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Gordo

Just curious, anyone have a plan, or preps for global pandemic?

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from worldometer:

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alert China: Hubei Province issued today a "Notice on the Correction of the Number of New Coronary Pneumonia Cases Diagnosed and the Number of Diagnosed Deaths in Wuhan" in which it reported 1,290 additional deaths that had not been previously counted and reported, bringing the total number of deaths in Wuhan from 2,579 to 3,869, an increase of 50%, as the result of a revision by the Wuhan New Coronary Pneumonia Epidemic Prevention and Control. As part of this revision, 325 additional cases in Wuhan were also added. Separately, China's National Health Commission (NHC) reported 26 new cases (and no deaths) in its daily report [source] [source] [source]

Looks like China and The Economist are conspiring to scare the crap out of us.

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UK

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Prof Anthony Costello, professor of global health and sustainable development at [ University College London] Institute for Global Health says [the UK] could see 40,000 deaths by the time this wave of coronavirus is over.

But even with those numbers only 10 to 15% of the population might have had the illness, and developed immunity. 

He says that is why it is crucial to keep the spread of the disease down until a vaccine is ready.

[...]Costello also suggested offering incentives to 10% of the population to stay socially isolated in order to get the economy going again.  "We have to get the economy going and if it means locking down 10% of our population, even giving them incentives to stay in quarantine and with digital apps to help monitor their symptoms and give them support, that’s the way to really keep this going until we get a vaccine and safe herd immunity."

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3 hours ago, Gordo said:

...Even in New York City where some random testing has been done, only 15% are showing antibodies (study cited in prior post by myself), and that's in the worst hit city in the country.  We are NO WHERE NEAR "most people having immunity"..

Actually, 15% to 25% infection rate is what generally ends the flu season, so the infection rates we see from Covid-19 serologic tests are rather similar. Herd immunity of 60%+ effectively stops transmission over time, but as Covid-19 is more akin to the other coronaviruses which have come into annual circulation in the last two decades, as well as to influenza, the 15%-20% appears right.

The numbers in NY simply do not make sense and that NYT chart appears to pick data and data period for maximum visual impact rather than for accuracy. Right now, it's impossible to compare apples to apples in NY, so politics and sensationalism is driving the Covid-19 news cycle juggernaut.

This should give some food for thought:

Total U.S. death rate is below average, CDC says

"... Even if states had trouble keeping track of COVID-19 cases, a severe COVID-19 outbreak would lead to increases the number of people going to doctor for influenza-like illnesses, or dying from “pneumonia and influenza.”

A severe COVID-19 outbreak that killed people without causing pneumonia would show up in the CDC’s “deaths from all causes” data.

The new weekly COVID-19 report shows that the percentage of all U.S. deaths caused by pneumonia and influenza spiked to epidemic levels in the week ending March 28 — the last week in the pneumonia and influenza chart.

But, even at the end of March, the percentage of all deaths recorded as being caused by pneumonia or influenza was still below the level recorded in late 2017.

A set of tables linked to that chart shows that states first began reporting a significant number of deaths caused by COVID-19 during the week ending March 21.  The percentage of all deaths classified as being caused by COVID-19 increased to 11 percent during the week ending April 11, from 1percent  during the week ending March 21.

The death count totals for the weeks ending April 4 and April 11 are still incomplete. The CDC death count tables for earlier weeks show that, from the week ending Feb. 1 through the week ending March 28, the average number of deaths was about 6 percent below the three-year average. ...

The weekly COVID-19 report also includes a chart show trends in outpatient office visits for influenza-like illness as a percentage of all office visits.

The typical level is 2 percent.

The level for the week ending April 4 was 3.9 percent, but that’s down from a recent peak of about 6.4 percent during the week ending March 21."


 

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I'm genuinely hoping someone will find a flaw in the following analysis, the bottom line of which suggests the US will likely see ~200k deaths in this first wave of covid-19.

I think it is interesting to look at Italy as a harbinger of where we in the US are headed, since they are about two weeks ahead of us on the epidemic curve. Here is the total deaths curves for a bunch of countries including Italy and the US from The Financial Times:

Screenshot_20200417-131206_Chrome.jpg

As you can see, Italy seems to have reached a plateau of about 500-600 deaths per day, while the US seem to be flattening out around 2000 deaths per day.

Another helpful way to look at Italy's situation is total number of active cases (from worldometer):

Screenshot_20200417-131644_Chrome.jpg

It's pretty clear that Italy is slowly asymptoting to around 110K active cases. Here is the graph of Italy's daily new cases vs. new recoveries, showing new cases are very slowly drifting down and new recoveries are creeping up:

Screenshot_20200417-132005_Chrome.jpg

When they meet the total number of active cases will stop climbing, and when they cross the number of active cases will slowly start to come down. In the meantime, total deaths in Italy keeps climbing pretty linearly, at about 500 new deaths per day:

Screenshot_20200417-132333_Chrome.jpg

As I've pointed out before, this contrasts pretty sharply with the rapid decline in daily deaths predicted by the IHME model for Italy:

20200417_134132.jpg

But perhaps the most interesting (and troubling) graph is this one:

Screenshot_20200417-134256_Chrome.jpg

It shows the percent of resolved Italian cases that end in death (orange) vs. recovery (green). The fraction of cases ending in death is so high (currently ~36%) presumably because only seriously ill people who visit the hospital are getting tested. If testing is increasing so more mild cases were being diagnosed, you'd expect the percent of cases ending in death to drop towards a very low number (i.e. the case fatality rate). It is definitely dropping, but not at all quickly. Projecting forward, it looks like it will take weeks for the orange death percentage curve to drop to 25%.

