Results for “Tests” 628 found
A vaccine from Oxford?
In the worldwide race for a vaccine to stop the coronavirus, the laboratory sprinting fastest is at Oxford University.
Most other teams have had to start with small clinical trials of a few hundred participants to demonstrate safety. But scientists at the university’s Jenner Institute had a head start on a vaccine, having proved in previous trials that similar inoculations — including one last year against an earlier coronavirus — were harmless to humans. That has enabled them to leap ahead and schedule tests of their new coronavirus vaccine involving more than 6,000 people by the end of next month, hoping to show not only that it is safe, but also that it works.
The Oxford scientists now say that with an emergency approval from regulators, the first few million doses of their vaccine could be available by September — at least several months ahead of any of the other announced efforts — if it proves to be effective.
Here is more from the NYT. I do not have a personal opinion on the specifics of this development, but it seems worth passing along.
The Decline of the Innovation State is Killing Us
The latest relief bill contains another $320 billion in small business relief and $25 billion for testing. Finally, we get some serious money to actually fight the virus. But as Paul Romer pointed out on twitter, this is less than half of what we spend on soft drinks!!! (Spending on soft drinks is about $65 billion annually). Soda is nice but it is not going to save lives and restart the economy. Despite monumental efforts by BARDA and CEPI we are also not investing enough in capacity for vaccine production so that if and when when a vaccine is available we can roll it out quickly to everyone (an issue I am working on).
The failure to spend on actually fighting the virus with science is mind boggling. It’s a stunning example of our inability to build. By the way, note that this failure has nothing to do with Ezra Klein’s explanation of our failure to build, the filibuster. Are we more politically divided about PCR tests than we are about unemployment insurance? I don’t think so yet we spend on the latter but not the former. The rot is deeper. A failure of imagination and boldness which is an embarrassment to the country that put a man on the moon.
In Launching the Innovation Renaissance I said the US was a welfare/warfare state and no longer an innovation state. The share of R&D in the Federal Budget, for example, has diminished from about 12% at its height in the NASA years to an all time low of about 3% in recent years. We are great at spending on welfare and warfare but all that spending has crowded out spending on innovation and now that is killing us.
Covid-19 liability reform for the eventual reopening
That is the topic of my latest Bloomberg column, here is one excerpt:
If an infected but asymptomatic worker shows up at work and sickens coworkers, for example, should the employer be liable? The answer is far from obvious. Liability exists not to shift unmanageable risk, but rather to induce management to take possible and prudent measures of precaution.
Another problem with liability law in this context is that the potential damages are high relative to the capitalization of most businesses. Covid-19 cases often pop up in chains; there have been many cases from a single conference, or in a single church choir, or on a single cruise ship. If a business or school is host to such a chain, it could be wiped out financially by lawsuits. In these cases the liability penalties do not have their intended deterrent effect because the money to lose simply isn’t there…
Another problem with liability in this setting has to do with jury expertise. Are random members of the public really the best people to determine acceptable levels of Covid-19 risk and appropriate employer precautions? Juries are better suited for more conventional applications of liability law, such as when the handyman fixing your roof falls off your rickety ladder. Given the unprecedented nature of the current situation, many Covid-19 risk questions require experts.
Finally, there is the issue of testing. Businesses could be of immeasurable help by testing their employees for Covid-19, as additional testing can help limit the spread of the virus (if only by indicating which workers should stay home or get treatment). Yet the available tests are highly imperfect, especially with false negatives. If businesses are liable for incorrect test results, and their possible practical implications, then business will likely not perform any tests at all, to the detriment of virtually everybody.
I recommend modest liability for some sectors, and zero liability, bundled with a New Zealand-like accident compensation system, for other sectors. And of course some very dangerous sectors should not be allowed to reopen at all, though I am more sympathetic to regional experimentation than are some people on Twitter.
The Roadmap to Pandemic Resilience
Led by Danielle Allen and Glen Weyl, the Safra Center for Ethics at Harvard has put out a Roadmap to Pandemic Resilience (I am a co-author along with others). It’s the most detailed plan I have yet seen on how to ramp up testing and combine with contact tracing and supported isolation to beat the virus.
One of the most useful parts of the roadmap is that choke points have been identified and solutions proposed. Three testing choke points, for example, are that nasal swaps make people sneeze which means that health care workers collecting the sample need PPE. A saliva test, such as the one just approved, could solve this problem. In addition, as I argued earlier, we need to permit home test kits especially as self-swab from near nasal appears to be just as accurate as nasal swabs taken by a nurse. Second, once collected, the swab material is classified as a bio-hazard which requires serious transport and storage safety requirements. A inactivation buffer, however, could kill the virus without killing the RNA necessary for testing and thus reduce the need for bio-safety techniques in transportation which would make testing faster and cheaper. Finally, labs are working on reducing the reagents needed for the tests.
