April Coronavirus Open Thread
Last month’s Coronavirus Open Thread did a fantastic job at being a place for coronavirus-related information and questions that didn’t merit a top level post, but at almost 400 comments, many of which were great at the time but are now obsolete, it’s getting a little creaky. So for the next month (probably. Who knows what’s going to happen in that month) this is the new spot for comments and questions about coronavirus that don’t fit anywhere else and aren’t worth a top level post.
Wondering what happened in last month’s thread? Here are the timeless and not-yet-eclipsed-by-events highlights:
Spiracular on why SARS-Cov-2 is unlikely to be lab-created.
Two documents collating estimates of basic epidemiological parameters, in response to this thread
Discussion on whether the tuberculosis vaccine provides protection against COVID-19.
Suggestive evidence that COVID-19 removes sense of taste and smell.
Could copper tape be net harmful?
Want to know what’s coming up in the future? Check out the Coronavirus Research Agenda and its related questions.
Wondering why the April thread is going up on 3/31? Because everything’s a little more confusing on 4⁄1 and I didn’t want the extra hassle.
In most major countries, daily case growth has switched from exponential to linear, an important first step towards the infection being under control. See https://ourworldindata.org/grapher/daily-covid-cases-3-day-average for more, you can change which countries are on the graph for more detail. The growth rate in the world as a whole has also turned linear, https://ourworldindata.org/grapher/daily-covid-cases-3-day-average?country=USA+CHN+KOR+ITA+ESP+DEU+GBR+IRN+OWID_WRL . Since this is growth per day, a horizontal line represents a linear growth rate.
If it was just one country, I would worry it was an artifact of reduced testing. Given almost every country at once, I say it’s real.
The time course doesn’t really match lockdowns, which were instituted at different times in different countries anyway. Sweden and Brazil, which are infamous for not taking any real coordinated efforts to stop the epidemic, are showing some of the same positive signs as everyone else—see https://ourworldindata.org/grapher/daily-covid-cases-3-day-average?country=BRA+SWE—though the graph is a little hard to interpret.
My guess is that this represents increased awareness of social distancing and increased taking-things-seriously starting about two weeks ago, and that this happened everywhere at once because it was more of a media phenomenon than a political one, and the media everywhere reads the media everywhere else and can coordinate on the same narrative quickly.
I’d like to point out that the growth in India is still exponential (linear on the log-scale) https://www.worldometers.info/coronavirus/country/india/. This could be or become true of other developing countries.
India and other developing countries probably have a harder time controlling the outbreak (and governments and the young, food-insecure populations may judge the economic cost of social distancing to be higher than the risk of the virus).
There was a time when the number of worldwide cases appeared to stagnate because of the Chinese lockdown, but this number just hid the exponential growth of the European+US outbreaks.
What I said doesn’t contradict any explicit statement in your comment, I just want to argue against the hypothetical deduction from “the growth rate of the world as a whole has also turned linear” to “and this means that the world is over the hill”.
I would like this to be true, but two days on from the above comment, I am not seeing any linearity in the world growth rate (second link above), just three points in a nearly horizontal line a few days ago. The link for BRA+SWE shows the same thing for Brazil even more dramatically. New daily cases is a noisy enough measurement that I wouldn’t entertain hope that we are past the exponential phase until seeing at least a week of a flat or declining rate.
The site I usually look for stats on is https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6
The graph at the bottom right still looks like exponential runaway, even when you switch to daily instead of cumulative cases. And just like the above links, a few days ago there was a period of a few days of seeming flatness in new cases, but it didn’t mean anything.
Edit: corrected corrupted URL to the arcgis.com site.
How much of that is a delayed effect of distancing and how much is saturation of test capacity? American capacity hasn’t increased in days, and by both my and the Imperial College of London’s calculations, at least 3 million Italians are probably already infected...
I’ve been forecasting a high probability that almost all of the low case count growth in Africa and Southeast Asia as limited testing.
I think there’s a decent amount of correlation with between lockdown dates and entering linear growth. Below are the lockdown dates and starts of the linear phase for some of the worst hit countries.
China 23rd Jan → 5th Feb
S. Korea 20th Feb → 1st March (This wasn’t a mandated government lockdown but people did seem to stay inside in the worst hit areas)
March:
Italy 9th → 21st
Spain 15th → 26th
Germany 16th → 27th
France 17th → not yet linear (last 2 days have been high)
Switzerland 20th → 21st
US 22nd (NY) → not yet linear
UK 23rd → approaching linear? Possibly already there
These are remarkably consistent at 10-14 days, apart from Switzerland (very fast) and France (looked like it had gone linear at about the normal time but has increased again).
This graph shows the same data but is annotated with containment steps taken by each country (it isn’t averaged over 3 days so the exact numbers don’t match up but the same pattern applies).
New post by Tomas Pueyo, the author of ‘Coronavirus: The Hammer and the Dance’:
‘Coronavirus: Out of Many, One’
[EDIT: Bucky points out that these cases make up too high a proportion of new cases for novel reinfection to be the primary mechanism, which means that there’s negligible evidence to move the basic prior that general immunity should persist for a while (once the virus is well and truly defeated by the immune system).]
In South Korea, 2% of previously recovered patients have again tested positive and are again in isolation. There are several other explanations besides a general lack of acquired immunity (which would be the worst possible case, from a public health standpoint). But it seems critical that someone look at the evidence for the most dangerous possibility.
The magnitude of the numbers here seem wrong to represent people being infected twice.
From April 9-17 there were 74 newly discovered positive tests in those who had previously recovered. Over the same period there were only 203 new cases discovered. If the 74 received a new infection then they are getting infected at 2000x the rate of the general population.
Obviously there are a fair few reasons why they might be getting reinfected at a higher rate but I can’t think of a way it would be that much more. The reoccurrence of an existing infection would make a lot more sense.
Great point. And South Korea is one of the few places I trust to have counted almost all of their cases, so that calculation has to be basically right. I think that completely settles it.
There’s a fascinating post by Patrick McKenzie (software engineer at Stripe, American living in Japan) on the Covid-19 situation in Japan.
With others, he did independent research into the issue when public sentiment stated the virus was well under control. They circulated this research privately and publically (anonymously), and the story even involves a cryptographic hash to pre-register their research (reasoning).
The whole post is interesting, and contains this gem that reminded me of LW concepts like a Crisis of Faith or Yudkowsky’s Civilizational Inadequacy:
I think the post may be of sufficient interest to LWers that it could warrant a top-level post, but I don’t know how to make such a post a valuable one. Consider this an invitation to this effect.
I’ve seen an image on social media that suggests postural drainage, a physical therapy practice used mostly for cystic fibrosis, as a way to cope with COVID-19 at a sub-hospitalization stage; the shared image suggests that draining the mucus can keep a patient from needing a ventilator. (I’ll transcribe the actual text attached to the image in a subthread, but it’s of pretty low quality; I’ve written here what I think is the only interesting point.)
Unfortunately, Googling “postural drainage coronavirus” just gets me all the medical pages on postural drainage (because they now have headers about coronavirus).
It’s a very cheap intervention for patients not on ventilators, the mechanism seems at least plausible, and it’s the sort of thing that medical professionals might fail to consider. Is it worth taking a closer look?
This is the link I was looking for (but couldn’t find!) for my previous answer:
Proning the non-intubated patient
Written by Josh Farkas assistant professor of Pulmonary and Critical Care Medicine at the University of Vermont.
Some general comments.
Positioning affects lung capacity and function.
Images (figure 1) and information to see the effects of gravity and compression of the lungs here and here.
Definitions:
supine ~ “facing up”
prone ~ “facing down”
More info: prone-ventilation-for-adult-patients-with-acute-respiratory-distress-syndrome
Proning the non-intubated patient
Movement is also important to help prevent congestion and keep the lungs inflated, for example post-op care for thoracotomy patients (where the chest wall has been opened which collapses the lung) is mainly about mobilisation—getting out of bed and walking around as soon as possible.
So yes, definitely worth a closer look.
On TWiV 595 they did an interview with a doctor who said he’d been able to get the survival rate of intubated patients up to 50% by using proning, though I don’t recall them going into the details.
Supine positioning is the easiest position for intubation but once the endotracheal tube is in-situ it makes physiological sense to turn the patient over if possible. Assuming the tube is secured in place—which it should be.
Main issues with a prone intubated patient are medical staff accessing/assessing/maintaining the tube—requiring suitable facilities or having to kneel on the floor!
Supine and immobile for days—not good.
Some more on proning in this NYT article:
So wow, something like this (just the basic version like leggi was discussing, putting patients on their belly instead of their back) is proving to be strikingly effective:
Text of the shared image; don’t say I didn’t warn you about the quality of the writing, and [sic] for the whole thing. It does read like it could really be from an elderly physical therapist.
...and there the image ends.
So wow, something like this (just the basic version, putting patients on their belly instead of their back) is proving to be strikingly effective:
Thanks for the shout-out, but I don’t think the thing I proposed there is quite the same as hammer and dance. I proposed lockdown, then gradual titration of lockdown level to build herd immunity. Pueyo and others are proposing lockdown, then stopping lockdown in favor of better strategies that prevent transmission. The hammer and dance idea is better, and if I had understood it at the time of writing I would have been in favor of that instead.
(there was an ICL paper that proposed the same thing I did, and I did brag about preempting them, which might be what you saw)
Link to paper, the relevant figure is on page 12.
I think you’re being too modest, but I’ve removed it since you think it’s been eclipsed by something better.
