Results for “rapid test” 292 found
Where and when does herd immunity kick in?
Here is the abstract of a new paper by
We have studied the evolution of COVID-19 in 12 low and middle income countries in which reported cases have peaked and declined rapidly in the past 2-3 months. In most of these countries the declines happened while control measures were consistent or even relaxing, and without signs of significant increases in cases that might indicate second waves. For the 12 countries we studied, the hypothesis that these countries have reached herd immunity warrants serious consideration. The Reed-Frost model, perhaps the simplest description for the evolution of cases in an epidemic, with only a few constant parameters, fits the observed case data remarkably well, and yields parameter values that are reasonable. The best-fitting curves suggest that the effective basic reproduction number in these countries ranged between 1.5 and 2.0, indicating that the curve was flattened in some countries but not suppressed by pushing the reproduction number below 1. The results suggest that between 51 and 80% of the population in these countries have been infected, and that between 0.05% and 2.50% of cases have been detected; values which are consistent with findings from serological and T-cell immunity studies. The infection rates, combined with data and estimates for deaths from COVID-19, allow us to estimate overall infection fatality rates for three of the countries. The values are lower than expected from reported infection fatality rates by age, based on data from several high-income countries, and the country population by age. COVID-19 may have a lower mortality risk in these three countries (to differing degrees in each country) than in high-income countries, due to differences in immune response, prior exposure to coronaviruses, disease characteristics or other factors. We find that the herd immunity hypothesis would not have fit the evolution of reported cases in several European countries, even just after the initial peaks; and subsequent resurgences of cases obviously prove that those countries have infection rates well below herd immunity levels. Our hypothesis that the 12 countries we studied have reached herd immunity should now be tested further, through serological and T cell immunity studies.
Via Alan Goldhammer.
Addendum: From Catinthehat in the comments:
It’s a simple homogeneous model Ni(t+1)= Ni(t) * Ro * Si(t) / Ntot -> Infected at time t+1 = Infected at time t * Ro * the proportion ( of the population) susceptible at time t. where t is discretized.
They fit the step t to an infection duration , then they fit Ro, to reproduce the shape of the curve for each country and at each step they multiply the infected by a parameter p (the undetected case ratio) to fit to the total population. This acts as an accelerant to the epidemic . Each country has its own p.
The main issue is that you can look at any epidemic curve and fit it that way and you will rather automatically reproduce this high immunity threshold which comes from your homogenous model.In Europe you can’t assume the undetected ratio is so high ( 1000x to 2000 x) so you must conclude social distancing stopped the epidemic, because your strategy would not fit experimental data.
In the countries fitted , the paper must conclude the epidemic raged fairly undetected, fairly quickly and infected most of the population.
Monday assorted links
1. Might changes in proton density, spurred by solar wind, predict earthquakes? If true, this would really be something.
2. Violates Godwin’s Law right upfront anyway speak for yourself! I genuinely find such hostile intentions difficult to understand.
3. Will a growth drug undermine “dwarf pride”? (NYT).
4. Robin Hanson on how and why remote work will matter.
5. Economics of the energy transition. Some subtle and underpromoted points in this one.
6. Why we can’t have good things: I am not sure how much public health experts are to blame for the problems in this article about why we don’t have home testing. The FDA won’t approve it? Do something about that! (Where is the outcry, other than from Paul Romer?) The American people aren’t ready for it? Well, are they ready for the alternatives you are proposing? Overall I found this NYT piece a depressing sign of American and perhaps also public health malaise.
7. Using banned cell phones for prison extortion by calling loved ones back home, excellent NYT piece, amazing investigative journalism.
Further results on the return to talk therapy
Here was my original post, here is an email response from a specialist in the area, channeled by a reader:
The issue is really, really complicated. I have a lot of data on it because I spent time with Mark Goldenson, interviewing a lot of folks segmented by those who chose to seek mental health assistance from a clinician, those who stayed with that treatment versus those who turned away relatively early, and those who experienced severe mental health conditions that make them think that they should have seen a therapist, but ultimately chose not to, for reasons other than economic ones.
And we also talked to clinicians on the other side of that equation.
So between that and knowing the literature reasonably well, I have a lot of perspective on this.
