By Luc Montagnier and Jed Rubenfeld
Jan. 9, 2022 5:20 pm ET
----WSJ
I did not believe the user of this website was really about reason, as this post was devoted greatly.
Should be smaller R0. However, I meant not to fix it. It took 22 months that CDC start considering to recommend N95 and some areas (Salt Lake city) starts giving free N95.
People who did not understand the richness,fastness, unpredictable, of COVID's mution could not appreciate my conclusoin two years ago.
For knowing this result, u need not to have an "accurate" model with many dependence assumptions.
Clearly the Exponential function dominate the linear function (benefit of vaccines/re-infect immune) in UK.
https://twitter.com/DrEricDing/status/1473752247376961543
Another reason is, all extra dependent hypothesis will be explored equally at an earlier stage of a research topic. In brief, most trash papers compete each other at the earlier stage and only after some time, a dominate theory/model will be established. The competition process actually is very similar as the process of virus evolution. At the earlier stage, there is no reason to assume a dominate new model yet. Thus, no heterogenous should be assumed.
There is no reason to assume heterogeneous, as the COVID is so new and the information/knowledge about its mutation direction is very shallow till now.
With the speed of double 1-3 days, I did not believe other details/aspects played any significant role. Only the transmission control/observe has relationship with the true reality.
Omicron doubles in 1.5 to 3 days in areas. https://www.reuters.com/business/healthcare-pharmaceuticals/omicron-cases-doubling-15-3-days-areas-with-local-spread-who-2021-12-18/
Mathematically, the consequence caused by the transmission >>>>> death rate.
Let's assume there were many COVID mutated variants. What is the best model for the average of the spreading path of all those mutations? It is the SIR model, as it has less dependency. More "accurate" models have more assumptions, hypothesis and depended conditions, which are not reliable. In brief, any other models looks more or less like the result of the SIR model. The difference cancels out.
https://twitter.com/DrEricDing/status/1469723185084084225?s=20
Please check the calculation part. I wish the health system would not stress out by the omicron.
"the chance of contracting disease at all compared with those who are not vaccinated (~40-70% for Delta, reduced to maybe ~10-30% for Omicron);"
Do you have a link to the peer review papers about the above item?
"6 months ago I wrote about how 30-year-olds should basically go back to normal and no longer take many COVID precautions."
Will the hospital system stress out again in many states because people did not control the transmission? We will see soon. I just did not understand that why so many people did not understand the power exponential functions.
Currently, the omicron doubles 3-4 days (Germany and British data). Let's assume the vaccines reduce the severity into the swan flu level. Now, what will the swan flu that doubles in each 4 days will lead? Simple math will tell us, it is UNACCEPTABLE.
If 30s live as normal, the transmission will not be controlled and the health system will stress out even further.
https://www.news5cleveland.com/news/continuing-coverage/coronavirus/local-coronavirus-news/gov-dewine-orders-ohio-national-guard-to-help-understaffed-hospitals-with-rising-covid-cases
I am surprised that most people did not read the virus spreading dynamics even after two years of COVID. For any large scale plague, the transmission will cause the most life loss. Assuming the serenity of COVID is reduced to the same level as a flu and ignore the long COVID.
Now, Think about a ten times transmissible swan flu. Individual tends to think it is acceptable. However, it could cause millions of life loss in US alone.
It is not possible that 100% will get it.
https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology
Please do some simple calculation by using the SIR model. https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology
Simple calculation suggests the transmission rate contribute much more to the life of loss than the mortality rate. Any measures improve the transmission will cancel the vaccines' linear contribution to the death rate. The first priority of the vaccine should be prevent transmission, not mortality rate.
After the two horrible years, any new thoughts?
We need to think earlier, before too late. Nobody could exclude the possibility that the COVID would last tens of years. I did have some knowledge of genetic algorithm and understand the power of small mutation. Hope more research could be done to control/predict the consequence of mutation, as the mutation itself is not predictable.
The vaccine does not prevent the transmission (to my best knowledge, for both Delta and Omicron; I am not anti-vaccine). A simple calculation suggests that the linear contribution (reduce the death rate, etc.) provided by the vaccines was dominated by the exponential contribution of increasing R0 of the Omicron. It looks like the only equilibrium is still universal N95/other PPE, in theory.
Do you have any new thoughts after two years?
After two years, should this post get more upvotes?
Any new thoughts? It seems that the mutation of RNA is too fast.
