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They're only testing the folks most likely to have it, symptomatic etc, and only 10% are positive.
I don't know why people think this is any more contagious than any other coronavirus. We are just laser focused on the numbers.
They're only testing the folks most likely to have it, symptomatic etc, and only 10% are positive.
About 35,000 people die in car accidents every year in this country. Many more are maimed and crippled. That works out to about 100 dead per day, half of them under the age of 50. Globally, the yearly death toll is more than a million. An immense amount of pain, misery, destruction, and death is absolutely guaranteed every year that we allow cars to remain on the roads. We all know this. Yet almost no one ever suggests that all cars be banned. Indeed, rarely is it even argued that the speed limits be dramatically reduced. Even something like raising the driving age to 30 — a move that would save thousands of young lives — is not seriously suggested or considered.
Ya'll put way too much emphasis on your first case dates for states and not nearly enough on the size of the infected population in a given state currently. The start date isn't very accurate, and it isn't important. What is important is reaching a level where you are achieving rapid community spread and whether or not you're taking steps to limit that spread. #2 is a clear no in those states, and the numbers point to there definitely being rapid community spread occurring.
^. People aren't understand why "we're freaking out." If we had 20 hospital beds per patient (we have 2.8) and an excess of supplies to treat illnesses like these, we probably don't shut down. It's not about the virus in a vacuum, but about our inability to deal with such a crisis.
Trump: No big deal!
DarrinS: No big deal!
Trump: Well actually it is a big deal!
DarrinS: OMG WE’RE ALL GONNA DIE
Trump: jk lol no biggie
DarrinS: we good everybody!
Car accidents are communicable diseases now?
I think !looking at Washington is a good model. They got it early, but reacted early.
Feel free to jump off a bridge
Truth hurts, I know.
I don't get this logic because if Arizona tested half their population tomorrow and the positive rate was 5 percent, which is 175000 people, the plot on the graph would show a massive exponential increase, and you would say that Arizona is "more infected" than New York and has the worst curve in the world, but the key detail is that Arizona tested a massive amount of more people. New York's positive rate is like 25 percent. They are "more infected" despite way fewer cases.
What numbers do you want to see? What's the sample size of tests you require relative to population size of the state to start drawing conclusions? New York's positive results have held pretty firm at 25ish percent for the past week.
https://covidtracking.com/data/state/new-york/
Texas is at 3.5 percent positive, performing over 11,000 tests.
https://covidtracking.com/data/state/texas/
New York's rate has never been below 13 percent. Not sure how you can conclude Texas is prone to be another New York or Italy when looking at these numbers.
I'm financially set to weather this mf. I'm concerned about my fellow Americans who aren't.
I ordered some take out food and left a generous tip.
I know it's only small gesture, but the delivery guy was very thankful. We need to help each other.
In my area, people are helping out neighbors, getting drugs/groceries for elderly folks.
So much misinformation that you can't think of one real instance.
That's not how dishing it/taking it works
The transmission is just like any other flu, tbh.
SMH muh right to party
DrDarrin
Is the Coronavirus as Deadly as They Say?
Current estimates about the Covid-19 fatality rate may be too high by orders of magnitude.
By Eran Bendavid and Jay Bhattacharya
March 24, 2020 6:21 pm ET
A line at an emergency room in Brooklyn, N.Y., March 19.
Photo: andrew kelly/Reuters
If it’s true that the novel coronavirus would kill millions without shelter-in-place orders and quarantines, then the extraordinary measures being carried out in cities and states around the country are surely justified. But there’s little evidence to confirm that premise—and projections of the death toll could plausibly be orders of magnitude too high.
Fear of Covid-19 is based on its high estimated case fatality rate—2% to 4% of people with confirmed Covid-19 have died, according to the World Health Organization and others. So if 100 million Americans ultimately get the disease, two million to four million could die. We believe that estimate is deeply flawed. The true fatality rate is the portion of those infected who die, not the deaths from identified positive cases.
The latter rate is misleading because of selection bias in testing. The degree of bias is uncertain because available data are limited. But it could make the difference between an epidemic that kills 20,000 and one that kills two million. If the number of actual infections is much larger than the number of cases—orders of magnitude larger—then the true fatality rate is much lower as well. That’s not only plausible but likely based on what we know so far.
Population samples from China, Italy, Iceland and the U.S. provide relevant evidence. On or around Jan. 31, countries sent planes to evacuate citizens from Wuhan, China. When those planes landed, the passengers were tested for Covid-19 and quarantined. After 14 days, the percentage who tested positive was 0.9%. If this was the prevalence in the greater Wuhan area on Jan. 31, then, with a population of about 20 million, greater Wuhan had 178,000 infections, about 30-fold more than the number of reported cases. The fatality rate, then, would be at least 10-fold lower than estimates based on reported cases.
