[CCP Genocide] The Truth of CCP Virus: The Only Way to Get Out of the “Curse of Sisyphus”

Author: Sister Karamazov [G-Translators/Authentic writing Team]

Proofread by: Cutecutepunk

Source: https://brazilian.report/opinion/2020/05/16/health-minister-nelson-teich-chose-president-jair-bolsonaro-sisyphus/

The outbreak of the CCP virus has brought colossal suffering to the whole world, and human life was therefore forced to change. Apart from wearing suffocating masks and practicing social distancing, many people may have formed a new habit: checking all kinds of fancy CCP virus data visualization such as daily/cumulative infection counts, hospitalization counts, death counts, 7-day average counts, R number, future forecasts, etc. For some of them, following these numbers has even become something like a daily ritual.

However, there are more than plenty of these data visualizations and infographics in the “big data” era. All kinds of media platforms, government websites, research institutes, or NGOs have been mass-producing data charts and graphs 24/7. But the real question is: how useful are they? For CCP virus-related infographics, how much real “info” can we get from the “graphics”?

In my humble opinion, those popular data visualizations and infographics are only helpful to some extent. However, concerning our grand and final goal, which is completely overcoming the virus, the function of these data visualizations is somewhat limited and might have even diverted too much public attention. To make matters worse, so many of the forecasts based on the epidemiological data are just totally wrong. In contrast to tons of data visualizations achieving so little (regarding the goal of overcoming the virus), the importance of Dr. Li-Meng Yan’s three reports (with the fourth one on the way) has become increasingly significant. It is because the fundamental point of the pandemic is never about the superficial epidemiological infection data. Instead, the truth lies in this human-modified virus nature from a virology point of view. And so far, nobody else has done more extensive, detailed, and thorough work on decomposing the structure and manufacturing process of the CCP virus than Dr. Li-Meng Yan.

Descriptive Data – Very Detailed and Fancy. So What?

It is not hard to find rather complicated and delicate CCP virus infection data visualizations with all kinds of measures online nowadays. For example, the following data dashboard is from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU):

Source: https://coronavirus.jhu.edu/map.html

As can be seen from the snapshot above, readers can find measures such as daily or cumulative infection counts, death counts, test counts, and death rates within a couple of clicks. Hmmm, it looks like a very informative dashboard, right?

Also, there is CCP virus infection data published by each county in the US. Take LA County as an example (shown below): the county-level data is really getting into nuts and bolts. You can even find hospitalizations, positivity rate, and death rate by race or poverty level. And LA County is definitely not the only county with such detailed interactive data visualizations. You can find similar information on almost any of the counties within the US.

Source: http://publichealth.lacounty.gov/media/coronavirus/data/index.htm

The third example is an article illustrating the R number from BBC. The author first introduced the concept of R number – “The R number is a way of rating coronavirus or any disease’s ability to spread. R is the number of people that one infected person will pass on a virus to, on average.” (Source: https://www.bbc.com/news/health-52473523) Then the time series of R number in the UK was shown:

Source: https://www.bbc.com/news/health-52473523

Similar to the three examples shown above, millions of CCP virus infection descriptive data graphs were produced and constantly updated in the past year, giving readers an impression that they might have provided a lot of useful information. Well, first we have to thank these data scientists, statisticians, or infectious disease scientists for taking the time and effort to make these graphs and data visualizations. From an epidemiology point of view, these data visualizations are indeed helpful, and the public can be informed of the pandemic situation through these detailed data. However, forgive me for being blunt – so what? More than one year after the initial outbreak, we’re still in the middle of a horrible pandemic, various mutants are still popping up here and there all over the world, and people die every minute because of the CCP virus. The diverse descriptive data visualizations are no more than describing the symptoms of a very ill patient: symptoms description can only play a supporting role if the doctor cannot find where the underlying disease is.

