Covid-19 at a homeless shelter in Boston: Implications for universal testing
medrxiv.org
medrxiv.org
It seems the more important point here is that the majority of the COVID-positive individuals were asymptomatic, putting another datapoint towards the conclusion that there are orders of magnitude more people that have this disease than have tested positive.
We need more studies to gather data on these asymptomatic cases if we want to reopen the economy soon. Imagine if 10+% of the population already had COVID and where immune, we'd be much closer to heard immunity than we currently think.
Longer term follow-up found ~25% of infections were asymptomatic.
In the US, we're now doing significant amounts of testing, but we're still primarily testing only those with severe symptoms. Even limiting our sample to people presenting with severe matching symptoms of COVID-19, we're finding ~20% PCR-positive for SARS-CoV-2.
Beyond that, we also have good evidence that the Basic Reproduction Number is somewhere between 2-3. Given a seeding of cases in mid-to-late January, even without social distancing measures, it would be unlikely to have 10% of the population infected yet.
Source: https://www.contagionlive.com/news/asymptomatic-influenza-in...
> Study results revealed an overall pooled prevalence for asymptomatic carriers of 19.1% for any type of influenza, 21.0% for influenza A, and 22.7% for influenza A(H1N1). For subclinical carriers, the overall pooled prevalence of was found to be 43.4% for any type of influenza, 42.8% for influenza A, and 39.8% for influenza A(H1N1).
The CDC is currently estimating an R0 of 5.7, which has likely been repressed by shelter in place orders. But even if it was only 4 before any interventions, there's still a chance there there are closer to 20M cases in the US rather than the 660,000 now.
My point is just that there's still a lot we don't know, and small changes in our understanding could implicate massive changes in the reality on the ground.
This would dovetail with the fact that places like sub-Saharan Africa and India haven't been hit very hard yet; they also happen to be _very_ young compared to Western countries.
If that's the case, it'd explain why developed countries are bearing the brunt of the pandemic. A very interesting example of similarly developed countries with comparable (but not equal) populations and population density, but different vaccination regimes are Portugal and Spain. Looking at their [2]respective [3]charts, the differences are stark. A similar difference can be seen between Ecuador and Argentina, even though the Greater Buenos Aires area is very population dense.
If this pans out, following the trends and [4] this map would validate that hypothesis.
[1]https://nypost.com/2020/04/14/coronavirus-death-rates-lower-...
[2]https://www.worldometers.info/coronavirus/country/portugal/
Obvious to me, perhaps dumb, question - if countries that were slower to change vaccination regimes have some advantage, then wouldn't we see older people in the countries that did change also at an advantage? Yet all reporting seems to be that older people are harder hit.
As someone else pointed out, the BCG needs a booster. While growing up I was given the initial one and two boosters. Most countries stopped doing that, which could also explain the effect. There's also the matter of which strain was used to create the vaccine: apparently some strains were better at fighting tuberculosis than others, so the same might apply (if the BCG vaccine actually made a difference) to COVID-19.
Anyway, my objective wasn't to start a conspiracy theory. We already have enough of those going around.
The BPR is estimated from the known cases, so it cannot tell you anything about a hypothetical large number of unknown cases.
Antibody tests in a recent German study of households gave 15% infection rate.
Also, the particular antibody tests they used appears to have a much higher FP rate than they claimed. Another study found 4% FPs rather than <1% like the press release for the Gangelt testing claimed.
If COVID-19 can spread to 15% of that particular population within that timeframe, and the estimate of a basic reproduction number of 2-3 predicts that this is not possible, then the estimate must be wrong.
> Also, the particular antibody tests they used appears to have a much higher FP rate than they claimed. Another study found 4% FPs rather than <1% like the press release for the Gangelt testing claimed.
Perhaps, but that doesn't put much of a dent into the results.
A FP rate that high will make a very significant difference in the results. (How large a difference depends on whether the 15% is a raw number or if it's been adjusted based on the expected test specificity and sensitivity. One would hope nobody would just report the raw numbers. But since all we've gotten from this study is basically a press release, expecting scientific rigor seems ill advised.)
However, if we apply the estimated mortality rate (0.37%) based on that to 11500 deaths in hot-spot New York, it would amount to over 3 million cases, again roughly 15% of the population.
Now perhaps Germans are so much healthier and their healthcare is so much better, making their real mortality rate so much lower. It's all speculation at this point, either way.
