<html><head><meta http-equiv="Content-Type" content="text/html; charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class="">It’s an interesting question what summary statistic would be informative across the several dimensions of context in demography. Total numbers are not by themselves, but neither are fractions.<div class=""><br class=""></div><div class="">Here’s my candidate, though it would require a parametric model of some sort:</div><div class=""><br class=""></div><div class="">Complementary cumulative probability: what is the probability a typical person in a given locale can get through a day and _not_ spend 15 minutes inhaling SARS-COV2 (at or above some threshold density)?</div><div class=""><br class=""></div><div class="">If crowd density is higher for a typical person (people in high-rises), the probability rapidly goes down with increasing average infection rate, while at low density (people in spread-out single-family dwellings) it goes down as a smaller power. The probability you can avoid the hazards also depends on connectivity and the likelihood that any given person can impact several environments.</div><div class=""><br class=""></div><div class="">Not easy to estimate, but not wildly harder at zeroth order than the kinds of models epidemiologists already make. It takes advantage of the invariants of the problem: every person lives 24 hours per day, and typically takes some part of a week to get sick and a couple of weeks to recover (with a distribution one could include if desired). So the bottom line for an individual anywhere is: what chance do I have of not being part of some chain of transmission.</div><div class=""><br class=""></div><div class="">Eric</div><div class=""><br class=""></div><div class=""><br class=""><div><br class=""><blockquote type="cite" class=""><div class="">On Nov 2, 2020, at 11:31 AM, Barry MacKichan <<a href="mailto:barry.mackichan@mackichan.com" class="">barry.mackichan@mackichan.com</a>> wrote:</div><br class="Apple-interchange-newline"><div class="">
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<div style="font-family:sans-serif" class=""><div style="white-space:normal" class=""><p dir="auto" class="">The other thing that has me tearing out my hair (really a serious problem at this point) is the maps where they shade the state or county by the cases or deaths relative to the population. The rates are correct, but a high rate in Elko County, Nevada hits you eye but the same rate in King County, New York (aka Manhattan) is almost invisible.<br class="">
—Barry</p><p dir="auto" class="">On 2 Nov 2020, at 11:09, Tom Johnson wrote:</p>
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<blockquote style="border-left:2px solid #777; color:#777; margin:0 0 5px; padding-left:5px" class=""><div id="AF0CFC4C-2B3E-4BF2-9C0B-FB0137C44FB9" class=""><div dir="auto" class="">Both numbers may be accurate but not particularly helpful bcs they lack context. One would have to start with, at least, a ratio of cases to some level of population, per 10,000 or 100,000,etc. <div dir="auto" class=""><br class=""><div dir="auto" class="">It drives me nuts that the state's health department or somebody publishes daily cases by ZIP without showing the population of those ZIPs. And then newspapers like the New Mexican publish the case numbers without context. <br class=""><div dir="auto" class="">T. </div></div></div></div><br class=""><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Mon, Nov 2, 2020, 9:01 AM Prof David West <<a href="mailto:profwest@fastmail.fm" class="">profwest@fastmail.fm</a>> wrote:<br class=""></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Which is scarier?<br class="">
<br class="">
-- 41 out of 50 states show an increase in COVID cases.<br class="">
-- 105 out of 3,141 counties show an increase in COVID cases.<br class="">
<br class="">
Which is more accurate?<br class="">
<br class="">
Which number should guide policy?<br class="">
<br class="">
davew<br class="">
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