The first thing is that talk therapy is in general not effective for most people. And I know the paper under examination showed that it’s more effective than antidepressants, but in general, most people do not generally stick with talk therapy. They get a benefit at a reasonably low rate for a reasonably short period of time…
Moreover, there’s some pretty strong evidence that talk therapy or at least CBT is becoming less effective over time – the effect sizes in studies & meta-analyses are going down. And there could be reasons for that that aren’t an indictment of the therapeutic model.
So for example, the modern world could just be becoming more stressful and the therapy is less equipped for it… It could be that as the treatment becomes more popular, rather than the more advanced or cutting-edge therapists using it, it’s used by an increasingly broad set of therapists that include low-skilled or ineffective ones.
So there are a lot of reasons that may not have to do with the merits of CBT as an approach, but the data are reasonably convincing on that front.
I think a lot of people are making a reasonably rational choice that, especially if they’re not going to stick with it for a long period of time, even starting therapy is a low-value proposition.
George Ainslie (the psychologist) has this kind of notion of playing a prisoner’s dilemma with your [future] self… let’s just say I want to start an exercise habit… there are a lot of parallels with exercise and talk therapy.
If I knew for a fact that I was going to stop doing it after one month, it actually doesn’t make sense to start at all. Right, because the benefits of accrued will pretty rapidly deteriorate and it’ll be as if I never did it…
People are not just considering, “Should I try talk therapy?”, they’re considering, “Will I do this for a sufficiently long period of time, or especially can I afford it for a long period of time, to where I will get and maintain the benefits from doing it?”
And many people do in fact have misinformation about how quickly they can experience certain types of benefits, and how much work is involved – it’s clear that there’s a lot of work involved, and many people don’t want to do that work.
From an operant conditioning standpoint, the experience of a therapy session is frankly more punishing than it is rewarding (for many people, a lot of the time). Like any negative stimulus, they’re going to engage in behaviors that cause that stimulus to be experienced at a lower rate.
Sometimes the benefits don’t accrue during the session, they accrue afterwards. It takes a lot of work to experience them and [can] involve emotional trauma to even retrieve them.
It’s not consistent with people’s ROI calculation, or what they would like to see in their ROI calculation. Again, it’s really similar to physical exercise – we know physical exercise works. It works better than antidepressants. It accrues all the benefits that this paper Cowen cited discovered in terms of energy and mood and earnings and so on and so forth.
But people still don’t engage in exercise, and in fact I think the rate of physical activity is actually on the decline, in the industrialized world at least.So, it’s more complex than “Does the behavior accrue benefits if you do it consistently?” It’s also not entirely about access because many forms of physical activity are free, and as the paper examines the seeking of talk therapy is not super sensitive to [price].
So it goes beyond the mere cost of the service, although the cost of the services is definitely prohibitive for a large cross-section of people.
How does ketamine or any other substance relate to this?
I think it relates very favorably in that people may actually have the opposite misconception around psychedelic-assisted therapy. They might view regular talk therapy as something where they’re going to have to do this tedious hour a week for months before they get any benefits or they solve any problems in their lives.
[With ketamine] they probably think that they’re going to do one ketamine session, and all of their issues are going to be solved right their PTSD is cured and they no longer experience any symptoms of anxiety, depression, etc… It’s probably a little bit overhyped in the minds of people who have only casually exposed themselves – they’re seeing an article in The New Yorker, or they’re seeing it on a blog, or someone goes on a podcast and talks about an experience. They’re not looking at it with the measured view of someone from the Johns Hopkins team or whatever. So I think that it does work in your favor….
People may overestimate the level of benefit they’re likely to achieve and it seems like the medicine is doing the work, rather than them. Even though I know that that isn’t really the case….
By the way, fun stuff from that research sprint we did with Goldenson – the average person in our cohort (who did ultimately get therapy), put it off for over two years.
It was a pretty wide range – some people sought help after, perhaps, six weeks I think was the shortest. Nobody has a bad day or think they’re experiencing depression or experiencing dysfunction in their work life or their romantic life or whatever it is and goes straight to a therapist…
They also tend to do a fair bit of research – they research different therapeutic methods and kind of choose one that fits their personality or their values, almost more so than efficacy.