Its not a significant amount of testing if we're only testing those with severe symptoms
Here in SF at least there are scores of people I know who all were sick with a "weird flu" at some point from February to now who would all kill for a test to show that they have COVID immunity. These are people who could potentially go back into society and do more work / enjoy their lives instead of being shuttered inside with anxiety
It is currently _IMPOSSIBLE_ to get any kind of test to prove that
Like how does it work? Would love to go do that
it’s free and they do it for everybody
Do we know this for sure? I've read that we're seeing re-infections of people who previously had it, but it wasn't clear exactly what was happening.
the hardest thing about this is finding real information and then accepting it.
Is a pretty hard line to take when all the facts are evolving and we’re learning more every day
> These are people who could potentially go back into society
Nobody knows if they could go back or not but the statement that they might is somehow the most dangerous form of misinformation.
https://ourworldindata.org/grapher/full-list-cumulative-tota...
(I'm using cumulative because the daily numbers are missing for Germany, France, and the Netherlands)
I ask because the data I have shows the US solidly in the middle for the big EU countries over the past month (Germany and France don't show up on the daily graphs)
- Italy started earliest, but the daily per-capita tests have been higher every day except Apr 4: https://ourworldindata.org/grapher/full-list-daily-covid-19-...
- Austria, Czechia, and Portugal have been higher than the US more often than not: https://ourworldindata.org/grapher/full-list-daily-covid-19-...
- Belgium is on par; the UK & the Netherlands are definitely worse: https://ourworldindata.org/grapher/full-list-daily-covid-19-...
Drawing the line at ~5M inhabitants, the following countries/territories have conducted more tests per million population than the US: The UAE, Norway, Switzerland, Germany, Portugal, Italy, Ireland, Austria, Hong Kong, Australia, Spain, New Zealand, Israel, Czechia, Singapore, Canada, Belgium, South Korea, and Russia.
There's the old adage "nine women can't make a baby in a month". Sometimes there are real-world limits to scaling, and the U.S. does have an incredible amount of tests to create.
And it's not like Portugal, Italy, Spain, or the Czech Republic are particularly small or particularly wealthy.
https://www.worldometers.info/coronavirus/country/south-kore...
Since they have coronavirus relatively under control and have been doing extensive testing and contact tracing for months, it's plausible that they've caught most cases. It's wishful thinking to believe the infection fatality rate is an order of magnitude lower.
Here's the result of randomized testing in Iceland, which also has the virus under control and has done even more testing per capita (https://www.nejm.org/doi/full/10.1056/NEJMoa2006100?query=fe...)
Randomized testing was still finding 0.6% of the population (outside those otherwise quarantined already) actively infected. This means even in Iceland, less than half of infections were being caught.
Iceland's CFR right now is 0.74% using deaths/recovered (or if you use an ultimate 20% hospitalization fatality rate, around 0.87%). If they missed half of infections, you get an IFR down to under 0.5%, though I'll admit they are doing better by keeping their most vulnerable population from being infected. (note the low infection rate for people 70+ at https://www.covid.is/data).
So no, not an order of magnitude lower, but 3x lower (0.7%) is looking pretty reasonable. Imperial College's latest estimate is 0.66% for China (https://www.medrxiv.org/content/10.1101/2020.03.09.20033357v...).
and meanwhile germany's data points toward a similar conclusion as the south korean data
singapore also has a 1.5% deaths/recovered... and it's been higher in the past if you've been following closely (as high as 2%... question is where have all the new cases been coming from?)
icelands data could easily be skewed if they avoided a nursing home getting infected, given their low number of cases, and even there 1% fatality seems plausible.
Germany and Singapore are also missing many infections. A recent serological study in Germany actually argued for 0.4%.
You are correct that IFR is skewed by who the population is and what interventions are done. But then again, so is the often cited flu benchmark (where we have targeted vaccinations of at-risk groups).
(And age is a huge thing to be aware of skewing the data. e.g. the pediatric IFR from covid is on the order of seasonal flu)
And taiwan is at 1.5% CFR as well?
And still 60 or so unresolved diamond princess cases with ~7 in critical condition?
Every country is missing large numbers of cases, so CFR doesn't mean much - randomized testing is what is needed.
Imperial College's paper (linked above which gives a 0.7% population IFR) uses Diamond Princess as an input. The relative risk ratio they give for someone age 70 (mean age on Diamond Princess) is something like 4.5x (IFR ~3%), so you'd natively guess about 80 deaths from the 2,666 passengers.
I'd just like to point out that this is still a far cry from the wishful thinking a lot of people are putting forth, claiming an IFR of < 0.1%, that 30% of the population have already had the disease and that herd immunity is both imminent and feasible.