Some points from an interview with virologist Hendrik Streeck who is leading a systematic study in the German town of Gangelt in the county of Heinsberg, one of the epicenters of Corona in Germany (https://www.zeit.de/wissen/gesundheit/2020-04/hendrik-streeck-covid-19-heinsberg-symptome-infektionsschutz-massnahmen-studie/komplettansicht, ZEIT online, April 6, interviewed by Jakob Simmank and Florian Schumann):
The team is testing, for the first time, a representative sample (1,000 from 500 households) for Germany on whether they are infected with Corona virus (smear test and antibody blood test).
There was a famous carnival event in Heinsberg and in Germany it is kind of common knowledge by now that the large outbreak in Heinsberg can be traced back to that event. In the study, people were asked whether they attended that event, whether they had pre-existing conditions or take any medications; and all participants of that event were finally tested, and the researchers are reconstructing who sat next to whom and talked to whom. People had assumed that infection had spread via insufficiently clean draft-beer glasses; this seems to be wrong, most people had bottled beer. Moreover, people got ill a day or so after the event, which does not fit the incubation time. There is a school nearby in which seemingly almost all pupils and parents were ill in January. These people are now tested for antibodies.
In February, during the initial breakout in Heinsberg, the homes/apartments/houses of infected people where tested, and this is now done for newly infected as well. This includes taking air samples and samples from remote controls and door knobs. Up to now: 70 households, but they are planning for a larger sample.
They found viruses on things or door knobs and (once) in toilet water when somebody had diarrhea, but not once did the researchers succeed in breeding intact viruses from these samples. This suggests that most people are not infected via surface viruses.
The team had been among the first to find loss of taste and smell as a symptom. Now the data shows that about a third of patients have diarrhea, sometimes for several days, which is more than was assumed. Moreover, Streeck says his team heard from somewhere else several times (but not yet found in their own samples) that people report of deafness and dizziness. He says that these are things nobody originally paid attention to because they do not fit a respiratory disease. The interviewers note that it fits reports of headache and other nerve-system symptoms including findings of brain damage in the case of deceased patients (https://pubs.rsna.org/doi/10.1148/radiol.2020201187). Streeck notes that Sars-CoV-2 is a surprising virus and mentions a two-phase pattern (pharynx first, lung later). He also mentions that authors of another study found the virus in blood samples, while the Heinsberg researchers did not find that among their 70-person sample (and that it could be possible that the virus only enters the blood in severe cases, but not the mild ones).
Both from Heinsberg and from other cases, Streeck states that infection mostly seems to happen via relatively close contact (he mentions that transmisisons of/via haircutters, taxidrivers etc did NOT seem to happen in one famous and well-researched case in Munich, but that basically the whole network of infection can be often be reconstructed).
He notes that sitting in your apartment and not getting any sun is bad for your immune system, and curfew-like restrictions and behavioral recommendations should be more evidence-based.
If they have a different pattern that might mean that their local strain has a mutation and actually there’s a different pattern.
I’ve seen news about this study, but no preprint. It’d be really helpful if we could get it.
I’ve written a blog post on “Body Mass and Risk from COVID-19 and Influenza”, available at https://radfordneal.wordpress.com/2020/04/06/body-mass-and-risk-from-covid-19-and-influenza/
Here’s the intro:
Understanding the factors affecting whether someone infected with COVID-19 will become seriously ill is important for treatment of patients, for forecasting and planning, and — with factors that can be changed — for personal decisions aimed at reducing risk. Despite our current focus, influenza also remains a serious disease, so understanding its risk factors is also important.
Here, I’ll look at some of the evidence on how body mass — formalized as Body Mass Index (BMI, weight in kilograms divided by squared height in metres) — influences prognosis for respiratory diseases. Information specific to COVID-19 is still scant, but there is more data on influenza and on other respiratory infections (which includes coronaviruses other than COVID-19). Information on how BMI relates to general mortality should also be helpful.
Below, I’ll look at two relevant papers, plus a preliminary report on COVID-19. To preview my conclusions, it seems that being underweight and being seriously obese are both risk factors for serious respiratory illness. Furthermore, it seems that “underweight” should include the lower part of the “normal weight” category as defined by the WHO. Official advice in this respect seems dangerously misleading.
This is fantastic. Can I encourage you to make it a top level post?
Thanks for the suggestion. I’ve put it up as a post now.
(meta note: you can make posts “link posts”, by clicking the link icon in the Edit Post page. I did that for your post so its now a proper link post)
A laboratory study (preprint) showed that covid could live in cat and ferret respiratory systems, but not dogs, pigs, chickens, or ducks. It further found that covid could be passed to from an infected to an uninfected cat in an adjacent cage (doesn’t look like they tested transmission in ferrets).
A survey (preprint) of Wuhan cats indicated 15% of cats surveyed after the outbreak had antibodies to covid. 0 of 39 cats caught before the outbreak had antibodies. (ETA 4/9: They don’t mention what they were sampling from, from what they imply I think 15% is an overestimate but still enough to establish the possibility).
At least one zoo tiger has test-confirmed covid, and several big cats at the zoo are showing symptoms
So it seems pretty likely cats are vulnerable to covid, and may be able to pass it to humans.
Does the fact that 15% of cats had antibodies suggest that far more Wuhan residents were infected than the official totals? Officially I think only around 1 in 200 Wuhan residents were infected. It says that the cats were sampled from animal shelters or pet hospitals so maybe the workers there had to keep coming in every day to care for the animals even during lockdown and thus were more at risk.
Where do you get that the cats were sampled from shelters and hospitals? I see
So hospitals and shelters were certainly part of the sample, but it seems like they also tested some human patients’ cats.
https://en.wikipedia.org/wiki/Pneumocystis_pneumonia
Could the coronavirus be interfering with the immune system in a way that is allowing Pneumocystis pneumonia to thrive?
My understanding is that Pneumocystis pneumonia, otherwise known as PJP or PCP, is caused by a fungus. It is highly opportunistic, and is rarely seen in people with healthy immune systems. It is highly associated with AIDS/HIV.
The fungus that causes it is widespread, and likely exists in the lungs of most healthy people.
I’m not sure about this one, but it seems relatively difficult to test for. Given it’s rarity, it seems it is most are assumed to have it if they present symptoms and are positive for HIV/AIDS. If they are suspected, it seems they test for HIV/AIDS first.
Symptoms sound similar, if not the same as coronavirus
CT scans of those suffering from coronavirus and PJP are very similar, and both are very different from more typical pneumonia.
I have seen some papers that indicated the coronavirus may lower CD4 T-Cell counts, which is one of the reasons PJP is seen in HIV/AIDS patients. Not sure if this has been well studied and peer reviewed yet.
There exists a very effective treatment for PJP, Bactrim.
I could see how this might be overlooked given its rarity. Also I am not sure how much experience China has with HIV/AIDS and PJP. I also not an expert, and this might look foolish to someone who is more knowledgeable. I hope that person is out there and can weigh in.
I emailed this comment and my reply to Elodie Ghedin, a molecular parasitologist and virologist at NYU for her thoughts on this. Here is her reply (posted with permission):
“Thanks for reaching out.
To my knowledge, there has not been an association of PCP with COVID-19. The percentages compared in that comment are not really comparing the same thing.
In severe COVID-19 cases there is indeed pneumonia but that’s a general term indicating inflammation due to the virus itself. It can however be followed by an opportunistic infection, mostly from bacteria.
At first blush SARS-CoV-2 is not doing anything all that different to the immune system than any other acute virus infection. ”
″ At first blush SARS-CoV-2 is not doing anything all that different to the immune system than any other acute virus infection. ″
Not sure about this, it does seem to create a cytokine storm and lypmphopenia in a minority.
MD here.
Could the coronavirus be interfering with the immune system in a way that is allowing Pneumocystis pneumonia to thrive?
A: Yes. COVID has been linked to CD4+ (T helper) lymphopenia, possible via cytokine storm. The main risk factor for PCP is CD4+ lymphoooenia caused by HIV.
My understanding is that Pneumocystis pneumonia, otherwise known as PJP or PCP, is caused by a fungus. It is highly opportunistic, and is rarely seen in people with healthy immune systems. It is highly associated with AIDS/HIV.
A: Its actually rarely seen with HIV these days (due to ART), useally in transplant or immunosupressed patients.
The fungus that causes it is widespread, and likely exists in the lungs of most healthy people.
I’m not sure about this one, but it seems relatively difficult to test for. Given it’s rarity, it seems it is most are assumed to have it if they present symptoms and are positive for HIV/AIDS. If they are suspected, it seems they test for HIV/AIDS first.
A: It is easy to test for (PCR for the gene or antibody based assays), but a bronchoscopy is needed to get fluid from the lung. As this causes aerosolization, this shouldn’t be done in a COVID patient. You are right to say that a positive test cannot distinguish between colonization or infection, although infectious burden (DNA copy number by PCR) is becoming a useful marker.
Symptoms sound similar, if not the same as coronavirus.
A: Yes, although PCP doesn’t cause a quick and spectacular deterioration like COVID.
CT scans of those suffering from coronavirus and PJP are very similar, and both are very different from more typical pneumonia.
A: Yes, but the image is really not specific and can be caused by any atypical infection, drug reaction or auto-immunity.
I have seen some papers that indicated the coronavirus may lower CD4 T-Cell counts, which is one of the reasons PJP is seen in HIV/AIDS patients. Not sure if this has been well studied and peer reviewed yet.
There exists a very effective treatment for PJP, Bactrim.
I could see how this might be overlooked given its rarity. Also I am not sure how much experience China has with HIV/AIDS and PJP. I also not an expert, and this might look foolish to someone who is more knowledgeable. I hope that person is out there and can weigh in.
A: You make an interesting point. Could the deterioration in COVID patients be caused by a secondary infection (PCP) due to COVID’s effects on the immune system?
