many people have observed the disconnect between resurgent and exacerbated COVID cases this fall without commensurate death increases. Here is plot of detailed Toronto information (pop 3 MM) color coded by age subgroup, paneled to Young, Middle and Old.
2/ nearly all of the present surge in cases is among 20-60 year olds, but this component of surge has had negligible impact on hospitalizations and even less on deaths (almost non-existent in this cohort)
3/ main factor in reduced hospitalizations and deaths in present surge appears to be lower COVID case rates among over-80s thus far as compared to spring.
4/ shutting down working class BBQ restaurants serving under 40s seems like a blunt tool to protect over-80s, whether they are in retirement homes or in their own houses.
5/ We know a lot more about this virus than we did in the spring, especially about the ability of young and middle-aged people to resist and recover. Policies that were justifiable in the spring are not necessarily justifiable now.
6/ the focus of media on cases, while ignoring the much lower death rates in fall recurrence, is needlessly feeding anxiety.
7/ as someone MUCH closer to 80 than to 40, I have zero desire or with to increase economic hardship of earnest young small businesses and do NOT want governments to make their lives any harder in the supposed cause of my age cohort and older.
8/ in Canada, it appears that we will not have a vaccine until spring at best - needless to say, precisely when virus will probably recede seasonally. So these decisions will be with us for next 5 months unfortunately.
• • •
Missing some Tweet in this thread? You can try to
force a refresh
we are inundated with statements about COVID deaths, but how often have you seen charts showing COVID deaths in context of all deaths? Not very often, if ever.
I've done one for Ontario (where I live). Population 14.8 MM.
2/ All-causes death rates are ~2100. Total COVID deaths to date ~2500. Here is diagram showing COVID deaths (black) stacked with (estimate of) all other deaths.
3/ in Ontario (as with CDC), all-cause death data is appalling delayed. Thus, reported 2020 all-cause values substantially under-state eventual results. Data is updated monthly (latest Nov 26). Deaths trend up with increasing population; recent "decline" is reporting artifact
@kylenabecker@Barnes_Law here's one pushback. You say "Yet, Biden leads in Michigan, Pennsylvania, and Wisconsin because of an apparent avalanche of black votes in Detroit, Philadelphia, and Milwaukee."
Trump won more votes than Clinton in Philadelphia. Avalanche was in (white) suburbs.
@kylenabecker@Barnes_Law 2/ while there was obviously little enthusiasm for Biden - not enough to bother going to physically vote - there may have been enough enthusiasm to fill out a mail-in ballot when Dem collector came to harvest.
@kylenabecker@Barnes_Law 3/ to be precise, there was an avalanche for Biden in Philadelphia, but there was bigger avalanche for Clinton. So nothing anomalous about Philadelphia avalanche. Swing to Biden occurred b/c he did better than Clinton in liberal suburbs.
tho ughtful comment on Georgia filing from knowledgeable Oregon Dem. It's hard to believe that a purportedly democratic nation could make such a mess of electoral procedures.
abandonment of/failure to carry out verifiable election processes in battleground states has left US courts in an absurd situation: it's impossible for them to have any founded confidence either that election wasn't stolen or that it was stolen.
in a local election, solution would be easy: a do-over run-off election in which rules obeyed. But this doesn't appear possible given mandated electoral college date cutoffs. What a mess.
before accepting calculations from anonymous (or even non-anymous) sources, insist on a citation to original data so that it can be checked. I'm told (but haven't seen reference by Werise) that this comes from NYT json data static01.nyt.com/elections-asse…. I'll comment on this data.
2/ first, I extracted the data into a csv format using the R package jsonlite. I hadn't used this program before but it worked easily first try.
3/ A first and important problem with this dataset: it does NOT give actual vote counts in integers. It gives total votes and Trump and Biden share only to 3 digits. As a result, candidate counts are accurate only to nearest ~3000. Calculated increments can be off up to ~6000.
I'm re-examining MI Oakland County from first principles, since I agree that similarity of 2016 and 2020 doesn't preclude manipulation in both elections, tho I think that it counts against it.
2/ Technically, Shiva's comparison of total margins to straight ticket margins is (at best) "exploratory" - a term of art in statistics. We don't know anything about the properties of this statistic. So Shiva's claim to have PROVED manipulation is unjustified armwaving.
3/ Having said that, merely showing (as I did last night) that there were similar patterns in 2016 Oakland Co doesn't end the discussion, as commenters rightly observed, since we still don't really understand the result.
there's been considerable publicity about Dr Shiva's characterization of the plot shown at right for Oakland County MI 2020 results. However, Oakland MI 2016 plot in same format yields nearly identical results. So, unfortunately, his plot proves nothing.
2/ Dr Shiva said that the downward slope PROVED use of an algorithm to tamper with data. It doesn't. Slope has something to do with straight ticket vs all vote results; and is not due to malicious algorithm.
here's my understanding of phenomenon. Straight Ticket Republicans obviously voted for Trump. So difference arises from balance between "Paul Ryan" non-Trump Reps and "50 Cent" pro-Trump Dems. Presumably pct of Paul Ryans vs 50 Cents increases in strongly R precincts.