How the US slowed the spread of covid
These findings show that one respiratory virus can block infection with another through stimulation of antiviral defences in the airway mucosa, supporting the idea that interference from rhinovirus disrupted the 2009 IAV pandemic in Europe. These results indicate that viral interference can potentially affect the course of an epidemic, and this possibility should be considered when designing interventions for seasonal influenza epidemics and the ongoing COVID-19 pandemic.”
The above from Yale scientist at
Go to https://ourworldindata.org/coronavirus >>Cases>>remove other countries leaving just USA>>just above the graph, change the metric to Reproduction rate>>use the slider under the graph to start in early October.
Did the government release fomite-spread rhino viruses to kill the run away covidcases at the end of last year?
- The dips in the R number are sharp.
- Unlike the rest of the year, suddenly different states are moving in unison (not natural spread of a cold).
- The CDC says hand-washing not so important.
- In disparate states cases peaked within a couple of days January 9th
- There are still colds going around in June.
You’d have to make several assumptions to believe that theory;
1. That the government wants to slow or stop the spread of covid. Given that we now know that the virus was created in large part by high ranking members of the US government and the US government doesnt do things without a reason. It seems logical that those who created it probably disseminated it. So, why would those who created and disseminated a virus suddenly want to slow or stop it’s spread? IDK, that could be the case but it doesnt seem likely to me. Everything that I have witnessed over the last two years…the false information, the suppression of alternative treatments, the fear campaign…all strongly suggest the opposite.
2. One simple study suggesting the possibility of one virus disrupting another doesnt really mean much. Remember 2/3rds of all studies cannot be replicated, that means the conclusions reached by the great majority of all studies are wrong.
3. Assuming the information suggested in the graph and data represented is accurate.