Understanding the choke points is a big step towards increasing the quantity of tests.
Monday assorted links
2. An extensive and pretty devastating article on the testing fail of the CDC. Again, our regulatory state has been failing us. And coverage from the NYT.
3. At the margin: “Results show that informants were given approximately 70 East German marks worth of rewards more per year in the areas that had access to WGTV, as compared with areas with no reception—ironically an amount roughly equivalent to the cost of an annual East German TV subscription.”
4. “Bars and Restaurants Peel Cash From Walls to Help Idled Workers” (NYT).
5. Scott Sumner watch the islands. This piece seems to imply that in-migration is a major source of heterogeneity. I’ve also been receiving some emails from Xavier suggested tourist inflow is a major cause of heterogeneity, due to an ever fresh supply of hard to trace cases. No rigorous test yet of that one, but it is certainly in the running as a hypothesis. And if true, it suggests many parts of Africa may not be hit that hard.
6. Karlson, Stern, and Klein on Sweden.
7. South Africa and HIV/AIDS: will the latter have been good training for Covid-19? (Economist)
8. The danger of “herd immunity overshoot.”
9. Singapore government and the Virus Vanguard.
10. Beloit University moves to more flexible two-course module system. For now at least.
COVID Prevalence and the Difficult Statistics of Rare Events
In a post titled Defensive Gun Use and the Difficult Statistics of Rare Events I pointed out that it’s very easy to go wrong when estimating rare events.
Since defensive gun use is relatively uncommon under any reasonable scenario there are many more opportunities to miscode in a way that inflates defensive gun use than there are ways to miscode in a way that deflates defensive gun use.
Imagine, for example, that the true rate of defensive gun use is not 1% but .1%. At the same time, imagine that 1% of all people are liars. Thus, in a survey of 10,000 people, there will be 100 liars. On average, 99.9 (~100) of the liars will say that they used a gun defensively when they did not and .1 of the liars will say that they did not use a gun defensively when they did. Of the 9900 people who report truthfully, approximately 10 will report a defensive gun use and 9890 will report no defensive gun use. Adding it up, the survey will find a defensive gun use rate of approximately (100+10)/10000=1.1%, i.e. more than ten times higher than the actual rate of .1%!
Epidemiologist Trevor Bedford points out that a similar problem applies to tests of COVID-19 when prevalence is low. The recent Santa Clara study found a 1.5% rate of antibodies to COVID-19. The authors assume a false positive rate of just .005 and a false negative rate of ~.8. Thus, if you test 1000 individuals ~5 will show up as having antibodies when they actually don’t and x*.8 will show up as having antibodies when they actually do and since (5+x*.8)/1000=.015 then x=12.5 so the true rate is 12.5/1000=1.25%, thus the reported rate is pretty close to the true rate. (The authors then inflate their numbers up for population weighting which I am ignoring). On the other hand, suppose that the false positive rate is .015 which is still very low and not implausible then we can easily have ~15/1000=1.5% showing up as having antibodies to COVID when none of them in fact do, i.e. all of the result could be due to test error.
In other words, when the event is rare the potential error in the test can easily dominate the results of the test.
Addendum: For those playing at home, Bedford uses sensitivity and specificity while I am more used to thinking about false positive and false negative rates and I simplify the numbers slightly .8 instead of his .803 and so forth but the point is the same.
It’s Time to Build — Marc Andreessen reemerges
Here is the short essay, opening excerpt:
Every Western institution was unprepared for the coronavirus pandemic, despite many prior warnings. This monumental failure of institutional effectiveness will reverberate for the rest of the decade, but it’s not too early to ask why, and what we need to do about it.
Many of us would like to pin the cause on one political party or another, on one government or another. But the harsh reality is that it all failed — no Western country, or state, or city was prepared — and despite hard work and often extraordinary sacrifice by many people within these institutions. So the problem runs deeper than your favorite political opponent or your home nation.
Part of the problem is clearly foresight, a failure of imagination. But the other part of the problem is what we didn’t *do* in advance, and what we’re failing to do now. And that is a failure of action, and specifically our widespread inability to *build*.
We see this today with the things we urgently need but don’t have. We don’t have enough coronavirus tests, or test materials — including, amazingly, cotton swabs and common reagents. We don’t have enough ventilators, negative pressure rooms, and ICU beds. And we don’t have enough surgical masks, eye shields, and medical gowns — as I write this, New York City has put out a desperate call for rain ponchos to be used as medical gowns. Rain ponchos! In 2020! In America!
