Results for “pandemic model”
66 found

Medical ethics? (model this)

Steven Joffe, MD, MPH, a medical ethicist at the University of Pennsylvania, said he doesn’t believe clinicians “should be lowering our standards of evidence because we’re in a pandemic.”

Link here.  That sentence is a good litmus test for whether you think clearly about trade-offs, statistical and speed trade-offs included, procedures vs. final ends of value (e.g., human lives), and how obsessed you are with mood affiliation (can you see through his question-begging invocation of “lowering our standards”?).  It is stunning to me that a top researcher at an Ivy League school literally cannot think properly about his subject area at all, and furthermore has no compunction admitting this publicly.  As Alex wrote just earlier today: “Waiting for more data isn’t “science,” it’s sometimes an excuse for an unscientific status-quo bias.”

To be clear, we should run more and better RCT trials of Ivermectin, the topic at hand for Joffe (and in fact Fast Grants is helping to fund exactly that).  But of course the “let’s go ahead and actually do this” decision should be different in a pandemic, just as the “just how much of a hurry are we in here anyway?” calculus should differ as well.  I do not know enough to judge whether Ivermectin should be in hospital treatment protocols, as it is in many countries, but I do not condemn this simply on the grounds of it representing a “lower standard.”  It might instead reflect a “higher standard” of concern for human lives, and you will note the drug is not considered harmful as it is being administered.

If you apply the standards of Joffe’s earlier work, we should not be proceeding with these RCTs, including presumably vaccine RCTs, until we have assured that all of the participants truly understand the difference between “research” and “treatment” as part of the informed consent protocols.  No “therapeutic misconception” should be allowed.  Really?

If the pandemic has changed my mind about anything, it is the nature of expertise.

Preparing for a Pandemic: Accelerating Vaccine Availability

In Preparing for a Pandemic, (forthcoming AER PP), by myself and a host of worthies including Susan Athey, Eric Budish, Canice Prendergast, Scott Duke Kominers, Michael Kremer and others equally worthy, we explain the model that we have been using to estimate the value of vaccines and to advise governments. The heart of the paper is the appendix but the paper gives a good overview. Based on our model, we advised governments to go big and we had some success but everywhere we went we were faced with sticker shock. We recommended that even poor countries buy vaccines in advance and that high-income countries make large investments in vaccine capacity of $100b or more in total.

It’s now obvious that we should have spent more but the magnitudes are still astounding. The world spent on the order of $20b or so on vaccines and got a return in the trillions! It was hard to get governments to spend billions on vaccines despite massive benefit-to-cost ratios yet global spending on fiscal support was $14 trillion! Even now, there is more to be done to vaccinate the world quickly, but still we hesitate.

I went over the model for Jess Hoel’s class and we also had a spirited discussion of First Doses First and other policies to stretch the vaccine supply.

The end of the Swedish model

The government this week proposed an emergency law that would allow it to lock down large parts of society; the first recommended use of face masks came into force; and the authorities gave schools the option to close for pupils older than 13 — all changes to its strategy to combat the pandemic.

“I don’t think Sweden stands out [from the rest of the world] very much right now,” said Jonas Ludvigsson, professor of clinical epidemiology at Karolinska Institutet in Stockholm. “Most of the things that made Sweden different have changed — either in Sweden or elsewhere.”

…Sweden has reported more than 2,000 Covid-19 deaths in a month and 535 in the past eight days alone. This compares with 465 for the pandemic as a whole in neighbouring Norway, which has half the population. As Sweden’s King Carl XVI Gustaf said just before Christmas: “We have failed.”

Here is more from the FT.  U.S. Covid deaths per day have now exceeded 4,000 for some days, and they are running at about 50% of the normal number for total daily deaths.  And no, it is not that the payments to classify these as Covid deaths have increased, rather the virus and the deaths have increased.  So the “no big deal” question we now can consider settled?  The new and more contagious strains haven’t even started playing a major role yet in the United States.

Minimum wage laws during a pandemic

From Michael Strain at Bloomberg:

In July 2019, the nonpartisan Congressional Budget Office estimated that a $15 minimum wage would eliminate 1.3 million jobs. The CBO also forecast that such an increase would reduce business income, raise consumer prices, and slow the economy.

