COVID-19: Two new omicron variants are spreading in N.Y. and elsewhere. Here's what we know

On Wednesday, health officials in New York said that two new omicron variants are spreading rapidly in the state. The variants appear to be causing a small surge in cases in central New York state, the department of health said.

Known as BA.2.12 and BA.2.12.1, the variants are closely related to the BA.2 variant – a version of omicron that has caused surges across Europe and is now dominant across the U.S.

Together the two new variants now comprise 90% of cases in central New York.

But one of them, BA.2.12.1, contains a mutation that appears to give the variant an advantage, computational biologist Cornelius Roemer wrote on Twitter. The mutation resides on the part of the virus that binds to human cells. And in previous variants, this mutation has helped the virus infect cells, studies have found. The BA.2.12. variant appears to have a growth advantage of about 30% to 90% per week over BA.2, Roemer estimates. ...

I'd been noticing on the NY Times heat maps over the past week or so that patterns are starting to resembe other early outbreaks. Most activity is in urbanised or high-travel states after finally burning out in rural ones --- Idaho and Maine had been the last-remaining hot-spots of the Omicron wave. Now cases are rising in the Northeast, Washington, DC (notably the Gridiron gala), Colorado, and Illinois. The pattern once again is of emergence in specific spots, many either high-population or high-travel, and I expect to see spread outwards from those regions once again.

https://www.nytimes.com/interactive/2021/us/covid-cases.html

No idea what the morbidity and mortality will be, though it's worth noting that the Omicron wave ended up showing a peak on 3 Feb 2022 comparable to the 27 Jan 2021 previous peak: 2,652 peak daily deaths as compared to 3,310. This was based on a far higher measured case count, though testing coverage may also have increased between the two waves.

If there's good news, this wave is emerging during Northern springtime, as people are increasingly outdoors and buildings are better ventilated, both of which tend to reduce spread. As summer emerges and people congregate inside air-conditioned structures, beginning in the Southeast, I expect severity may increase.

Watch Florida and Texas.

https://www.npr.org/sections/goatsandsoda/2022/04/14/1092812456/two-new-omicron-variants-are-spreading-in-n-y-and-elsewhere-heres-what-we-know

#covid19 #ba_2_12 #ba_2_12_1 #pandemic #epidemiology #sarscov2

COVID-19: Fred Hutchison Researcher Thread on Omicron

(17 Dec 2021)

Trevor Bedford is a biostatistician working on viruses and vaccines at the Fred Hutchison Cancer Centre. The linked thread is a few days old but gives some strong insights on Omicron's spread in several countries: South Africa, UK, Denmark, Germany, and the US. It supports much of what I've read and feared regarding Omicron's spread, though does not speak to its severity.

The first set of graphs is a log-linear plot of growth over time. On this plot, exponential growth appears as a straight line. I'd mentioned yesterday in a comment that it's likely that many countries, the US included, effectively have two simultaneous epidemics presently: Covid-Delta and Covid-Omicron. These plots support that view, with the delta and omicron infection rates presented. Several insights:

• Omicron growth is virtually straight-line dating as far back as October. And if you look at the South Africa plot, the case numbers bend upwards, that is, measured growth is accelerating. Note that on a log plot, even only slight deviations from a straight line actually represent large changes in absolute numbers.
• It's possible to extrapolate numbers backwards to determine likely first cases at the x-intercept (where the red line crosses the bottom of the graph). This puts Omicron in the US and Germany around 10 November. The UK's severe Omicron outbreak actually seems to have begun later than the US's. If you've been taking comfort that UK experience is a future portend of what's coming to the US (as I have), you might care to revise that view, though the UK's growth rate is higher than the US's, for now. As a lower graph shows, the King County, WA, doubling time is 2.2 days, faster than the UK.
• There's been little deviation from an exponential rate across four orders of magnitude. I do expect the trend to become sigmoidal / cyclical as numbers reach into the millions. But that's not happened yet.

As the text notes, doubling times range from 2.3 days (UK) to 3.3 (Germany). This is faster than the initial COVID-19 outbreak at its highest. Case numbers increase by an order of magnitude, that is, a multiple of ten, per week.

As with all surveillance and testing data, these plots are a look backwards in time, and reflect only the observed tested cases. Depending on testing lags, we've probably got another 3-7 days of actual incidence. This means multiplying by both additional growth (an order of magnitude per week) and accounting for non-tested cases (a multiplier of perhaps 3--6). Given the nearly 5 days elapsed since the thread was posted, there are two doubling periods as well. The total multiplier then is as much as 100x for present actual incidence, another two orders of magnitude over values shown. At the lower end, perhaps 30x if growth is slower and testing more comrehensive.

