The current COVID-19 (COVID) panic is born from and is being fostered by defective science.
The steps used by officials/politicians during the pre virus identification, and steps until the declaration of a panic.
The actions taken by those parties after the declaration of a pandemic, and inclusive as of today.
OED defines as:
“a method or procedure that has characterized natural science since the 17th century, consisting in systematic observation, measurement, and experiment, and the formulation, testing, and modification of hypotheses"
Wikipedia adds that
“It involves careful observation, applying rigorous skepticism about what is observed, given that cognitive assumptions can distort how one interprets the observation.”
Conclusions or summations being published and acted on, despite clear logical gaps or contrary to the scientific method. This could also include conflicts of interest, prophetic fulfillment, lack of rigorous skepticism, and other res ipsa logical problems.
SUMMARY OF POSITIONS:
Officials and decision-makers state there is a virus with known transmission methods. It can be identified accurately and Its mortality is significant. However, these positions are inaccurate. Those statements are made, and continuously made based on defective science.
- The definition of COVID is problematic
- Diagnosing COVID is based on RT-PCR alone
- The transmission method claimed is demonstrably inaccurate, and possibly flat out wrong
- The testing methodology is inconsistent and flawed
- The testing method produces inexplicable results
- 20 global reports of unknown sourced infection
COVID has no clear demonstrable clinical presentation. Normally a suspect case is required (as seen in SARS 
), with the foundation of the clinical elements of disease. A confirmed case protocol is then followed up. This did not occur with COVID. The definition of COVID is not based on clear clinical elements. Science without clarity, we look for specific answers. However, COVID is turning into a catch-all nonspecific defined disease. This non-specific catchall methodology is contrary to the scientific method.
FIRST PHASE IDENTIFICATION AND TESTING
The first “confirmed case” of COVID 
did not originally require the criteria for a suspect case to be met, but simply a positive RNA test. It did not require any symptoms or evidence of contact with previous suspect cases, illustrating total faith in the RT-PCR technology used in the RNA test .
There is a fundamental problem when the foundation of the disease definition is based on an RNA test and not supported by clinical and trace findings. There is no element of skepticism here. That omission is contrary to the scientific method.
We have been told extensively that COVID is a highly contagious disease. After the first person to person contact reported in the US, contact by that couple was traced to 347 people. None of those people ended up testing positive, despite close contact with the two infected people.
Nearly 80% of COVID patients in Japan have not passed on the infection to others even if very ill. [38
] This flies in the face of the infectious rhetoric we receive on a daily basis.
The Princess cruise line only had a positive test rate of 19% despite the incredibly close and hostile environment. Of those not infected, it included people isolated with infected people in the same cabin. Over 50% who tested positive did not have symptoms, therefore only 8% of all Diamond Princess has any form of symptoms. 
To support the assumed model of infectiousness, officials struggle to find any contact correlation whatsoever to build their infection tree. Fundamentally, the belief that COVID positive tests are caused by contact drives the necessity to preserve the infectious paradigm. There is no clear skepticism about the COVID infection model. On the contrary, an affirmation of infection is assumed even with the slightest evidence of an association between an old case and a new case. This is bad science. Officials are taking information and trying to make it fit their model, rather than try to discount it. Defective science.
In short, the testing model is so erratic, inconsistent, and in some cases, unreliable, the accumulated statistics reported, even in small regions are nearly meaningless.
COVID has been genotyped, and segments of the RNA (its genetic core) had been isolated. The RNA in coronavirus is about 33 thousand base pairs long.
Testing is performed using Polymerase Chain Reaction (PCR), a method to replicate DNA to levels high enough so they can be measured, as seen in this Mayo clinic release. 
The slight problem is that RNA is not DNA, so you must first convert RNA to DNA.
This process is called RT-PCR.
Once DNA is created, it’s heated to be separated, and then a copy is made using “DNA ingredients”. Once a pair is made, it is heated again for the replication of double the strands. Rinse and repeat. Each iteration is a cycle. Each cycle doubles the number of strands in an exponential process.
The number of cycles used to test is proportional to the sensitivity level. If you replicate too many times you add noise or false positives because errors naturally occur in the replication process. If you do not replicate enough, you may not find enough to measure. The number of cycles required for identification is called signal saturation.
Companies use assays as a framework for testing. The assay defines the segment or segments of RNA they compare against, the number of cycles, and the virus source (blood, sputum, feces, etc). The FDA approved 33 emergency RT-PCR tests with varying assays.
These tests look at different base pairs sequences, segments, cycle counts, etc. The cycle counts vary. Some had recommended counts of 30 cycles, 31, 35, 36, 37, 38, and 39.12 companies chose 40 cycles and two say 43 and 45.
With that background, we can now see some significant issues with the testing.
- Testing is widely inconsistent, with dozens of different approaches, all with different cycles, and base-pair matching segments.
- Therefore testing in one method may produce a positive or negative that will be different by another.
- The variability of cycle numbers cannot be understated. Because we are dealing with an exponential representation, when you look at the starting numbers, you see the highest difference.
- Example. If there is 1 copy of the virus, and it is cycled 35 times you get 2^34.That is about 17 billion.
- If there are 2 copies of the virus and it is cycled 35 times you get 35 billion copies. A 17 billion difference because of one extra copy at the beginning.
- The concentration of the virus is traditionally a measure of how infectious a person is. But without standardized testing, we cannot correlate virus amount, clinical symptoms, and transmission risk. We cannot use test results from company A and compare them with company B.
The test results clearly depend on the level of virus in the body at a given time. If someone has a lot of viruses in them, the start of the test cycles will be significantly higher. This is where we run into problems with the COVID testing models. As I show later on the numbers and level of virus detected have not shown a correlation to symptoms. People with significant levels of positive detection are not necessarily sicker than those with very low levels. This causes a problem because the foundation of the COVID panic is that this virus causes illness. However, the cause-effect is not seen in the testing (even when tested with the same methodologies). Note the graph 3A in the supplement content of this Singapore study 
. Graphs 1-6 are all people who had the worst clinical presentations. Each day the virus was measured with the same testing model. There is no correlation between their recorded symptoms and viral count. 
The inconsistent testing means the numbers of infected and recovered, which are updated and continuously presented by officials, are virtually meaningless and are when looked at cumulatively are based on poor science.
POSITIVE NEGATIVE TEST FLIP FLOP
There are dozens of reports of questionable test results. Negatives with symptoms, then positive without symptoms, than negatives. A few examples. China  
Rather than look at these tests as evidence to question the overall premise of the infectiousness of COVID, the results are categorized as outliers. This is not representative of the skepticism needed as part of the Scientific Method.
NO KNOWN SOURCE
There are many examples of positive tests without a known source. Knowing the source is the foundation of the germ model and the premise of tracking the disease. When there are positive tests outside the known circle of infection, this can cause significant alarm. Imagine if an ebola case popped up in the middle of Oslo, with no known source. Serious questions would be made about what we understood about ebola. However, in COVID unexplained cases are accepted.
The Director-General of the WHO stated on Feb 22nd that cases with no know clear epidemiological link is a concern. 
That is evidence that the assumptive models are defective, and proper science needs to be used to get a better understanding, rather than build upon a week foundation.
Here is a list of the countries with unknown COVID sources.