I think that it is unlikely. One, the crash seen around day 7-10 in COVID patients is really rapid, happening in the course of a few hours/a day. This is strongly suggestive of an immune cause. Even a typical, aggressive bacterial. pneumonia wouldn’t progress that quickly. PCP is more indolent and symptoms progress over days to weeks.
Secondly, the immune suppression is too short for PCP to take hold. For example, when we do stem cell transplants, we kill off the host immune system. To prevent PCP, we start Bactrim around day 30 after the transplant. Despite the patient having next to no immune system for a month, I have never seen PCP develop in that time frame. In addition, after we treat PCP infection, we would start Bactrim at lower doses to prevent recurrence. Generally we have a couple of weeks before the risk of infection increases to start it.
So, in sum, interesting idea, outside the box and impressive if you are not in the medical field. But no, I really don’t think that this is what is happening.
The current evidence seems to point to immune dysregulation causing the severe cases. Generally, when there is such a wide discrepancy in symptoms (most patients are asymptomatic and a minority die in the ICU), something funky is going on in the immune system. There is clearly a cytokine storm happening in some patients (reflective of an inability to clear the viral infection) and this is probably causing the severe lung disease (ARDS-acute respiratory distress syndrome).
Why this is happening is unclear, personally i think that ADE (antibody dependent enhancement) may play a role. Briefly, an non-neutralizing antibody, possibly from previous coronavirus infection, binds the virus and the binds to the macrophage. The virus then infects the macrophage (which it can’t normally since the macrophage doesn’t express the receptor (ACE-2)) and replicates. Macrophage freaks out and activates, which can then activates CD4+ and CD8+ lymphocytes causing the cytokine storm. Incidentally, this has been well described in other infections that target macrophages, such as leishmania. For some unclear reason, the CD8+ lymphocytes can’t kill the macrophages.
Anyways, hope this helped.
I have no medical background, but wanted to add that the prevalence of Pneumocystis colonization in the general population is approximately the proportion of cases of COVID-19 that are symptomatic (~70%). We don’t know either of these numbers with great confidence or precision, but these estimates appear consistent with your hypothesis.
Edit: additional source showing 68% of COVID-19 cases have a dry cough (page 4)
Hydroxychloroquine update!
A smart friend pointed me to this study that explains that mediocre antivirals only work if administered right after infection. By the onset of symptoms the effect is already much reduced. (The study isn’t clear as to what counts as “symptoms” except that they occurred 3 days before hospitalization, so maybe early warning signs like loss of smell don’t count). HCQ is, at best, a mediocre antiviral.
https://www.medrxiv.org/content/10.1101/2020.04.04.20047886v1
This model agrees with a new study from China (N=150) that showed zero effect when giving patients HCQ 16-17 days after the onset of the disease. Of note, the study compared Standard of Care to SOC+HCQ, and I have no idea what the Chinese SOC is beyond the minimal requirement of intravenous fluids, oxygen, and monitoring that’s mentioned in the paper. In particular, there’s no info on whether it includes antibiotics like azithromycin, and whether it includes zinc. It’s hypothesized that HCQ works partly by easing the entry of zinc into cells where it slows viral replication, and so they work well in conjunction.
https://www.medrxiv.org/content/10.1101/2020.04.10.20060558v1
Bottom line: it may still be worth it to take HCQ+zinc if you cough and lose your sense of smell two days after going through an airport, but HCQ may not be of any help to heavily symptomatic people (and it still has nasty side effects).
Real bottom line: now that hydroxychloroquine is a politicized issue, you can’t trust anything journalists have to say about it and have to read the studies yourself.
Scott talked some days ago about how Brazil didn’t take real coordinated efforts, and as a brazillian living in the country’s largest city, I’m here to both defend my country and say that things are much worse than a lack of coordination.
The government, both at federal and state level, took quarantine measures earlier than other countries did (according to the Johns Hopkins institute), compared to number of confirmed cases. We officially closed schools at about 100~ confirmed cases, with parents refusing to take their kids to school much earlier. (Per comparison, Lombardy closed at about 200 cases or so).
They’re also taking measures to have a semi-UBI going on so people have spending money for basics and utilities. Brazil also has much more robust work laws than US, thus people aren’t at such a risk of sudden unemployment without social safety nets that can last them through this pandemic. They’re also pushing for landlords and companies to open negotiations for rent and utilities.
BUT
The biggest factor here is the brazillian people, which are simply not caring about what the media has to tell them. Barely two weeks of quarantine in, and people are going outside and finding ways to restart their normal convenient lives.
Stores will open and work normally, but restrict the number of clients inside, thus forcing people to wait in long queues on the sidewalks (which is legally public and thus the store can’t be blamed for gathering people despite the current quarantine orders going on. Also for cultural reasons, no one is going to give the 5ft space recommendation. All the queues are as dense and closely packed and near the entrance as they can get away with).
During weekends, people will still throw large illegal parties (baile funk) or go outside to walk in parks, eat street food and unwind under the sun or on our beaches.
I believe that a reason this is happening is because people here largely distrust the media and will prefer to receive their news from chain messages on Whatsapp(Page 122). Thus misjudging the risk or severity of the situation. Another root cause might be risk compensating behavior; since everyone is now washing their hands and using alcohol gel and taking care to not cough on others, thus is safe to go outside and behave normaly if everyone is taking these precautions.
Bonus point: Who has a very dense public transport system and a city population higher than NYC and close to Tokyo’s thus making any attempt of “social distancing” very moot? Yes, Brazil.
This is going to be quite a ride for most of us.
I’ve been playing with the Kinsa Health weathermap data to get a sense of how effective US lockdowns have been at reducing US fever. The main thing I am interested in is the question of whether lockdown has reduced coronavirus’s r0 below 1 (stopping the spread) or not (reducing spread-rate but not stopping it). I’ve seen evidence that Spain’s complete lockdown has not worked so my expectation is that this is probably the case here. Also, Kinsa’s data has two important caveats:
People who own smart thermometers are more likely to be health conscious which makes them more likely to be health conscious than the overall population. Kinsa may therefore overstate the effect of the lockdown by not effectively sampling the health apathetic people more likely to get the virus.
Kinsa data cannot separate coronavirus fever symptoms with flu fever symptoms. At the early stages of coronavirus spread, seasonal flu illness dominates coronavirus illness and seasonal flu r0 is between 1-2. This means that a lockdown can easily eliminate symptoms caused by seasonal flu illness by reducing flu r0 below zero without reducing coronavirus’s r0 below zero.
I’m addressing this by comparing the largest amounts of observed atypical illness over the last month in different locations with their current total illness to get a conservative estimate of how much coronavirus %ill have changed.
With this in mind, my overall conclusion is that the Kinsa data does not disconfirm the possibility that we’ve reduced r0 below 1. Within the population of people who use smart thermometer’s, we’ve probably stopped the spread but it may/may not have stopped in the overall population. Here are my specific observations:
The overall US %ill weakly suggests we may have reduced r0 below 1. It maxed out at around 5.1% ill compared to a range of 3.7-4.7 %ill . This indicates that 0.4-1.4% of overall illness was due to coronavirus and currently total illness is only 0.88%. This means that, for many values in that range, our lockdowns are actually cutting into the percent of people getting coronavirus and therefore that the virus is not growing.
New York county NY %ill weakly suggests that we may have reduced r0 below 1. It maxed out at 6.4 %ill compared to a typical range of 2.75-4.32, indicating that 2.1-3.65% of people had coronavirus. Currently, total illness is 2.56%. Again, for most values in that range, it looks like we’re reducing the absolute amount of coronavirus.
Cook county IL (Chicago) %ill is very weakly positive on reducing r0 below 1. It maxed out at 5.4 %ill with a range of 2.8-4.9 indicating that 0.5-2.6% of people had coronavirus. Currently the total is 0.92% which suggests we’ve likely cut into coronavirus illness. The range of typical values is so large though that its hard to reach a conclusion
Essex country NJ (Newark) %ill doesn’t say much about r0. It maxed out at 6.1 compared to a typical range of 2.9-4.5 which implies a range of coronavirus %ill of 1.6-3.2 The current value is 2.63% which is closer to the higher end of the range so there’s no evidence that we’ve reduced the amount of coronavirus. Still %ill is continuing to trend down so this may change in the future.
I also considered looking at Santa Clara County CA, Los Angeles County CA, and Orleans Parish LA (New Orleans) but their %ill never exceeded the atypical value by a large enough amount for me to perform comparison.
On Mar28, the overall US %ill changed from a steep linear drop of ~-0.3%ill/day to a weaker linear drop of ~-0.1%ill/day. Also, on Mar28, both Newark’s and New York’s fast linear drop is broken with a slight increase in illness and it looks like we’re on our second leg down there now. Similar on Mar27, Chicago’s fast linear drop is broken with a a brief plateau and second leg down. No idea why this happened.
The Kinsa data is barely even weak evidence in favor of R0 < 1. The downward trend in fever readings are confounded, likely severely, by their thermometers having to be actively used vs. being a passive wearable. It seems plausible that more people will check their temperature when they are concerned about COVID-19, and since most people are healthy this will spuriously drive average fever readings down. Plausibly the timing of increased thermometer use will coincide somewhat with shelter-in-place orders since they correlate with severity & awareness of the local outbreak.
Their FAQ notes that they have seen 2-3x normal usage of their thermometers (this was as of March 29, they haven’t updated this part of their FAQ since) and consider this “healthcare seeking behavior” a potential driver of their trends. This has not stopped them from promoting their data to government agencies and NYT, without mentioning this or any other limitations whatsoever (at least to the NYT).