(Caveat: that's my Space Alien Cat interpretation. I'm not a statistician or virologist. I'm pretty comfortable making that observation though.)

In the UK, cases are growing fastest presently in London. As of December 14 (a week ago), 72% of London cases tested were Omicron. I expect that other countries, inlcuding the US, will see a similar trend. Given the "two epidemics" model, in the US, this means:

• An extant and well-established Delta wave that's been progressing north-to-south across northerly states from Minnesota to Maine, and from a south-west path from Minnesota to New Mexico. This has been gradually progressing southwards and eastwards for the most part.
• An emerging Omicron wave beginning in urban and transport hubs. New York City seems to be the present epicenter, though looking at the New York Times's hot-spot maps, Miami, FL, has emerged as an uncharacteristically hot spot in what should be the state's winter respite. Cases may be increasing in the Houston area as well. Ithaca, NY, has seen the highest rate of infections, supplanting Wheeler County, OR. Cities such as Chicago may well be at the intersection of both waves now. Illinois had been a less-affected state among the midwestern states until this past week, but is now getting hotter, with some rise in the highly urban Chicago region, but also very high rates in more rural downstate counties. Cleveland, OH, also appears hard hit.
• Several states still have very low vaccination rates, at or below 50%, notably Idaho, Wyoming, Alabama, and Mississippi. These will likely be hit very hard.
• Even previously spared (and well-vaccinated) states and territories such as Hawaii and Puerto Rico are now reporting explosive Omicron growth.

As I noted, this thread does not address severity of Omicron.

I've been watching the reports on this closely. Early suggestions were that Omicron was less severe than earlier Covid waves. The revised information from multiple sources, including South African medical authorities, the WHO, and UK, is that this is not well supported, and that the apparent mildness of Omicron may be due to its infecting large numbers of previously-exposed or vaccinated individuals. Information continues to develop, the situation is fluid, and as evidence changes, understanding and advice will likely do so as well. I'm simply relaying what I'm reading and hearing as accurately as I can.

GET VAXXED AND GET BOOSTED IF AT ALL POSSIBLE

This is Bedford's advice in a subsequent thread.

[T]he single best action individuals (and governments) can be taking to reduce impact of the Omicron wave is to get booster dose if already vaccinated and to get vaccinated if not.

Vaccination does greatly reduce severity of Omicron. "Fully vaccinated" now means two Pfizer doses and a booster.

Oh, and holiday travel and gatherings are doing absolutely nobody any favours.

As I've noted before, I expect that the US and UK will go into hard lockdown within weeks. It should do so now. I had hoped the UK would do so before Christmas, it has not. Medical advisors are telling the PM that a 2022 lockdown will be too late. Dr. Fauci in the US has been mooting the prospect of lockdowns, though Biden to date has resisted these, though less strongly over the past few days.

The biggest concern is impacts on medical and other services. Hospitals and other facilities will be and are being overwhelmed. Non-Covid issues won't be treatable due to shortages of space, equipment, and staff. Medical and other personnel will be impacted by the disease. John Campbell, RN, PhD, has noted that as much as 10% of medical staff at UK hospitals are quarantining after exposure or infection. Similar rates can be expected across all employment sectors, exacerbating production, distribution, and services further.

I'd made a lay prediction for the August 2021 -- January 2022 period that deserves review

You'll find that here.

https://joindiaspora.com/posts/52b6aff0e8f30139ede5002590d8e506

I'll admit that the US fall experience has been less severe generally than I'd feared. Vaccination seems to be helping blunt the fall-winter wave considerably. I also anticipated a greater contribution from the 2021 Sturgis Rally (which was held) than seems to have emerged. That was limited at best.

Other than that ... I think my predictions as to where outbreaks would originate and how they'd propagate were spot on.

And I'd had this to say about possible mutations:

Likely mutations are to easier transmission, longer incubation time (Covid tends to spread amongst asymptomatic carriers before onset of symptoms), efficacy of shedding, and lower-severity symptoms. This could lead to a less harmful form becoming prevalent and out-competing more harmful or lethal variants.

I'd also noted that Dr. Fauci saw US control over Covid-19 by Spring 2022 at the earliest. Given the Omicron wave ... that seems somewhat optimistic, though its spread through the population could finally precipitate a general natural immunity. Hopefully without too many more deaths, though I strongly expect the 1 million threshold to be crossed.

Again: I am not a specialist in this area, just someone following best science and reporting as I can. You are strongly encouraged to verify all content, and please comment with any concerns or inaccuracies.

https://threadreaderapp.com/thread/1471651826554470402.html

#Covid19 #Omicron #TrevorBedford #Pandemic #Epidemiology #FredHutchisonCenter #FredHutch #Projections #Reviews #GetVaxxed #GetBoosted

COVID-19: The Trouble With the Case Curve During the Holidays

The familiar curve chart that shows new Covid-19 cases will be disrupted by testing and data reporting interruptions just as people prepare for another holiday season in the United States. Last year, the national case curve showed pronounced declines after Thanksgiving and Christmas that did not reflect real decreases in new infections.