These interpretations seem less likely to be true today than they did two weeks ago, and even then they were making assumptions on the optimistic side.
An IFR of 0.5% and a hospitalization rate in the high single digits is still a big frickin problem for society, when the disease has a reproduction number greater than 2 (and in the absence of countermeasures, more likely in the region 2-5).
South Korea had 5 deaths per day on both 2/29 and 4/13 that’s a horrible sign.
Looking at the raw data, I don't think Korea ever had below 3% using deaths/recoveries. Iceland is at 0.75% right now. Two weeks after peak, Korea was at a crude CFR (deaths/total confirmed) at 1.1%
Also, deaths/recoveries is the least useful metric to compare countries as people with minimal symptoms recover first, again look at the South Korea or China graphs of the number of infected over time.
Based on other available data the severity of the Covid symptoms are dependent on both the length of exposure and total exposure to the virus. A cruise ship with recirculated air likely means prolonged exposure to high concentrations of the virus that won't fit most other patients exposure.
May be about double that. Per wiki article, I noticed this a few days ago (https://en.wikipedia.org/wiki/2019%E2%80%9320_outbreak_of_no...)
"Initial estimates of the basic reproduction number (R0) for COVID-19 in January were between 1.4 and 2.5,[381] but a subsequent statistical analysis has concluded that it may be much higher.[382]"
From that link wiki provided (https://wwwnc.cdc.gov/eid/article/26/7/20-0282_article) (edit: and note this is published on the CDC website)
"...we calculated a median R0 value of 5.7 (95% CI 3.8–8.9)"
So this sucks harder then.
> Given a seeding of cases in mid-to-late January, even without social distancing measures, it would be unlikely to have 10% of the population infected yet
This sounds like circular logic - how we know it indeed started in January and not before? How do we know how many had it asymptomatically if we didn't test 99% of the population? We're making assumption based on knowledge gathered by select sample of 1% chosen by severity of symptoms - how can we make conclusion about how it works in the rest of the population and what's the dynamics there?
[1] https://www.sccgov.org/sites/covid19/Pages/dashboard.aspx
Indeed, without serological testing, we're necessarily making some assumptions. But there's other evidence that points this way, too.
Genetic analysis based on samples from around China and the world point to a single index patient in Hubei Province around late November. China's case tracing has not gotten to an index patient, but it appears to be pretty close, and also points to an initial case around the same time.
If there was a single case in late November, then we'd expect a few hundred cases by January. Some number of these will have traveled, but many would not.
While the US clearly had major blind spots in testing, we were explicitly looking for symptomatic cases from China. The first detected case was Jan 21. There may have been a few earlier, but genetic analysis of cases in Washington point to an index patient in the Seattle area around that time.
I don't understand what's so hard about measuring population infection rate.
Assuming the population infection rate is between 1-10%, we would only need to do around ~500 randomized tests to achieve a 95% confidence interval of +/- 1%.
For example, let's say we tested 500 random NYC residents for COVID and found that 10 were positive -- a 2% infection rate. The standard error (binomial approximation) for this sample is 0.6%. So, by doing a mere 500 randomized tests, we have a 95% confidence interval of 0.8%-3.2%.
Given that overall population infection rate is so important in planning for re-opening, why are we not doing randomized testing in hotspots like NY on a regular basis? Am I missing something?
In a hypothetical scenario of 1% of the population actually having COVID and 9% testing false positive, you could ask all 10 positive results to self-quarantine and that'd probably be a pretty effective way of shutting down the virus without asking the whole population to stay home.
Once the virus spreads more, it gets even more reasonable. (If it gets crushed more and there are very few cases, it does get a little extreme to call it useful though).
99,0000.09 = 8,910 false positives 99,0000.91 = 90,090 true negatives 1,0000.99 = 990 true positive 1,0000.01 = 10 false negative
So going through your plan will isolate 8,910 + 990 = 9900 people (9.9% of the population), catching 990 actual cases and letting loose 10 cases.
In other words we can isolate 10% of the population and reduce the number of carriers by 990:10 ratio. This seems more effective than the current 100% isolation. All we need is the tests now.
Plus people to then do what they are told...
1. Efficacy of a test can only be judged against specific priors, not any possible circumstance.
2. Likewise, the goal of a test is to assist in making a particular decision, not all possible decisions.
Specifically 9% false positive rate is not useful for testing general population where effect size is expected to be on the order of 1-10%. However it is very useful in testing groups where expected effect size is much larger for example "all symptomatic people who came to hospital" at 50% or "all contacts of a known case" at 20%. The numbers are made up to illustrate the math.