The U.S. economy will be very weak throughout 2021. The nation will need more business income, not less; more jobs, not fewer; and faster, not slower, economic growth. A $15 minimum wage would move the economy in the wrong direction across all these fronts.

I fully agree, and in fact would go further.  On Twitter I wrote in response to Noah:

Surely in a pandemic these businesspeople are right and the accumulated non-pandemic research literature doesn’t apply so much, right? Pretty much all models imply we should cut the minimum wage, if only temporarily, for small business at the very least.

Put in whatever exotic assumptions you wish, a basic model will spit out a lower optimal minimum wage for 2020-21, again for small business at the very least.  This is the advice that leading Democratic economists should be offering to Biden.

Dark matter, second waves and epidemiological modelling

Here is a new paper from Karl FristonAnthony Costello, and Deenan Pillay:

Background Recent reports based on conventional SEIR models suggest that the next wave of the COVID-19 pandemic in the UK could overwhelm health services, with fatalities that far exceed the first wave. These models suggest non-pharmaceutical interventions would have limited impact without intermittent national lockdowns and consequent economic and health impacts. We used Bayesian model comparison to revisit these conclusions, when allowing for heterogeneity of exposure, susceptibility, and viral transmission. Methods We used dynamic causal modelling to estimate the parameters of epidemiological models and, crucially, the evidence for alternative models of the same data. We compared SEIR models of immune status that were equipped with latent factors generating data; namely, location, symptom, and testing status. We analysed daily cases and deaths from the US, UK, Brazil, Italy, France, Spain, Mexico, Belgium, Germany, and Canada over the period 25-Jan-20 to 15-Jun-20. These data were used to estimate the composition of each country’s population in terms of the proportions of people (i) not exposed to the virus, (ii) not susceptible to infection when exposed, and (iii) not infectious when susceptible to infection. Findings Bayesian model comparison found overwhelming evidence for heterogeneity of exposure, susceptibility, and transmission. Furthermore, both lockdown and the build-up of population immunity contributed to viral transmission in all but one country. Small variations in heterogeneity were sufficient to explain the large differences in mortality rates across countries. The best model of UK data predicts a second surge of fatalities will be much less than the first peak (31 vs. 998 deaths per day. 95% CI: 24-37)–substantially less than conventional model predictions. The size of the second wave depends sensitively upon the loss of immunity and the efficacy of find-test-trace-isolate-support (FTTIS) programmes. Interpretation A dynamic causal model that incorporates heterogeneity of exposure, susceptibility and transmission suggests that the next wave of the SARS-CoV-2 pandemic will be much smaller than conventional models predict, with less economic and health disruption. This heterogeneity means that seroprevalence underestimates effective herd immunity and, crucially, the potential of public health programmes.

This would appear to be one of the very best treatments so far, though I would stress I have not seen anyone with a good understanding of the potential rotation (or not) of super-spreaders, especially as winter comes and also as offices reopen.  In that regard, at the very least, modeling a second wave is difficult.

Via Yaakov Saxon, who once came up with a scheme so clever I personally sent him money for nothing.

Pandemics and persistent heterogeneity

It has become increasingly clear that the COVID-19 epidemic is characterized by overdispersion whereby the majority of the transmission is driven by a minority of infected individuals. Such a strong departure from the homogeneity assumptions of traditional well-mixed compartment model is usually hypothesized to be the result of short-term super-spreader events, such as individual’s extreme rate of virus shedding at the peak of infectivity while attending a large gathering without appropriate mitigation. However, heterogeneity can also arise through long-term, or persistent variations in individual susceptibility or infectivity. Here, we show how to incorporate persistent heterogeneity into a wide class of epidemiological models, and derive a non-linear dependence of the effective reproduction number R_e on the susceptible population fraction S. Persistent heterogeneity has three important consequences compared to the effects of overdispersion: (1) It results in a major modification of the early epidemic dynamics; (2) It significantly suppresses the herd immunity threshold; (3) It significantly reduces the final size of the epidemic. We estimate social and biological contributions to persistent heterogeneity using data on real-life face-to-face contact networks and age variation of the incidence rate during the COVID-19 epidemic, and show that empirical data from the COVID-19 epidemic in New York City (NYC) and Chicago and all 50 US states provide a consistent characterization of the level of persistent heterogeneity. Our estimates suggest that the hardest-hit areas, such as NYC, are close to the persistent heterogeneity herd immunity threshold following the first wave of the epidemic, thereby limiting the spread of infection to other regions during a potential second wave of the epidemic. Our work implies that general considerations of persistent heterogeneity in addition to overdispersion act to limit the scale of pandemics.