What does this mean? With a pretty steady number of active cases (~110K) and a 25% eventual death rate when these (biased towards serious) cases get resolved, it looks like at least ~27K additional deaths are likely to occur among the current Italian population of active (biased towards serious) cases, more than doubling Italy's eventual death toll from this first wave to around 50K. That's a pretty sobering number, equivalent to 250K deaths in the US, since we're 5x more populous than Italy.

What if looks at the actual data from the US and do the same analysis?

We've currently got about 600K active cases at it appears on a trajectory to asymptote at about 650K active cases:

20200417_135143.jpg

 

While we've oscillated around quite a bit, we seem to have settled into about the same 60/40 split of recovery vs. death as Italy has been experiencing:

Screenshot_20200417-135654_Chrome.jpg

Let's (optimistically) assume over the next few weeks or months the percent of case resolutions ending in death drift down towards 25% as they have in Italy. That would suggest that 25% of the ~650K active cases in the US will end in death. That would be 163K additional deaths on top of the 35K so far, for a total in the first wave of nearly 200K deaths.

Although this jibes with my extrapolation from the Italian data (~250K deaths in the US), it also seems like a very high number to me. It would mean the ~2000 deaths per day the US is experiencing would have to continue unabated for three more months, which seems hard to imagine except if we end the lockdown in places with lots of cases soon resulting in a flare-up in cases.

But I can't seem to see the error in this analysis. Diagnosed cases are skewed towards the very sick because of limited testing. As a result a high percentage of those "lucky" enough to be tested and diagnosed eventually die (~25%). The US has a large backlog of these quite sick active cases (approaching 650k), and if things continue like they are trending towards based on both Italian and US data, ~25% of them appear likely to eventually succumb to the disease, resulting in a larger-than-currently-anticipated US death toll (~200k).

Even worse, this analysis assumes that pool of 650k active US cases peaks and starts dropping pretty rapidly, rather than getting replenished with new active cases and lingering relatively high for a long period of time, which unfortunately seems likely given what we're seeing in Italy (i.e. It appears even with the lockdown as implemented in the US and Italy, the effective reproduction rate of the virus is around 1.0 rather than a number significantly less than one which would cause the number of new cases per day to drop towards zero like it apparently did in Wuhan).

This is one time I'm genuinely hoping someone will find a flaw in my analysis, since nearly everyone (including me) is hoping US will come in under 100k deaths in this first wave.

Please let me know what I'm missing.

--Dean

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17 hours ago, Ron Put said:

am not even sure what exactly you are arguing, so I wasn't even going to respond, but here we go.... The closest the US has ever had to an authoritarian government is in fact the reign of FDR, for whom the US constitution was a mere inconvenience. He wanted to serve four terms and stacked the Supreme Court so that can can effectively rule by decree. FDR disliked Churchill (who thought FDR was an idiot), had a great admiration for Stalin and felt that the US should be ideologically closer to the USSR than to an "empire" like Britain. Also, it is grossly simplistic and inaccurate to say that FDR "dealt" with the economic problems at the time, at least not successfully. The US economy did not show significant growth until the country engaged in WWII and even though the economy started growing during the war time, it was largely due to government procurements directly to fight the war, which is not how you build a sustainable economy. The reality is that the US economy returned to real growth only at the time of FDR's death in 1945, after government spending dried up and private investment increased dramatically. Don't have time to look now, but here is some of the data based on a quick search:

What Really Ended the Great Depression?

This is not the forum to debate this, but statements like "xenophobia and racial hatred is a very useful tool in the U.S" are just nonsense -- enforcing national immigration laws is hardly xenophobia, and compared to places like the EU, Australia or much of Asia, the US has practically open borders. But race and class baiting has been on the rise for a while, so I expect it to be duly amplified now.

 

I agree totally with Ron.

There is one president who was even more dictatorial than FDR:  Lincoln.  Lincoln brought about the highly desirable Emancipation Proclamation -- but when the Supreme Court met to determine it's constitutionality (which would probably have not gone well -- the majority on the Court were from Southern states) -- Lincoln had soldiers with aimed rifles to "help" them make their decisions.

P.S.: Most economists believe that FDR's New Deal slowed down the recovery from the Great Depression.

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Vox just published a new article that goes into some depth on the troubling observation that I made at the end of my previous post, namely that while we appear to have halted the exponential growth of the virus in the US via pretty serious social distancing, there is mounting evidence that it may have only brought the effective reproduction rate down to about 1.0, rather than the less than 1.0 necessary if we are going to have wiggle room to relax restrictions without new cases blowing up again. From the article:

Here’s the dilemma: Ideally, case numbers would now start falling. If the effective reproductive number (the R0) of the virus has been pushed below 1 with the shelter-in-place orders around the country, then every day should see fewer infections than the day before, and case numbers should fall, hopefully as quickly as they rose.

Most schemes to reopen the country rely on this: They require that case numbers fall for 14 days before the US starts loosening restrictions. The idea is that two weeks of falling cases is enough that it can’t just be a coincidence, and enough to lower the overall case count so regions can trace contacts and use more intensive monitoring approaches in a targeted way.