And most of the people who ended up with a stable relationship with a provider trial between two and five different folks.
Those words are from Chris York, via MR reader Milan Griffes.
The Beginning of the End?
It’s taken far too long and it’s still not FDA approved for at-home use or for asymptomatic individuals but the new $5,15-minute, easy to use, Abbott test and the Trump administration’s promise to purchase 150 million of them is a big deal. Abbott has been building capacity for months according to their lead scientist interviewed in the Atlantic by Alex Madrigal and in a few weeks will be producing 50 million tests a month:
Madrigal: Fifty million tests a month is a huge number. That’s more than twice the number of tests the U.S. completes in a month. How did you ramp up production so massively?
Hackett: This was the challenge of this program. We needed some sort of reliable testing that could be affordable and that doesn’t require instrumentation. You need scale. The more frequently you could test people, frankly, even tests with lower sensitivity would be very effective at identifying people quickly and slowing the spread. As we were developing the test, there were people working in parallel looking at supply chain and logistics. Abbott took a lot of risk—hundreds of millions of dollars were spent building two new manufacturing facilities focused solely on those tests. We hoped we could come to a solution that would be where we needed it from an overall accuracy perspective, but if you weren’t building capability simultaneously, there was no way it could be the answer.
The US has performed about 80 million tests since the pandemic began, so an additional 50 million tests a month is a big increase in capacity. As noted, the test is not approved for at-home use but it’s a CLIA-waived test which means that a doctor’s office, a CVS or Walmart clinic, even a school nurse could qualify for a waiver and perform the tests. The test is not approved for asymptomatic individuals but I suspect that won’t mean much in practice, it can be prescribed off-label although the fact that a prescription is required is limiting. I hope the necessity for a prescription will be lifted as we get more experience with these tests. False positives (~1.5%) are low and by taking the strain off the PCR system we can improve triage and afford to do more double checks. False positives will be more of an issue as we wipe out the virus but that will take time.
I hope these tests will open up air travel within a month or two. I also hope to see more of these types of tests approved. Derek Lowe has more technical details.
It won’t be all smooth sailing, Abbott may not be able to produce as much or as quickly as they say they can and quality in the field may fall. The government may distribute the tests poorly. The virus could pickup in the fall, as in 1918. I expect more problems and challenges but we now have a chance to get ahead of the virus which is very welcome news.
Addendum: This type of public-private partnership with private firms building capacity in advance of approval for tests and vaccines on the foundation of government push and pull funding is exactly the structure that the Accelerating Health Technologies team has been recommending both to the US government and to governments around the world.
Shoring Up the Vaccine Supply Chain
Supply chains were hit hard early in the pandemic. Disinfectant couldn’t be produced because of a lack of bottles, tests couldn’t be processed because nasal swabs or PPE wasn’t available, the decline of passenger air traffic hit commercial delivery and so forth. I worry about forthcoming stresses on the vaccine supply chain. Billions of doses of vaccine will be demanded in the next year and a lot will depend on complicated supply lines including cold storage, air traffic, styrofoam, vials, bags, needles and many other inputs. Companies and the awesome team at CEPI (give them all a Nobel prize) are planning for vials and needles and other inputs but there are many non-obvious inputs higher up in the supply chain that also need shoring up.
Writing in Bloomberg, Scott Duke Kominers and I look at some of the odder inputs to vaccines like horseshoe crab blood, shark livers and the vaccinia capping enyzme, VCE. We are actually not too worried about horseshoe crab blood and shark livers as these are used in other industries. Shark livers, for example, are used to produce a lot of cosmetics so we should be able to divert supply as needed. VCE, however, is rarer.
DNA and mRNA vaccine technologies have shown promising results, and two of the leading vaccine contenders, from Pfizer Inc. and Moderna Inc., use mRNA technology. But mRNA has never been used to produce a commercial vaccine for humans, let alone at scale. And scaling these technologies may not be easy. In particular, mRNA degrades rapidly. To prevent this, it must be “capped” by a very rare substance called vaccinia capping enzyme.
Just over 10 pounds of this VCE is enough to produce a hundred million doses of an mRNA vaccine — but the current manufacturing processes for VCE require so much bioreactor capacity that making 10 pounds would cost about $1.4 billion. More important, global bioreactor capacity cannot support production at that level while also producing other vaccines and cancer-fighting drugs.
