This coronavirus is very nasty business and I hope this catches you still free of Covid-19.
The most disheartening aspect of the Covid-19 pandemic is that nothing in the numbers presented gives any indication of hope of the end being in sight. This with all the graphs continuing to point upwards, coupled with the fake news media inflammation of an already dire situation. https://www.businessinsider.com/figures-show-us-soon-coronavirus-worse-china-2020-3
The spread of Covid-19 is exponential in nature with one person infecting another and then two people infecting two others and then four people infecting four others etc.. This makes the rate of increase in new cases equivalent to acceleration which is also exponential.
Basic physics tells us that force equals mass times acceleration (f=ma) so as long as the force and mass remain constant so will the acceleration.
In the case of Covid-19 “force” is equivalent to how infectious the coronavirus is and the number of people who infected people come in contact with. Then “mass” is equivalent to measures being taken to stop the spread, so any changes in either of these two factors will affect the acceleration which is the percentage of new cases per day.
New York City has become the epicentre for Covid-19 in the USA because of the sudden introduction of 100 new cases in New Rochelle demonstrated by the change in the curve around March 15, 2020
If you look at this graph as the number of cases versus time, being equivalent to distance versus time which is essentially velocity (first derivative of distance with respect to time) and take the second derivative which is equivalent to acceleration, the plot of percentage increase in new cases per day creates a downward pointing graph. This because the same increase relative to a larger number becomes a smaller percentage, which if nothing else gives some degree of hope that the end is in sight!
Because a same large number of new cases as a percentage of progressively larger numbers of total cases produces lower and lower percentage values, after reaching a peak value of 116.5% the percentage rapidly drops back to the background rate as demonstrated by this plot of the New York City data as a percentage increase in new cases per day.
This is the same plot at a smaller scale with the curve plotted from March 20 after the recovery from the spike showing a trajectory to zero percent new cases sometime in the not too distant future.
This plot of percentage increase in new cases per day is very sensitive to any large cluster of new cases as demonstrated by the one day peak on March 25.
If this plot is done for individual states it will provide an alternative metric demonstrating the effectiveness of measures being taken for each state and a rough estimate of when the point of zero new cases will be reached.
Since this uses the same official numbers for each state that are plotted as the total number of cases, there is no fiddling with the data in producing this graph.
The best thing about plotting the numbers in this way (percentage derived from new cases each day divided by previous total) is that the curve points down instead of up, giving the public hope by demonstrating that the measures being put in place are having the effect intended.
To date projections have been based on models of “flattening the curve” from the maximum destructive curve to a more benign curve modelled on the basis of measures being put in place.
Models are based on assumptions and are only as good as the assumptions being made. By contrast by plotting the actual data in this way makes no assumptions and therefore the data driven projections of reaching zero new cases are perfectly valid with the only questionable aspect being how the trajectory will behave and how long it will take to reach the zero point.
There is something to be said for plotting data by hand because unlike having a computer plot data from a computer generated spreadsheet, the individual numbers plotted have meaning and small anomalies can be identified that would never be seen in computer outputs which would likely average out anything that does not fit the curve.
A perfect example of this is Admiral Titley presenting a temperature graph plotted against CO2 claiming correlation at Ted Cruz’s December 8, 2015 hearing at which you were one of the witnesses.
This is what was presented as evidence of correlation which clearly does not hold up when the temperature data is analyzed in detail and the actual emissions are plotted at each of the temperature inflection points.
On April 18, 2007 I gave this presentation to the Rotary Club of Calgary Centennial which included this hand drawn graph showing the departure of increasing CO2 emissions from decreasing global temperature in 2002 for which I received a Certificate of Appreciation for “Speaking on Global Warming Ended in 2002!!!”
That same year Al Gore blamed global warming on increasing CO2based on Hansen’s 1988 computer model prediction for which Al Gore was awarded the Nobel Prize for warning the world about man made climate change that never actually existed!
Unlike the faked computer models responsible for creating the UN’s fabricated climate emergency https://clintel.nl/wp-content/uploads/2019/09/ecd-press-briefing.pdf the models used in predicting the outcome of the Covid-19 epidemic are both useful and necessary .Yet equally necessary is to use data based predictions as a complimentary alternative to the model based predictions that provides the positive outlook that a stressed US population is in desperate need of.
Please pass this on to your Whitehouse contacts with my compliments