Highly recommended.
Estimating the COVID-19 Infection Rate: Anatomy of an Inference Problem
That is a recent paper by Manski and Molinari, top people with econometrics. Here is the abstract:
As a consequence of missing data on tests for infection and imperfect accuracy of tests, reported rates of population infection by the SARS CoV-2 virus are lower than actual rates of infection. Hence, reported rates of severe illness conditional on infection are higher than actual rates. Understanding the time path of the COVID-19 pandemic has been hampered by the absence of bounds on infection rates that are credible and informative. This paper explains the logical problem of bounding these rates and reports illustrative findings, using data from Illinois, New York, and Italy. We combine the data with assumptions on the infection rate in the untested population and on the accuracy of the tests that appear credible in the current context. We find that the infection rate might be substantially higher than reported. We also find that the infection fatality rate in Italy is substantially lower than reported.
Here is a very good tweet storm on their methods, excerpt: “What I love about this paper is its humility in the face of uncertainty.” And: “…rather than trying to get exact answers using strong assumptions about who opts-in for testing, the characteristics of the tests themselves, etc, they start with what we can credibly know about each to build bounds on each of these quantities of interest.”
I genuinely cannot give a coherent account of “what is going on” with Covid-19 data issues and prevalence. But at this point I think it is safe to say that the mainstream story we have been living with for some number of weeks now just isn’t holding up.
For the pointer I thank David Joslin.
Saturday assorted links
1. How to take care of your green onions.
2. New U. Texas mortality projections model. Here is an accompanying paper.
3. Spit tests seem to work pretty well relative to swabs.
5. Precht designs Parc de la Distance for outdoor social distancing.
6. “We are the neurotypicals of the COVID-19 world.”
8. A history of unemployment insurance.
9. “Outpacing the Virus: Digital Response to Containing the Spread of COVID-19 while Mitigating Privacy Risks,” by Glen Weyl, et.al.
10. They are the Chinese restaurant road trippers.
11. Measured effective reproduction numbers for various states (please note this comes with the usual data problems!). Will social distancing get these much below 0.8? I found this link to be quite pessimistic in its implications.
Is there a Hawaii heterogeneity?
Via Michael A. Alcorn, the vertical axis refers to new cases, here is the underlying code. Or if you care about text:
Hawaii Department of Health officials said today that the state’s tally of coronavirus cases has risen to 553, up 12 from Thursday.
Of all the confirmed cases in Hawaii since the start of the outbreak, 48 have required hospitalizations, with three new cases reported today, health officials said.
The state’s coronavirus death toll stands at nine, unchanged from Thursday. Six of the deaths were on Oahu, while three were in Maui.
The population of Hawaii is about 1.4 million. Three days ago, Hawaii was the lowest infection rate in the United States, but of course more and better data are needed. We’ll see, with the passage of time, if this remains a true heterogeneity. But do note this:
It is also noteworthy that Hawaii tests for coronavirus at a considerably higher rate than most states. According to data compiled by Vox, Hawaii continues to rank among the top 10 states for testing per capita, which suggests Hawaii’s infection rate may be more accurate than rates reported by some other states.
Developing…
“Social Distancing is Working so Well!”
How do you feel about that statement? I take this as one psychometric test.
If your reaction is: “My goodness, these are tragic times but it is splendid and noble how we all can come together and sacrifice for a common endeavor!”…well…
…you have failed my test and I will suspect a wee bit of mood affiliation. Most likely it is bad news if the relative safety (for some) of the current moment comes from social distancing. Because at some point social distancing must end, or at least be significantly curtailed, and then a higher danger level may well reemerge.
Possibly you have inside information that a cure will be ready next week, but somehow I doubt it. You are happy because you like something about the process.
Alternatively, if you hear “social distancing is working so well!” and immediately feel a deep sense of foreboding, and begin to calculate whether good short-term results are correlated with better or worse long-term results. And then you calculate how how long the distancing can last for, due to governmental budget constraints, and then try to figure out what kinds of progress we might make in the meantime while the distancing lasts, and then start worrying about how reliant on social distancing we are becoming…
…But then you undertake a second-order calculation about how the greater danger spurred by the forthcoming decline in social distancing also might spur innovation…
And then you think “would it not be better if the current progress came from a more sustainable source, what might that be, how about faster than expected herd immunity amongst a relatively small group of heterogeneous super-spreaders, now what is the chance of that?”…
…and finish your analysis confused…
Then you are my kind of weirdo.
We are living in a time of psychometric tests.