Here is the full paper by Alexei Tkachenko, et.al., via the excellent Alan Goldhammer.  These models are looking much better than the ones that were more popular in the earlier months of the pandemic (yes, yes I know epidemiologists have been studying heterogeneity for a long time, etc.).

A multi-risk SIR model with optimally targeted lockdown

Or you could say “all-star economists write Covid-19 paper.”  Daron Acemoglu, Victor Chernozhukov, Iván Werning, and Michael D. Whinston have a new NBER working paper.  Here is part of the abstract:

For baseline parameter values for the COVID-19 pandemic applied to the US, we find that optimal policies differentially
targeting risk/age groups significantly outperform optimal uniform policies and most of the gains can be realized by having stricter lockdown policies on the oldest group. For example, for the same economic cost (24.3% decline in GDP), optimal semi–targeted or fully-targeted policies reduce mortality from 1.83% to 0.71% (thus, saving 2.7 million lives) relative to optimal uniform policies. Intuitively, a strict and long lockdown for the most vulnerable group both reduces infections and enables less strict lockdowns for the lower-risk groups.

Note the paper is much broader-ranging than that, though I won’t cover all of its points.  Note this sentence:

Such network versions of the SIR model may behave very differently from a basic homogeneous-agent version of the framework.

And:

…we find that semi-targeted policies that simply apply a strict lockdown on the oldest group can achieve the majority of the gains from fully-targeted policies.

Here is a related Twitter thread.  I also take the authors’ model to imply that isolating infected individuals will yield high social returns, though that is presented in a more oblique manner.

Again, I would say we are finally making progress.  One question I have is whether the age-specific lockdown in fact collapses into some other policy, once you remove paternalism as an underlying assumption.  The paper focuses on deaths and gdp, not welfare per se.  But what if older people wish to go gallivanting out and about?  Most of the lockdown in this paper is for reasons of “protective custody,” and not because the older people are super-spreaders.  Must we lock them up (down?), so that we do not feel too bad about our own private consumption and its second-order consequences?  What if they ask to be released, in full knowledge of the relevant risks?

The macroeconomics of pandemics

By Eichenbaum, Rebelo, and Trabandt:

We extend the canonical epidemiology model to study the interaction between economic decisions and epidemics. Our model implies that people’s decision to cut back on consumption and work reduces the severity of the epidemic, as measured by total deaths. These decisions exacerbate the size of the recession caused by the epidemic. The competitive equilibrium is not socially optimal because infected people do not fully internalize the e§ect of their economic decisions on the spread of the virus. In our benchmark scenario, the optimal containment policy increases the severity of the recession but saves roughly 0.6 million lives in the U.S.

I would add this: if you hold the timing and uncertainty of deaths constant, death and output tend to move together. That is, curing people and developing remedies and a vaccine will do wonders for gdp, through the usual channels.  The tricky trade-off is between output and the timing of deaths.  Whatever number of people are going to die, it is better to “get that over with” and clear up the uncertainty.  Policy is thus in the tricky position of wishing to both minimize the number of deaths and yet also to speed them along.  Good luck with that!  In terms of an optimum, might it be possible that some of the victims do not…get infected and die quickly enough?  Might that be the more significant market failure?

Via Harold Uhlig.  In any case, kudos to the authors for focusing their energies on this critical problem.

Maybe We Won’t All Die in a Pandemic

The high frequency of modern travel has led to concerns about a devastating pandemic since a lethal pathogen strain could spread worldwide quickly. Many historical pandemics have arisen following pathogen evolution to a more virulent form. However, some pathogen strains invoke immune responses that provide partial cross-immunity against infection with related strains. Here, we consider a mathematical model of successive outbreaks of two strains: a low virulence strain outbreak followed by a high virulence strain outbreak. Under these circumstances, we investigate the impacts of varying travel rates and cross-immunity on the probability that a major epidemic of the high virulence strain occurs, and the size of that outbreak. Frequent travel between subpopulations can lead to widespread immunity to the high virulence strain, driven by exposure to the low virulence strain. As a result, major epidemics of the high virulence strain are less likely, and can potentially be smaller, with more connected subpopulations. Cross-immunity may be a factor contributing to the absence of a global pandemic as severe as the 1918 influenza pandemic in the century since.