If we work hard now, we may be able to find more efficient means of producing VCE. Expanding bioreactor production and repurposing bioreactors from existing large-scale industrial applications will also help to lessen the pressure on the supply chains for multiple types of vaccines.
In addition to supply chains per se we also face the problem that companies are not raising prices enough. Ironically, this means that we need more public investment.
Of course, we might think that private companies would have incentives to coordinate supply chains themselves — and to some extent, they are doing so. But many have pledged to keep their vaccine prices close to costs, both out of altruism and because they may fear public backlash (or legal action) if they’re perceived as “price gouging” in the middle of a pandemic. And if companies don’t stand to profit much from Covid-19 vaccines, then they don’t have much incentive to invest in increasing capacity. In short: If prices can’t rise, then the only way to encourage companies to invest more in production is to reduce their costs — and that means we need public investment.
More generally, it’s not too late to be building more vaccine capacity and to repurpose bioreactor capacity from non-GMP sources, perhaps including veterinary and food sources. There are lots of vaccines in development. The science is promising. We need to take action now so that we can deliver on that promise.
Read the whole thing.
Why herd immunity is worth less than you might think
That is the topic of my latest Bloomberg column. The evidence in favor of at least partial herd immunity continues to pile up, but still don’t get too cheery. One worry is that herd immunity might prove only temporary:
First, many herd immunity hypotheses invoke the idea of “superspreaders” — that a relatively small number of people account for a disproportionate amount of the contagion. Perhaps it is the bartenders, church choir singers and bus drivers who spread the virus to so many others early on in the pandemic. Now that those groups have been exposed to a high degree and have acquired immunity, it might be much harder to distribute the virus.
That logic makes some sense except for one issue: namely, that the identities of potential superspreaders can change over time. For instance, perhaps choir singers were superspreaders earlier in the winter, but with most choral singing shut down, maybe TSA security guards are the new superspreaders. After all, air travel has been rising steadily. Or the onset of winter and colder weather might make waiters a new set of superspreaders, as more people dine inside.
In other words, herd immunity might be a temporary state of affairs. The very economic and social changes brought by the virus may induce a rotation of potential superspreaders, thereby undoing some of the acquired protection.
In other words, the fight never quite ends. Here is another and possibly larger worry:
Another problem is global in nature and could prove very severe indeed. One possible motivation for the herd immunity hypothesis is that a significant chunk of the population already had been exposed to related coronaviruses, thereby giving it partial immunity to Covid-19. In essence, that “reservoir” of protected individuals has helped to slow or stop the spread of the virus sooner than might have been expected.
There is a catch, however. If true, that hypothesis means that the virus spreads all the more rapidly among groups with little or no protection. (Technically, if R = 2.5, but say 50% of the core population has protection, there is an R of something like 5 for the unprotected population, to get the aggregate R to 2.5.) So if some parts of the world enjoy less protection from cross-immunities, Covid-19 is likely to ravage them all the more — and very rapidly at that.
Again, this is all in the realm of the hypothetical. But that scenario might help explain the severe Covid-19 toll in much of Latin America, and possibly in India and South Africa. Herd immunity, as a general concept, could mean a more dangerous virus for some areas and population subgroups.
There are further arguments at the link.
The case for GPT-3
That is the topic of my latest Bloomberg column, here is one excerpt:
As a very rough description, think of GPT-3 as giving computers a facility with words that they have had with numbers for a long time, and with images since about 2012.
The core of GPT-3, which is a creation of OpenAI, an artificial intelligence company based in San Francisco, is a general language model designed to perform autofill. It is trained on uncategorized internet writings, and basically guesses what text ought to come next from any starting point. That may sound unglamorous, but a language model built for guessing with 175 billion parameters — 10 times more than previous competitors — is surprisingly powerful.
The eventual uses of GPT-3 are hard to predict, but it is easy to see the potential. GPT-3 can converse at a conceptual level, translate language, answer email, perform (some) programming tasks, help with medical diagnoses and, perhaps someday, serve as a therapist. It can write poetry, dialogue and stories with a surprising degree of sophistication, and it is generally good at common sense — a typical failing for many automated response systems. You can even ask it questions about God.
