher than for influenza, especially seasonal influenza. While the true mortality of COVID-19 will take some time to fully understand, the data we have so far indicate that the crude mortality ratio (the number of reported deaths divided by the reported cases) is between 3- 4%, the infection mortality rate (the number of reported deaths divided by the number of infections) will be lower. For seasonal influenza, mortality is usually well below 0.1%. However, mortality is to a large extent determined by access to and quality of health care."
It's the same thing with the new expression so popular among the Press, the Reproduction Rate (or Replication Rate). They say it's so high...blablabla...The thing is that they forget to mention that the Replication rate always starts high and then falls shortly towards, and eventually below, 1.
It's not that this situation doesn't call for attention, and action, it is just the tone which is irresponsible by many people in position of authority. We are not helping ourselves by being anxious, and projecting this anxiety on our environment and loved ones.
For example, an early estimation of the H1N1 virus Pandemic was for:
In the end there were:
61,8 Million illnesses
A departure, but early forecasts often suffer from this wide excesses. But, lets contain, lets mitigate, and let's not panic...and tell your media they should learn to speak English or French instead of Anxiety, music having always been my first language (as a listener of course)...
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[Updated] DigitalNotchedBattery v1.1.0
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[Updated] VolVibes v1.4
[Updated] TrueCuts v1.0.0-2
[Updated] AirPort for iOS 13
[Updated] Dots 2 v0.3
[Updated] ColorizeChat v1.0.2
[Updated] SBWelcomesYou v1.1
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[Updated] QuickLS v2
[Updated] Compactor v1.0.2
[Updated] FlashBack v4.0.1
[Updated] TokeTime13 v1.0-2
[Updated] Foldy v1.1
[Updated] Cartella v0.6
[Updated] Pastel v1.2