To make matters worse, these data visualizations and infographics have grasped a considerable portion of public attention, which, in my opinion, could have been focused more on tracking the origin of the CCP virus, identifying who is responsible, and ultimately finding the cure. It is the only way for humanity to walk out of the pandemic curse. Take another example: Mr. Olaf Gersemann is a journalist and the head of the business, finance, and real estate department of the WELT Group. He has been working very hard every day to update various kinds of CCP virus data graphs on his Twitter account for quite a while now. The graphs include but are not limited to daily/7-day average cases/deaths in Germany, case comparison with the previous week, R number in Germany, etc. Below are some graphs from his Twitter account:

Source: https://twitter.com/OlafGersemann?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor

Perhaps Mr. Gersemann’s mood fluctuates with the data fluctuations while he was making these graphs every day. Perhaps he’s crossing his fingers every day hoping the R number to be below 1, or the trend of the daily cases to downwards. And perhaps he’s wondering all the time when this whole pandemic is going to end, just like all of us. I feel him, and I admire his devotion to drawing graphs every day. 

I have to admit that I also kept checking all of these infection data during the first couple of weeks of the CCP virus outbreak. I was also desperately hoping the infection and death numbers to go down every day (Obviously, I was let down again and again). Luckily, later I gradually learned about the true nature of this virus, which is a lab-modified, unrestricted bioweapon created by CCP through Lude Media with Dr. Li-Meng Yan as an anonymous whistleblower. Then it suddenly occurred to me that all the graphs and data visualizations, however fancy they look, are just descriptions of “symptoms”, rather than the “underlying disease”. And then I began reflecting: did I focus too much on the superficial symptoms but not pay enough attention to the real cause of the pandemic?

Or did some of the policymakers make the same mistake as I once did? Have they made decisions primarily based on these superficial infection data? Are we now stuck in the seemingly endless debate about whether to wear masks or whether to open schools and restaurants just because the infection time series has gone up or down? Have we wasted huge energy and resource on debating these issues? Are we tired of these debates? I’m afraid all the answers are yes, at least in my opinion. Had more policymakers realized the vital importance of Dr. Yan’s whistleblowing earlier, would they act a bit differently, and rely more on the essence of the CCP virus instead of the roller-coaster-like infection data to make decisions?

Predictive Data – “An economist is an expert who will know tomorrow why the things he predicted yesterday didn’t happen today.”

This is a joke mocking economists about their always not-so-accurate forecasts. It seems like this joke can not only apply to economics, but also to the forecasting behavior based on historical infection data. For example, as one of the rather authoritative forecasting institutes in the US, IHME (Institute for Health Metrics and Evaluation) has employed some very complicated models to forecast many CCP virus infection measures in the past year or so. Looking back at some of their past predictions and comparing them with what actually happened, it seems like the accuracy of the infection forecast desperately needs improving. The graph below shows IHME’s forecasted deaths (caused by CCP virus) in the US in April 2020 (Dotted red line indicates forecasted number, red shadow indicates forecasted range):

Source: https://ktla.com/news/nationworld/influential-ihme-model-projects-coronavirus-deaths-will-stop-after-june-21-but-experts-are-skeptical/

We all knew what happened afterward. Obviously, the IHME forecast shown above was too optimistic, which is reflected as just a unimodal forecasting curve. It did not consider the likelihood of the second, third, or even the fourth infection wave. Also, the red shadow area (i.e. forecasted range) was too wide to be of much practical value.

There are plenty of flawed forecasts like this one out there. These forecasting models are inevitably blended with human weaknesses (e.g., overly optimistic or putting faith in luck), raw data quality issue (e.g., false positive or false negative test results), and the limitation of the models themselves – these models can only predict future based on historical data and do not have the ability to take other factors into consideration. Again, take the IHME forecast above as an example. It is obvious that the model adopted failed to consider the core issue here – the CCP virus is an unrestricted bioweapon, which indicates the fundamental difference from other pandemic caused by natural germs. Also, potential mutations that might pop up in the future from time to time all over the world are making the virus more infectious and more deadly. Plus, there might not be enough study on the potential side effects of mass vaccinations such as blood clots or immune escape, and potential ADE (antibody-dependent enhancement) effect. With all of the factors that the model failed to consider combined, it is not surprising that the accuracy of the model forecast, however intricate it was, turned out to be not very satisfying.

As of now, IHME is still working very hard on using complicated statistical models to predict CCP virus infection every day. I’m not sure if IHME has or will spend some time reading Dr. Yan’s reports instead of getting mesmerized in the number games, since her report, which thoroughly illustrated the fundamental reason behind the fluctuating infection numbers, might be really helpful for improving the model specifications.