The impact of holidays may be even more noticeable this time around, as illustrated by the recent Labor Day holiday, because states are reporting data less consistently than they did a year ago. In response, we’re updating the averaging methodology that turns the daily case and death data we report into a curve chart. ...

...

The average number of cases reported each day declined after both Thanksgiving and Christmas, but these drops corresponded to decreases in the number of tests performed, meaning that more cases were likely going uncounted. Once tests returned to previous levels, cases quickly climbed again to their peak in early January. Those deceiving decreases were not reflected in the chart showing the number of people hospitalized with Covid-19 during the holiday season.

The number of people hospitalized with Covid is a lagging indicator of community infection rates, but is less sensitive to holiday data interruptions because data from hospitals is reported directly to the Department of Health and Human Services. And if a hospital fails to report for a single day, the same number of Covid-19 patients from the previous day can be easily carried forward. ...

I've pointed out the distortionary effects of holiday dates on COVID-19 (and other healthcare and epidemiological) reporting previously. That's occasionally been met by some strong pushback by those insisting that the changes in reported data reflect actual incidence fluctuations.

The simple truth is that they do not, that the same fluctuations appear across numerous data series ... and are curiously absent from others. As the New York Times article notes, the pronounced "notches" which appear at Thanksgiving and Christmas in US data (and smaller notches appearing at Memorial Day, Independence Day, and Labour day, all large US national holidays often coinciding with travel) are not reflected by fluctuations in hospital admissions, which are both a lagging and more persistent trend.

I'd first noticed this about two decades ago in an earlier life doing health care data anlysis. I'd been tasked with making sense of a set of outpatient data for a clinic --- mostly dealing with minor or routine issues. The daily-level data were quite noisy, and what I quickly learnt applying different smoothing intervals was that at a weekly level virtually all of the daily "noise" disappeared, though what remained was a pronounced 7-day trend with large drops on weekends. This pattern also persisted when longer multiples-of-seven smoothing periods were applied: 14, 21, and 28 days. I (and most official data analyses) tend to stick with the shortest useful smoothing period, though for smaller countries with thinner data, I often wish that a longer (2--4 week) smoothing period would be applied as data over shorter periods remain quite noisy and give the spurious appearance of real changes which are not in fact warranted..

The other strongly evident pattern was that claims dropped precipitously on and around holidays, with the largest declines appearing at Thanksgiving and Christmas for US data. This was evident across several years' worth of data. And so when I started looking at COVID19 data, the holiday dips stood out to me as they occurred, first with a late-May dip (Memorial Day, US), then the 4th of July (Independence day), Labour Day, and of course the larger Thanksgiving and Christmas / New Years dips (there's a trend that flows through the period from the 25th of December through the 1st of January).

And now the New York Times seem to have recognised this.

You'll find similar patterns for data in other countries, of course. In Canada, Canada Day (July 1), Labour Day (Sept. 6), and Thanksgiving (October 12) are visible, along with the much more pronounced Christmas holiday:

https://www.worldometers.info/coronavirus/country/canada/

In Germany, Easter Week shows a major dip:

https://www.worldometers.info/coronavirus/country/germany

Again, what characterises a holiday data reporting dip is:

• A fall in reported data that are themselves based on direct and often voluntary assessment or filing times. Covid tests and (in many countries) coronors' reports if based on filing date rather than date of death will follow these trends. Hospital admissions rather less so as these facilities both remain operational and are accessed when truly needed.
• A resumption of an earlier trendline. You can usually imagine a consistent smoothed line running through the time period.
• An absence of a sharp exponential growth trend. Again, where there are triggering events leading to outbreaks, what is not seen is a sudden spike in cases, but a slow growth following a doubling trend. As has been observed, "Exponential growth doesn't mean 'fast', it means 'imperceptibly at first, then suddenly overwhelming'." A holiday-induced outbreak would look much more like South Dakota following the 2020 Sturgis Rally (~8--19 August, 2020), with a slow rise appearing by the 23d, and a sharp climb from the 25 of August through September 3. Cases subsequently fell, but began rising again after the 14th of September, and continued to climb until their November 15 peak. For South Dakota in 2020, both the Thanksgiving and Christmas notches appear as the overall case rate was falling rapidly.

https://www.worldometers.info/coronavirus/usa/south-dakota/

https://www.nytimes.com/interactive/2021/11/22/us/covid-data-holiday-averages.html

#COVID19 #epidemiology #pandemics #thanksgiving #christmas #statistics

COVID-19: "Antibodies to SARS-CoV-2 were detected in 40% of wild white-tailed deer sampled from four U.S. states in 2021."