But even in the parts of the country that have now been living under extensive restrictions for several weeks, case numbers aren’t falling across the board — though in some areas (most crucially New York) they do seem to be. More reliable measures like hospitalizations and deaths aren’t falling, either. That’s why the CMMID estimates the RO in the US at about 1 — each sick person is infecting about one more person.

But that estimate has a lot of uncertainty, too, and that uncertainty is significant. If the number is a little smaller than 1, new case numbers will decline. If it’s a little larger than 1, new case numbers will keep increasing. And there are some signs that it might, indeed, be larger than 1, considering that case numbers aren’t consistently decreasing anywhere. “We are still moving in an uphill direction,” Becca Bartles, Providence St. Joseph Health’s executive director of infection prevention, told The BMJ.

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A preliminary Stanford University antibody study has found Covid-19 infection rates in a single county to  be  50-85 times higher than reported.  Lots of caveats though.

COVID-19 Antibody Seroprevalence in Santa Clara County, California

Full text:   https://www.medrxiv.org/content/10.1101/2020.04.14.20062463v1.full.pdf
 

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Discussion

After adjusting for population and test performance characteristics, we estimate that the seroprevalence of antibodies to SARS-CoV-2 in Santa Clara County is between 2.49% and 4.16%, with uncertainty bounds ranging from 1.80% (lower uncertainty bound of the lowest estimate), up to 5.70% (upper uncertainty bound of the highest estimate). Test performance characteristics are the most critical driver of this range, with lower estimates associated with data suggesting the test has a high sensitivity for identifying SARS-CoV-2, and higher estimates resulting from data suggesting over 30% of positive cases are missed by the test.

These results represent the first large-scale community-based prevalence study in a major US county completed during a rapidly changing pandemic, and with newly available test kits. We consider our estimate to represent the best available current evidence, but recognize that new information, especially about the test kit performance, could result in updated estimates. For example,if new estimates indicate test specificity to be less than 97.9%, our SARS-CoV-2prevalence estimate would change from 2.8% to less than 1%,and the lower uncertainty bound of our estimate would include zero. On the other hand, lower sensitivity, which has been raised as a concern with point-of-care test kits, would imply that the population prevalence would be even higher. New information on test kit performance and population should be incorporated as more testing is done and we plan to revise our estimates accordingly.

The most important implication of these findings is that the number of infections is much greater than the reported number of cases. Our data imply that, by April 1 (three days prior to the end of our survey) between 48,000 and 81,000 people had been infected in Santa Clara County. The reported number of confirmed positive cases in the county on April 1 was 956, 50-85-fold lower than the number of infections predicted by this study.17

The infection to case ratio, also referred to as an under-ascertainment rate, of at least 50,is meaningfully higher than current estimates.10,18This ascertainment rate is a fundamental parameter of many projection and epidemiologic models, and is used as a calibration target for understanding epidemic stage and calculating fatality rates.19,20 The under-ascertainment for COVID-19 is likely a function of reliance on PCR for case identification which misses convalescent cases, early spread in the absence of systematic testing, and asymptomatic or lightly symptomatic infections that go undetected.

The under-ascertainment of infections is central for better estimation of the fatality rate from COVID-19. Many estimates of fatality rate use a ratio of deaths to lagged cases (because of duration from case confirmation to death), with an infections-to-cases ratio in the 1-5-foldrange as an estimate of under-ascertainment.3,4,21 Our study suggests that adjustments for under-ascertainment may need to be much higher. We can use our prevalence estimates to approximate the infection fatality rate from COVID-19 in Santa Clara County. As of April 10, 2020, 50 people have died of COVID-19 in the County, with an average increase of 6% daily in the number of deaths. If our estimates of48,000-81,000 infections represent the cumulative total on April 1, and we project deaths to April 22 (a 3 week lag from time of infection to death22), we estimate about 100 deaths in the county. A hundred deaths out of 48,000-81,000 infections corresponds to an infection fatality rate of 0.12-0.2%. If antibodies take longer than 3 days to appear, if the average duration from case identification to death is less than 3 weeks, or if the epidemic wave has peaked and growth in deaths is less than 6% daily, then the infection fatality rate would be lower. These straightforward estimations of infection fatality rate fail to account for age structure and changing treatment approaches to COVID-19. Nevertheless,our prevalence estimates can be used to update existing fatality rates given the large upwards revision of under-ascertainment.

While our prevalence estimates of 2.49% to4.16%are representative of the situation in Santa Clara County as of April 4, other areas are likely to have different seroprevalence estimates based on effective contact rates in the community, social distancing policies to date, and relative disease progression. Our prevalence estimate also suggests that, at this time, a large fraction of the population remains unexposed in Santa Clara County. Repeated serologic testing in different geographies, spaced a few weeks apart, could establish extent of infection over time.

This study had several limitations. First, our sampling strategy selected for members of Santa Clara County with access to Facebook and a car to attend drive-through testing sites. This resulted in an over-representation of white women between the ages of 19 and 64, and an under-representation of Hispanic and Asian populations, relative to our community. Those imbalances were partly addressed by weighting our sample population by zip code, race, and sex to match the county. We did not account for age imbalance in our sample, and could not ascertain representativeness of SARS-CoV-2 antibodies in homeless populations. Other biases, such as bias favoring individuals in good health capable of attending our testing sites, or bias favoring those with prior COVID-like illnesses seeking antibody confirmation are also possible. The overall effect of such biases is hard to ascertain.