Imagine a Siri-like voice-activated assistant that actually did your intended bidding. It also has the potential to outperform Google for many search queries, which could give rise to a highly profitable company.
GPT-3 does not try to pass the Turing test by being indistinguishable from a human in its responses. Rather, it is built for generality and depth, even though that means it will serve up bad answers to many queries, at least in its current state. As a general philosophical principle, it accepts that being weird sometimes is a necessary part of being smart. In any case, like so many other technologies, GPT-3 has the potential to rapidly improve.
There is much more at the link.
Rescheduling for thee, but not for me
When Wisconsin Republicans refused to move their election day, Democrats, experts, and various media types decried the decision as immoral and dangerous during a pandemic. “Regularly scheduled, orderly elections with direct governmental consequences were either too dangerous, or insufficiently compelling,” Adam wrote in a late-night email. “Contrast that, of course, with Democrats’s evident belief that we absolutely must not delay these protests against police brutality. The protests—spontaneous not scheduled, disorderly not orderly, emotive not concretely consequential—simply had to go on.”
Protests and demonstrations are more important and indispensable than elections. The deliberate act of voting, essential to a democracy, can be put on a schedule delay but political catharsis must proceed on its own schedule. Mario Cuomo used to say that “We campaign in poetry but we govern in prose.” Now it’s poetry or nothing.
Here is more by Jonah Goldberg. I am not looking to attack or make trouble for any individual person here, so no link or name, but this is from a leading figure in biology and also a regular commenter on epidemiology:
“As a citizen, I wholeheartedly support the protests nonetheless.”
My worries run deep. Should the original lockdown recommendations have been asterisked with a “this is my lesser, non-citizen self speaking” disclaimer? Should those who broke the earlier lockdowns, to save their jobs or visit their relatives, or go to their churches, or they wanted to see their dying grandma but couldn’t…have been able to cite their role as “citizens” as good reason for opposing the recommendations of the “scientists”? Does the author have much scientific expertise in how likely these protests are to prove successful? Does typing the word “c-i-t-i-z-e-n” relieve one of the burden of estimating how much public health credibility will be lost if/when we are told that another lockdown is needed to forestall a really quite possible second wave? Does the author have a deep understanding of the actual literature on the “science/citizen” distinction, value freedom in science, the normative role of the advisor, and so on? Does the implicit portrait painted by that tweet imply a radically desiccated, and indeed segregated role of the notions of “scientist” and “citizen”? Would you trust a scientist like that for advice? Should you? And shouldn’t he endorse the protests “2/3 heartedly” or so, rather than “wholeheartedly”? Isn’t that the mood affiliation talking?
On May 20th, the same source called a Trump plan for rapid reopening (churches too, and much more) “extraordinarily dangerous” — was that the scientist or the citizen talking? And were we told which at the time? Andreas’s comments at that above link are exactly on the mark, especially the point that the fragile consensus for the acceptability of lockdown will be difficult to recreate ever again.
If you would like a different perspective, bravo to Dan Diamond. Here is his article. And here are some better options for public health experts. Here is a useful (very rough) estimate of expected fatalities from the protests, though it does not take all-important demonstration effects into account. I can say I give credit to the initial source (the one I am criticizing) for passing that tweet storm along.
We really very drastically need to raise the quality and credibility of the advice given here.
Friday assorted links
How tourism will change
That is the topic of my latest Bloomberg column, here is one bit:
Some of the safer locales may decide to open up, perhaps with visitor quotas. Many tourists will rush there, either occasioning a counterreaction — that is, reducing the destination’s appeal — or filling the quota very rapidly. Then everyone will resume their search for the next open spot, whether it’s Nova Scotia or Iceland. Tourists will compete for status by asking, “Did you get in before the door shut?”
Some countries might allow visitors to only their more distant (and less desirable?) locales, enforcing movements with electronic monitoring. Central Australia, anyone? I’ve always wanted to see the northwest coast of New Zealand’s South Island.