From a new paper in bioRxiv, the biological preprint service analagous to arXiv.

Hat tip: Paul Kedrosky.

Canada: An Official Strong Recommendation for First Doses First

Canada’s National Advisory Committee on Immunization (NACI), a scientific advisory group to the government, has made a forceful and dramatic statement strongly favoring First Doses First (delay the second dose.) This is a very big deal for the entire world. Basically NACI have endorsed everything that Tyler and I have said on First Doses First since my first post tentatively raised the issue on December 8. I am going to quote this statement extensively since it’s an excellent summary. No indentation.

—-NACI Statement—-

Based on emerging evidence of the protection provided by the first dose of a two dose series for COVID-19 vaccines currently authorized in Canada, NACI recommends that in the context of limited COVID-19 vaccine supply jurisdictions should maximize the number of individuals benefiting from the first dose of vaccine by extending the second dose of COVID-19 vaccine up to four months after the first. NACI will continue to monitor the evidence on effectiveness of an extended dose interval and will adjust recommendations as needed. (Strong NACI Recommendation)

    • In addition to emerging population-based data, this recommendation is based on expert opinion and the public health principles of equity, ethics, accessibility, feasibility, immunological vaccine principles, and the perspective that, within a global pandemic setting, reducing the risk of severe disease outcomes at the population-level will have the greatest impact. Current evidence suggests high vaccine effectiveness against symptomatic disease and hospitalization for several weeks after the first dose, including among older populations.

Protecting individuals

  • By implementing an extended four month interval strategy, Canada will be able to provide access to first doses of highly efficacious vaccines to more individuals earlier which is expected to increase health equity faster. Canada has secured enough vaccines to ensure that a second dose will be available to every adult.
  • As a general vaccination principle, interruption of a vaccine series resulting in an extended interval between doses does not require restarting the vaccine series. Principles of immunology, vaccine science, and historical examples demonstrate that delays between doses do not result in a reduction in final antibody concentrations nor a reduction in durability of memory response for most multi-dose products.
  • Assessment of available data on efficacy and effectiveness of a single dose of mRNA vaccine was a critical factor in assessing the impact of a delayed second dose at this time. The two available clinical trials for mRNA vaccines (Pfizer-BioNTech and Moderna) provide evidence that indicates that efficacy against symptomatic disease begins as early as 12 to 14 days after the first dose of the mRNA vaccine. Excluding the first 14 days before vaccines are expected to offer protection, both vaccines showed an efficacy of 92% up until the second dose (most second doses were administered at 19-42 days in the trials). Recently, real world vaccine effectiveness data presented to or reviewed by NACI assessing PCR-positive COVID-19 disease and/or infection from Quebec, British Columbia, Israel, the United Kingdom and the United States support good effectiveness (generally 70-80%, depending on the methodology used and outcomes assessed) from a single dose of mRNA vaccines (for up to two months in some studies). While studies have not yet collected four months of data on effectiveness of the first dose, the first two months of population-based effectiveness data are showing sustained and high levels of protection. These data include studies in health care workers, long term care residents, elderly populations and the general public. While this is somewhat lower than the efficacy demonstrated after one dose in clinical trials, it is important to note that vaccine effectiveness in a general population setting is typically lower than efficacy from the controlled setting of a clinical trial, and this is expected to be the case after series completion as well.
  • Published data from the AstraZeneca clinical trial indicated that delaying the second dose to ≥ 12 weeks resulted in a better efficacy against symptomatic disease compared to shorter intervals between doses.
  • The duration of protection from one or two doses of COVID-19 vaccines is currently unknown. Experience with other multi-dose vaccines after a single dose suggests persistent protection could last for six months or longer in adolescents and adults. Longer-term follow-up of clinical trial participants and those receiving vaccination in public programs will assist in determining the duration of protection following both one and two doses of vaccination. NACI will continue to monitor the evidence on effectiveness of an extended interval, which is currently being collected weekly in some Canadian jurisdictions, and will adjust recommendations as needed if concerns emerge about waning protection.