The Premier Biotech serology test used in this study has not been approved by the FDA by the time of the study, and validation studies for this assay are ongoing. We used existing test performance data to establish a range of sensitivity and specificity, including reliable but small-size data sourced at Stanford. Test sensitivity varied between the manufacturer’s data and the local data. It is possible that asymptomatic or mildly symptomatic individuals may generate only low-titer antibodies, and that sensitivity may be even lower if there are many such cases.23Additional validation of the assays used could improve our estimates and those of ongoing serosurveys.

Several teams worldwide have started testing population samples for SARSCoV-2 antibodies, with preliminary findings consistent with a large under-ascertainment of SARS CoV-2 infections. Reports from the town of Robbio, Italy, where the entire population was tested, suggest at least 10% seropositivity;24and data from Gangelt, a highly affected area in Germany,25point to 14% seropositivity. . A recent effort to test the town of Telluride, Colorado is underway, and interim results suggest a prevalence just under 2%.26 Our data from Santa Clara county suggest higher spread of the infection than Telluride but lower than some areas in Europe.

We conclude that based on seroprevalence sampling of a large regional population, the prevalence of SARS-CoV-2 antibodies in Santa Clara County was between 2.49% and 4.16%by early April. While this prevalence may be far smaller than the theoretical final size of the epidemic,27it suggests that the number of infections is 50-85-fold larger than the number of cases currently detected in Santa Clara County. These new data should allow for better modeling of this pandemic and its progression under various scenarios of non-pharmaceutical interventions. While our study was limited to Santa Clara County, it demonstrates the feasibility of seroprevalence surveys of population samples now, and in the future, to inform our understanding of this pandemic’s progression, project estimates of community vulnerability, and monitor infection fatality rates in different populations over time. It is also an important tool for reducing uncertainty about the state of the epidemic, which may have important public benefits.

 

The Guardian:
 

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[...] The study has been interpreted by some to mean we are closer to herd immunity – the concept that if enough people in a population have developed antibodies to a disease that population becomes immune – than expected. This would allow some to more quickly get back to work, a strategy currently being deployed in Sweden. But researchers behind the study said not to jump to conclusions or make policy choices until more research has been done.

The study confirms the widely-held belief that far more people than originally thought have been infected with the coronavirus, said Arthur Reingold, an epidemiology professor at UC Berkeley who was not involved in the study, but it doesn’t mean the shelter-in-place order will be lifted any time soon.“The idea this would be a passport to going safely back to work and getting us up and running has two constraints: we do not know if antibodies protect you and for how long, and a very small percentage of the population even has antibodies,” he said.

Even with the adjusted rate of infection as found by the study, only 3% of the population has coronavirus – that means 97% does not. To reach herd immunity 50% or more of the population would have to be infected and recovered from coronavirus.

It is also unclear if the study, conducted exclusively on residents of Santa Clara county, is representative of the rest of the United States, researchers said.  “It is absolutely critical that similar studies be done all around the country,” said Jayanta Bhattacharya, a professor at Stanford and author on the study. “It’s very clear that the virus is more prevalent in some areas than in others, and understanding the prevalence of viruses in each region is is a critical step forward to making some policy.”

Other large-scale sample studies are currently underway. The National Institute of Health is testing 10,000 people. UC Berkeley is going to test 5,000 healthy volunteers to see if they have, or have ever had, the coronavirus.

 

Edited by Sibiriak

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Thanks Sibiriak, I saw that Santa Clara article too. The full text of the study preprint [1] talks about just how sensitive their results are to the quality of the serology test they use, in particular the specificity of test, its ability to avoid false positives. From the article:

We consider our estimate to represent the best available current evidence, but recognize that new information, especially about the test kit performance, could result in updated estimates. For example, if new estimates indicate test specificity to be less than 97.9%, our SARS-CoV-2 prevalence estimate would change from 2.8% to less than 1%, and the lower uncertainty bound of our estimate would include zero.

In other words very small changes in the test manufacturer's advertized false positive rate for the test would mean only 1%, rather than 2.8%, had previously been infected by the virus, and the confidence interval for their estimate of population infection rate would include 0%. Given how new the serology tests are, it seems too early to draw strong conclusions other than a very small fraction of the general population has been infected so far. 

The "50-85x" estimate of infection prevalence in the population relative to diagnosed cases quoted in the media would vary wildly depending these same test performance parameters and should be taken with a very big grain of salt. 

--Dean 

---

[1] COVID-19 Antibody Seroprevalence in Santa Clara County, California

Eran Bendavid, Bianca Mulaney, Neeraj Sood, Soleil Shah, Emilia Ling, Rebecca Bromley-Dulfano, Cara Lai, Zoe Weissberg, Rodrigo Saavedra, James Tedrow, Dona Tversky, Andrew Bogan, Thomas Kupiec, Daniel Eichner, Ribhav Gupta, John Ioannidis, Jay Bhattacharya

doi: https://doi.org/10.1101/2020.04.14.20062463

This article is a preprint and has not been certified by peer review [what does this mean?]. It reports new medical research that has yet to be evaluated and so should not be used to guide clinical practice.