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Next, the northeastern Italian town of Vò, near the provincial capital of Padua. On March 6, all 3,300 people of Vò were tested, and 90 were positive, a prevalence of 2.7%. Applying that prevalence to the whole province (population 955,000), which had 198 reported cases, suggests there were actually 26,000 infections at that time. That’s more than 130-fold the number of actual reported cases. Since Italy’s case fatality rate of 8% is estimated using the confirmed cases, the real fatality rate could in fact be closer to 0.06%.
In Iceland, deCode Genetics is working with the government to perform widespread testing. In a sample of nearly 2,000 entirely asymptomatic people, researchers estimated disease prevalence of just over 1%. Iceland’s first case was reported on Feb. 28, weeks behind the U.S. It’s plausible that the proportion of the U.S. population that has been infected is double, triple or even 10 times as high as the estimates from Iceland. That also implies a dramatically lower fatality rate.
The best (albeit very weak) evidence in the U.S. comes from the National Basketball Association. Between March 11 and 19, a substantial number of NBA players and teams received testing. By March 19, 10 out of 450 rostered players were positive. Since not everyone was tested, that represents a lower bound on the prevalence of 2.2%. The NBA isn’t a representative population, and contact among players might have facilitated transmission. But if we extend that lower-bound assumption to cities with NBA teams (population 45 million), we get at least 990,000 infections in the U.S. The number of cases reported on March 19 in the U.S. was 13,677, more than 72-fold lower. These numbers imply a fatality rate from Covid-19 orders of magnitude smaller than it appears.
How can we reconcile these estimates with the epidemiological models? First, the test used to identify cases doesn’t catch people who were infected and recovered. Second, testing rates were woefully low for a long time and typically reserved for the severely ill. Together, these facts imply that the confirmed cases are likely orders of magnitude less than the true number of infections. Epidemiological modelers haven’t adequately adapted their estimates to account for these factors.
The epidemic started in China sometime in November or December. The first confirmed U.S. cases included a person who traveled from Wuhan on Jan. 15, and it is likely that the virus entered before that: Tens of thousands of people traveled from Wuhan to the U.S. in December. Existing evidence suggests that the virus is highly transmissible and that the number of infections doubles roughly every three days. An epidemic seed on Jan. 1 implies that by March 9 about six million people in the U.S. would have been infected. As of March 23, according to the Centers for Disease Control and Prevention, there were 499 Covid-19 deaths in the U.S. If our surmise of six million cases is accurate, that’s a mortality rate of 0.01%, assuming a two week lag between infection and death. This is one-tenth of the flu mortality rate of 0.1%. Such a low death rate would be cause for optimism.
This does not make Covid-19 a nonissue. The daily reports from Italy and across the U.S. show real struggles and overwhelmed health systems. But a 20,000- or 40,000-death epidemic is a far less severe problem than one that kills two million. Given the enormous consequences of decisions around Covid-19 response, getting clear data to guide decisions now is critical. We don’t know the true infection rate in the U.S. Antibody testing of representative samples to measure disease prevalence (including the recovered) is crucial. Nearly every day a new lab gets approval for antibody testing, so population testing using this technology is now feasible.
If we’re right about the limited scale of the epidemic, then measures focused on older populations and hospitals are sensible. Elective procedures will need to be rescheduled. Hospital resources will need to be reallocated to care for critically ill patients. Triage will need to improve. And policy makers will need to focus on reducing risks for older adults and people with underlying medical conditions.
A universal quarantine may not be worth the costs it imposes on the economy, community and individual mental and physical health. We should undertake immediate steps to evaluate the empirical basis of the current lockdowns.
Dr. Bendavid and Dr. Bhattacharya are professors of medicine at Stanford. Neeraj Sood contributed to this article.
https://www.wsj.com/articles/is-the-....co/Gdo9sg67Pp
Atleast he's financially set and leaving big tips tbh
So the point of testing is to discover the amount of true infections in an area, right? So if we could test all of AZ tomorrow, then we know the true number of infections right? But we can't do that, so we're left with partial data that is really horrible right now. In areas that are not NYC, WA, and CA, testing is still absolute , so we have a partial number based on the people who have been tested, right? But this number is not a good look. It's incomplete and its acquired through a biased selection.
So lets look at NYC and see where they were a week ago.