That's the headline finding in "SARS-CoV-2 exposure in wild white-tailed deer (Odocoileus virginianus):

Abstract: Widespread human SARS-CoV-2 infections combined with human-wildlife interactions create the potential for reverse zoonosis from humans to wildlife. We targeted white-tailed deer (Odocoileus virginianus) for serosurveillance based on evidence these deer have ACE2 receptors with high affinity for SARS-CoV-2, are permissive to infection, exhibit sustained viral shedding, can transmit to conspecifics, and can be abundant near urban centers. We evaluated 624 pre- and post-pandemic serum samples from wild deer from four U.S. states for SARS-CoV-2 exposure. Antibodies were detected in 152 samples (40%) from 2021 using a surrogate virus neutralization test. A subset of samples was tested using a SARS-CoV-2 virus neutralization test with high concordance between tests. These data suggest white-tailed deer in the populations assessed have been exposed to SARS-CoV-2.

One-Sentence Summary: Antibodies to SARS-CoV-2 were detected in 40% of wild white-tailed deer sampled from four U.S. states in 2021.

There's some evidence that deer are not themselves symptomatically ill on account of the coronavirus. That itself may not give much human comfort.

My immediate thoughts are:

• This means that there's a strong potential for a pervasive animal reservoir of SARS-COV-2 in North America, even if the virus is eradicated amongst the human population. (Itself apparently no certainty.)
• Ongoing mutation and evolution are likely within that reservoir population.
• If SARS-COV-2 can spread throughout North American wildlife populations, it can most likely survive in wildlife populations elsewhere. (The fact that it's widely believed to have originated amongst some animal population only lends more credence to this possibility.)

This is not especially surprising news. Though it's most definitely not good.

HN discussion: https://news.ycombinator.com/item?id=28030368

https://www.biorxiv.org/content/10.1101/2021.07.29.454326v1.full

#covid19 #deer #AnimalReservoirs #viruses #epidemiology #SarsCov2

COVID-19: With 55% weekly new-case growth, the US is again back at uncontrolled spread rates

I've been watching the Covid story since mid-January, 2020, and one of the initial quite-worrying signs was viral infections doubling every 8--10 days early in the China emergence.

Checking at Worldometers right now, the US is reporting a 55% weekly growth rate, which is the same as doubling every 8.9 days. That means that with vaccines and masks, the virus is once again spreading effectively without resistance through the population.

This is not a good sign.

As with earlier cautions of rising rates, the real questions are when will effective measures to control spread be implemented, how widely will they be adopted, and when will their impacts be felt. As before, there seems to be roughly a two-week lag between implementation of effective measures, and their effect on spread being visible.

https://www.worldometers.info/coronavirus/weekly-trends/#weekly_table

#covid19 #usnews #pandemics #epidemiology

I don't agree with everything said (nor the philosophy/epistemology behind it). I think it important to listen and consider more/all perspectives. I do think it vital to be wary of the one true "truth" we're not allowed to waver from.
~ "I may not agree with what you say, but I will defend, to my death, your right to say it."

listening to this immunologist https://sp.rmbl.ws/s8/2/H/h/N/H/HhNHb.caa.1.mp4?u=0&b=0

reminds me of ... https://joindiaspora.com/posts/18175698 ... long before he mentioned the #nuremberg code.

#civilliberties #existential #humanrights #coercedmedicalproceedures #antivaxxers #anyvaxxers #neither #immunology #health #rights #logic #coercion #silence #duress #morality #moralconscience #moralcoward #selfcensorship #censorship #decide #freedom #noveltechnology #humantesting #plandemic #geneticengineering #fuseagenicprotien #warn #warning #worrying #coagulation #arrogance #stonewalling #denial #marketing #clots #thevaccineisnotavaccine #humancull #thinkaboutit #costriskanalysis #medicalethics #manslaughter #massmurder #softkill #eugenics #tyranny #vaccinepassport #digitalidentity #privelidgelevels #coerce #totalitariancontrol #behaviourcontrol #mindcontrol #obey #disobey #tonybliar #killbill #canttrustgovernment #corporateterrorism #epidemiology #topupvaccine #howstupidarewe #stultophobia #wealthextractionmaximisation #regulatorycapture #regulatoryskip #hippocraticoath #donoharm #depopulation #nocullnecessary #prisonplanet #unethical #toxicology #costbenefit #riskanalysis #doctorsmustknow #nursesmustknow #avoidabledeaths #hierarchy #massmedia #howthefewcontrolthemany #experimentaltreatment #treatment #mistreatment #nurembergcode #councilofeurope

frustrating how suppressed such messages have been getting... and more frustrating how many succumb to the danger of the little bit of information they're allowed, epistemologically missing that absence of evidence is not evidence of absence, and blundering into a dozen other fallacies and unquestioning group think, saturated in the propaganda parroted back, not even questioning who's making bank, who's got conflicts of interest, not following the money, not considering cui-bono. very disappointing, humanity, very disappointing, and all the more for all you keep quiet for the fear of what other people think. you would hand us all over to eugenicists and tyrants just to play play along for an easy life. ... "First they came..."