AbstractInfo/HistoryMetrics Preview PDF

Abstract

Background Addressing COVID-19 is a pressing health and social concern. To date, many epidemic projections and policies addressing COVID-19 have been designed without seroprevalence data to inform epidemic parameters. We measured the seroprevalence of antibodies to SARS-CoV-2 in Santa Clara County. Methods On 4/3-4/4, 2020, we tested county residents for antibodies to SARS-CoV-2 using a lateral flow immunoassay. Participants were recruited using Facebook ads targeting a representative sample of the county by demographic and geographic characteristics. We report the prevalence of antibodies to SARS-CoV-2 in a sample of 3,330 people, adjusting for zip code, sex, and race/ethnicity. We also adjust for test performance characteristics using 3 different estimates: (i) the test manufacturer's data, (ii) a sample of 37 positive and 30 negative controls tested at Stanford, and (iii) a combination of both. Results The unadjusted prevalence of antibodies to SARS-CoV-2 in Santa Clara County was 1.5% (exact binomial 95CI 1.11-1.97%), and the population-weighted prevalence was 2.81% (95CI 2.24-3.37%). Under the three scenarios for test performance characteristics, the population prevalence of COVID-19 in Santa Clara ranged from 2.49% (95CI 1.80-3.17%) to 4.16% (2.58-5.70%). These prevalence estimates represent a range between 48,000 and 81,000 people infected in Santa Clara County by early April, 50-85-fold more than the number of confirmed cases. Conclusions The population prevalence of SARS-CoV-2 antibodies in Santa Clara County implies that the infection is much more widespread than indicated by the number of confirmed cases. Population prevalence estimates can now be used to calibrate epidemic and mortality projections.

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One third of participants in Massachusetts study tested positive for antibodies linked to coronavirus

Didn't dig into this, but I wonder about specificity and false positives.  1/3 is the highest penetration in random sampling I've seen so far, from anywhere...

Also note they say "200 residents on the street in Chelsea, MA" it sounds like they just went out to the streets to collect samples - maybe the people out and about on the streets are the most at risk for infection?

Edited by Gordo

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13 minutes ago, Gordo said:

One third of participants in Massachusetts study tested positive for antibodies linked to coronavirus

Didn't dig into this, but I wonder about specificity and false positives.  1/3 is the highest penetration in random sampling I've seen so far, from anywhere...

Also note they say "200 residents on the street in Chelsea, MA" it sounds like they just went out to the streets to collect samples - maybe the people out and about on the streets are the most at risk for infection?

Quote

 

Great to see you @ the CR Meeting online Gordo.  Here from your article:

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The Mass. General study took samples from 200 residents on the street in Chelsea, MA. Participants remained anonymous and provided a drop of blood to researchers, who were able to produce a result in ten minutes with a rapid test.

[...]

Doctors used a device made by BioMedomics to analyze the samples. The test hasn’t been approved by the FDA, but Mass. General approved the device for use.

While the participants appeared healthy, about half told the doctors that they experienced at least one symptom of COVID-19 in the past four weeks. Since participants remained anonymous, the doctors could not inform those who tested positive of their results.

 

 

 

 

 

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In the meantime, over in China where they’ve eradicated the virus and haven’t had any new cases in a while except for those pesky “imported cases”:

 

 

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2 hours ago, Gordo said:

1/3 is the highest penetration in random sampling I've seen so far, from anywhere...

That is pretty high. But that town (Chelsea) has been hit very hard with 712 diagnosed cases and 39 deaths so far among a population of only 40k. That equates to a 0.325% interim mortality rate.  But 70% of cases in Massachusetts are still active. Assuming Chelsea is similar to the whole state and 70% of cases are still active, that suggest the ultimate mortality rate will likely at least double to around 0.65% when the outstanding cases get resolved, which is in the neighborhood of other credible estimates out there.

On the surface that doesn't seem so bad compared to the 1-2% mortality some (including me) have speculated about. But this virus is so virulent it wouldn't be surprising if it eventually infects 50% of the population before this is over, especially if/when we relax social distancing measures. Herd immunity would require 70-80% penetration for a virus this contagious.

That mortality rate and penetration would equate to 1.0 to 1.75 million dead Americans, which is very similar to the estimated death toll from Imperial College London if we do nothing. And that assumes the healthcare system doesn't get overwhelmed leading to more loss of life which would seem almost inevitable with that many sick and dying people. That kind of death toll doesn't seem tenable to me.

--Dean

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I've come to realize many of my fellow Americans are just nuts. Look at these scenes from today/yesterday showing a protest in Texas and beach goers in Florida:

Screenshot_20200418-184208_Google News.jpgScreenshot_20200418-185259_Chrome.jpg

If it weren't for the collateral damage they are likely to cause to innocent and vulnerable people, I'd say let them learn the consequences of ignoring the advice of health experts. 

It will be very interesting to see the outcome of the various natural experiments it appears we'll be conducting around the US in the coming weeks...

--Dean

 

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It was good meeting many of you during the Google Meet earlier today. Vitamin D was one thing I had mentioned and I came across this paper in the journal Nutrients (which I had not heard of until now, but apparently it is peer-reviewed and has been around since 2009). I think people here are well aware of the need for the sunshine vitamin, but this reaffirmed it's importance for me. My only concern is the second author's conflict of interest... which should perhaps be a big concern.

https://www.mdpi.com/2072-6643/12/4/988/htm

Open AccessReview

Evidence that Vitamin D Supplementation Could Reduce Risk of Influenza and COVID-19 Infections and Death