Importantly our goal is not to detect all infected people ending the epidemic in XYZ days flat, it is to reduce the viral spread factor below 0.5, halving the epidemic every XYZ days. Thinking in absolutes is counter-productive.
Bearing all this in mind, the test can be useful. For example let's take a pool of people who are symptomatic, and say we expect 50% were in fact infected. The false positives (9% of the healthy 50% == 4.5%) will be outnumbered by true positives (* 99% of the infected 50% == 49%). So now you're looking at 49% vs 4.5%, a respectable 11:1 accuracy under the given priors. Not bad, for some applications.
And here's one good application: test a group of people from the pool of the currently symptomatic, quarantine them for two weeks, then release them into the wild without self-isolation rules. They will all be healthy due to quarantine, and 10/11 will be immune. If we keep releasing 91% immune groups of people into the general population the virus will die off.
* the antibody test I was referring to has 99% true positive and 9% false positive rates.
[0] varies by region, but see for example this graph of London: https://pbs.twimg.com/media/EVlO-28XQAA30xu?format=jpg&name=...
Here is an article by a Stanford epidemiologist calling for random testing a month ago: https://www.statnews.com/2020/03/17/a-fiasco-in-the-making-a...
We need data to act objectively. As far as I can tell US state governments are doing all they can to prevent random data from being released.
I see plenty of motivation in some states to try to minimize the potential magnitude of the problem, or avoid "bad" numbers of infection rates.
I don't see a motivation for "milking" the crisis. The current actions by governments are restricting economic activity and revenue streams. Many states only make money on sales tax and that's probably not going super well right now.
I agree that there is hesitation to do widespread testing, but I don't know which government would intentionally cause a recession using this virus as an excuse.
I'm sure Trump was just itching to shut down international travel, recommend reduced productivity, and mail everyone checks. He just needed a good excuse.
https://news.usc.edu/168497/antibody-testing-covid-19-pandem...
Happy to change my mind -- but there simply hasn't been any effort to use population testing in this way -- which is one of the only useful forms of testing. Otherwise, why test people in the hospital? It doesn't change treatment. Finding asymptomatics is actually useful -- and random sampling seems critical for understanding whether we are simply fucked or actually fubar'd.
[1] https://www.wsj.com/articles/health-authorities-roll-out-new...
Another point of reference is this: according to [1], a back-of-the-envelope estimate of the number of Covid-infected is 100 times the number of dead. It's about 8000 now in NYC, so about 800k infected. That's about 10% of NYC's population, exactly the number you mentioned.
[1] https://www.solipsys.co.uk/new/BackOfTheEnvelopeCOVID19.html...
That said, it's a whole lot more likely that you're right than the December-California-epidemic folks, just saying that that type of anecdotal evidence is fairly easy to come by in the winter.
The problem is that according to the data, none of us that had the "weird cold" in February ever went to the hospital and tested positive for COVID-19. So it was probably something else, but it is weird that there was a widespread "weird cold" in New York right before the COVID-19 cases, isn't it?
If so, what does that mean for herd immunity?
By some estimates we need over 80% of the population to get it in order to achieve herd immunity, so if only 10% of the population has it we're not very close at all to this being over.
We’d be about 1/7 the way there. Meaning we’d see another 6x current deaths to get through this. That’s not even close to acceptable.
And the 10% aren’t realistic, anyway. At least nationally. Assuming nationwide rates of 10%, then scaling up by death numbers, would put infection rates far above 100% in New York.
I didn't say that. In fact, I said the exact opposite: to get to 10% nationwide while explaining the rather large per capita differences in deaths, you'd need either negative infection rates in some places (say, Alaska), or >100% infection rates in hotspots. Neither is possible.
Is there a choice?
Does it mean the period for which a patient is infectious before they become symptomatic?
Or do some people never become symptomatic?
And if it's the second, do they cease to be infectious like symptomatic individuals after a time? Or are they infectious long term (aka carriers)?
That makes sense and that's a useful word, thanks!
I've heard it takes about a week before you get symptoms and then they last for 2 days on average. So even if everyone developed symptoms only 22% of people with the disease would have symptoms on a given day...
We also know from contact tracing and mass testing instances that only something like half of cases stay asymptomatic forever. It is likely that the people in this study are either asymptomatic because they no longer have the disease or because they haven’t developed symptoms yet. Also, they might just not be recognizing their symptoms. Even without covid-19, way more than 7.5% of homeless people are typically coughing.