I was completely wrong, I don’t think their data is subject to this worry. They now have a preprint up. From supplementary methods:
So lots of repeat readings shouldn’t affect the gauge, and neither should more of their user base taking readings. Unless they are seeing a lot of new users, or lots of returning users that haven’t used the thermometer in over a year, both of which seem somewhat unlikely, their metric should be fine.
Thanks for pointing this out. Having recently looked at Ohio County KY, I think this is correct. %ill there max’d out at above 1% the typical range but has since dropped below 0.4% of the typical range and started rising again (which is notable in contrast with seasonal trends) [Edit to point out that this is true for many counties in the Kentucky/Tennessee area]. This basically demonstrates that having a reported %ill now that is lower than previous in the Kinsa database is insufficient to show r0<1. Probably best to stick with the prior of containment failure.
I’m personally quite worried about disruptions to the food supply chain severe enough to cause food shortages in e.g. the Bay Area in the next few months but not sure what to do with that worry other than to stock up more on non-perishables. Would very much appreciate seeing more people thinking and researching about this.
The shift of the supply chain from commercial food supply to residential is part of the problem, and is being highlighted right now with producers destroying milk and produce while food banks face severe shortages. These commercial supply chains aren’t easily converted to being direct to consumer, but the government should be purchasing and diverting these excesses to food banks. As individuals, we could all sign up for local CSA veggie boxes, which could contract with commercial suppliers to augment their produce to meet the increased demand. This would reduce collective demand on the direct consumer supply lines (ie: grocery stores). We could also purchase food in bulk from commercial suppliers, and coordinate with neighbors if a single bulk unit is too much to consume before it spoils.
The other part of the equation is a potential food shortage in the fall due to farmers being unable to get temporary migrant workers in the spring planting season and projecting that same difficulty for the fall harvest, so they’ve elected to further reduce their spring planting. Unfortunately, I think this has already happened across Europe and North America.
https://www.theglobeandmail.com/world/article-abrupt-shortage-of-seasonal-farm-workers-threatens-to-create-food/
As individuals, we could all plant subsistence gardens right now to help reduce the collective demand for food in the fall.
The various things I’ve seen about this claim that food banks aren’t processors (who take industrial-sized portions of foodstuffs and pack it into commercial or individual-sized portions), and so aren’t able to handle a 55-gallon drum of milk better than anyone else.
For things like milk, I’d expect that’d pose a problem. But for things like fresh produce, food banks can handle industrial-sized portions of these, or at least I’ve seen it handled at my local food bank, and my local food bank probably isn’t unique in this.
Summarizing an article on gloves: https://www.n-tv.de/panorama/Einweghandschuhe-so-wichtig-wie-Masken-article21689035.html (April 2)
First, about virus survival on surfaces in general:
Germany’s (kind of celebrity) virologist Christian Drosten’s (Berliner Charité hospital) opinion on the study about survival rates of Sars-CoV-2 on surfaces and the possibility of smear infection:
He hypothesizes that for the experiment, dass für den Versuch Viruses in a larger drop were put on the surface, and even though in this way you can verify infectiosity even after hours, probably only very few viruses survived. On fingers, the amount of viruses decreases further and gets into contact with the acidic milieu of the skin, and it is unclear whether anything remains; similarly simple experiments cannot simulate that. The German federal institute for risk assessment states that it currently does not know of Sars-CoV-2 infections via touching surfaces.
Note that I neither checked the statements cited, nor the sources; this is simply a translation and summary of a paragraph from the article. Starting from this, the article writes about disposable gloves.
The article states that gloves are of course considerable but that you of course should not touch your face with the gloves, and that it should also be considered that Sars-CoV-2 viruses seem to survive longer on plastics etc than on skin, that also bacteria thrive on gloves more than on hands and gloves distribute them more than hands do. Moreover, if you wear them for a longer time, the skin sweats and swells, which opens an entry to the body for viruses and bacteria. Finally, taking the gloves off without touching them is not as easy as you might think, and disposing them should of course be done properly, some people just leave them in the shopping cart.
The article basically recommends to prefer washing your hands and not touching your face over using disposable gloves. It also kind of suggests that gloves can be the opposite of face masks in one sense: Simple face masks do protect other people, while gloves may even make matters for other people worse.
The Imperial college model (yes, that one) has just been released, open-source, for anyone to look at. We don’t actually know what the model’s precise projections were (thanks to the UK governments lack of transparency), but we do know that ever since the UK’s lockdown was declared the model had been predicting a peak on Easter weekend, which was, in fact, what happened in the UK. Imperial supposedly has the best infectious disease modellers in the world, and this is the code of the model that led the UK to dramatically reverse course, so its probably pretty accurate. Here’s the GitHub.
I’ve heard people with good judgment criticize the Imperial College modelling for countries outside the UK because the forecasts proved to be too pessimistic repeatedly. That’s interesting because I know that their UK forecasts were slightly too optimistic. They predicted 20k deaths for the UK initially, then updated to “probably a bit less than that” shortly afterward. And now we’re at 21k deaths already (but daily deaths slowed down a lot). I would imagine that their forecasting is the most accurate for the UK numbers because that’s what their main task is about.
I don’t understand that either—its like they got the shape and timing of the epidemic right but had a mistake in the scaling factor.
The other models I’ve been looking at that seem sensible—Covid-19 projections and the FHI both try to model scenarios where social distancing is relaxed to various degrees at the start of June, which is realistic. Both actually look decently optimistic to me, with no giant second wave anytime soon (instead more of a ‘slow burn’ with R about 1) even with ‘weak mitigation’ - which is basically what happens by default when people say ‘Morituri Nolmus Mori’. With moderate to strong mitigation imposed constantly (a big ask, admittedly) it stays gone.
Those roughly agree with the Imperial model, at least based on what we’ve heard of it—all of them agree that R is currently decently below 1 so we have a bit of headroom to relax restrictions in June without it blowing up in our faces immediately.
I might later write a more indepth post, but for now the core idea. Controling a building to have between 50% and 60% humidity might reduce the amount of spread of corona. There are multiple studies that you can reduce the spread of infections in schools and hospitals that way.
There was also a paper out that suggested COVID-19 to spread less in climates with high moisture and temperature.
If anyone wants to collaborate with me on writing a post about this, please contact me.
Can you link whatever you have on this, even before you write it up? Articles, the paper you mentioned, the studies about reducing infections this way, where you get the idea in general?
(Everyone, please do this! It is really helpful, and it’s probably easier for you to re-find things than for people to try to find them based on your comment! I wish LW had a rule about doing this!)
I think it would be really stupid to have such a rule that you should have to put effort into asking for help to write a post.
I think downvoting a post that asks for help to flash out a given case more because the post doesn’t flash out the case enough is rather demotivating to the actual production of the case getting written up.
For what it’s worth, I upvoted your comment.
But since you stated that you had a source already, I don’t see how it’s asking much for you to post a link to the source you already said you had.
[EDIT: After a couple days, I regret the tone of my comments here. I don’t want to discourage anybody from writing posts, or asking for help in composing posts. And I think “there oughta be a rule” was a poor summary of my position and sounded pretty hostile. I think it would be nice if people mentioning the existence of sources would link the sources they mention, and in general I’d like it if people linked source more often. But that wasn’t really directed at you personally, it was spillover from elsewhere.]
This suggests that a humidity between 50-60% is optimal https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2863430/
If you are wondering what the typical humidity is where you live, note that almost any build that is 10 deg F or more warmer than its surrounding because it is heated will have relative humidity (which is what parent means by “humidity”) well below .6.
Relative humidity is expensive to control partly because it is expensive for an automated system to measure. My guess is that on the margin, distributing fresh surgical masks to everyone entering the building would be much cheaper (and more effective) even when we adjust for the fact that an emergency ramp-up of mask production is much more expensive than normal, “peacetime” mask production. (I added “on the margin” to adjust for the fact that surgical masks are most efficiently produced in a facility that can supply many orders of magnitude of buildings.)
Measuring humidity is cheap. You only have to pay 2,86 € for the cheapest one on Amazon. Automated systems are more expensive but even a non-automated system could do the job with a bit on manual effort.
Effort-wise I think it’s less effort to do manual humidity control then to wear a surgical mask. My first experience with wearing a surgical mask for longer was to wear it 5 hours for a SlateStarCodex meetup I organized for the 8.3. Afterwards, I had a headache and didn’t wear my masks in a meeting the next day even through I’m a well-prepared rationalist who brought his masks a year in advance.
Comparing it to mask wearing however misses the point. It’s quite likely that we want to do multiple interventions to keep down infection rates.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4420971/ is skeptical of cloth masks. Does anyone have any thoughts on it, or know any other studies investigating this question?
It seems the primary argument for clothing masks that’s made is that if an infected person wears them it’s less likely that they will infect others.
That’s not the purpose that was tested in “A cluster randomised trial of cloth masks compared with medical masks in healthcare workers” that study seemed more about whether or not the masks have protective qualities for the wearer.
It seems only skeptical of cloth masks as compared to surgical masks, which isn’t really very interesting to me in the current circumstances, since most people don’t have access to surgical masks.
No, it says:
Mea culpa, that’s more of a condemnation than I thought.
I noticed this too. When wearing a bandana as a mask, humidity is building up while exhaling, causing it to be slightly damp.
Should there be a non-coronavirus Open Thread?
Some potentially useful numbers I’ve been working on estimating:
1. The number of days lag between registered cases and deaths
2. The adjusted CFR for each country taking this into account
The method is essentially to try different lags (dividing current deaths by cases from x days ago) and see which length of lag gives a constant CFR over time (normally CFR increases with time as the growth rate of cases slows earlier than that of deaths).