Nutrients 2020, 12(4), 988; https://doi.org/10.3390/nu12040988
Received: 12 March 2020 / Revised: 30 March 2020 / Accepted: 31 March 2020 / Published: 2 April 2020
Abstract
The world is in the grip of the COVID-19 pandemic. Public health measures that can reduce the risk of infection and death in addition to quarantines are desperately needed. This article reviews the roles of vitamin D in reducing the risk of respiratory tract infections, knowledge about the epidemiology of influenza and COVID-19, and how vitamin D supplementation might be a useful measure to reduce risk. Through several mechanisms, vitamin D can reduce risk of infections. Those mechanisms include inducing cathelicidins and defensins that can lower viral replication rates and reducing concentrations of pro-inflammatory cytokines that produce the inflammation that injures the lining of the lungs, leading to pneumonia, as well as increasing concentrations of anti-inflammatory cytokines. Several observational studies and clinical trials reported that vitamin D supplementation reduced the risk of influenza, whereas others did not. Evidence supporting the role of vitamin D in reducing risk of COVID-19 includes that the outbreak occurred in winter, a time when 25-hydroxyvitamin D (25(OH)D) concentrations are lowest; that the number of cases in the Southern Hemisphere near the end of summer are low; that vitamin D deficiency has been found to contribute to acute respiratory distress syndrome; and that case-fatality rates increase with age and with chronic disease comorbidity, both of which are associated with lower 25(OH)D concentration. To reduce the risk of infection, it is recommended that people at risk of influenza and/or COVID-19 consider taking 10,000 IU/d of vitamin D3 for a few weeks to rapidly raise 25(OH)D concentrations, followed by 5000 IU/d. The goal should be to raise 25(OH)D concentrations above 40–60 ng/mL (100–150 nmol/L). For treatment of people who become infected with COVID-19, higher vitamin D3 doses might be useful. Randomized controlled trials and large population studies should be conducted to evaluate these recommendations.

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6 hours ago, Dean Pomerleau said:

 ... beach goers in Florida   ... ignoring the advice of health experts. 

 

From  "Professor" Knut Wittkowski's  video posted here by Ron Put (and appearing all over the net):

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WITTKOWSKI [10:46]:   ...There are no indications that anything is different from a regular flu. Maybe  one that's a bit worse than other flus—could be? 

For a respiratory disease, the flu ends during  springtime, and people spend more time outdoors,  because outdoors the viruses cannot easily spread. That is a form of containment, spending more time outdoors.

Q:  So, we’re now spending more time indoors. We’ve been told to go indoors. Isn’t that—doesn’t that help keep the virus going?

WITTKOWSKI: It keeps the virus healthy, yeah.

Q:  So we should be told to go outdoors?

WITTKOWSKI: Yeah.  Going outdoors is what stops every respiratory disease.

😈

Edited by Sibiriak

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For those who haven’t found the time to watch “Professor”  Knut Wittkowski's widely viewed video, which is being promoted by Ron Put and many others, I’ve made a brief outline of it.    You can find a transcript of the video here.  All bracketed time references below refer to that transcript, not the video itself. 

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“Professor” Wittkowski’s Natural Herd Immunity Theory

  • COVID-19 is like any other respiratory disease such as the seasonal flu.
  • All respiratory disease outbreaks-- if left alone--will peak in two weeks and will be entirely over in four weeks. All that needs to be done is to refrain from interfering and allow natural herd immunity to emerge.   [00:44.20]  [03:23.16] 
  • Herd immunity requires about 80% of the population to get infected.  [01:45.00] 
  • Herd immunity lasts for a couple of years. [28:06.21] 
  • Vaccination is not necessary, but it would help to create herd immunity a bit faster. [17:49.20] 
  • Ultimately,  what we  need to do is put our trust in Mother Nature:

“For some reason that we haven’t fully understood yet, humankind has survived all sorts of respiratory diseases. Nature has a way of making sure that we survive.” [18:34.04]

“We should not believe that we are more intelligent than Mother Nature was when we were evolving. Mother Nature was pretty good at making sure that we’re a good match for the disease that we happen to see virtually every year.”  [39:45.18] 

 

“Professor” Wittkowski’s   Simple Strategy  [00:44.20] [01:45.00] 

  • Allow herd immunity to be created as fast as possible.  Allow children to go to school, mingle freely, and contract the virus. Allow  parents to go to work, enjoy the outdoors,  and contract the virus. Allow children and parents to infect each other. 
  • Separate the elderly and fragile from the children and their parents.
  • Close down nursing homes.
  • Allow the elderly to return and meet their children and their  grandchildren after about four weeks “when the virus has been exterminated.”

 

The U.S.

If the simple natural herd immunity strategy had been followed, the outbreak would already be over, having lasted only four weeks.    Some 10,000 people would have died, similar to recent flu outbreaks.

2% of all symptomatic cases will die. That is 2% of the 25,000 a day. So that is 500 people a day, and that will happen over 4 weeks. So, that could be as high as 10,000 people. Now, that compares to the normal numbers of flus during the flu season, and we have in the United States about 35,000 deaths due to flu every year during the flu season. So, it would be part of the normal situation during the flu season.”  [07:06.26] 

 

China and South Korea vs. Western Countries

In China, herd immunity was reached before social distancing was started.   That is why the pandemic has ended  there. [12:37.18] 

In South Korea, the imposition of social distancing after the peak prevented herd immunity from “getting to the final point.” “This is why we are still seeing new cases in South Korea, several weeks after the peak.”   [13:53.23] 

In both China and in South Korea, “social distancing started only long after the number of infections had already started to decline, and therefore had very little impact on the epidemic.” [13:53.23] 