"Upon observing a cluster of COVID-19 cases from a single large homeless shelter in Boston, Boston Health Care for the Homeless Program conducted symptom assessments and polymerase chain reaction (PCR) testing for SARS-CoV-2 among all guests residing at the shelter over a 2-day period. Of 408 participants, 147 (36.0%) were PCR-positive for SARS-CoV-2"
The key thing here is they tested people from one single homeless shelter. Is this one homeless shelter in Boston representative of all homeless shelters in Boston? There is not enough info in this article to make that assumption. Nor is it enough information to make generalizations about the populations of the homeless who do not live in shelters.
I actually think we can say for certain this is NOT a good representation of the general population. The general population has not been living in close quarters with multiple COVID positive people.
Submitted title was 'Boston study finds 35% of homeless are Covid+ (via PCR)'
https://www.nbcnewyork.com/news/local/nyc-hospital-finds-hig...
So is it possible this disease appeared in China way before December, maybe a few months back. Then in December it reached a saturation point (i.e. infected hundreds of thousands of people, so deaths started to get noticed by respiratory specialists). Then around January/February this disease spread to the world, but it went unnoticed. Then in March it reached the same saturation point that the deaths became too many not to notice.
Is this a realistic scenario?
We know from genomic analysis [1] that "The common ancestor of circulating viruses appears to have emerged in Wuhan, China, in late Nov or early Dec 2019."
It's fun to speculate about other possibilities, but I personally like to stick to what the science tells us.
[1]: https://twitter.com/nextstrain/status/1248707010750640128
That’s why are deaths to cases ratio is so high before the new infection rate has dropped to near zero for weeks. Adjusting for infection vs death curve in China US deaths from people currently infected are likely to hit in the ballpark of 50k to 100k. Assuming we get new infections under control in the next few days and keep it low for the next month.
Science is a term being twisted out of its original context. if people keep doing that, its going to be a pejorative.
Also, do you have a source?
For Google searches to spike, information about the disease would need to be public knowledge. And at that point, you wouldn’t need Google search traffic to prove anything.
Also: Google is blocked in China, isn’t it?
I'll leave it for readers to determine whether it holds up.
It was a search for 'coronavirus' in china between 8/1/2019 and 01/01/2020. It showed a spike of 100 on September, 21, 2019 and another in December of 2019. However, this search no longer works. All of the traffic came from Hubei Province.
what gnulinux said about '90' searches is wrong. This is 'Google Trends is a search trends feature that shows how frequently a given search term is entered into Google's search engine relative to the site's total search volume over a given period of time.'
IAmEveryone comments are just all wrong. this doesnt mean 'it would have had to been public knowledge'. It means searches on google were performed on that term, specifically in Hubei Province. and yes, google is 'blocked' in china. That doesnt mean its not used there, just less often.
The article you cite says: "The common ancestor of circulating viruses appears to have emerged in Wuhan, China, in late Nov or early Dec 2019. Accordingly, the majority of sampled and inferred ancestral cases were located in Asia during this early period. "
On top of that it’s spread rapidly across every country outside of China and there uncontrolled growth rate showed a similar doubling every few days.
I see this repeated often but this is a misrepresentation of the statistics. Case fatality rate (CFR) is not the same as mortality rate. The CFR is the ratio between deaths and confirmed cases, mortality rate is the ratio between deaths and total number of people infected. The former number is 2% in South Korea now, the latter can not be measured directly without testing 100% of the population continually during the entire timespan of the outbreak, but it is (practically speaking) by definition significantly lower than 2%, unless there is a huge number of unaccounted Covid-19 deaths, which is very unlikely.
That said, your definition is also in common usage and less ambiguous.
Just compare new cases vs new deaths here: https://en.wikipedia.org/wiki/2020_coronavirus_pandemic_in_S... vs new cases here: https://en.wikipedia.org/wiki/2020_coronavirus_pandemic_in_I...
On the positive side, they seem to have contained the virus extremely successfully which was likely aided by a low population density, early reaction, and good testing.
covid-19 is bad, but seems nowhere near comparable to what much of the developing world faces on a daily basis (4000 kids die a day from malaria?!?)
Face masks for the general public being the most obvious example.
The University Hospital handles most of the cases (and all except two deaths). According to their statistics* a total of 25 have been in ICU ("Á gjörgæslu frá upphafi"), total of 13 on ventilators from the beginning ("Í öndunarvél frá upphafi"), with as-of yesterday 3 still on ventilators ("Í öndunarvél") and 6 deaths ("Andlát samtals vegna Covid-19"). So that's less than a quarter of ICU cases dying and half of those on vents.
