A lot of "fake" anxiety as you described can be cured by just doing _something_.
It's easy to get overwhelmed by a large task and psych yourself out. Motivation stems from _action_; the key is just start doing something small related to the task. This action leads to inspiration, which then leads to motivation, and the effect snowballs from there.
I think this deal was more about Visa seeing a not so distant future where the payment rails shift to an API based interaction, not the data that Plaid has. Visa already probably has more data than they know what to do with.
> I think it is important that engineering organizations identify a primary thing that they want to excel at. It affects the day to day mindset of the engineers, gives context to help with decisions, and provides guidance for what types of engineers we want to hire.
Aligning expectations is extremely important, especially when hiring - the culture of an engineering organization has an outsized impact on the final product. I have seen a lot of contention when people are brought into an environment where they expect to be moving fast, only to realize soon into the job that stability is much higher valued than frequent releases.
I agree with your general point about engineering culture (there's also more broadly the company culture) and it's always frustrating when management can't decide what the engineering values actually are to any real specificity, leaving it in the hands of each team doing interviews.
I disagree though that stability and frequent releases need to be traded off -- it seems only at infrequently releasing orgs do people think that. As for new people coming in and being surprised by the slowness, I see it most happen with acqui-hires rather than new hires, since new hires who want to avoid being surprised by such things tend to ask enough questions (like "how are releases done?") in the interview stage.
> Cough (7.5%), shortness of breath (1.4%), and fever (0.7%) were all uncommon among COVID-positive individuals
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.
We already have lots of data that says this is not true. For instance, on the Diamond Princess, widespread testing of a confined group initially found many asymptomatic infections, but the majority of those turned out to be pre-symptomatic, not asymptomatic.
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.
> In the US, we're now doing significant amounts of testing, but we're still primarily testing only those with severe symptoms
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
We're about as confident that people gain long-lasting immunity as we are that it's a simple respiratory tract infection. It's not beyond question, but to raise the questions without mentioning that we're pretty confident in the consensus answers is either misinformed or misleading.
For some people any statement which is not their preferred taste of doom, disaster and panic is considered misinformation. The post they are complaining about doesn’t even supply a concrete statement:
> 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.
The US is doing a significant amount of testing compared to what's available almost anywhere else on the planet. I think Germany is still beating them, but they're basically the only large country that is at this point. Journalists here in the UK have actually been pointing to the US as one of the examples that proves we're the ones failing at testing for a while now.
It depends on where you draw the line for "large", but the US is really not doing all that great (though it has caught up significantly over the last month): https://www.worldometers.info/coronavirus/
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.
The U.S. has more resources per capita at its disposal, but if everyone is starting at zero tests, I'd still expect smaller countries to be able to ramp faster relative to their population, and all the countries you've listed are indeed smaller than the U.S.
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.
Yeah. One big reason I'm not counting small countries (that is, anyone much smaller than Germany) is that there's a limited global supply of pretty much every consumable you need to test for coronavirus, and smaller wealthy countries can get a major boost in per-capita testing by just buying up more of the supply. So there's a whole bunch of smaller countries with really substantial per-capita testing advantages over all the larger ones.
I'm not expecting the US to rise to the lofty levels of the Faeroe Islands, but it seems to me that you're grading on an excessively lenient curve here. Germany is the 19th most populous country in the world, and of the top 19, only 3 are bona fide first world countries (the US, Japan, and Germany) , and even one of the marginal candidates (Russia) is beating the US in tests.
And it's not like Portugal, Italy, Spain, or the Czech Republic are particularly small or particularly wealthy.
Where are you getting that data? The 'Our World in Data' site says reporting is inconsistent, but the data they do have shows per-capita Germany, Italy, Belgium, and Austria doing more than the US, and France, the UK, and the Netherlands doing less.
It's certainly possible other countries have passed the US since I last looked, the exact leaderboard does seem to vary depending on things like which country is hitting the biggest roadblocks in their test rollout right now. (Though Italy's lead in total per-capita tests is mainly because they were the first Western country to have their outbreak hit catastrophic levels and they ramped up testing fairly aggressively early on in response to that. I presume their testing wasn't so great prior to the collapse of their healthcare system, since they went from reporting only three cases to disaster alarmingly quickly.)
Are you using some metric other than "per-capita" for tests? I could understand something like "per-positive case", but it's unclear from your comments.
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)
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.
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).
That's a much more reasonable interpretation of the available data, although there are still plenty of unknowns here, as sibling comments are pointing out. My interpretations the last week are converging on the same ballpark figures.
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).
There is a very economical way to figure out this: test the majority of the US population. Sure, the government will have to spend a few billion dollars, but that would possibly save a few trillion dollars in GDP losses. The fact that people and businesses are not requiring this right now from the government makes my head explode!
Half of Iceland's population lives in the Reykjavik area. The rest are thinly spread out over the country - which of which is still inaccessible due to ice (usually until April/May). Comparing this to a densely populated South Korea is incredibly difficult.
