Results for “pandemic model”

107 found

1. Vomit fraud in Uber.

2. Wikipedia on monoclonal antibodies.  And in the Covid-19 context.

3. Semi-herd immunity has come to Manaus without a lockdown (to be clear, I am not recommending this approach!  But you should use this data to recalibrate your mental models).

4. Finis Welch has passed away.

5. “We find no consistent relationship between gender [of leadership] and pandemic outcomes.”

Tyler and I have been pushing pooled testing for months. The primary benefit of pooled testing is obvious. If 1% are infected and we test 100 people individually we need 100 tests. If we split the group into five pools of twenty then if we’re lucky, we only need five tests. Of course, chances are that there will be some positives in at least one group and taking this into account we will require 23.2 tests on average (5 + (1 – (1 – .01)^20)*20*5). Thus, pooled testing reduces the number of needed tests by a factor of 4. Or to put it the other way, under these assumptions, pooled testing increases our effective test capacity by a factor of 4. That’s a big gain and well understood.

An important new paper from Augenblick, Kolstad, Obermeyer and Wang shows that the benefits of pooled testing go well beyond this primary benefit. Pooled testing works best when the prevalence rate is low. If 10% are infected, for example, then it’s quite likely that all five pools will have at least one positive test and thus you will still need nearly 100 tests (92.8 expected). But the reverse is also true. The lower the prevalence rate the fewer tests are needed. But this means that pooled testing is highly complementary to frequent testing. If you test frequently then the prevalence rate must be low because the people who tested negative yesterday are very likely to test negative today. Thus from the logic given above, the expected number of tests falls as you tests more frequently (per test-cohort).

Suppose instead that people are tested ten times as frequently. Testing individually at this frequency requires ten times the number of tests, for 1000 total tests. It is therefore natural think that group testing also requires ten times the number of tests, for more than 200 total tests. However, this estimation ignores the fact that testing ten times as frequently reduces the probability of infection at the point of each test (conditional on not being positive at previous test) from 1% to only around .1%. This drop in prevalence reduces the number of expected tests – given groups of 20 – to 6.9 at each of the ten testing points, such that the total number is only 69. That is, testing people 10 times as frequently only requires slightly more than three times the number of tests. Or, put in a different way, there is a “quantity discount” of around 65% by increasing frequency.

Peter Frazier, Yujia Zhang and Massey Cashore also point out that you could also do an array-protocol in which each person is tested twice but in two different groups–this doubles the number of initial tests but limits the number of false-positives (both tests must be positive) and the number of needed retests. (See figure.).

Moreover, we haven’t yet taken into account the point of testing which is to reduce the prevalence rate. If we test frequently we can reduce the prevalence rate by quickly isolating the infected population and by reducing the prevalence rate we reduce the number of needed tests. Indeed, under some parameters it’s possible to increase the frequency of testing and at the same time reduce the total number of tests!

We can do better yet if we group individuals whose risks are likely to be correlated. Consider an office building with five floors and 100 employees, 20 per floor. If the prevalence rate is 1% and we test people at random then we will need 23.2 tests on average, as before. But suppose that the virus is more likely to transmit to people who work on the same floor and now suppose that we pool each floor. Holding the total prevalence rate constant, we are now likely to have a zero prevalence rate on four floors and a 5% prevalence rate on one floor. We don’t know which floor but it doesn’t matter–the expected number of tests required now falls to 17.8.

The authors suggest using machine learning techniques to uncover correlations which is a good idea but much can be done simply by pooling families, co-workers, and so forth.

The government has failed miserably at controlling the pandemic. Tens of thousands of people have died who would have lived under a more competent government. The FDA only recently said they might allow pooled testing, if people ask nicely. Unbelievably, after telling us we don’t need masks (supposedly a noble lie to help limit shortages), the CDC is still disparaging testing of asymptomatic people (another noble lie?) which is absolutely disastrous. Paul Romer is correct, testing capacity won’t increase until we put soft drink money behind advance market commitments and start using techniques such as pooled testing. Fortunately or sadly, depending on how you look at it, it’s not too late to do better. Some universities are now proposing rapid, frequent testing using pooling. Harvard will test every three days. Cornell will test frequently. Delaware State will test weekly. Lets hope the idea spreads from the ivory tower.