* https://www.landspitali.is/default.aspx?pageid=b629a8e0-b262...
Have you got a source for that?
Given that most ill people don't get lung complications, that likely means Nov. was the real patient zero. This is close to 100% certain. It should be verified by looking at Chinese sites.
This seems to have the toughest aspect of gaining consensus.
Need antibody tests now, and ones that work reliably.
We have no reliable antibody tests, and then won't give the unreliable ones people because they are so rare. So where does the confidence come from that we can rule out this possibility?
What if this is already the "second wave". Something with lower symptoms wouldn't have warranted checking for a new strain, complications and deaths would fit into the normal distribution of last fall's flu season with no outlier spikes. We would then be deep into the second wave and can't even test most for people currently exposed to it, much less having already been exposed. This is enough not to dismiss the hypothesis.
It's not. What is preposterous is advancing the hypothesis without any meaningful evidence, to the detrement of more useful topics, or even useful discussion of the same topic.
It's one thing to be an epidemiologist saying "hey, let's see how we can double check the exact origins of this", which is a potentially useful line of inquery. It's another to take some hypochondriac's third hand retelling of someone else's flu symptoms back in December and jump to the same untested unproven unscientific fear-driven conclusions they did on little more than their wild speculation. Even if they do somehow end up being right, they'll be right in the "broken clock is right twice a day" sense rather than a bringing anything useful to the table sense.
So far, alternative suggestions as to the virus's origins have looked more similar to the latter - with perhaps some undertones of (completely understandable and expected) attempted political diversion (I sure wouldn't want fault for this mess hanging on my neck!) If you have some epidemiologist's proposed study that you're trying to drive funding towards - something that even remotely looks like the former - it'd be a welcome breath of fresh air on the topic, and I'd suggest sharing that as a far more useful and constructive way of advancing the hypothesis.
People like you can help fill gaps of savants saying "this isn't a constructive methodology but it isn't inherently without merit" versus "I don't want to believe that because thats not consensus right now" versus "hm thats interesting maybe worth a look sometime before the 2022 congressional committee to fund the epidemiologists?"
As for hiding within the flu statistics, we test for influenza so these deaths would not have been classified as flu related.
It's possible to have flu and covid-19 but early on it would have been rare. It's probably still somewhat rare.
I am sure flu surveillance samples will be retroactively tested for the virus that causes covid-19. Stanford did this and they have mid to late Feb.
> What if this is already the "second wave". Something with lower symptoms wouldn't have warranted checking for a new strain, complications and deaths would fit into the normal distribution of last fall's flu season with no outlier spikes.
I think you are misunderstanding what wave means here. It means an earlier not as deadly strain/conditions had already occurred, just like in the Spanish Flu in the spring versus the fall deadly resurgance, but at lower orders of magnitude. The strain itself doesn't have to be different if the co-morbidities were different - such as different opportunistic viruses or bacterias being present.
The virus could be the same, the opportunistic additional virus/bacteria could be different.
just like HIV causes no symptoms, until your immune system is down and a different infection (caused by bacteria or virus) kills you. Maybe even a normal "gut" bacteria, or something in your body usually present, is what kills you.
There is research pointing to Sars-Cov-2 attacking T cells directly. Instant AIDS.
In the fall and early winter, there could have been different variables that made it less debilitating and deadly than the spring variables. And in that case the fall and early winter deaths and pneumonia would have blended in to normal distribution.
They do so all the time. They'll advance hypothesis even without ancedotes on the vaguest of hunches, and bias towards Type I pattern recognition errors (https://www.youtube.com/watch?v=1AjLmU0Sfu4). This is in fact half the problem - why anecdotes alone aren't terribly useful, and must be treated with so much caution and skepticism. Needless to say that primary caregivers and epidemiologists are already drowning in such ancedotes as well, they're not exactly aided in us adding more noise to the discussion where they're not even looking.
There is such a thing as "enough anecdotes" - when they're numerous enough to rise to the quantity of being actual statistically measurable data. But you yourself practically admit such ancedotes fail to meet that bar by suggesting some secret first wave that's indistinguishable from the regular flu season in the data. Either the ancedotes form useful data or they don't - you can't have it both ways.