Some of the world’s poorer countries might pursue a “herd immunity” strategy, not intentionally, but because their public health institutions are too weak to mount an effective response to Covid-19. A year and a half from now, some of those countries likely will be open to tourism. They won’t be able to prove they are safe, but they might be fine nonetheless. They will attract the kind of risk-seeking tourist who, pre-Covid 19, might have gone to Mali or the more exotic parts of India.
And:
laces reachable by direct flights will be increasingly attractive. A smaller aviation sector will make connecting flights more logistically difficult, and passengers will appreciate the certainty that comes from knowing they are approved to enter the country of their final destination and don’t have to worry about transfers, delays or cancellations. That will favor London, Paris, Toronto, Rome and other well-connected cities with lots to see and do. More people will want to visit a single locale and not worry about catching the train to the next city. Or they might prefer a driving tour. How about flying to Paris and then a car trip to the famous cathedrals and towns of Normandy?
Maybe. But I might start by giving Parkersburg, West Virginia, a try.
Universities with Hospitals and Labs
Mitch Daniels, the President of Purdue, has outlined a preliminary plan to reopen involving test, trace and supported isolation on campus.
We intend to know as much as possible about the viral health status of our community. This could include pre-testing of students and staff before arrival in August, for both infection and post-infection immunity through antibodies. It will include a robust testing system during the school year, using Purdue’s own BSL-2 level laboratory for fast results. Anyone showing symptoms will be tested promptly, and quarantined if positive, in space we will set aside for that purpose.
We expect to be able to trace proximate and/or frequent contacts of those who test positive. Contacts in the vulnerable categories will be asked to self-quarantine for the recommended period, currently 14 days. Those in the young, least vulnerable group will be tested, quarantined if positive, or checked regularly for symptoms if negative for both antibodies and the virus.
This paper provides details on transforming a university lab into a testing center. In essence, a major university with a hospital (which Purdue doesn’t have) should be able to do it technically but to work to reopen for students it probably has to be a university located outside of a major urban area. Here are a few possibilities:
- Baylor University
- Vanderbilt University
- University of Michigan, Ann Arbor
- University of Virginia
- University of Iowa
- University of Utah
- University of Alabama
Mitch Daniel also notes:
Our campus community, a “city” of 50,000+ people, is highly unusual in its makeup. At least 80% of our population is made up of young people, say, 35 and under. All data to date tell us that the COVID-19 virus, while it transmits rapidly in this age group, poses close to zero lethal threat to them.
which does seem to miss (ahem) an important group necessary for reopening.
Spit Works
A new paper finds that COVID-19 can be detected in saliva more accurately than with nasal swab. As I mentioned earlier a saliva test will lessen the need for personnel with PPE to collect samples.
Rapid and accurate SARS-CoV-2 diagnostic testing is essential for controlling the ongoing COVID-19 pandemic. The current gold standard for COVID-19 diagnosis is real-time RT-PCR detection of SARS-CoV-2 from nasopharyngeal swabs. Low sensitivity, exposure risks to healthcare workers, and global shortages of swabs and personal protective equipment, however, necessitate the validation of new diagnostic approaches. Saliva is a promising candidate for SARS-CoV-2 diagnostics because (1) collection is minimally invasive and can reliably be self-administered and (2) saliva has exhibited comparable sensitivity to nasopharyngeal swabs in detection of other respiratory pathogens, including endemic human coronaviruses, in previous studies. To validate the use of saliva for SARS-CoV-2 detection, we tested nasopharyngeal and saliva samples from confirmed COVID-19 patients and self-collected samples from healthcare workers on COVID-19 wards. When we compared SARS-CoV-2 detection from patient-matched nasopharyngeal and saliva samples, we found that saliva yielded greater detection sensitivity and consistency throughout the course of infection. Furthermore, we report less variability in self-sample collection of saliva. Taken together, our findings demonstrate that saliva is a viable and more sensitive alternative to nasopharyngeal swabs and could enable at-home self-administered sample collection for accurate large-scale SARS-CoV-2 testing.
The FDA has also just approved an at-home test collected by nasal swab, a saliva test should not be far behind.
Hat tip: Cat in the Hat.
What should we believe and not believe about R?
