#LongCovid dashboard USpol
NCHS estimates of #LongCovid—based on Household Pulse Survey—provide for volatile projections.
Census Bureau to drop next round of data June 28. Best guess projections until then.
As more and more folk experience Long Covid, fewer and fewer staff our hospitals.
#ThisIsOurPolio #CountLongCovid
#CovidIsNotOver #MassDisablingEvent
[This is first toot of a weekly thread, updated daily, providing various dataviz of ongoing #pandemic.]
Last week: https://mastodon.social/@beadsland/110532486263305910
hospital & ICU capacity trend USpol
HHS adds 3 days data on Friday, then rolls back 5. Working from archive.
Critical Staffing still at 2021 levels.
Capacity Level has been elevated since independence from the virus was declared—as fewer & fewer professionals are available to staff hospital beds.
#ThisIsOurPolio #hospitals #LongCovid #CovidIsNotOver #nurses #MassDisablingEvent #CovidIsAirborne #BringBackMasks #dataviz #datavis
pediatric & PICU capacity trend USpol
Pediatric staffing never recovered to pre-omicron levels. Rather, one in five pediatric beds reported last May: now missing.
PICU Capacity Level (not shown): 68%.
Weekly average ~60 PICU beds were covid patients.
We're failing our kids. The emergency is over.
#ThisIsOurPolio #hospitals #LongCovid #CovidIsNotOver #nurses #MassDisablingEvent #CovidIsAirborne #BringBackMasks #dataviz #datavis
pediatric & PICU capacity map USpol
HHS state level data was rolled back; facilities level data saw no update this week.
As of last week's data, some 164 counties have pediatric care near or over capacity (≥90%).
Of 258 counties reporting any PICU capacity, near one in six are near or over full.
#ThisIsOurPolio #pediatric #hospitals #pedsICU #RSV #Strep #Flu #LongCovidKids #CovidIsNotOver #nurses #MassDisablingEvent #CovidIsAirborne #BringBackMasks #dataviz #datavis
pediatric & PICU capacity rank USpol
Counties by pediatric capacity (darkest counties on map above—old data):
⒈ Coconino, AZ ≥150%
⒉ St. Landry Parish, LA ≥133⅓%
⒊ Brown, SD—150%
⒋ Wood, WV—150%
Idaho—135%
⒌ Wicomico, MD—117%
⒍ Anoka, MN—100%
⒎ Wood, WI—100%
⒏ Cayey Municipio, PR—100%
⒐ Scott, MN—100%
#ThisIsOurPolio #RSV #Strep #Flu #LongCovidKids #CovidIsNotOver #BringBackMasks
adult hospital & ICU capacity map USpol
Facilities data still a week out of date.
Some 71 counties ≥ 100% capacity per HHS data. Reporting ≥90%: 201—over 8⅛% of those with any capacity.
Near full can mean E/Rs with day-long wait times.
For counties w/ ICUs—near one in six are full or near full.
#ThisIsOurPolio #hospitals #LongCovid #CovidIsNotOver #nurses #MassDisablingEvent #CovidIsAirborne #BringBackMasks #dataviz #datavis
adult hospital & ICU capacity rank USpol
Counties by adult hospital capacity (darkest counties on map above—old data):
⒈ Warren, NY—134%
⒉ Wise, VA—121%
⒊ Guam, GU—115%
⒋ Yuma, AZ—110%
⒌ Smyth, VA—110%
⒍ Kenton, KY—109%
⒎ Boone, KY—108%
⒏ Buchanan, MO—107%
⒐ Berkeley, SC—104%
⒑ St. Mary's, MD—101%
#ThisIsOurPolio #hospitals #LongCovid #CovidIsNotOver #nurses #MassDisablingEvent #CovidIsAirborne #BringBackMasks
covid variants forecast USpol
Seventh week of post-Kraken soup, Nowcast adds FE.1.1, EU.1.1, XBB.1.5.68.
Unmitigated XBB.1.5 has birthed over 100 variants—latest dashboard for Kraken clan shows no major contender.
Top non-Kraken XBB: Arcturus (1.16), Hyperion sib (1.9.2), Hyperion (1.9.1).
[Srcs: https://covid.cdc.gov/covid-data-tracker/#variant-proportions
https://public.tableau.com/app/profile/raj.rajnarayanan/viz/USAVariantDB/VariantDashboard]
#ThisIsOurPolio #Covid #Covid19 #SARS2 #CDC #variants #CovidIsNotOver #CovidIsAirborne #WearAMask #BetterMasks
covid variants map USpol
Arcturus XBB.1.16 clan (incl FU*) above ⅓ of non-Kraken XBB in GISAID—CDC has as ⅒ to ¼ of CDC specimens all regions.
Hyperion XBB.1.9.1 (incl FL*) & sib XBB.1.9.2 (incl EG*) together over another third of non-Kraken XBB; with latter prominent in Mnts/Dakotas.
#ThisIsOurPolio #Covid #Covid19 #SARS2 #CDC #variants #CovidIsNotOver #CovidIsAirborne #WearAMask #BetterMasks
covid variants map USpol
@beadsland don't wait for the new variant to rise out of nowhere predict it yourself with jax. https://www.researchsquare.com/article/rs-2797280/v2
covid variant data, machine learning
@tavoglc Not sure if this link is specifically addressed to me or to folk in general.
In either case, while this work is certainly intriguing (although perhaps your Medium link would be a better intro for the uninitiated?) it seems a bit beyond the capacity for most to DIY.
Also, the term "jax" doesn't turn up in a text search of the PDF of your preprint. From context, guessing you mean the vectorizing alternative to TensorFlow?
covid variant data, machine learning
@beadsland you're right is the alternative to TensorFlow, although there's also a TensorFlow version of most of the models. There are examples linked on the GitHub repo in the paper. And live examples at kaggle for most of the models and datasets.
covid variant data, machine learning
@tavoglc Now see, if you'd lead with a live demo link that would have made a world of difference! Not finding "kaggle" in the PDF preprint either, BTW.
Sounds like this is all a *very* big idea and figuring out how to package it for easy dissemination & adoption by others ain't gonna be easy. It may all fit in your head, may all be readily visible to you, because is your brainchild, but for the rest of us, is proverbial elephant with us only grasping blindly.