This means you can use #bacteria to determine whether someone has just been to the beach, got off the train in the city centre or taken a walk in the woods. This opens up new possibilities within #medicine, #epidemiology and #forensics.
#police #surveillance #location #technology #privacy #news #future
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.
#covid19 #ba_2_12 #ba_2_12_1 #pandemic #epidemiology #sarscov2
(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:
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:
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.
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.
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
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:
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
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 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
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
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."
#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."
"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
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.
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.
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
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
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
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.
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
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
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
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.
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.
The fomula for exponential growth is:
y(t) = a * e^(k * t)
See: https://www.mathsisfun.com/algebra/exponential-growth.html
Where:
"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.
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:
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.
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.
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 |
https://pastebin.com/raw/Sn2jrG5f
Please note any observed errors / corrections.
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