"Enough ancedotes" led us to our current conclusions, not this hypothetical secret first wave stuff you suggest. "Enough ancedotes" say "don't bother looking over there, we've figured it out", on account of failing to rise to the standard of useful statistical data to contradict our current conclusions. We've been checking, and are going to continue to check, for contradictory evidence anyways - despite the lack of usefully contradictory ancedotes - just for thoroughness, given the size, scope, and impact of the epidemic.
> People like you can help
...by spending my time embracing social distancing, masks, calming the histronics of hypochondriacs, and by guiding doubt towards those who deserve it the most and where it's going to be the most useful, actionable, and effective. We have a number of botched pandemic responses - exacerbated by censorship, political manuvering, misinformation, and so much more - with direct lesson to teach us about how to properly react next time, and parts of our government and geopolitics we need to fix. There are ongoing problems like the lack of randomized testing to give us an idea of where we're at now. Things we have hard data for. Things we can change by voting or lobbying our congresscritters or influencing debate or through direct individual action to support those in need, or helping the helpers.
But entertaining the conspiracy theorist fueling armchair "hypothesizing" about alternative virus origin stories? Does our political and epidemic policy really change if, say, technically this started several months earlier than we realized in Russia? This "maybe hypothetically a second wave!" still kicked off in China, regardless. I don't see the hypothetical policy changes. I don't see the upside. I don't see how this hypothesizing "helps". I think, in fact, that it hurts - by further stressing people out when they're already stressed out (which has real health impacts), and driving them towards unreasonable levels of distrust in the scientific method and community (which has been very upfront about the many things that are actually properly unknown about this epididemic). I think it's the same nonsense that leads to the antivax mindset. It reminds me of gaslighting.
And so, on this topic, I think helping means poking all the obvious holes I can in said "hypothesizing" - as a perhaps useful example to follow - as to how one might differentiate this armchair debate from actual reasonable doubt. On the off chance that I'm wrong, it invites a proper useful rebuttal or counter-argument. On that note, I again emphasize:
>> If you have some epidemiologist's proposed study that you're trying to drive funding towards [...] it'd be a welcome breath of fresh air on the topic, and I'd suggest sharing that as a far more useful and constructive way of advancing the hypothesis.
But since you seem to be hypothesizing about 2022 congressional committees instead of any of the stuff actively being looked into while half the bloody economy has been put on hold, I won't hold my breath.
Thanks for the invitation. Lets see where the goal post is here
> Either the ancedotes form useful data or they don't - you can't have it both ways.
In this case they do if you test for antibodies. We can't test for antibodies. It has been gaslighting from you to willingly ignore what the limitations of data are and how to solve them, if this is a term that bothers you make sure to look at it from my perspective as well, are you recycling this argument for everything epidemiologists aren't currently doing and everything that doesn't currently have consensus? Or is it just for me, either way I don't think you have factored in the exact argument here and I'll get to that:
> Does our political and epidemic policy really change if, say, technically this started several months earlier than we realized in Russia?
Yes it does, because it means the bay area is safe and can change its own policy. I don't see how you missed this in your effort to convert the word hypothetical into a pejorative.
> But since you seem to be hypothesizing about 2022 congressional committees
This was hyperbole but also likely what is going to happen. After the dust has cleared, Congress makes committees to see what exacerbated dysfunction - and they may then notice this discrepancy in the bay area as well. Meaning that it has nothing to do with a national policy decision because my hypothesis is relevant on a local level for the Bay Area as this whole thread made abundantly clear. For everywhere else it is merely interesting.
The difference is that one major economic center of the US can resume with a level of certainty and forward guidance.
My primary point of this exercise is that you are rejecting this possibility out of principle, recycled from rejecting a wide universe of possibilities, without factoring in what this one actually could change. So I hope thats clear now.
> In this case they do if you test for antibodies
I'm not sure if this is a disagreement in framing or what. I encourage retroactive testing for antibodies, however, I do so in spite of ancedotal data failing to form useful data that suggests such tests will contradict the currently understood origins of the virus. Finding antibodies for samples taken in December, for example, sounds like it would be interesting and potentially useful science - for the very reason that it contradicts the data formed by said ancedotes.
>> Does our political and epidemic policy really change if, say, technically this started several months earlier than we realized in Russia?
> Yes it does, because it means the bay area is safe [...]
Wait what? How the heck would that conclusion follow? If there was some safer first wave originating from outside of China, and we're now in the middle of a dangerous second wave, then we're still dealing with a second dangerous wave! Are you... assuming some confounding variable is what's causing this to be dangerous, and assuming said confounding variable isn't present in the bay area? On what basis? For how long? There are confounding variables - the age of the victims, societal mask use, possibly weather conditions, and more to boot I'm sure... but I'm completely unaware of any that would suggest the bay area is somehow "safe", regardless of the origin of this thing. If anything, it might suggest that the confounding variables are changing to make this thing more dangerous.
