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Thinking out loud here. Really just brainstorming. Sorry this is
long and a bit "wandering" but that's the nature of research. <br>
<br>
How might we score residential tax parcels for COVID-19 risk
appropriately, with an eye towards limited surveillance testing
resources?<br>
<br>
First we need to know - is any resident of that tax parcel a
COVID-19 "vulnerable person"?<br>
<br>
<b>#1 Elderly individuals.</b><br>
<b>#2 Individuals with serious underlying health condition</b><b>s</b><br>
(or both)<br>
<br>
On the "Elderly" factor we have Census data, including the 2020
Census that is related to a Master (postal) Address File and asks
the question specifically: (for each person living in that
household) <i>"What is Person 1's age and what is Person 1's date
of birth?</i>...." Of course Census data is for the most part
totally unavailable at such a granular level, this however might be
an excellent "exception". <br>
<br>
On a secondary basis we might proxy "age of resident" by number of
years the house was owned from when it was last purchased? That
obviously would miss a great number of elderly residents, renters,
etc. <b><br>
</b><b><br>
</b><b>Qualified medical conditions </b>(Individuals with serious
underlying health conditions)<br>
<br>
"Individuals with serious underlying health conditions, including <br>
<ul>
<li>high blood pressure </li>
<li>chronic lung disease </li>
<li>diabetes </li>
<li>obesity </li>
<li>asthma, and </li>
<li>those whose immune system is compromised such as by
chemotherapy for cancer and other conditions requiring such
therapy."</li>
</ul>
Thinking off the top of the head, could prescription drug purchases
identify many of those individuals? <br>
<br>
Of course confidentiality of CENSUS and health care information
would be a huge issue. <br>
<br>
The above are just the two easiest to understand criteria. <br>
<br>
Heroic assumption: the (one or more) "Vulnerable Person(s)" is fully
informed, responsible, sheltering in place and following all CDC
guidance. (later this assumption can be relaxed.) Then the question
becomes, <b><i>who else lives in that household</i></b><b> </b>and
how "exposed" are they during their typical day? Are they possibly
asymptomatic or with very mild symptoms, fully at risk of
inadvertently bringing COVID-19 through the front door, exposing the
1+ <i>vulnerable </i>people who live there?<br>
<br>
Speculating:<br>
<ul>
<li>a greater number of residents in the household who are "out
and about" increases the chance <i>any one </i>will carry
COVID-19 in. </li>
<li>if a resident is a front line health care worker the risk goes
up (but the awareness/knowledge/compliance might also be
better?)</li>
<li>if a resident works in an "essential business" there is a
greater chance they have been exposed on the job, or will be
exposed</li>
<li>has a resident traveled through / lingered in any known hot
spot? </li>
<li>more youthful/healthful residents are the MOST likely to
carry, but remain asymptomatic (and hence the least tested group
so far.) </li>
<li>if a resident is employed in a "non-essential business" they
may have been sheltering at home in the recent past, BUT are
about to return to work. How will all that play out?<br>
</li>
</ul>
<b>Classic Demographics</b><br>
<br>
We can easily factor in some great Census data at the Block Group
level to identify residential parcels that score high on demographic
data that is well known to correlate highly with COVID-19 severity,
items such as:<br>
<ul>
<li>household income</li>
<li>ethnic origin</li>
<li>residential density</li>
<li>urban / rural differentials</li>
<li>air quality? (as a proxy for lung disease and asthma - e,g,
LA, NY, air quality measures?)<br>
</li>
</ul>
<b>Local transportation statistics</b><br>
<br>
I'm not familiar with this area at all so I can only speculate. I'm
aware of CENSUS commuter statistics regarding commuters living in
one county and working in another (commuting across county lines for
work every day). <br>
<br>
Very large employers commuter maps may have been constructed as
large employment centers were modeled added or subtracted?<br>
<br>
License plate capture on toll / traffic cameras?<br>
<br>
Again, I have no solid background here but I speculate
transportation experts might have "network models" that could be
juiced to contribute an individual COVID-19 tax parcel risk score? <br>
<br>
<b>Classic Marketing Data</b><b><br>
</b>Marketing in the USA is very sophisticated. Much is well known
about each postal address, including past purchases by product type,
brand preferences, estimated ages of residents at that address, etc.
(Next to CENSUS data this data set is pretty granular and accurate.)<br>
<br>
For instance I'm sure its possible to get postal lists of heavy
buyers of OTC medications for allergy/asthma/lung disorders (or any
of the other conditions above). And that's just a start...<br>
<br>
<a class="moz-txt-link-freetext" href="https://www.mrisimmons.com/">https://www.mrisimmons.com/</a><br>
<br>
Credit card data could also be useful. Especially in real time. <br>
<br>
<b>Cell phone location tracking</b><b><br>
</b><br>
They know where you are, when, and for how long you stayed. They
know where that phone sleeps. They infer if you are in a car vs.
walking vs. riding a bike vs. in a kayak....<br>
<br>
Some of that data is already VERY widely available to marketers.
Some studies might be run by the "data holders" that do not actually
reveal <i>personal identity</i>, but may be linked (with relative
ease) to postal address. Obviously there are great sensitivities
here. However if the relationship were "many-to-many" it probably
would fly. <br>
<br>
Example to make it "many-to-many" - build up a "points system": <br>
<ul>
<li>traveled to any of the following locations and remained more
than 1 day in the past 90 days: 10 points</li>
<li>traveled to and spent time at a known drug store 3 standard
deviations higher than average in the past month: 5 points </li>
<li>owns a cell phone and uses it more than x times per week but
otherwise shows very little movement around, does not use much
data. (proxy for obesity and/or old age). 15 points</li>
<li>watches more than 4 hours of daytime TV. 15 points<br>
</li>
<li>does not have a cell phone and does not have any form of
Internet, yet lives in a wired/urban location: 20 points</li>
<li>...more like above<br>
</li>
</ul>
Data vendor runs the analysis and gives you the top scoring 5% of
your county population, as just a list of post office addresses,
given back to you in random order. <br>
<br>
County sends post cards... "<i>Your household may be at elevated
risk - more info available by phone or on the Internet...FREE
CONFIDENTIAL TESTING. Only requires a mouth swab, available TODAY!
Takes only 15 minutes."</i><br>
...<br>
<br>
Any other ideas how to create a tax parcel score for COVID-19 risk?
<br>
<br>
Above of course would be pretty much a hypothesis to begin with.
Would want to close the loop with actual surveillance testing
results. Then "train the model" further with even wider data to try
to improve the pin pointing. <br>
<br>
Ideas? <br>
<br>
Rick <br>
<br>
<pre class="moz-signature" cols="72">--
Richard J. Labs, CFA, CPA
CL&B Capital Management, LLC
Phone: 315-637-0915
E-mail (preferred for efficiency): <a class="moz-txt-link-abbreviated" href="mailto:rick@clbcm.com">rick@clbcm.com</a>
3213 Yorktown Dr, Tallahassee, FL 32312-2015
June-August: 408B Holiday Harbour, Canandaigua, NY 14424</pre>
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