An econometrician on the SEIRD epidemiological model for Covid-19
There is a new paper by Ivan Korolev:
This paper studies the SEIRD epidemic model for COVID-19. First, I show that the model is poorly identified from the observed number of deaths and confirmed cases. There are many sets of parameters that are observationally equivalent in the short run but lead to markedly different long run forecasts. Second, I demonstrate using the data from Iceland that auxiliary information from random tests can be used to calibrate the initial parameters of the model and reduce the range of possible forecasts about the future number of deaths. Finally, I show that the basic reproduction number R0 can be identified from the data, conditional on the clinical parameters. I then estimate it for the US and several other countries, allowing for possible underreporting of the number of cases. The resulting estimates of R0 are heterogeneous across countries: they are 2-3 times higher for Western countries than for Asian countries. I demonstrate that if one fails to take underreporting into account and estimates R0 from the cases data, the resulting estimate of R0 will be biased downward and the model will fail to fit the observed data.
Here is the full paper. And here is Ivan’s brief supplemental note on CFR. (By the way, here is a new and related Anthony Atkeson paper on estimating the fatality rate.)
And here is a further paper on the IMHE model, by statisticians from CTDS, Northwestern University and the University of Texas, excerpt from the opener:
- In excess of 70% of US states had actual death rates falling outside the 95% prediction interval for that state, (see Figure 1)
- The ability of the model to make accurate predictions decreases with increasing amount of data. (figure 2)
Again, I am very happy to present counter evidence to these arguments. I readily admit this is outside my area of expertise, but I have read through the paper and it is not much more than a few pages of recording numbers and comparing them to the actual outcomes (you will note the model predicts New York fairly well, and thus the predictions are of a “train wreck” nature).
Let me just repeat the two central findings again:
- In excess of 70% of US states had actual death rates falling outside the 95% prediction interval for that state, (see Figure 1)
- The ability of the model to make accurate predictions decreases with increasing amount of data. (figure 2)
So now really is the time to be asking tough questions about epidemiology, and yes, epidemiologists. I would very gladly publish and “signal boost” the best positive response possible.
And just to be clear (again), I fully support current lockdown efforts (best choice until we have more data and also a better theory), I don’t want Fauci to be fired, and I don’t think economists are necessarily better forecasters. I do feel I am not getting straight answers.
Discovering Safety Protocols
Walmart, Amazon and other firms are developing safety protocols for the COVID workplace. Walmart, for example, will be doing temperature checks of its employees:
Walmart Blog: As the COVID-19 situation has evolved, we’ve decided to begin taking the temperatures of our associates as they report to work in stores, clubs and facilities, as well as asking them some basic health screening questions. We are in the process of sending infrared thermometers to all locations, which could take up to three weeks.
Any associate with a temperature of 100.0 degrees will be paid for reporting to work and asked to return home and seek medical treatment if necessary. The associate will not be able to return to work until they are fever-free for at least three days.
Many associates have already been taking their own temperatures at home, and we’re asking them to continue that practice as we start doing it on-site. And we’ll continue to ask associates to look out for other symptoms of the virus (coughing, feeling achy, difficulty breathing) and never come to work when they don’t feel well.
Our COVID-19 emergency leave policy allows associates to stay home if they have any COVID-19 related symptoms, concerns, illness or are quarantined – knowing that their jobs will be protected.
Amazon is even investing in their own testing labs.
Amazon Blog: A next step might be regular testing of all employees, including those showing no symptoms. Regular testing on a global scale across all industries would both help keep people safe and help get the economy back up and running. But, for this to work, we as a society would need vastly more testing capacity than is currently available. Unfortunately, today we live in a world of scarcity where COVID-19 testing is heavily rationed.
If every person, including people with no symptoms, could be tested regularly, it would make a huge difference in how we are all fighting this virus. Those who test positive could be quarantined and cared for, and everyone who tests negative could re-enter the economy with confidence.
Until we have an effective vaccine available in billions of doses, high-volume testing capacity would be of great help, but getting that done will take collective action by NGOs, companies, and governments.
For our part, we’ve begun the work of building incremental testing capacity. A team of Amazonians with a variety of skills – from research scientists and program managers to procurement specialists and software engineers – have moved from their normal day jobs onto a dedicated team to work on this initiative. We have begun assembling the equipment we need to build our first lab (photos below) and hope to start testing small numbers of our front line employees soon.
Actions and experiments like these will discover ways to work safely till we reach the vaccine era.
Covid-19 testing in New Jersey
Hey people, what is up with this?
Via John V. And in the meantime, the virus has now affected 70% of New Jersey’s long-term care centers.
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.