Here are the results for a few countries:
China: 9 day lag, CFR=4%
USA: 7 day lag, CFR=6.5%
Italy: 4 day lag, CFR=14.5%
Spain: 2 day lag, CFR=10.5%
Germany: 10 day lag, CFR=3.5%
France: 10 day lag, CFR=24%
Switzerland: 6 day lag, CFR=4%
UK: 4 day lag, CFR=18.5%
I’m not sure about these, especially UK but they do create nice constant values for CFR over a period of 2-4 weeks (UK only 10 days) which suggests a predictable pattern, despite variation in testing.
The France result is also not quite as consistent as the others and is surprisingly high so I don’t quite trust it either. I could make a case that for an estimate of 7 day lag and 20% CFR.
Interesting COVID-19 vaccine development landscape publication in Nature.
Here’s a snippet from it...
It also links to WHO’s spreadsheet of COVID-19 candidate vaccines, which looks like a good resource in its own right (although this particular copy is probably a bit old).
A nice graphical guide on COVID-19 vaccines: https://www.nature.com/articles/d41586-020-01221-y
Metaculus is running a competition for accurate, publicly-posted, well-reasoned predictions about how COVID-19 will hit El Paso, Texas, in order to help the city with its disaster response. The top prize is $1,000.
Dr. Birx said in the press conference today that the US is counting any patient who dies with COVID-19 as dying of COVID-19. Are deaths during pandemics normally counted this way? Is it legitimate to call this number inflation—i.e., is there something fishy going on here—or is this standard practice?
Not a doctor, but it doesn’t seem fishy to me: most people do not die, most of the time. If you sample a random person it’s highly likely that they’ll survive the next two weeks. This is true even among high-risk groups (the elderly, obese, etc.). If you hear that someone died, and that they had a CV19 diagnosis, you should not put much weight on the hypothesis that they died of something unrelated, just because of this low base rate.
It makes more sense to worry about the opposite thing: people dying without formal CV19 diagnoses being excluded from the official statistics. For example, right now in New York about 200 people are dying at home each day, up from a baseline of 20 to 25, according to the city’s department of health: https://gothamist.com/news/surge-number-new-yorkers-dying-home-officials-suspect-undercount-covid-19-related-deaths These are not presently counted as CV19 deaths, but probably a lot of them are.
Cardboard and plastic: Tottori Prefecture goes low-tech to protect officials from COVID-19
This made my day.
Cheap, low-tech prevention measures.
‘“I hope this system will send out a message that even Tottori, where no infections have been reported yet, is being very vigilant.”’ – Yes!
I want Tottori spirit everywhere.
Is the Chinese coronavirus data fake?
If so, what’s a good estimate of the actual number of Chinese cases & actual number of Chinese deaths?
It would be interesting to look at Apple or Fitbit data to see patterns of ILI in China but I doubt they will be willing to share given the politics of it.
Hi all,
Does anyone know why US cases and deaths tend to be lower on Sundays and (especially) Mondays, compared to other days of the week? Is it something with the timing of how the data are processed?
I put up some quick plots here:
https://twitter.com/Nick_Lutsko/status/1254067996990959616
Apologies if this has been explained before and I missed it.
What should be the relative importance of natural herd immunity vs vaccination, in anti-corona strategy?
Scott Atlas argues that mass isolation prolongs the problem by delaying natural herd immunity. Meanwhile, countries like Australia and New Zealand have engaged in national isolation as well, creating entire national populations where natural immunity will be rare.
Will we see the world divided between countries that rely on natural herd immunity, and those which rely on the artificial herd immunity of vaccination? Does it make sense to have a differentiated strategy within a single country, with natural herd immunity encouraged in some subpopulations but not others?
There are also time issues here: vaccines don’t exist yet or are not available in large quantities; and coronavirus immunity may fade out after a year or two.
I assume these issues have been discussed somewhere, and would even be part of public health strategies for well-known diseases like the flu, but I seem to have overlooked such discussions.
P.S. I am looking for nuance, something about the appropriate relative importance of natural versus artificial herd immunity.
Hi,
I need your input on something. Sweden, as many of you know they are going for the herd immunity. Choosing to walk an unorthodox way.
They are still only reporting around ten thousand cases but with a death toll of 600 and rising. Probably the true number of infections is around 40k-100k depending on what mortality/asymptomatic cases we assume. The current rate, as in the number of hospitalizations per day is still within the the limits. It could probably go as high as double the death rate, before breaching the limit.
To reach heard immunity they need 60% of population (10 million) immune I’m told, with the other important goal of not breaching the capacity of the healthcare system.
With some calculations we could land on the maximum rate of infections of say 10k per day, before our healthcare starts to break apart. The exact figure is not important, the approximate magnitude is. With 10k infections per rate, Sweden would need 600 days to develop her immunity.
Recently some info from Denmark has indicated 27/1000 blood donors have tested positive for antibodies in one population and 0⁄244 in an other. I can share the source (Danish) unfortunately not much details on type of test or other information. But this has me wondering if you sit on any information of corona virus test false positives. cellax seems to.be around 1.5-3%
In case herd immunity, is the way to go having potentially tens of thousands of asymptomatic cases is great news as a tenfold increase in actual infections coupled with the current death rates will give Sweden a time to herd immunity of only a few months.
Of course percentage required would go up as R0 in that case is a little higher than what we initially thought.
From what I’ve read from other studies this is unlikely and herd immunity is as far away as the (winter) Olympics.
Anyways what are your thoughts? do you happen to sit on some data on false positive rates for various tests?
Video explaining the Czech Republic’s experience of having everyone make home-made masks in about 10 days, from a starting point of almost no masks being worn in public in the country.
Here is their COVID19 infection curves on a log chart (seems to be flattening).
With noticeably different governance and social reactions in different locations, I wonder if this situation will spur migration in the coming few years. At what point is it worth moving to somewhere with more sane (still broken; nowhere is perfect) government and social behaviors, even if it’s more distant from your personal networks?
Seattle and the Bay Area are looking pretty good compared to New York and Florida (this could reverse over the next few months, but it’s unlikely that by end of year there’ll be no difference in terms how we evaluate their reactions and outcomes).
Similarly for urban vs more spread-out locations. Especially as many of us learn that we CAN be fairly productive anywhere there’s internet service, I wonder if more of us will opt to be around fewer strangers standing so close all the time.
I predict not much movement—people have a learned helplessness about governments, and easily forget that they have choices. And the advantages of cities remain powerful. And, importantly, most people don’t actually have quite as much future financial and social freedom as LW readers tend to.
My response here is pretty useless but I have relevant personal experience and not many human interactions recently so I might as well...
I’m an EU citizen and as such I benefit from almost 30 sovereign countries competing over my presence (I could also move to a non-EU country but staying within EU is much easier). This has been my perspective for quite a few years. I have recently moved to a new country mostly because it offers low taxation for high-income individuals and the bureaucracy is generally friendly towards expat entrepreneurs and freelancers. I keep my investments/savings and even current accounts in more dependable countries.
Following reports of how different countries respond to the pandemic, I’ve been also considering moving to another EU country for a month or two and staying there in an AirBnB. The primary criteria would be the political response to the crisis (the more rational the better; the less constraining individual freedom the better) and expected quality of healthcare, should I require it. (Another factor is possible barriers when I try to come back to my country of residence.) The one thing stopping me from doing that is lack of reliable data that would help me estimate the risks.
So how much of the differences between the Bay Area and NY do you attribute to a difference in government action?
I have heard, and give some credit to, the theory that silicon valley tech company culture played a role in the bay area’s response being relatively early. Tech companies were making contingency plans and sending their employees home, well before there was any kind of government action here. I don’t know what fraction of employees / day-to-day interactions that represents. But e.g. all Google employees working from home seems like it could have played a nontrivial role in Mountain View, which was the epicenter of the bay area coronavirus outbreak.
Hard to separate government from cultural/behavioral issues—they reinforce each other. But variance in behaviors regionally (differences in how quickly governments signaled action and how well populations complied) seems likely to be a significant driver of variation of outcomes.
Wild guesses: 30% from different patterns of trade and interaction with the broader infected world, 25% from different social structures and living situations (types of corner store and shopping/entertainment mechanisms), 45% from behavioral differences and reaction time. I don’t think I can defend these guesses, and would be interested to hear other perspectives and missing causes of variation.
Note that we don’t actually know yet if NYC is all that much worse off than San Francisco. It looks that way currently, but a lot can change in a few weeks.
Late Edit: Pangolins with this viral infection have been found from smuggled ones from both Guangxi and Guangdong provinces, but do not show up in wild pangolin populations in general.
Despite the virus being characterized in pangolins, after looking into this, I now think it is basically incorrect to think of this as primarily a “pangolin virus.” The pangolins were a dying canary in a coal mine, and probably caught it from something else that serves as the real reservoir species for this nCOV precursor*.
These pangolins were being smuggled when they were captured by the authorities in Guangxi. They were dying of probably several diseases; they had lesions in their skin, intense congestion, and were in generally atrocious condition when they got sequenced for viruses. They turned up positive for all manner of things (herpes out the wazoo, but also a sendai virus which was most closely related to the sequence of a human-taken sample, a paramyxovirus, and yes, several coronaviruses).
Here’s the original article on the pangolins whose virome they sequenced, and the article noting its relatedness to nCOV.
Given that so many of the pangolins died, the pangolins look more like a highly-susceptible secondary species, than a mostly-asymptomatic primary reserve species* to me.
* GD/PIL or GD/P2S is thought of as a possible nCOV progenitor, alongside bat-virus RaTG13. GD/PIL’s receptor-binding motif (RBM) in particular is identical to SARS-2′s, although nCOV otherwise appears more closely related to RaTG13.