I don't agree with everything said (nor the philosophy behind it). I think it important to listen and consider more/all perspectives. I do think it vital to be wary of the one true "truth" we're not allowed to waver from.

Listen to those who seek the truth, run from those who claim to have found it.

yet, "no valid medical reason whatsoever" & "no chance, impossible" & "not viable, not plausible" surely needs give you pause for concern and consideration. and he can say "I don't know" often too, and consider possibilities and uncertainties. Sounds like someone who investigates rather than parrots unquestioningly. still some flies in his ointment still, but far less than the jar of flies-only on offer.

remember they already bought themselves #NoLiability ... :-[

achieve totalitarian domination, AND triple your billionaire wealth, risk-free? no-brainer for the #ruthless.

#wakethefuckup #weareinalotoftrouble

and still the words of that middling insider from years ago caught on audio recording scoffing something like "after the terrorist thing wears off, we're going to have people running scared of a disease without symptoms".

... and initiated at the end of the year with the biggest % of the world population protesting. and largely protesting the banker/corporate-polluter/politician class... same folks who promote #thegreatreset #geoengineering and ploys text-book copy of simulations like #event201 ... got enough smoking gun yet? got enough pieces in place to make a reasonable guess at the picture yet? or is it still as those who have most to gain and most to lose say to you?

we needn't have this bs, we needn't keep succumbing to their terrorising us into accepting, even pleading for, their toxic paternalism. we could have anarchism, and that would be a #good thing.

"hope will be ok" #positivityninnyism #no #wearenotdoingthatanymore

"They can't believe people can be that evil. I remind them, Pol Pot, Stalin, Hitler, they do exist."

#beliefism #denial #evil

"All it takes for evil to succeed is for good people to do nothing."

"One death is a tragedy; a million, a statistic." ... numb yet? :-/

"I don't believe anything I'm told anymore." YAY! non-belief. best protection to deceivers. #keepquestioning

#relax #keeplearning
#goodlisten

London will be overwhelmed by covid in a fortnight says leaked NHS England briefing

London’s hospitals are less than two weeks from being overwhelmed by covid even under the “best” case scenario, according to an official briefing given to the capital’s most senior doctors this afternoon.

NHS England London medical director Vin Diwakar set out the stark analysis to the medical directors of London’s hospital trusts on a Zoom call.

The NHS England presentation, seen by HSJ (see slides below story), showed that even if the number of covid patients grew at the lowest rate considered likely, and measures to manage demand and increase capacity, including open the capital’s Nightingale hospital, were successful, the NHS in London would be short of nearly 2,000 general and acute and intensive care beds by 19 January.

The briefing forecasts demand for both G&A and intensive care beds, for both covid and non-covid patients, against capacity. It accounts for the impact of planned measures to mitigate demand and increase capacity.

https://www.hsj.co.uk/acute-care/exclusive-london-will-be-overwhelmed-by-covid-in-a-fortnight-says-leaked-nhs-england-briefing/7029264.article

An item I've not yet posted by Zeynep Tufekci at The Atlantic puts forth the case that demolishes my cautious optimism about the high-infectiousness SARS-CoV-2 variant: higher infectiousness, unlike higher mortality, exhibits exponential growth rather than a linear trend increase, and hence is actually a far greater problem.

#covid19 #uk #epidemiology #PublicHealth #NHS

The National Respiratory and Enteric Virus Surveillance System -- National Trends

The following surveillance information only applies to the four common human coronavirus types, not SARS-CoV2 or COVID-19. These four common types include 229E, NL63, OC43, and HKU1.

The four coronaviruses shown, and percent-positivity in testing, suggest that early-November levels are about 1/5 to 1/6 of winter peaks. How closely this models SARS-CoV2 behaviour is unknown, though suggestions are interesting.

COVID19 infections are running about 150,000/day in the US. Five times that is 750,000/day. Six times is 900,000/day.