Unfortunately, it seems that in western countries where the story of China was already known, people started with social distancing, as imperfect as it is, before the epidemic could reach the level that is needed to develop herd immunity. [“29:44.16]

 

Contact Tracing

Contact tracing for a respiratory disease like COVID-19 is “impossible” because airborne transmission creates too many contacts to be traced.  [16:16.20]

 

“Flattening the curve” via Sheltering at Home and Social Distancing (lockdowns)

  • There is no reason to try to “flatten the curve”   Lockdowns  before the peak of infections has been achieved impede natural herd immunity, prolong the outbreak and guarantee a second wave of infections.   [02:47.05] [28:06.21] [30:21.00]    
  • Hospitals getting overwhelmed is not a big problem  "Funding for hospitals has, as everybody knows, not increased recently. So, hospitals had to cut down, and, therefore, they now have to run their emergency plans, which is not terrible. That’s what they have been planning for, for decades, so if they have to put up some tents in Central Park, that’s not the end of the world. The tents are there, they’re maintained very well, and they will be there for a few weeks—three, four, maybe, and then the crisis will be over. This is not a situation nobody has ever thought about." [08:29.26] 
  • Shortages of PPE are not a big problem Why should there be a shortage of those things?” “Because people are getting crazy now and it’s almost like the toilet paper [...]”  “Like the toilet paper. Shortages happen now and then, all the time.” [09:21.13] [09:28.00] [09:36.02]   [09:51.18] 
  • Social distancing will cause more elderly people to die    "[With the policy that we are following now, the shelter in place] we will see maybe a total of fewer cases—that is possible. However, we will see more cases among the elderly, because we have prevented the school children from creating herd immunity. And so, in the end, we will see more death because the school children don’t die, it’s the elderly people who die, we will see more death because of this social distancing. [27:14.01] 

 

Contagiousness

Covid-19 is not more contagious than any seasonal flu.  “I don’t know where that opinion [that it is much more contagious than the flu] comes from. We have no—the data that we have speaks against it.” [18:55.08]   [19:22.03]       

-------------------------------------------------------------------------------------------------------------------------------

(In my considered opinion,  Wittkowski is a nut and his theory is rubbish.  YMMV.)

Edited by Sibiriak

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13 hours ago, Dean Pomerleau said:

That is pretty high. But that town (Chelsea) has been hit very hard with 712 diagnosed cases and 39 deaths so far among a population of only 40k. That equates to a 0.325% interim mortality rate.  But 70% of cases in Massachusetts are still active. Assuming Chelsea is similar to the whole state and 70% of cases are still active, that suggest the ultimate mortality rate will likely at least double to around 0.65% when the outstanding cases get resolved, which is in the neighborhood of other credible estimates out there.

On the surface that doesn't seem so bad compared to the 1-2% mortality some (including me) have speculated about. But this virus is so virulent it wouldn't be surprising if it eventually infects 50% of the population before this is over, especially if/when we relax social distancing measures. Herd immunity would require 70-80% penetration for a virus this contagious.

That mortality rate and penetration would equate to 1.0 to 1.75 million dead Americans, which is very similar to the estimated death toll from Imperial College London if we do nothing. And that assumes the healthcare system doesn't get overwhelmed leading to more loss of life which would seem almost inevitable with that many sick and dying people. That kind of death toll doesn't seem tenable to me.

--Dean

Yes, but the biggest question seems to be - what level of penetration is really required for herd immunity? 


For those that didn’t follow the math above... When widespread (accurate) antibody testing becomes available we’ll have a better picture, but just based on the preliminary data mentioned above, 1/3 of Chelsea may have been infected (most with mild or no symptoms),  so that makes the real number of infected 40,000 * (1/3) = 13,333.  I’ll go with your projected death rate putting total estimated deaths from the “already infected” at 2*39=78, making the fatality rate approximately 78/13,333=0.00585 (or 0.585%).  I’m going with 60% penetration required for herd immunity (which may not be accurate), with a US population of 328,239,523

So that gives us 328239523*0.60*0.00585 =1,152,120 (dead Americans).  This assumes that effective treatments and vaccines don’t change the death rate.  Also ignores the probable benefit from increasing vitamin D and other factors as we approach Summer which could lead to lower deaths even if the infection rate remains the same.

A demographic adjustment is also missing: According to the U.S. Census Bureau, in 2016 over 575,000 Connecticut residents were age 65 and older, making up an estimated 16% of the state's total population of 3.6 million. In the United States, the population age 65 and over numbered 49.2 million in 2016 (the most recent year for which data are available). They represented 15.2% of the population, about one in every seven Americans.

Also when penetration gets to 30-40% of the population maybe it will spread so slowly that much of the population can “hold out” a year when hopefully a vaccine is available.

 

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7 hours ago, Sibiriak said:

(In my considered opinion,  Wittkowski is a nut and his theory is rubbish.  YMMV.)

Some of his ideas were worth considering but as the data comes in it seems clear he is wrong on many of the points you highlighted. Also if this thing does seriously subside in the Summer it’s likely to just return with a vengeance in the Fall or Winter. Also many ideas work in theory but not in practice. If we all seriously locked down worldwide for a few weeks the virus would go extinct, good luck with that, if we locked down only the vulnerable while the rest go out and deliberately try to get infected, well:  Over 70 million adults in U.S. are obese (35 million men and 35 million women). 99 million are overweight (45 million women and 54 million men). Again, good luck with that. The real decision seems to come down to what is better - get it over with fast and accept that millions will die, or try hard to keep it from spreading while economies suffer greatly, with possible starvation, mental health toll, suicide, poverty and other risks.  Maybe the best time to let the foot off the brake is in the Summer (when people have stronger immune systems and viruses can’t survive outside a host as long).