Also, different genetics, different climate, different culture, different population density, different connectedness to China, and a 2 orders of magnitude difference in population size. As a rule of thumb, whenever you want to compare Iceland to another country, you probably shouldn't.
The comparison between the two countries is so difficult. The population of Iceland is around 360K, while South Korea is over 51 million (~140x). The population density is in the same order, 3 per Km^2 to 500 per Km^2 respectively (~160x).
I'm extrapolating from hospitalization rates for that reason. Otherwise, you'd get a 0.45% CFR in Iceland.
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%
With a sample size of 8 deaths, 0.45% is easily comparable to 1.1%. We are talking different infected populations, different healthcare systems, and so forth it’s expected to see that kind of a range. The US had what ~19 deaths out a single nursing home.
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.
But you just ignored the evidence from the diamond princess?
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.
An average age of 59 on the Diamond Princess (1.8% CFR) is not representative of the general population. This is a substantially more at-risk group.
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)
Yes, I agree it is preliminary and not too much should be drawn off it.
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.
The Diamond Princess likely had a very atypical (older) population than the general population though.
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.
5.7 in Wuhan. That could be true for NYC as well, but is far less likely to be true in the rest of the lower density country. That said I agree with you that infections are likely in the 10M range.
> The Diamond Princess likely had a very atypical (older) population than the general population though.
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.
I don't know if people are aware of this, but there's an increasing suspicion that countries still using the BCG vaccine for tuberculosis might be seeing [1]way lower infection rates.
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.
The map in [4] suggests that every adult in France would have received the BCG vaccine, as they only stopped in 2017 (and receive it as an infant). There are so many confounding variables here, I don't think anyone should be deriving any evidence by looking at the case charts like that.
I agree. I'm not arguing this is 'fact', just an interesting correlation - until proven anything stronger than that - that would explain why some countries got hit worse than others.
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.
Thanks for the 4th link, I couldn't really understand the really sad images and videos coming out of Guayaquil (Ecuador) when comparing them to the rest of South America. Apart from Buenos Aires there are also big metropolises like Sao Paulo or Rio de Janeiro where you couldn't see the same things that were happening in Guayaquil. That vaccination map for South America partly explained the difference for me, at least by correlation.
Yeah, what's happening in Ecuador is incredibly sad. My whole family is in Argentina and I was worried sick for them for a while... who knows, this BCG theory gives me some hope things will be better for them than they are here. Fingers crossed.
"there's an increasing suspicion that countries still using the BCG vaccine for tuberculosis might be seeing [1]way lower infection rates"
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.
BCG vaccine requires a booster every few decades, so it becomes less effective over the years. Apart from health issues related to age, this would explain why old people are at higher risk.
>We already have lots of data that says this is not true... Longer term follow-up found ~25% of infections were asymptomatic.
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.
"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]"
> 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).
What's "significant amount of testing"? In Santa Clara country, for example, 16585 tests have been done[1], that's less than 1% of the population. I know from personal experience of several people that it's next to impossible to get tested if you don't have very severe symptoms. Positivity rate is 10.8% - over people which are sick enough to get tested.
> 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?
> This sounds like circular logic - how we know it indeed started in January and not before?
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.
It's worth noting that the German study in Gangelt was done because it was considered a hot-spot - this wasn't an attempt to estimate the infection rate across the country. It does however demonstrate that even in places where lots of people have had it, herd immunity still looks a long way off.
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.
It wasn't a study of representative German households, but just those in a Gangelt. A small town that was basically the ground zero for the epidemic in Germany. Those numbers do not generalize to Germany as a whole, let alone other countries.
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.
It does generalize to my point, because we're talking about the basic reproduction number of the virus. It is not supposed to vary dramatically across populations.
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.
R0 isn't a fundamental constant, but just an estimated average. It's affected both by human behavior and random chance. In this case, the town had a very early superspreader event, which shifted them far ahead of the curve.
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.)
> We need more studies to gather data on these asymptomatic cases if we want to reopen the economy soon.
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?
Ah, I see. Is it true that these tests are a work in progress and the false positive rate will drop in the coming weeks/months? If so, it seems like we should start doing random sampling and saving the samples for future testing (so we can see how infection rate trended). This could be invaluable data that we're losing.
It would be the test using QPCR to amplify the virus genomic material from samples collection. That test is a lot more specific though there can be issues with how the samples were obtained.
Great question, and the one I struggled with a lot. When I finally saw the light it was like find the glasses I have misplaced three days ago. Here:
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.
I mean, it's not great, but imagine if it was so cheap and fast we could test the entire population on a consistent basis.
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).
You need to also take into account the false positive too (1% iirc). Under your priors a group of 100,000 people will have 1000 infected and 99,000 not infected. Specifically:
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.
There's good reason to suspect strong regional variation, which means that finding a representative sample of 500 is hard. But that would still be technically possible (look at South Korea and China); we clearly lack to political will to do proper testing in USA.