Europe is seeing slightly over 200,000 cases/day. Five times that is 1,000,000 cases/day. Six times is 1,200,000/day.

https://www.cdc.gov/surveillance/nrevss/coronavirus/natl-trends.html

#covid19 #epidemiology #pandemics

COVID-19: Texas has overtaken California for most cases to date

• TX: 889,513 (30,677/1M)
• CA: 888,305 (22,482/1M)

https://www.worldometers.info/coronavirus/country/us/

Again, days earlier than projected.

(I'm trying to lean conservative in forecasting.)

Texas, Illinois, and Wisconsin exceeded California's new-case count, Texas by more than double (6,414 vs. 3,047).

#covid19 #texas #california #worldometers #coronavirus #epidemiology

COVID-19: The True Coronavirus Toll in the U.S. Has Already Surpassed 200,000

Across the United States, at least 200,000 more people have died than usual since March, according to a New York Times analysis of estimates from the Centers for Disease Control and Prevention. This is about 60,000 higher than the number of deaths that have been directly linked to the coronavirus....

Counting deaths takes time and many states are weeks or months behind in reporting. The estimates from the C.D.C. are adjusted based on how mortality data has lagged in previous years. Even with this adjustment, it’s possible there could be an underestimate of the complete death toll if increased mortality is causing states to lag more than they have in the past or if states have changed their reporting systems.

But comparing recent totals of deaths from all causes can provide a more complete picture of the pandemic’s impact than tracking only deaths of people with confirmed diagnoses....

The Times's data run through July 25, 2½ weeks ago. At about 1,000 deaths per day, reported, the NYT reports 219,000 deaths through the reporting period, to which we can add about another 20,000 for the thousand additional souls extinguished every day -- half a 9/11. That's approaching a quarter million dead.

So far.

https://www.nytimes.com/interactive/2020/08/12/us/covid-deaths-us.html

#covid19 #mortality #epidemiology

Covid-19: 1 in 1,000 people worldwide confirmed to have had disease

Looking through Worldometer's COVID-19 page just now, I was startled by the worldwide case count, initially misreading it as total population. But no, 7,709,358 cases have been diagnostically confirmed, just a hair under one per thousand of global population.

Given testing lags and shortfalls, the ground truth is undoubtedly higher, though by how much is in question.

Growth has been less than exponential for quite some time, though accellerating again for the past few weeks. How much this represents actual containment and how much is measurement artefact is also unclear.

Brazil, India, and Russia are rapidly gaining on the US for total cases (Brazil's daily new count surpassed the US some days ago). African numbers remain implausibly low.

#Covid19 #coronavirus #epidemiology

https://www.youtube.com/watch?v=JeX8lu-OIvQ

#consider #reconsidering #germtheory #interesting #paradigmshattering #questioneverything #apatientcuredisacustomerlost
#bioelectric #cellularhealth #immunology #virology #epidemiology #koch #ickonic #drandrewkaufman #confirmationbias #conformity #scientism #immunesystem #biome #exosomes #virome #health #logicalfallacy #logicalfallacies #alopathy #study #falsepositive #illness #contageous #pandemic #plandemic #scamdemic #panicdemic #excessmortality #perspective #maybe #propaganda #covid19 #coronavirus #flu #pneumonia #architectureofoppression #911again #billgatesisthedisease

Discussion elsewhere: Does COVID-19 first affect vulnerable populations such as the elderly or those with comorbidities?

This question popped up in another thread. It's an interesting suggestion, but my very strong sense is that it's a false impression.

My read is that an infectious agent has no specific capability to seek out vulnerable populations, but rather infects opportunistically. What it will do is spread through hospitable environments, whether the hosts themselves are vulnerable or not: asymptomatic carriers, super-spreaders, tight quarters, high-probability of transmission, etc.

For a previously non-vigelant region, that spread, especially if via no- or low-symptom carriers, won't be evident until the severely sick, or dead, start showing up.

Perception is "this targets highly-vulnerable populations".

Reality is that highly-vulnerable populations are your perception system.

The epidemic is widespread, but (perceptually) cryptic. It isn't until vulnerable populations are affected, and consequences have time to develop, that it becomes manifest. Which is precisely the circumstances of the Washington State outbreak: six weeks of cryptic spread, likely hitting an. elder-care home about two weeks before its residents began waking up dead.

Not only is the map not the territory, but the vista is not the geography.

#covid19 #epidemiology #MapTerritoryConfusion #media #Perception #PerceptionVsReality #SeeingLikeAState

#Simulating an #epidemic - YouTube

This is an interesting video about how to model the spread of epidemics and how they're affected by factors such as #Hygiene and #SocialDistancing.

https://www.youtube.com/watch?v=gxAaO2rsdIs

#3Blue1Brown #Mathematics #Maths #MathematicalModelling #Modelling #Epidemiology

COVID-19 A Laycat's US Outbreak Model

This is a non-expert's simple extrapolation of the past 11 days' COVID-19 experience within the US, projecting both further likely spread of the COVID-19 outbreak and the possible actual extent of infected individuals based on a presumed testing lag.