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28 minutes ago, Gordo said:

The real decision seems to come down to what is better - get it over with fast and accept that millions will die, or try hard to keep it from spreading while economies suffer greatly, with possible starvation, mental health toll, suicide, poverty and other risk

Is there no middle ground? 

How about relaxing the lockdowns,  keeping some restrictions eg on restaurants,  sports and entertainment venues etc;  plus getting people wearing face coverings in crowded areas /continue social distancing habits;  and, critically, taking concrete actions to  shield the vulnerable,  including and especially those in  nursing homes etc.?  Meanwhile,  work on treatments and vaccines, of course.

No need for lockdown: pandemic restrictions plus citizens’ response helped Hong Kong keep Covid-19 at bay, says HKU study (April 19)

 

Quote

The HKU researchers found that social distancing and other measures, coupled with the correct response of citizens who heeded advice to wear masks and avoid crowded places, helped keep the virus at bay. The findings of their study were published in The Lancet Public Health journal.

Hong Kong has demonstrated that Covid-19 transmission can be effectively contained without resorting to the highly disruptive complete lockdown adopted by mainland China, the United States, and Western European countries,” said lead researcher Benjamin Cowling, a professor of epidemiology and biostatistics at HKU’s school of public health.

[...]To curb the spread of the coronavirus, Hong Kong implemented border restrictions, suspended schools and encouraged employers to let their staff work from home, with the civil service leading the way. Sports and leisure venues were closed and restaurants told to limit customers to half their seating capacity.

Unlike in Wuhan, where Covid-19 originated, as well other countries badly affected by the virus, people in Hong Kong were not ordered to remain at home at all times.

The HKU study found that over eight weeks from February, as the restrictions were put in place gradually, the reproduction number – those who contract the virus from an infected person – remained at around one, suggesting no worsening of the situation.

[...]HKU assistant professor Dr Wu Peng, a co-author of the study, said the 2003 experience of the severe acute respiratory syndrome (Sars) outbreak helped prepare Hongkongers to combat the Covid-19 epidemic. “Improved testing and hospital capacity to handle novel respiratory pathogens, and a population acutely aware of the need to improve personal hygiene and maintain physical distancing, put them in good stead,” Wu said.

While Hong Kong had shown signs of improvement in keeping down the spread of Covid-19 over the past week, other experts said the social-distancing measures had to stay for a while. Professor David Hui Shu-cheong, a respiratory medicine expert from Chinese University, said some measures, such as maintaining a certain distance between tables in restaurants and wearing masks on public transport and in crowded places, needed to be in place until the middle of next year, the earliest when vaccines were expected to be available.

“Without a vaccine, there will still be risks of seeing community cases,” he said.

[...]“Outdoor recreational facilities, such as playgrounds and soccer fields, could be opened first as their risks are relatively lower,” he said. But places such as bars and karaoke lounges, where crowds were likely to gather indoors, should be opened later.

Dr Leung Chi-chiu, chairman of the Medical Association’s advisory committee on communicable diseases, said Hong Kong needed about a week more before it could assess whether it could begin to ease the restrictions.

 

Edited by Sibiriak

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1 hour ago, Gordo said:

1/3 of Chelsea may have been infected (most with mild or no symptoms),  so that makes the real number of infected 40,000 * (1/3) = 13,333.  I’ll go with your projected death rate putting total estimated deaths from the “already infected” at 2*39=78, making the fatality rate approximately 78/13,333=0.00585 (or 0.585%)

The article says "30%" of Chelsea residents tested positive for antibodies, not 1/3rd, which accounts for the difference between our estimated mortality rates (0.585 vs. 0.65%). But that's a small difference.

1 hour ago, Gordo said:

A demographic adjustment is also missing: According to the U.S. Census Bureau, in 2016 over 575,000 Connecticut residents were age 65 and older, making up an estimated 16%

Chelsea is in Massachusetts not Connecticut, but MA also has about 16% people aged 65+, which isn't much different from the national average of 15.2%.

A more significant demographic mismatch that would point in the other direction (i.e. a higher national mortality rate than the 0.65% estimate for Massachusetts) is the fact that prevalence of obesity (a known covid-19 mortality risk factor) is much lower in Massachusetts than the national average. According to the CDC obesity page, the obesity rate in Massachusetts is "only" 25.7% compared with the national average of 42.4%, a 65% difference. As we discussed earlier, obesity doubles the risk of ICU admittance and therefore (presumably) risk of death. Extra obesity nationally would therefore likely push the mortality rate closer to 1.0%.  

1 hour ago, Gordo said:

Also when penetration gets to 30-40% of the population maybe it will spread so slowly that much of the population can “hold out” a year when hopefully a vaccine is available

That's definitely a possibility, but I think it would be a much smaller effect than the risk of higher mortality than 0.65% due to the healthcare system becoming overwhelmed by a flood of patients if we get anywhere near 30-60% of the population being infected over the next 12-18 months while we are waiting for a vaccine.

I think putting it all together, the Chelsea data suggests we'd likely have to accept at least a million dead Americans if we hope to achieve herd immunity without effective treatments/prophylactics or a vaccine.

--Dean

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