Well we need to measure if people have it and if people have had it. The have it test is currently $3000 a pop, and the had it test is hitting accuracy road blocks [1].
Amen. The whole scenario seems like a bad statistics lesson.
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.
Agreed generally, but my understanding is that testing people in the hospital does have value. Just for one example, if you're negative, then you can be placed in a non-Covid wing (otherwise you're liable to catch the coronavirus on top of whatever flu or whatever you actually have), staff don't need to use precious PPE when treating you, etc.
Another explanation is that the exact infection level isn’t important when the observed all-causes fatality rate is double [0] its seasonal average and everyone is therefore busy firefighting the immediate and obvious problems.
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.
A month ago I would’ve agreed with the article you linked to. I’m suggesting that right now there’s probably enough information in merely the death count to say “we need to do something! Argh, panic!” — and governments, regional and national, are doing just that worldwide.
What would a government, and specifically a ruling party gain from slowing down the economy and alienating its people (which always happens to some non-zero extent when you quarantine and constrain people)?
Milk it by tanking GDP? I'm sure that will do well for government revenue this year.
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.
My bet is that it's much more than 10%. In NYC I think it's at least 20%-30% by now. Obviously, I don't have any more data than what you can find online, but anecdotally, I think one of my sons had it; he most likely got it from his piano teacher who exhibited all the Covid symptoms (fever, dry cough, loss of smell), but was never tested. I think at least myself and my wife got this from him as well; likely his two siblings, but they were completely asymptomatic. Our kids' nanny has Covid now (tested at the hospital), and her son too (he's in ICU). I have other anecdotes, and no, I don't have systematic data, but I believe at least 20%-30% of NYC has had Covid by now.
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.
15% of pregnant woman in NYC that delivered had active Covid infections (https://www.nejm.org/doi/full/10.1056/NEJMc2009316) detectable by PCR. It's highly unlikely under 20% of the population was infected. I'd bet closer to the 30% in fact.
Does this study change your mind on that 20-30% figure? After all, the study measured a 35% infection rate for people living in close quarters with other infected individuals. Surely, the overall population would yield a much lower infection rate than this?
As a counterpoint, I saw a lot of people supporting the idea (with similar anecdotes) that there was a huge outbreak in California in December before we were even testing for COVID at all. Which was empirically demonstrated to be untrue by the later Seattle Flu Study testing of the viral genome.
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.
Also from bk. I'll anecdotally second this sentiment. I even got sick with a terrible cough for 2 weeks in the end of February and lost my sense of smell for 2-3 days. My roommate didn't get sick, but I just spoke to my neighbors and they mentioned similar symptoms the week after me.
Do you think this happened in early February? If so, I agree. Sort of.
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?
Or the majority were presymptomatic. They don't give a timeline of how long after the first cases in the cluster were detected that the testing took place, so these individuals might have been in the first week of the infection.
> Imagine if 10+% of the population already had COVID and where immune
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.
The first 10% to be infected, are also likely to be the highest spreaders (people in close contact with a high amount of strangers). The effect should hopefully be higher than 10% reduction in the reproduction rate.
There are several variables here but it's also important to keep in mind that the 36% positive rate in this cohort presumably does not include people who had the virus and recovered.
A Diamond Princess ex-passenger died as late as March 24. The first patient was tested positive on February 1, and the ship was quarantined on Feb 4, and everyone left the ship on March 1. It doesn't seem unlikely you can die from this virus a month (or even two months?) after testing positive. Or maybe I'm missing something?
Why? 23 days for the longest time to succumb isn't surprisingly long. small exposure, healthy person / lucky genes, good medical treatment, but not quite healthy/lucky/good enough.
Well the longest possible according to this data would be 54 days. We don't know the identity of patients who died, could be one of the people who tested positive on Feb 1 (or sometime in early Feb).
It is very important to distinguish asymptomatic (does not have symptoms and will never develop them) from presymptomatic (does not have symptoms yet, because the test caught the infection sufficiently early). The people in this study are mostly presymptomatic, and will develop symptoms later.
Asymptomatic means the person had no symptoms at the time of the test, and possibly never developed symptoms. They could also have been presymptomatic, in which case they subsequently developed symptoms after the test. This study didn't do any follow-ups to differentiate.
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...
0.2% of nyc has already died from Covid-19. That will probably get to around 0.4% or so. This is a lower bound for lethality of the disease.
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.
> Imagine if 10+% of the population already had COVID and where immune, we'd be much closer to heard immunity than we currently think.
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.
Why would the rest of the country have the exact same percentage of infected as New York City, the only place in the country where most people don't own cars, and almost everyone takes cramped public transportation. NYC clearly has a MUCH higher infection rate than anywhere else in the country.
> Why would the rest of the country have the exact same percentage of infected as New York City?
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.
It's easy to get overwhelmed by a large task and psych yourself out. Motivation stems from _action_; the key is just start doing something small related to the task. This action leads to inspiration, which then leads to motivation, and the effect snowballs from there.