This is from my email, highly recommended, and I will not apply further indentation:
“Although there’s a lot of pre-peer-reviewed and strongly-incorrect work out there, I’ll single out Kevin Systrom’s rt.live as being deeply problematic. Estimating R from noisy real-world data when you don’t know the underlying model is fundamentally difficult, but a minimal baseline capability is to get sign(R-1) right (at least when |R-1| isn’t small), and rt.live is going to often be badly (and confidently) wrong about that because it fails to account for how the confirmed count data it’s based on is noisy enough to be mostly garbage. (Many serious modelers have given up on case counts and just model death counts.) For an obvious example, consider their graph for WA: it’s deeply implausible on its face that WA had R=.24 on 10 April and R=1.4 on 17 April. (In an epidemiological model with fixed waiting times, the implication would be that infectious people started interacting with non-infectious people five times as often over the course of a week with no policy changes.) Digging into the data and the math, you can see that a few days of falling case counts will make the system confident of a very low R, and a few days of rising counts will make it confident of a very high one, but we know from other sources that both can and do happen due to changes in test and test processing availability. (There are additional serious methodological problems with rt.live, but trying to nowcast R from observed case counts is already garbage-in so will be garbage-out.)
However, folks are (understandably, given the difficulty and the rush) missing a lot of harder stuff too. You linked a study and wrote “Good and extensive west coast Kaiser data set, and further evidence that R doesn’t fall nearly as much as you might wish for.” We read the study tonight, and the data set seems great and important, but we don’t buy the claims about R at all — we think there are major statistical issues. (I could go into it if you want, although it’s fairly subtle, and of course there’s some chance that *we’re* wrong…)
Ultimately, the models and statistics in the field aren’t designed to handle rapidly changing R, and everything is made much worse by the massive inconsistencies in the observed data. R itself is a surprisingly subtle concept (especially in changing systems): for instance, rt.live uses a simple relationship between R and the observed rate of growth, but their claimed relationship only holds for the simplest SIR model (not epidemiologically plausible at all for COVID-19), and it has as an input the median serial interval, which is also substantially uncertain for COVID-19 (they treat it as a known constant). These things make it easy to badly missestimate R. Usually these errors pull or push R away from 1 — rt.live would at least get sign(R – 1) right if their data weren’t garbage and they fixed other statistical problems — but of course getting sign(R – 1) right is a low bar, it’s just figuring out whether what you’re observing is growing or shrinking. Many folks would actually be better off not trying to forecast R and just looking carefully at whether they believe the thing they’re observing is growing or shrinking and how quickly.
All that said, the growing (not total, but mostly shared) consensus among both folks I’ve talked to inside Google and with academic epidemiologists who are thinking hard about this is:
- Lockdowns, including Western-style lockdowns, very likely drive R substantially below 1 (say .7 or lower), even without perfect compliance. Best evidence is the daily death graphs from Italy, Spain, and probably France (their data’s a mess): those were some non-perfect lockdowns (compared to China), and you see a clear peak followed by a clear decline after basically one time constant (people who died at peak were getting infected right around the lockdown). If R was > 1 you’d see exponential growth up to herd immunity, if R was 0.9 you’d see a much bigger and later peak (there’s a lot of momentum in these systems). This is good news if true (and we think it’s probably true), since it means there’s at least some room to relax policy while keeping things under control. Another implication is the “first wave” is going to end over the next month-ish, as IHME and UTexas (my preferred public deaths forecaster; they don’t do R) predict.
- Cases are of course massively undercounted, but the weight of evidence is that they’re *probably* not *so* massively undercounted that we’re anywhere near herd immunity (though this would of course be great news). Looking at Iceland, Diamond Princess, the other studies, the flaws in the Stanford study, we’re very likely still at < ~2-3% infected in the US. (25% in large parts of NYC wouldn’t be a shock though).
Anyways, I guess my single biggest point is that if you see a result that says something about R, there’s a very good chance it’s just mathematically broken or observationally broken and isn’t actually saying that thing at all.”
That is all from Rif A. Saurous, Research Director at Google, currently working on COVID-19 modeling.
Currently it seems to me that those are the smartest and best informed views “out there,” so at least for now they are my views too.