1400 cases. But we KNOW for a fact that they weren't at 1400 cases because even with a doubling time of 3 days, you don't go from 1400 to 25,000 in 7 days. So, the NYC curve isn't bad because its gone from 1400 to 25000. Its bad because its likely gone from something like 40,000 to 150,000 in that time frame. We don't have those true numbers, but based on hospitalization this is a much better guess than 1400 to 25000. We KNOW that even with severe testing, these numbers are a big time underestimation of the current infection. We just don't know what the real numbers are, but we can estimate them.
So when we see a 20% or whatever testing rate in Lousiana, for instance, that doesn't mean we're more accurately testing the population there. It means that we're more accurately applying our selection bias to only test a very likely population of possible candidates. The same goes for all of these southern states with really low testing numbers compared to the big 3 areas. What this means is that the true of the extent of the infection is not known, but we know the numbers are high enough for rapid transference throughout the population and we know that these states are taking very little action to limit said transmission.
So what works in their favor? Lower population density for sure. They're not likely to be the next NYC just because of this factor. But they also don't need to be the next NYC to be pretty ing bad. They also have fewer resources than NYC, so they smaller metros might actually exceed their healthcare capacity faster because of this. The R0 in NYC is definitely going to be worse than in smaller cities, but its not going to stop these areas from having very serious situations in the very near future. This is why certain state governments are acting before the numbers get high in their areas.
There are already relatively high numbers of cases in these areas and our detection of them is far worse than in the major hotspots. This is a very very big problem.
People just don't get it. 3 weeks ago almost no one thought it was a big deal here. Now NYC is on the brink of collapse and people are still "Yeah BUT"ing this . Its amazing.
#allinthistogether
BTW, I never said Texas was going to be another Italy or NYC. Not sure why that's the goalpost here. I do think Texas will see numbers that overwhelm its healthcare system, which is frankly going to be more than bad enough.
So NYC still 2 weeks away from a peak after 2 weeks of WAYYYYYYYYYY more of a lockdown than any place in the south and the places in the south already have high numbers. This should definitely worry people. A lot.
EDIT: ALso the peak isn't the end. The peak just means new cases start to decrease. But there is a long time before a broken healthcare system goes back to normal and new cases STILL come in.
Yes, I agree that the sample sizes aren't big enough to draw any conclusions from many states. I would also say that Texas (13K tested) and Florida (16k tested) have tested a decent enough amount to give us some idea of their infection rate and how it's likely to progress. I don't see them being on anywhere near the same pace as NY. Would you disagree with that?
Second bolded point. Wouldn't this biased selection work in favor for states with limited testing capabilities? I would imagine states that need to ration tests are only testing the most obvious cases, so it might be skewing their infection higher than it actually is. But more data is needed.
Don't disagree with that.So lets look at NYC and see where they were a week ago.
1400 cases. But we KNOW for a fact that they weren't at 1400 cases because even with a doubling time of 3 days, you don't go from 1400 to 25,000 in 7 days. So, the NYC curve isn't bad because its gone from 1400 to 25000. Its bad because its likely gone from something like 40,000 to 150,000 in that time frame. We don't have those true numbers, but based on hospitalization this is a much better guess than 1400 to 25000. We KNOW that even with severe testing, these numbers are a big time underestimation of the current infection. We just don't know what the real numbers are, but we can estimate them.
But the selection bias works both ways, as I stated above. If LA is just testing obvious cases, we still have little clue the true rate of infection.So when we see a 20% or whatever testing rate in Lousiana, for instance, that doesn't mean we're more accurately testing the population there. It means that we're more accurately applying our selection bias to only test a very likely population of possible candidates. The same goes for all of these southern states with really low testing numbers compared to the big 3 areas. What this means is that the true of the extent of the infection is not known, but we know the numbers are high enough for rapid transference throughout the population and we know that these states are taking very little action to limit said transmission.
That's been my point all along (the important population density plays can't be understated, imo), and I was more applying it to the big states, since there's many fears the other +15 million dense states will become "the next New York." Other regions in that "green band" need to be on high alert. All states should act on high alert and just assume the worst to err on the side of caution. My main contention is this idea the entirety of the US will become like New York. I think many regions will be able to flatten the curve relatively quickly due to the advantages I've talked about ad nauseum. We'll just have to wait and see. I'm waiting for those "pending tests" in CA to be logged. Give us some more insight.So what works in their favor? Lower population density for sure. They're not likely to be the next NYC just because of this factor. But they also don't need to be the next NYC to be pretty ing bad. They also have fewer resources than NYC, so they smaller metros might actually exceed their healthcare capacity faster because of this. The R0 in NYC is definitely going to be worse than in smaller cities, but its not going to stop these areas from having very serious situations in the very near future. This is why certain state governments are acting before the numbers get high in their areas.
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