The worldwide spread is giving us a huge sample size under varying conditions, the better to understand said confounding variables. It seems like a bit of a stretch to assume that any additional hypothetical early sample points are going to give us terribly much more insight as to what said confounding variables are, when we've turned half the planet into an involuntary testbed already - if there are indeed such confounding variables that would make parts of the world "safe" - and a stretch beyond the breaking point of common sense to assume that those insights will automatically make the bay area specifically safe when this thing is killing people just up the coast.
> [...] and can change its own policy. I don't see how you missed this in your effort to convert the word hypothetical into a pejorative.
Hypothesizing is great, and a fundamental part of the scientific process - which is why I don't wish to sully the term by conflating it with biased wishful thinking and conclusion jumping. I also haven't missed e.g. the bay area and California at large joining the Western States Pact, and their ability to make/change their own policy.
What I have completely missed is even the hypothetical logic train to "the bay area is safe". I have attempted to guess at it above, but it has so many holes that I fear I must be (unintentionally, I promise!) strawmanning you - and that you clearly must have some other train of logic leading to that conclusion that I'm simply failing to synthesize on your behalf. Perhaps you stated it elsewhere and can simply point me to the right part of that thread?
"Safe" meant "maybe we don't need a lockdown as long either" or that "a larger portion of the population than you might think doesn't need to be in lockdown" and that it loses utility to keep them in lockdown. At the very least, it would allow for the Mayors offices in bay area counties, and the Governors offices to take a holistic view at the blanket order - or FUTURE blanket orders when this flares up again throughout the year. But only after antibody tests occurred.
https://www.sfchronicle.com/bayarea/article/Exclusive-Autops...
https://www.cnn.com/2020/04/23/us/california-woman-first-cor...
tl;dr
first covid death (feb 6) is nearly a month earlier than previously official first US covid death (feb 26), and also from community spread. suggesting it was in the bay area at least half a month earlier than that.
So how do we move this from a collection of anecdotes and immediate dismissal to prioritizing some antibody tests to the bay area. Possibly reopening it faster and keeping it open.
Just acknowledging the possibility can influence public policy, and the research that some other people want to see before they can acknowledge it.
https://www.sfchronicle.com/bayarea/article/Exclusive-Autops...
https://www.cnn.com/2020/04/23/us/california-woman-first-cor...
tl;dr
first covid death (feb 6) is nearly a month earlier than previously official first US covid death (feb 26), and also from community spread. suggesting it was in the bay area at least half a month earlier than that.
[1]. https://www.bostonglobe.com/2020/04/15/opinion/how-get-bette...
The UK has some randomized seriological surveillance and it's about 13% prevalence. That matches with the pregnancy study of 15% prevalence (which may be enriched due to hospital visits).
New signs suggest coronavirus was in California far earlier than anyone knew: https://www.latimes.com/california/story/2020-04-11/bay-area...
Whoops.
"Stanford’s virology lab, looking retroactively at some 2,800 patient samples collected since January, did not find the first COVID-19 cases until late February — from two patients who were tested Feb. 21 and Feb. 23"
Again not inconsistent with the Seattle timeline.
But either way folks are probably being very careful because the results will have significant impact.
Also worth considering is the period mentioned in the correspondence is one in which there was very fast exponential growth. When more of the positive cases are recent, it's only normal that they don't show symptoms yet.
Besides the test used showing a lot of false positives, the population of pregnant women looked at had a much higher rate of symptomatic infections 1 month ago than the entire population does today... we should be very cautious about concluding much from that sample.
As always, the distinction between two tightly bound things is not important when you talking about features common to both, such as which people have them.
Humans tend to migrate toward shorter more pronouncable, easier to type terms. Language evolves like a virus ;-)
https://www.merriam-webster.com/words-at-play/synecdoche-met...
The best you can hope for is for people to call it the "COVID19 virus".
Doesn't matter for nearly 100% of my daily discussion about the pandemic.
I typically say Coronavirus or sometimes covid because it's faster to say or type.
For instance as of today, the number of unreported to reported cases in the US can't be more than about 500.
The time period when the disease could have been prevalent in the US can't have been very long, because at its present level it kills about 15000 per week -- a number that could not have gone unnoticed even a month ago.
Eventually, the curves of hopes, reported data, and reality will all have to intersect, I just don't know where or when.