As with my earlier China extrapolation: The real message here is how quickly experience deviates below the projection here, suggesting containment efforts are effective. In the case of China, that began about two weeks after my initial post. I am a space alien cat on the Internet, not an expert.

I've probably fucked up all kinds of things. Cluebats welcomed.

How this model works

I'm using a simple exponential growth formula, and basing the expected number of cases (and deaths) from the 5 March 2020 case and death counts, based on what appears to be native community spread rates through the US from 20 February 2020 through 5 March (the period of visible community spread). This is a short window though one showing rapid growth.

It is overwhelmingly evident that the US does NOT have a solid handle on monitoring, and likely won't for at least another week, possible several. This both makes the data presented and model based on them more uncertain, and means that as monitoring improves, apparent case counts will likely increase rapidly. Again, this reflects experience in China.

Virus behaviour, population behaviour, public health measures, weather changes, sunspots, and timelords could all change things markedly.

Exponential growth function

The fomula for exponential growth is:

y(t) = a * e^(k * t)

See: https://www.mathsisfun.com/algebra/exponential-growth.html

Where:

• y(t): quantity at time t
• a: initial quantity
• e: the natural log constant, about 2.7183
• k: the grow rate per period.
• t: the number of periods.

"Period" here is "days".

We can solve for k:

k = ln(y(t)/a)/t

This gives us the growth rate given two measurements t periods apart.

We can solve for t:

t = ln(y(t)/a)/k

In particular, if we solve for y(t) = 2 and a = 1, we get the doubling time.

I've written a simple gawk script which computes for k and doubling rate, and also projects the weekly (7 day) and fortnightly (14 day) growth rates.

Detection lag

A huge problem within the US is that confirmed cases are lagging actual infection dates by a substantial amount. How long that is is ... not entirely clear, though I'm going to assume a 14 day (two week) lag based on:

• Initial infection is followed by a non-symptomatic period of about a week on average.
• Seeking medical assistance has seen a further lag of several days in getting an appointment / performing a test.
• Test results themselves take 4 days based on information I've seen.

The total lag is about 2 weeks.

I'd suggested that this could lead to as much as a 100-fold understatement of actual cases. Based on current data, that seems pessimistic: it's "only" about 47x greater than the published confirmed cases count -- a number that's moved around considerably, by the way, so don't put too much faith in that either. But it gives an indication.

We also get a doubling time of about 2.2 days, which means that however bad the situation is now, it's going to be twice as bad in a little over 48 hours. When you hear statements that the situation is "rapidly evolving" this is what is being referenced. Things are changing very quickly. Locations which may have low risk today may have a high risk in a day or two.

You should be finalising preparations and supplies runs about now, if not already.

Again: non-expert extrapolation based on early data, a simple model, and many uncertainties. I expect we'll likely see following trend, if not overshooting it, for a week or two, mostly as monitoring catches up to reality. I'm very much hoping we'll start to see a low-side numbers starting about two weeks out (18-22 March), as containment efforts begin to be effective. The caveat is that I don't see effective containment measures being enacted, certainly not on the scale that China performed starting ~22 January. In which case the projection here could well fit actual experience for longer.

As before, I'm posting this as a line in the sand of what my projection was. I hope and expect to be proved wrong on this within a couple of weeks. I'm dying to see how well this matches reality.

The professionals are apparently doing this as well

Dr. Messonier of the CDC mentioned 5 March in an NPR interview that there were numerous groups doing epidemic modelling to try to estimate the actual spread of SARS-CoV-2 within the US, though she pointedly refused to give any numbers herself. I have yet to find any published projections, but would be interested in seeing any.

The script

Hardcoded in (edit to modify) are the initial and current case counts. You'll need to supply days between these measures as well. Data are taken from Wikipedia's 2020 Coronavirus Outbreak in the United States article.

The script calcuates the growth rate, with an arbitrary high and low bound (basically assuming one day more or less error in the reported range -- it's kind of weak sauce but gives some idea of sensitivity), the doubling time, the weekly growth rate, and the 14-day growth rate.

It then produces two reports, one every day for 29 days, the other every seven days for 200 days. Both cut off if the infected population exceeds total US population, given as 330.4 million. Shown are projected deaths, cases, cases at a low or high growth rate, and as "w/ 14 day lag" the possible ground truth of total cases from which confirmed cases are drawn. I'll note that this presently exceeds 10,000 cases, and ... doubles ever 2.2 days or so. A rate which will hit 1,000,000 by 18 March.