Operation Warp Speed is following the right plan by paying for vaccine capacity to be built even before clinical trials are completed. OWS, however, should be bigger and should have more diverse vaccine candidates. OWS has spent well under $5 billion. At current rates, the US economy is losing about $40 billion a week. Thus, if $20 billion could advance a vaccine by just one week that would be a good deal. As I said in the LA Times, “It might seem expensive to invest in capacity for a vaccine that is never approved, but it’s even more expensive to delay a vaccine that could end the pandemic.”

I am also concerned that OWS is narrowing down the list of candidates too early:

NYTimes: Moderna, Johnson & Johnson and the Oxford-AstraZeneca group have already received a total of $2.2 billion in federal funding to support their vaccine programs. Their selection as finalists, along with Merck and Pfizer, will give all five companies access to additional government money, help in running clinical trials and financial and logistical support for a manufacturing base that is being built even before it is clear which if any of the vaccines in development will work.

These are all good programs and one of them will probably be successful but we also want to support some long-shots because a small probability of a very big gain is still a big gain.

The five candidates also all use new technologies and are less diverse than I would prefer. There are a lot of different vaccine platforms, Live-Attenuated, Deactivated, Protein Subunit, Viral Vector, DNA and mRNA among others. The Accelerating Health Technologies team that I am a part of collected data on over 100 vaccine candidates and their characteristics. We then created a model to compute an optimal portfolio. We estimated that it’s necessary to have 15-20 candidates in the portfolio to get to a 80-90% chance of at least one success and that you want diverse candidates because the second candidate from the same platform probably fails if the first candidate from that platform fails. Moderna and Pfrizer are both mRNA vaccines–a platform that has never been used before–while AstraZeneca, Johnson and Johnson and Merck are using somewhat different viral vector platforms (Adenovirus for AstraZeneca and J & J and measles for Merck) which is also a relatively novel approach. I think it would be better if there were some tried and true platforms such as a Deactivated or Protein Subunit vaccine in the mix. As Larry Summers said, “if you will die of starvation if you don’t get a pizza in two hours, order 5 pizzas”. I would change that to order 10 pizzas and order from different companies!

One way to diversify the portfolio is to make deals with other countries to avoid the prisoner’s dilemma of vaccine portfolios. The prisoner’s dilemma is that each country has an incentive to invest in the vaccine most likely to succeed but if every country does this the world has put all its eggs in one basket. To avoid that, you need some global coordination. One country invests in Vaccine A, the other invests in Vaccine B and they agree to share capacity regardless of which vaccine works.

So my critique is that OWS is good policy but it would be even better if more vaccine candidates and more diverse vaccine candidates were part of the program. In contrast, the critiques being offered in Congress are ridiculous and dangerous.

Democrats in Congress are already seeking details about the contracts with the companies, many of which are still wrapped in secrecy. They are asking how much Americans will have to pay to be vaccinated and whether the firms, or American taxpayers, will retain the profits and intellectual property.

How much will Americans have to pay to be vaccinated??? A lot less than they are paying for not being vaccinated! The worry about profits is entirely backwards. The problem is that the profits of vaccine manufacturers are far too small to give them the correct social incentives not that the profits are too large. The stupidity of this is aggravating.

Skepticism about Trump administration policies is understandable but I am concerned that one of the best things the Trump administration is doing to combat the virus will be impeded and undermined by politics.

1. Sam Altman on idea generation.

2. Nuclear markets in everything: bid on plant reactor control and monitoring system.

3. Often immigrant restaurants are better prepared for the pandemic.

4. Why do humans help others, and how do financial markets affect the sociality of behavior?  Quite interesting, not just the usual b.s.  VTEKL.

5. Why men are pointing loaded guns at their dicks.

6. Why our regulatory state is still failing us.

7. Language Models are Few-Shot Learners.

8. What does it mean to decertify Hong Kong autonomy?