** On educated priors, I think the true reservoir is probably rats, bats, or (less likely) humans in the Guanxi, Hunan, and/or Hubei province.
Personally, I assign >90% on either rats (strong priors + skin lesions) or bats (strong priors + simplest story). But these were exotic animal smugglers; there is a small chance that the original reservoir species could be any animal.
I think the brief era of me looking at Kinsa weathermap data has ended for now. My best guess is that that covid spread among Kinsa users has been almost completely mitigated by the lockdown and current estimatess of r0 are being driven almost exclusively by other demographics. Otherwise, the data doesn’t really line up:
As of now, Kinsa reports 0% ill for the United States (this is likely just a matter of misleading rounding: New York county has 0.73% ill)
New York’s trend is a much more aggressive drop than what would be anticipated by Cuomo’s official estimate of r0=0.9.
None of these trends really fall in line with state-by-state r0 estimates[1] either
Georgia has the worst r0 estimate of 1.5 but Fulton County GA (Atlanta) has been flat at 0%ill since April 7 according to Kinsa
[1] Linking to the Twitter link because there is some criticism of these estimates: “They use case counts, which are massively and non-uniformly censored. A big daily growth rate in positive cases is often just testing ramping up or old tests finally coming back.”
[Years of life lost due to C19]
A recent meta-analysis looks at C-19-related mortality by age groups in Europe and finds the following age distribution:
< 40: 0.1%
40-69: 12.8%
≥ 70: 84.8%
In this spreadsheet model I combine this data with Metaculus predictions to get at the years of life lost (YLLs) due to C19.
I find C19 might cause 6m − 87m YYLs (highly dependending on # of deaths). For comparison, substance abuse causes 13m, diarrhea causes 85m YLLs.
Countries often spend 1-3x GDP per capita to avert a DALY, and so the world might want to spend $2-8trn to avert C19 YYLs (could also be a rough proxy for the cost of C19).
One of the many simplifying assumptions of this model is that excludes disability caused by C19 - which might be severe.
In addition to prediction markets, there are also, y’know, normal financial markets, which implicitly predict lots of things. But I don’t personally know how to speak the language. For example, does the market say anything about the price of food, or possibility of shortages, in three months? Like, shouldn’t there be some future / option / something whose price corresponds to a prediction about that? Or shortages of other things? Does anyone know?
A US study looking for recruits: NIH begins study to quantify undetected cases of coronavirus infection
Why does recovery data seem so sparse? I only seem to be able to find that data for the global dashboards but that means it should be available. I would think that would be easily found as reporting only deaths is, to be nice, creating a situation where people will be misinformed and overly fearful.
I took a quick look on the links database but nothing jumping out for me there.
New data out of Germany using serological testing found 14 % of the population presented antigens against COVID19. This is massive if nearly 1⁄7 of the population has already contracted the disease and developed some type of immunity. This is only one piece of data, however, and is even much higher than the Iceland estimates of 50% asymptomatic and 50% symptomatic.
German study
https://www.land.nrw/sites/default/files/asset/document/zwischenergebnis_covid19_case_study_gangelt_0.pdf
Iceland information
https://www.cnn.com/2020/04/01/europe/iceland-testing-coronavirus-intl/index.html
That German study only refers to Germany’s COVID-19 hotspot area and does not apply at all to the larger population.
There are also connections to a PR agency and one of that agency’s founders was the former head of the notorious “Bild” tabloid newspaper. Given the fact that on the one hand a PR agency should be able to provide a correct narrative, and on the other hand the study’s results have been reported wrongly quite often, you should take that study with a grain of salt.
(German) source: https://www.zeit.de/wissen/gesundheit/2020-04/heinsberg-studie-coronavirus-hendrik-streeck-storymachine-kai-diekmann/komplettansicht
I’m trying to make some educated guess about the situation, but it looks like the data are very lacking. Could someone validate my logic please?
1. Some people claim that SARS-CoV-19 could have been around for ages, “everybody’s bad flu last autumn was this thing” and basically nothing is happening except panic. People are dying not because of the virus, but because everybody is going to hospitals making them overcrowded. People dying of other reasons just happen to also have almost-harmless COVID-19. When I’ve heard this couple weeks ago I chuckled, then gave it a second thought and decided that it could be possible, though unlikely. Now, given we have some antibody tests results (San Miguel County, David Friedberg in SF − 1, 2), we can conclude that “the virus has been around long before Dec 2019″ theory is false. Otherwise much more than 1% of people would be positive for antibodies as it is very infectious. Sounds right?
A note. The tests mentioned above are very unreliable (San Miguel county didn’t explain the protocol, in the second case the test kits were provided to volunteers who would test their friends or whoever they find appropriate—so non-representative sample by any means). They show that it’s not 50% herd immunity, but don’t really provide data whether it’s 0.01% or 1%.
2. To understand what will happen next, we need to know CFR (Case Fatality Rate) or better yet IFR (Infection Fatality Rate). CFR is deaths / cases, IFR is the same, but tries to account for asymptomatic cases. And that’s the problem: we don’t know percentage of asymptomatic cases. Maybe 50 people of every 1′000 infected die, and that’s 5%, pretty scary. But maybe another 99′000 are also infected, but don’t even feel it, then we get 0.05%, less than flu.
I personally watch closely Iceland statistics: https://www.covid.is/data They have a government body testing those in high-risk groups and with symptoms, and a private biotech firm testing everybody. They have conducted about 29′000 tests, 8% of the country population. This data does not contain details about whether those tested had symptoms or not, whether positives developed symptoms later or not etc, but it seems to be the closest to “test big random sample of seemingly healthy population” research I want. 28′992 samples, 1′586 infected, 6 deaths. I feel an urge to count IFR, but it would still be highly inaccurate (there is a skew because partly they test high risk groups, because older age population probably strictly follows isolation and is not exposed, and other factors). Data from Italy, or Diamond Princess cruise ship, or anywhere is very different.
I see two different options here:
1) Low IFR less than 0.5%. Maybe less than flu (~0.1%). It is still dangerous because nobody is immune, and if everybody gets infected at the same time, hospitals are overcrowded and even young patients with typically good prognosis will have complications and die without adequate care. Here we can employ FlattenTheCurve ideas (because majority of cases are mild, don’t require hospitalisation and we can “burn through” the population fast), nobody should really worry going for groceries and the quarantine may finish soon.
2) High IFR. It’s very dangerous for older generation, it’s not nice for younger generation—one will probably not die, but have a reasonable chance of visiting ICU, which is not pleasant. Business as usual, stay at home, wash your hands, wait for a vaccine next year or two.
Which case looks more probable to you? Have you seen any high-quality data suggesting one of the cases? I stress high-quality here, there are lots of reports, but mostly data look unreliable or plainly false.
Thank you.
If you go through my LW comment history you’ll find that I’m in the camp of “The IFR is definitely >0.3%, and very plausibly >0.8%” and that I seem to care somewhat strongly about conveying this to others. :) Maybe you’ll find some of the discussions (or links therein) useful. (Unfortunately I can’t recommend any single resource that looks super convincing all on its own.)
Edit: By “very plausibly” I mean 25% likely rather than 50% likely. By “definitely” I mean 97% likely.
We’re finally getting some results on this. An antibody test in an Italian town an hour outside Milan has been done. 2000 people out of a population of 6169 have been tested.
Results: 13-14% of the population tested positive for the antibodies (~832 people).
The town had 27 confirmed cases, with 4 confirmed deaths. 6 deaths of all causes were recorded in March.
So this is arguably good news. It implies an IFR of about .5%. If the population here is older/unhealthier than average, as seems to be true for Northern Italy as a whole, then we could see that number dipping down further. Covid being less than an order of magnitude worse than the flu is starting to look likely but there is still a long way to go to reach herd immunity and I personally think that they should at least try for actual suppression unless we get really good numbers on the death rate and are more confident that survivors won’t have lasting side effects.
Someone in that twitter thread points out that with subtracting false positives, it implies that 10% would be the better guess, as opposed to 13-14%. Does that make sense? Then 4 Covid-confirmed deaths per 620 people would be 0.66%.
And what about sampling bias? I read that the tests were voluntary. Unless someone was extremely meticulous about trying to somehow get a representative sample, I don’t think it’s reasonable to treat this as random. It’s really quite obvious that people who had flu-like symptoms for a couple of days will be more curious to go among people and have a needle stuck into them. .
I share your rough estimates of IFR in your other comment here although I was concerned about how high IFR might be with overwhelmed hospitals.
Sampling bias at its worst here would mean that IFR is 3 times more than those calculations (i.e. 1.5-2%). If this is the worst case in Lombardy where the hospitals are overwhelmed then it is something of a relief to me that higher rates are unlikely.
Both good points. Hopefully we get more tests of the sort reported soon.
Very interesting, thanks. I think it’s 13% of tests, not 13% of entire population of 6′169, so not 832.
I don’t speak Italian and struggle to find any details. Actually they didn’t mention that all 2′000 samples were processed. On 5th April the mayor of the town posted a photo of newspaper mentioning “29% of 38 persons” positive. So I would not be surprised if they have taken blood from 2′000 people, processed 100 of them so far and this results got it to newspaper.
Very promising (we get more test data!), but I wouldn’t draw any conclusions on this yet.
IHME published a dashboard with state-by-state projections of coronavirus peaks: http://covid19.healthdata.org/projections
The accompanying FAQ is also interesting: http://www.healthdata.org/covid/faqs
The IHME Covid19 Model is Dangerously Flawed:
Thanks, hadn’t seen that.