By April 25, if present rates continue, the entire US is infected. At the WHO's 3.4% fatality rate, 11.2 million die, and given economic modelling, your retirement fund is trash.

(And then the disease may return in the fall....)

For Rest-of-world, you can substitute in values for that outbreak for a simiilar model. (I've got a separate script for this.) As values are hardcoded, it's a tad inflexible.

## Program Output

Minor reformatting aside, this is output as currently stands.

COVID-19 US Outbreak Model

Assumptions:
- init cases (2020-4-26): 14
- cases (2020-3-5): 175
- deaths (2020-3-5): 11
- daily growth rate: 1.316
- doubling time (days): 2.195
- 7 day growth: 6.83x
- 14 day growth/mon. lag: 46.59x

day	date	deaths	cases	@ lo dbl	@ hi dbl	w/ 14d lag
1	Mar 06, 2020	14	230	224	238	10,726
2	Mar 07, 2020	19	302	287	324	14,113
3	Mar 08, 2020	25	398	367	440	18,569
4	Mar 09, 2020	32	524	470	600	24,431
5	Mar 10, 2020	43	689	602	816	32,145
6	Mar 11, 2020	57	907	771	1,111	42,294
7	Mar 12, 2020	75	1,194	988	1,512	55,647
8	Mar 13, 2020	98	1,571	1,266	2,057	73,216
9	Mar 14, 2020	129	2,067	1,621	2,800	96,331
10	Mar 15, 2020	171	2,720	2,076	3,811	126,744
11	Mar 16, 2020	224	3,579	2,659	5,186	166,760
12	Mar 17, 2020	296	4,709	3,405	7,057	219,409
13	Mar 18, 2020	389	6,196	4,360	9,603	288,680
14	Mar 19, 2020	512	8,152	5,584	13,068	379,821
15	Mar 20, 2020	674	10,726	7,151	17,784	499,736
16	Mar 21, 2020	887	14,113	9,159	24,201	657,511
17	Mar 22, 2020	1,167	18,569	11,729	32,933	865,098
18	Mar 23, 2020	1,535	24,431	15,021	44,816	1,138,224
19	Mar 24, 2020	2,020	32,145	19,236	60,987	1,497,580
20	Mar 25, 2020	2,658	42,294	24,635	82,992	1,970,390
21	Mar 26, 2020	3,497	55,647	31,548	112,938	2,592,474
22	Mar 27, 2020	4,602	73,216	40,402	153,688	3,410,959
23	Mar 28, 2020	6,055	96,331	51,740	209,142	4,487,854
24	Mar 29, 2020	7,966	126,744	66,261	284,604	5,904,742
25	Mar 30, 2020	10,482	166,760	84,856	387,295	7,768,965
26	Mar 31, 2020	13,791	219,409	108,670	527,038	10,221,752
27	Apr 01, 2020	18,145	288,680	139,167	717,203	13,448,923
28	Apr 02, 2020	23,874	379,821	178,222	975,983	17,694,965
29	Apr 03, 2020	31,412	499,736	228,238	1,328,136	23,281,550

day	date	deaths	cases	@ lo dbl	@ hi dbl	w/ 14d lag
1	Mar 06, 2020	14	230	224	238	10,726
8	Mar 13, 2020	98	1,571	1,266	2,057	73,216
15	Mar 20, 2020	674	10,726	7,151	17,784	499,736
22	Mar 27, 2020	4,602	73,216	40,402	153,688	3,410,959
29	Apr 03, 2020	31,412	499,736	228,238	1,328,136	23,281,550
36	Apr 10, 2020	214,403	3,410,959	1,289,346	11,477,413	158,908,518
43	Apr 17, 2020	1,463,411	23,281,550	7,283,681	99,184,812	1,084,632,112
50	Apr 24, 2020	9,988,535	158,908,518	41,146,424	857,129,291	7,403,170,243

Source Code

https://pastebin.com/raw/Sn2jrG5f

Please note any observed errors / corrections.

Earlier

• March 3: "COVID-19: A Laycat’s guide to what to consider / watch for" https://joindiaspora.com/posts/3abaa5e03fca0138fcb6002590d8e506
• February 28: "A Laycat’s COVID-19 / Coronavirus Updates – February 28, 2020" https://joindiaspora.com/posts/bc04cb503c840138f4b8002590d8e506
• ~ January 22: "The Wuhan 2019nCoV Coronavirus Epidemic is growing by a factor of ten a week" https://joindiaspora.com/posts/6eab7f50245f0138ba5f002590d8e506

#coronavirus #covid-19 #covid19 #ncov2019 #epidemiology #epidemics #exponentialGrowth #IHopeIAmWrong #awk