Getting Out of the Curse of Sisyphus – The Essence of CCP Virus Revealed in Dr. Yan’s Reports

In Greek mythology, Sisyphus was punished by being forced to roll a huge boulder up a hill only for it to roll down every time it almost reached the top, repeating this action for eternity. Somehow I feel that we are in a similar seemingly endless struggle like Sisyphus: a lot of us are staring at the infection data charts and doing make work without knowing the real reason behind this pandemic every day, not sure why we have to endure this suffering, don’t know if there are other ways to get through life under pandemic apart from simply struggling, and worst of all, we might gradually lose hope because we don’t know when or if the pandemic will end. 

However, there might be a way to transcend this “Sisyphus curse”, because there is indeed hope. So far, only Dr. Yan’s reports systematically explained the essence of this pandemic, that means only by taking down the CCP can we get the early virus and backbone virus samples, which are essential for thoroughly understanding this virus, and then finish this pandemic and prevent the next “man-made” pandemic. Also, the better news is that we have learned from Miles Guo a couple of days ago that CCP actually has developed an antidote to this unrestricted bioweapon (Source: https://gtv.org/getter/60806a3f8616c84f0ff21ef1).

This time, we really have seen the dawn of hope. So, what are we waiting for? Take down the CCP!

P.S. links to Dr. Yan’s three reports can be found here:

1st report: https://zenodo.org/record/4028830#.YIZVfy2cB0t

2nd report: https://zenodo.org/record/4073131#.YIZViS2cB0t

3rd report: https://zenodo.org/record/4650821#.YIZViS2cB0v

(This article merely represents the author’s own opinion)

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甘博文
9 days ago

1kjhhlojpook、我又被钓鱼式收集假证造仇收集我假证的领导,被挑脚筋欺负人无法睡觉只能来求救,被请转发给认识“人脉甚广”的郭七爷救命提了爆尿革命和洗手以及车里临时直播拍保镖吃汉堡聊境界!!!这在我被诬陷某图之前请他放心。他就明白怎么回事我保证,不相信我请直接看第3段转告他具体事他马上就知道我!!!想骗您们不会还告诉你我被具体如何被误诊的事,我保证他马上知道我在说什么,请他救命告知他于您有利!我现在被国安公安相关的监视渠道的监视下发求救信息!!会有假求救信息跟发只有这一条真的!!!!!!只有郭七爷他曾经一次直播时看过我的真推特[email protected]里的成人内容,他应该记得!!!全文在那只有他明白什么意思!!!我父母十年对我不问事,没听说过这些军方普及的微波辐射感应电流设备和种种之事,及美方领事馆多有大使人员被相同设备攻击之事,以为我得了精神病钓鱼我去陪我妈看精神病院,到那我和医生吵架,父母把我关在门外待替我说话,最后医生和父母什么都不知道且生气下直接给我诊断个精神病,请千万别信这回事!如果我骗你更没有必要跟你提这些。
  
 2、请认识我且人脉甚广的郭七爷救救我!!!推特两篇有我被在领导中南海领导前被误会和被诬陷的事及我受到的伤害,我叫甘博文现命危在旦夕!请请您相信我救我一命于您有利!!!
 3、您去告诉他疫情有次直播时他先看电脑右下角,不知当时他是和这边领导达成协议说孩子这不是爆尿革命与边笑边借洗手拿我开玩笑,或他自己当时在监控我的电脑才会这样的事,以及他有期临时节目一大早在车里前面有个外国保镖,他在边吃汉堡边直播的事,只要你跟他说了我保证他马上就知道我是怎么一回事!!!还有他自己或者是被安排说特定的话,您只要跟他说了这他马上就知道怎么一回事以及我是谁!!!我当时被监视下被动和他“直播”!只有他能看懂我推特全文在说什么,此事涉及党和国家机密!!!
 近期有领导对我有大误会大误解,在加强收集我的假录音,根据我过去被在江派领导前被诬陷或被误会的事,用微波电磁辐射感应电流监控辐射设备辐射非自主移动录影诬陷,以及其他未知假录音剪辑等非我本意的假证,我被剪辑了很多假录音发给了他知道的我却不能提的人

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