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The Recovery to Death Ratio, and the Hope of a partial lifting of the ECQ

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Since March, I have been crowing about the success of the quarantine as a State led effort to mitigate the SARS -COVID2 epidemic. For this, I have been using the geometric average of the daily growth rates of cases, because that was the most available, and growth of the number of cases tended to show clearly whether the disease was on a massacre rampage, or that we were turning the tide, and "flattening the curve". See the blog post dated March 30, 2020 (We are Winning! Part 2) I had an intense interest in the curve because I really wanted a partial lifting of the quarantine, or at least a modification of the extended community quarantine, or ECQ, which for all practical purposes of confining the citizenry to their homes, was like a martial regime imposed control: curfew from 8 pm to 5 am of the next day, no movement outside of the house, and ONLY for the one authorized to shop essential supplies, nobody.  And, of course, that this movement to the store was only on certa

Statistical Inference as a Predictive Tool

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WE ARE WINNING, Part 2

As exercise in program/project evaluation (and to calm my nerves as a citizen who feels under siege with this lockdown), I have analyzed the daily incidence of infected cases. And as a netizen, I have posted on my FB page a truncated table showing the declining infected rates, and crowed, WE ARE WINNING! The short analysis focused on the declining infection rates from the inflection on March 24. two days straight. I know, two days are too short a period to draw conclusions from, but as the lockdown and extended lockdown were already implemented, I thought should see some real trends. I immediately received comments from a high school classmate who was (is?) a VP for promotions in one of the biggest services group in the country. I also got comments, also almost instantly, from former college students who are now professors, and government consultants on their own. The most incisive of all their comments was that the data set from which the analysis proceeded is too small to draw