Emergent Ventures Covid-19 prizes, second cohort
There is another round of prize winners, and I am pleased and honored to announce them:
1. Petr Ludwig.
Petr has been instrumental in building out the #Masks4All movement, and in persuading individuals in the Czech Republic, and in turn the world, to wear masks. That already has saved numerous lives and made possible — whenever the time is right — an eventual reopening of economies. And I am pleased to see this movement is now having an impact in the United States.
Here is Petr on Twitter, here is the viral video he had a hand in creating and promoting, his work has been truly impressive, and I also would like to offer praise and recognition to all of the people who have worked with him.
The covid19india project is a website for tracking the progress of Covid-19 cases through India, and it is the result of a collaboration.
It is based on a large volunteer group that is rapidly aggregating and verifying patient-level data by crowdsourcing.They portray a website for tracking the progress of Covid-19 cases through India and open-sources all the (non-personally identifiable) data for researchers and analysts to consume. The data for the react based website and the cluster graph are a crowdsourced Google Sheet filled in by a large and hardworking Ops team at covid19india. They manually fill in each case, from various news sources, as soon as the case is reported. Top contributor amongst 100 odd other code contributors and the maintainer of the website is Jeremy Philemon, an undergraduate at SUNY Binghamton, majoring in Computer Science. Another interesting contribution is from Somesh Kar, a 15 year old high school student at Delhi Public School RK Puram, New Delhi. For the COVID-19 India tracker he worked on the code for the cluster graph. He is interested in computer science tech entrepreneurship and is a designer and developer in his free time. Somesh was joined in this effort by his brother, Sibesh Kar, a tech entrepreneur in New Delhi and the founder of MayaHQ.
3. Debes Christiansen, the head of department at the National Reference Laboratory for Fish and Animal Diseases in the capital, Tórshavn, Faroe Islands.
Here is the story of Debes Christiansen. Here is one part:
A scientist who adapted his veterinary lab to test for disease among humans rather than salmon is being celebrated for helping the Faroe Islands avoid coronavirus deaths, where a larger proportion of the population has been tested than anywhere in the world.
Debes was prescient in understanding the import of testing, and also in realizing in January that he needed to move quickly.
Please note that I am trying to reach Debes Christiansen — can anyone please help me in this endeavor with an email?
Here is the list of the first cohort of winners, here is the original prize announcement. Most of the prize money still remains open to be won. It is worth noting that the winners so far are taking the money and plowing it back into their ongoing and still very valuable work.
Pandemic Policy in Developing Countries: Recommendations for India
Shruti Rajagopalan and I have written a policy brief on pandemic policy in developing countries with specific recommendations for India. The Indian context requires a different approach. Even washing hands, for example, is not easily accomplished when hundreds of millions of people do not have access to piped water or soap. India needs to control the COVID-19 pandemic better than other nations because the consequences of losing control are more severe given India’s relatively low healthcare resources, limited state capacity, and large population of poor people, many of whom are already burdened with other health issues. We make 10 recommendations:
1: Any test kit approved in China, Japan, Singapore, South Korea, Taiwan, the United States, or Western Europe should be immediately approved in India.
2: The Indian government should announce a commitment to pay any private Indian lab running coronavirus tests at least the current cost of tests run at government labs.
3: All import tariffs and quotas on medical equipment related to the COVID-19 crisis should be immediately lifted and nullified.
4: Use mobile phones to survey, inform, and prescreen for symptoms. Direct any individual with symptoms and his or her family to a testing center, or direct mobile testing to them.
5: Keep mobile phone accounts alive even if the phone bills are not paid, and provide a subsidy for pay-as-you-go account holders who cannot afford to pay for mobile services.
6: Requisition government schools and buildings and rent private hotel rooms, repurposing them as quarantine facilities.
7: Rapidly scale up the production and distribution of masks and encourage everyone to wear masks.
8: Truck in water and soap for hand washing and use existing distribution networks to provide hand sanitizers.
9: Accept voter identification cards and AADHAAR cards for in-kind transfers at ration shops.
10: Announce a direct cash transfer of a minimum of 3000 rupees per month (equivalent to the poverty line of $1.25 a day or $38 a month) to be distributed through Jan Dhan accounts or mobile phone applications such as Paytm.
See the whole thing for more on the rationales.
Addendum: As we went to press we heard that India will lift tariffs on medical equipment. My co-author lobbied hard for this.