@doublehalo
This is old news. The PCR test gives lots of false positives.
Exactly, it has always been an issue - particularly with tests where the actual incidence of an 'event' (ie presence of disease) is low.
I love the topic of false positives - part of signal detection theory.
Signal Detection Theory says that there are four possible outcomes: Hits, Misses (ie false negatives), False Alarms (ie false positives) and Correct Rejections.
Think of it like a grid. The columns (going down) will represent an 'event' ie an actual thing that is present eg being ill. The first column would represent the people who are actually ill and the second column would represent the people who aren't actually ill.
Then you have the rows. These represent the test - the 'signal'. The first row includes the people who test 'positive' and the second row the people who test 'negative'. If someone 'has' something and a signal is detected (ie tests positive), that is a 'Hit'. Underneath that, if someone 'has' something but tests negative that is a 'Miss' (a 'false negative' if you like).
The second column includes someone who isn't ill ie there is no 'event' yet there is a 'signal' - ie they still tested positive - which is the false alarm (ie a 'false positive') and then under that you have people who were neither sick nor tested positive for the 'signal' - these are the 'correct rejections'.
I'm happy to demonstrate with figures but it can get confusing to explain without drawing it.
But say you've got 200 sick people out of 100,000 - a low incidence. Therefore 99,800 healthy people.
Imagine the false positive rate is 1% (we don't know it). That means 1% of the 99,800 healthy people will have been falsely told they were sick ie 998 people. We'll come back to this later.
Say the test was 70% effective (again we don't know how effective/sensitive the tests are but I'll be prudent and say 70%). So 70% of the 200 people were correctly told they were sick when they were actually sick (these are the 'hits') ie 140 people. Well, that's okay, better than nothing.
Sadly 30% of the sick people were 'missed' (ie false negative) so sadly there would be 60 sick people who were told they were fine. Not great but they could always retest. And 60 out of 100,000 isn't too much of a problem.
Back to the healthy people - the 99,800. Out of the healthy people, 99% out of 99,800 were 'correct rejections' ie they were healthy and no 'signal was detected' so they correctly tested negative = 98,802 people. So that's good.
But hang on, 998 people were incorrectly told they were sick when they weren't. And actually only 200 people were sick (because incidence is low)...
See the issue? Without clinical diagnosis there is potential for way more false positive 'cases' than actual 'cases'.
The PCR test was never intended to be used for diagnosis of a virus in the absence of symptoms. It's a scandal and everyone who has studied this knows this.
I should add that if the PCR was voluntary - and simply used as a screening aid - fine. Or, if each 'case' had a 50% death rate or something then again, perhaps it would be better to be safe than sorry.
But in this case, to use these inflated PCR 'case' numbers to decide government, health and economic policy is knowingly misleading.
Related to this is the issue of the fluctuating cycle threshold that will influence how sensitive the test is ...
Bah! Don't get me started on PCR tests 
NB The history signal detection theory is v interesting and I believe it has been used in eye witness testimony and to test signals in warfare. There is no use in having a test (or eyewitness) that is over sensitive and says 'yes' to everything - it has to be balanced against the noise. Imagine if pregnancy tests were over sensitive and were telling men they were pregnant etc when they weren't.
In my opinion, the PCR test should not be used without clinical diagnosis, unless it is being used merely as a screening tool.