Also just saw this, which makes a lot of the same points: https://westhunt.wordpress.com/2020/04/04/ihme-projections/
I was suspicious of the IHME model several days ago when I first saw it, but couldn’t find a detailed description of their methodology. (It’s really well hidden, and doesn’t even appear in their FAQ section.) Finally found it yesterday, noticed the “similar to Wuhan” assumption, then saw the page criticizing it linked in the comments section for the paper.
Can anyone help me to understand these graphs? The ‘deaths per day’ graphs seem to incorporate actual data up to April 1st and to be projections from there, which suggests that they are using the actual death rates to calibrate some parameters of their model. But the ‘hospitalization’ graphs don’t seem to incorporate any actual data, and seem to be very different from the reality: for example, for April 1st (when the models were last updated), the “all beds needed” and “ICU beds needed” numbers for New York State are 50,962 and 10,050; but it looks like the actual number of people in hospital for Covid in NYS on that date was about 12,500, with about 3,000 in the ICU (source: ‘NYS total hospitalized’ graphs here [https://gothamist.com/news/coronavirus-statistics-tracking-epidemic-new-york]). Does that mean that they are not trying to fit their model to actual hospitalization data at all? If so that seems like a problem: hospitalization rates should be much better than death rates for estimating what effect social distancing measures are having, since there is so much time between infection and death.
Innoculate GI tract with live virus. Suffer GI symptoms. Get immunity. Avoid respiratory complications.
This is an interesting idea but would benefit from more elaboration.
Why the GI tract in particular—do you have evidence that this will significantly reduce the risk of respiratory symptoms, or just speculation / “common sense”? Is there evidence that the GI tract as the initial site of exposure will produce an infection / an immune response, but with a reduced chance of the infection spreading to the lungs / respiratory tract? Or with it taking longer to get there, similar to Robin Hanson’s thoughts about deliberate exposure with a low dose, like variolation of old?
If you have any links/references, please definitely post them. If it’s just speculation, it’s interesting speculation but tell us what it’s based on.
Exactly like variolation, except you do it intelligently to minimize lung infection.
SARS and SARS-Cov2 are both ACE2 dependent for cell entry.
ACE2 expression in AT2 cells in the lower respiratory track is known to be on the apical surface, that is the side of the cell facing airspace, not the basal surface facing vasculature. Hypothesis would be that lung infection is much more efficient and virulent by droplet delivery rather than by virus circulating in blood stream. I am also under the understanding that the kidney and heart complications are due to poor oxygenation due to the respiratory distress, not a primary viremia in those organs.
https://www.ncbi.nlm.nih.gov/pubmed/15141377
Tissue distribution of ACE2 protein, the functional receptor for SARS coronavirus. A first step in understanding SARS pathogenesis.
“In conclusion, ACE2 is abundantly present in humans in the epithelia of the lung and small intestine, which might provide possible routes of entry for the SARS-CoV. ”
ACE2 expression by colonic epithelial cells is associated with viral infection, immunity and energy metabolism
https://www.medrxiv.org/content/10.1101/2020.02.05.20020545v1.full.pdf
The digestive system is a potential route of 2019-nCov infection: a bioinformatics analysis based on single-cell transcriptomes
https://www.biorxiv.org/content/10.1101/2020.01.30.927806v1.full.pdf
Covid-19 and the Digestive System.
https://www.ncbi.nlm.nih.gov/pubmed/32215956
“Studies have identified the SARS-CoV-2 RNA in stool specimens of infected patients, and its viral receptor angiotensin converting enzyme 2 (ACE2) was found to be highly expressed in gastrointestinal epithelial cells. These suggest that SARS-CoV-2 can actively infect and replicate in the gastrointestinal tract. This has important implications to the disease management, transmission, and infection control.”
Severe acute respiratory syndrome and its lesions in digestive system
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4611772/
Cool, thanks for expanding on that. You might want to link this comment in your other comments about this idea, so people have some details to read. It’s a lot more informative than the one I was responding to!
Neat idea, although immune responses can be tissue specific (to a certain degree).
This article and the linked study propose that smokers are getting infected less often due to the nicotine rather than the smoking. Is it worth getting nicotine gum and patches?
Oxford COVID-19 vaccine begins human trial stage
I’m trying to formulate a response to, what at least in my circle of friends and acquaintances, is an increasing insistence that people had Covid-19 in November 2019.
If we assume someone did have Covid-19 in upstate NY for instance what else would have to be true?
I think mainly, that a novel virus made its way around the world without detection would be pretty major. And then it mutated in Wuhan, China unleashing this second, more virulent strain, meaning that the pattern of outbreak that we all witnessed beginning in Wuhan was some kind of ‘second phase’ and not the initial outbreak. It would also mean that current genetic tracing is probably wrong or not accounting for this alleged mutation.
All of this seems very unlikely to me. I don’t think I can prove with 100% certainty it is impossible. Even if I could I struggle to understand the emotional attachment people have to the claim that the illness they had was covid-19.
Unknown respiratory disease = Covid-19 strikes me as about as accurate as UFO = Aliens. But people seem to take it very personally when you tell them they could not possibly have had this illness 2months before the outbreak in Wuhan.
TL;DR: No. The earliest I’d buy for pandemic-track COVID is early-to-mid December, and in China or maybe Australia. Otherwise, it’d have to be a non-pandemic substrain that died out early, and left no children behind except the first Wuhan strain. The theory loses in an Occam’s Razor fight with “your friends probably had something else back then.”
ETA: This post mentions a second independent line of evidence on the matter (using antibodies), and also dates the first COVID-19 cases to no earlier than December.
I’m going to be basing most of this on nextstrain’s COVID-19 phylogeny data and their accompanying chart.*
The earliest sequenced US case we have came from Washington. The Washington strain’s earliest sequenced sample was 5 weeks of mutation out from the Wuhan strain at the time, leading to the inference that it arrived (or at least split off from the Wuhan gene-pool) in about mid-January. Australia seems to have a divergent strain that might have broken off even earlier, possibly as far back as mid-December.
Going on their graph, they dated the Wuhan last common-ancestor (LCA) strain to roughly mid-December, and the all-strain LCA is the same one.
(I’m not going to detail all of how this works, but LUCA is a similar concept.)
So, cases much earlier than mid-December (and in anyplace other than China or Australia) seem really unlikely to me**.
*Note that old phylogenies are often very inaccurate and rough, and rely heavily on your starting assumptions (I’ve played around with them, and it is wild how different the trees can be). But this is a fresh phylogeny, and should be a bit of a best-case-scenario. This a very recent series of mutations and splits, and on top of that, unlike with paleontology we can access, date, and sequence old blood samples just fine. I expect this phylogeny to get the occasional detail wrong, but to hit the broad-strokes and to be largely pretty accurate.
**If an earlier strain existed, it would have had to have left no lingering sequenced sub-strains in the present day. If even one of those theoretical highly-divergent sub-strains persisted, got sequenced, and were added to the phylogeny calculation… it would have introduced a new early-stage split into the phylogenetic tree that would have pushed the probable LCA way back into the past.***
*** I mention this in part because… HIV did have these. A non-pandemic ancestor that we found in some very old blood samples. It was nowhere near as contagious and persistent at the time, and even seemed to be a transient illness for those who caught it. But being a pretty unsuccessful virus at the time, only a very small pool of people had it back then.
Thanks for this, I was not aware of the Bedford lab’s work.
Wondering if you (or any other LW reader) has any thoughts on on the emotional aspect of this.
Seems folks are very attached to the idea that they had Covid-19 earlier than it was identified. It’s starting to get into that ‘can’t be reasoned out of something they haven’t reasoned into’ area.
I’m curious why people are so adamant that an extremely unlikely scenario is actually the most likely explanation. But i guess this cuts across all kinds of mental models and not just covid-19.
I don’t really know, I wouldn’t do this. Here are a couple of possibilities that ran through my mind.
COVID’s symptoms are basically “see: undefined flu-like symptoms.” This might just be an equivalent of “I looked up my symptoms on WebMD and it’s definitely cancer,” only with COVID.
There was that revelation that Washington got it earlier than expected. Maybe they’re pattern-matching blindly to this. It’s really easy to do so, especially if there was another nasty flu or cold going around back then (which there probably was).
Motivated reasoning
People want an excuse to go about their life as normal (or to complain if they’re not)
People especially hate taking the possibility of their own death seriously
Nobody wants to deal with the guilt of knowing that their “normal” actions may be endangering others (cough asymptomatic transmission), and they would rather believe something potentially-false than contend with that
It’s probably a mix of all three, or even more.
With all due affection, I’ve heard that New Yorkers as a whole are fairly prone to contrarianism. So the frequency with which you’re hearing this might also partially be local variance.
Ok, a question of which I assume that people here can quickly provide the answer:
How certain are we that age groups with low likelihood of developing symptoms—in particular, children—are actually infectious? Sure, there is asymptomatic spread in general, but I guess being asymptomatic also correlates with the ability to quickly kill the virus and thus not transfer it to others. So the first question is HOW infectious are people who are and stay asymptomatic? Is there good evidence on this?
Harvard published a study on link between PM2.5 pollution and mortality.
Places most hit so far are on the more polluted side so one can expect final CFR/IFR be lower than estimated from current data.
Mass testing seems like a promising brute force strategy that can keep R < 1 after lockdown, without requiring contact tracing. I’m pretty early in thinking about this but wanted to share my thoughts to encourage parallel efforts. A few possibilities (not mutually exclusive):
1) RNA testing: If everyone is given a daily RNA test and positives are isolated, transmission will likely be very close to 0. The US is still a factor of 1000 away from doing this (for comparison, RNA testing has scaled by 400x in the last month). However it seems likely that even testing on average every 10 days could keep R < 1. Key questions: i) what frequency of testing is enough? ii) what testing throughput is feasible?