Unfortunately, no other possible equilibrium point of the COVID evolution had been observed until now. On the contrary, more COVID variations appeared. I guess, soon or later, people as a whole will learn to use masks, and then better protection gears.
https://orgmode.org/worg/org-gtd-etc.html
I would like to recommend the emacs' org mode and some discussion about its relationship with GTD technique.
Even (1),(2) and (3) were proven true in the future, it was not apocalyptic scenario. People only need to wear serious respirators while not at home. It was not a big deal in my opinion.
I understood that there would be strongly against toward serious respirator. A picture of kids wearing scary respirator is kind of unthinkable to me. However, it is the only equilibrium point that I did not see any scientific uncertainties.
Besides the theoretical consideration, in reality, mine workers had used respirators to protect their lung for years.
Herd immunity may not be reachable since we did not know how long the immune effects could last for infected people.
It is not a joke; Also, English is not my mother tongue. However, the above proof is the only proof of the possible ending of COVID-19 since (as I posted in another topic):
"Everybody wearing a respirator could be one of the equilibrium point of the social evolution under the COVID-19, though may be not the only one. Unfortunately, I did not figure other equilibrium point yet. To my best knowledge, nobody gives other end point of the social evolution in a rigors way. "
If anybody could proposed other equilibrium point of social evolution, I would be more than happy.
The outlook of kids with a respiratory is kind of scary, as shown in HK. I did not see any other issue with this strategy.
Everybody wearing a respirator could be one of the equilibrium point of the social evolution under the COVID-19, though may be not the only one. Unfortunately, I did not figure other equilibrium point yet. To my best knowledge, nobody gives other end point of the social evolution in a rigors way.
In amzon, the 2097 filtering is more expensive than before. But still available.
I bought 3M respirators and filters several months ago. I did not use it since I worked from home and did not go shopping. Those devices are more cheaper than N95 mask.
If a researcher was given 1000X more data, 1000X CPU power, would he switch to a brute-force approach? I did not see the connection between "data and computation power" and the brute-force models.
Such a great article! I thought the AlexNet that led to the recent AI break through could be viewed as a discontinuity too. The background and some statistics result are well summarized in below link.
https://qz.com/1034972/the-data-that-changed-the-direction-of-ai-research-and-possibly-the-world/
The graph evolution system are of:
[a] easy to be stated
[b] Turing complete
Conway's Game of Life also has the above two properties.
From the perspective of mathematical logic, string replacement systems could be as powerful as a full functional computer. The proposed graph evolution systems are of the same power too. The author provided many well explained good features of the system and I was persuaded to try to think some science topics from the viewpoint of "graph evolution".
If in future the author or others can obtain new physics findings by using this system, then evidentially the new "fundamental ontology" had some advantages.
However, at this moment, I did ...
Can we reduce the issue of “we can't efficiently compute that update” by adding sensors?
What if we could get more data ? —— if facing such type of difficulties, I would ask that question first.
For training new graduates from computer science major, I often asked them to develop a simple website to predict the UP/DOWN probability of tomorrow’s SP index (close price), by using any machine learning model. Then, if the website reported a number that was very close 50%, I would say: the website worked well since the SP index was very close to random walk. “What is the meaning of the work!” Most of them would ask angrily. “50% visitors will be impressed by your website. “
I apologize if you feel the story is irrelevant. In my opinion, 50% prediction de
...Below is a simplified COVID-19 framework:
Data acquiring ---> social engineering based on model ----> better result
Yes. A better model will be definitely helpful. However, (as pointed out indirectly earlier by someone else), to my best knowledge, there were no good and robust model for large lag dynamic systems. Such kind of model could lead to Chaos and random like result easily. Thus, I believed that increasing the data acquiring capability was the key (South Korea's approach).
(1)
A wrong model could be useful if the action (based on the module) can compensate the models' error effectively. Usually, you need to know some properties of the model's error.
(2)
Even a wrong model could be very useful. For example, the earth is flat. That wrong model setup the question correctly and so that people could start thinking the shape of the earth.
Also, people can design a specific application environment to reduce the bad effects resulting from the error of the model.
A lesson from last 30 years AI development: data and computation power are the key factor of improvement.
Thus, IMPHO,,for obtaining a better model, the most reliable approach is to get more data.
https://www.sanders.senate.gov/wp-content/uploads/Masks-for-All-2022-Final-Text.pdf