2) Batch RNA testing: To the extent that reagents & machines rather than PPE & workers are the limiting factor in RNA test capacity, batch testing can be used, stopping the binary search at some threshold (e.g. at batch size 10). This of course results in lots of unnecessary isolation but still a small share of the population would be isolated. Key questions: i) are reagents & machines more limited than PPE & workers? ii) what’s the optimal specificity / stopping point?
3) Cheap symptom-based tests: Paul Romer has pointed out that even fairly poor tests (with specificity & sensitivity much lower than RNA tests) can significantly reduce transmission without requiring a very large share of the population to be isolated. Fever (present in 85% of mild cases) and anosmia (present in 60% of mild cases) can be tested for very cheaply. Isolating everyone with fever or anosmia eliminates nearly all transmission except for fully asymptomatic transmission. The importance of fully asymptomatic transmission is still pretty uncertain (asymptomatic shedding seems to be important but probably a nontrivial fraction of “asymptomatic” would have a mild fever or anosmia), so this might need to be combined with additional measures.
Some very encouraging developments. There is a PCR protocol that can test 100,000 samples in a single machine run, making millions of samples per day feasible, ignoring sample collection capacity. On that front, FDA just approved (EUA, limited scope for now) a sample collection protocol relying on saliva samples rather than nasopharyngeal swabs (would mean enormous increase in sample collection capacity). The prospects for plan #1 look dramatically better.
On plan #3, I was hoping this would work as a backup or low-tech option for poor countries but it looks like most estimates tend to put asymptomatic + presymptomatic transmission at 50%+ of all transmission, which makes this pretty limited.
How much do we know about gender differences?
I saw a reddit thread suggesting that women have different symptoms than men, though it was super anecdotal and I can’t find it now. I know women have a lower death rate, and I understand that was originally suspected to be because men smoke more but more recently maybe that turns out not to explain the whole difference? This paper suggests that men have more ACE2 than women, which is the enzyme the virus binds to.
Is this a thread that’s been well explored by others?
Something to keep an eye out for:
Source
NYT: “Some Coronavirus Patients Show Signs of Brain Ailments”
“The Case for Universal Cloth Mask Adoption & Policies to Increase the Supply of Medical Masks for Health Workers”
Excerpt from Twitter thread summarizing it:
https://www.dropbox.com/s/6ua7j979dbqb045/masks_final_n_HF_NA.pdf?dl=0
Does anyone have any idea / info on what proportion of the infected cases are getting Covid19 inside hospitals? This seems to have been a real issue for previous coronavirus.
I’d say there might be a stark difference between countries / regions in this area. Italian health workers seem to have taken a heavy blow. Also, 79 deaths in Brazil (total: 200) came from only one Hospital chain/ health insurer, which focus on aging customers (so, yeah, maybe it’s just selection bias?).
(Epistemic status: low, but I didin’t find any research on that after 30min, so maybe the hypothesis deserves a bit more of attention?)
South Korea, as always, are a treasure trove on information—they publish details every day which includes major outbreak clusters, some of which are hospitals. Of the non-cult related cases where they have managed to identify the source of the infection, hospital based infections account for 20%. If you include cases where they haven’t identified the source then it’s more like 10% which is probably a fairer reflection as hospital clusters probably mainly do get identified.
(They changed their reporting layout on March 25th and the new version doesn’t quite contain as much information so I’ve based this on the 24th)
Swiss cardiologist Nils Kucher is going to start a study to check whether blood thinners help covid-19 patients, suspecting that lung embolisms play a role in hospitalization and mortality. (https://www.n-tv.de/wissen/Helfen-Blutverduenner-bei-Corona-Infektion-article21722726.html)
I am not sure how it is possible that there are reports in the media claiming a low IFR (0.1%) when Lombardy has an official population fatality rate (i.e official COVID19 deaths over total population) of 0.12%, and unofficial one of 0.22% (measuring March and April all cause mortality there are ~10000 excess deaths) and a variability of up to 10x of casualties between towns more or less hit, indicating that only a small fraction (~10-20% imho) of the entire population was infected. I am pretty confident that the IFR is around 1% on average: it’s probably lower for younger people (0.2%) but as as high as 3% for people over 65. Moreover, Lombardy average age is less than the Italian average and the same as Germany. Even if there could be some age distribution difference they can’t explain the variation in the estimated IFR.
Hi all. I haven’t been to LessWrong in a while...but the mess in the world has reminded me how important it is for us to strive for clear thinking as a community. With that, I’d like to share a Coronavirus pandemic information site that has really good analysis for tracking the progress of the pandemic. It’s here: https://ourworldindata.org/grapher/daily-covid-cases-3-day-average
(it seems that I cannot embed or add images)
<iframe src=”https://ourworldindata.org/grapher/covid-confirmed-daily-cases-epidemiological-trajectory″ style=”width: 100%; height: 600px; border: 0px none;”></iframe>
How Large is the Iceberg? New Evidence from Kansas City
The details are currently vague, but I have tried to report key information, as this is one of the few efforts at random sampling that I am aware of. My main hope is to inspire someone with more expertise and ability to access details to expand the analysis.
Basics:
Johnson County, with a population of 602k, has reported on preliminary results from an effort to test a representative sample of its residents. I have been unable to find details about how they constructed the sample, so cannot assess how representative it is likely to be, but at least it’s not another dataset that selects on the sick. Out of 369 residents tested via PCR on Friday April 10th, 14 residents tested positive, for an estimated infection rate of 3.8%. The results were discussed at the 4⁄16 Board of County Commissioners Meeting. The video and slides are generally made available at some point after the meeting at https://boccmeetings.jocogov.org/onbaseagendaonline, but they have not been posted as I write this.
Hazarding to Interpret the Results:
Major Caveats
-I don’t know how representative the tested sample is of the Johnson County population.
-I don’t know details about the type of test they used, so I am not trying to account for false negatives or false positives.
-The test is not for antibodies, so it does not capture people who were infected and recovered.
-I’m ignoring statistical uncertainty.
-The results are from 6 days ago.
-Deaths and hospitalizations from those testing positive will increase with time.
As of April 16, 343 people have tested positive in Johnson County according to the county’s dashboard (available at https://public.tableau.com/profile/mapper.of.the.day.mod.#!/vizhome/covid19_joco_public/Dashboard). Ignoring the major caveats above, there should be 22,800 infections in Johnson County, suggesting that:
Infections are being undercounted by a factor of more than 60.
An estimate of infection fatality rate is .1%.
An estimate rate of admission to ICU is .2%
An estimate of the hospitalization rate (inpatient and outpatient) is 1.6%
How would 0.1% IFR square with New York’s state fatality rate of .08%? Seems unlikely it is representative, or it Johnson city was at earlier stage and had more recent exponential growth.
Why are surgical or self-made masks supposed to be better at protecting others than at protecting oneself? Naively, it seems to me that the percentage of filtered droplets/aerosol should be the same regardless of the direction in which it is breathed.
I think it’s mostly because the mask slows down the flow of exhaled air, which reduces the distance the droplets travel before they evaporate or fall to the ground. You can see this illustrated here and what happens without a mask here.
Aerosolization maybe happens more after exposure to air where water evaporates off of the lipid/protein shell. On exhale into mask it hasn’t had as much time to evaporate and is in humid environment, on inhale brought mask it has made its way over from another person and has had more time to aerosolize in drier air.
And as mentioned below, large droplets would be caught and kept off surfaces reducing hand to face transfer.
Don’t know about percentage of filtered droplets/aerosol in both directions, but I would guess the reasons include:
1. A mask doesn’t protect your eyes
2. You are likely to touch contaminated outer surface of your mask (and then touch your mouth, scratch your eye etc and get infected), but other people probably won’t touch the inner surface of your mask.
Idris Elba and his wife, two weeks after testing positive for the coronavirus, say they still have experienced no symptoms
The pool of people who 1. received a test while asymptomatic 2. tested positive and 3. are updating the public about their condition through mass media seems very small to me. The fact that two of them are turning out to remain fully asymptomatic seems to indicate that this is in fact likely to be a common thing. Somewhat surprising imo.
Interesting. I suppose another possibility is that both tests were false positives. Unlikely assuming that false positives are independent—but is that a reasonable assumption here? It seems possible they’d be correlated—e.g. if the tests were picking up some other infection.
Does anyone have a good understanding of this (in general, needn’t be SARS-cov-2 specific)?
Under what circumstances is it (un)reasonable to assume that false positives are independent?
Test reliability:
Sensitivity and specificity of the test. an image here
sensitivity = number of true positives / number of true positives + number of false negatives (true positives that test negative)
specificity = number of true negatives / number of true negatives + number of false positives (cross-reactions, other infections giving positive result)
Some info. I found here about covid19 PCR test. (It might not be the test that was was used but as far as I’m aware all current covid virus testing is via PCR so the tests should be of a very high specificity − 100%?!)
A bio-optical laser sensor for COVID testing is under development.
Technique/testing protocol.
Contamination of swabs is a possibility for positive results in a negative patient. (e.g. test personal is positive and contaminates sample, contamination in lab.)
Poor technique on sampling, ‘bad luck’ just missing the virus on sampling, using the wrong type of swab, poor handing of sample will give a negative result for a positive case.
Seems like a good model for estimating total infections, from my quick look: https://observablehq.com/@danyx/estimating-sars-cov-2-infections
I haven’t poked its methodology.
Which US federal agencies should receive more funding in response to coronavirus? Which should receive less?
From the Center for Health Security’s covid19 brief:
More info here. Maybe someone was listening to Scott’s surname-based lockdown suggestion.