Protecting populations

  • Although effectiveness after two-doses will be somewhat higher than with one dose, many more people will benefit from immunization when extending the interval between doses in times of vaccine shortage; offering more individuals direct benefit and also the possibility of indirect benefit from increasing population immunity to COVID-19 disease. Everyone is expected to obtain the full benefit of two doses when the second dose is offered after 4 months.
  • Internal PHAC modelling reviewed by NACI based on Canadian supply projections suggested that accelerating vaccine coverage by extending dose intervals of mRNA vaccines could have short-term public health benefits in preventing symptomatic disease, hospitalizations, and deaths while vaccine supply is constrained. Even a theoretical scenario analysis in which intervals were extended up to six months and protection was lost at a rate of 4% per week after the first dose also showed that extending the mRNA vaccine dose intervals would still have public health benefits. External modelling results have also suggested that extending dose intervals can avert infections, hospitalizations and deaths.
  • The impact on variants of concern by extending the interval between doses is unknown, but there is currently no evidence that an extended interval between doses will either increase or decrease the emergence of variants of concern. COVID-19 mRNA vaccines and AstraZeneca vaccine have shown promising early results against variant B.1.1.7. As effectiveness of the first dose against other variants of concern is emerging, ongoing monitoring will be required.
  • Vaccine distribution will be optimized through this strategy, and current vaccine supply projections will work well with an extended dose strategy that aims to immunize as many Canadians as efficiently as possible. Extending the dose intervals for mRNA vaccines up to four months has the potential to result in rapid immunization and protection of a large proportion of the Canadian population….

Tuesday assorted links

From the Comments, On FDF

Sure and Tom Meadowcroft have been hitting it out of the ballpark in the comments sections. Two examples.

Sure:

Protocol was made to serve man, not man to serve the protocol.

The reason we have protocols is because we need to weight the harms of waiting without a treatment against the harms that happen if the treatment is counterproductive in some unforeseen manner.

We can, normally, pretty easily measure the benefit side: count up the mortality and morbidity for the illness in question. The risk side is harder so we developed tests and processes to elucidate those: RCTs, literature reviews, regulatory oversight, mandatory waiting periods. At the end of the day though, the whole process is just one giant test to measure the likely harm of a new entity.

So when is a test worth doing? After all I do not order an MRI for every patient even though I could find a lot of early stage cancers that way.

..GSW to the abdomen with crashing bp with minimal response to volume? Straight to the OR. No matter the results of the CT scan they are still getting opened to stop the bleeding.

…So now we look at the vaccine approval process and methods to stretch doses. Pre-test probability that vaccines work? Inordinately high after passing Phase II. Odds that we hit on the precise optimal timing regimen on the first go? Nil.

The likelihood ratios for RCTs and approval mechanisms are powerful. But we are talking thousands of deaths per day. The odds that these tests will remotely alter management decisions is nil. It is malpractice to delay life saving treatment on tests exceedingly unlikely to change management decisions.

And remember the UK is not seeing horrid outcomes for doing this for a while now. A lot of theoretical failure mechanisms are now off the table.

Science is wholly about building a reliable model that accurately predicts future outcomes of current actions. While doing the actual experiment is the gold standard for knowledge acquisition, it is not the only option and in cases like this pandemic is not sufficiently better than past data to merit waiting.

As far as the regulators. I work with some of them directly. They are not overburdened to anywhere near the degree that the frontline clinicians have been hit. When I ask them to explain their cost benefit calculations, they have none. Not I cannot follow them. Not I disagree with them. They have done not an iota of math to justify their course of action.

Sorry, but I believe in evidence based medicine, not eminence based medicine. If you as a regulator cannot explain to me in technical terms the math behind your decision process, even if only back of the envelope, you are not worth putting in charge.

Approve all the vaccines, FDF, fractional dosing trials, and first dose followed by variolation trials should all be done now. It is was [what] the math demands.

Also this from Tom Meadowcroft:

Scientific researchers search for the truth. Medical clinicians use limited data balance cost and benefits in the face of uncertainty to save the most lives.

When searching for the truth, it is important to have high standards of statistical significance, integrity, and patience, because credibility and a reputation for integrity is everything. Every academic knows that a retracted paper or an accusation of playing fast and loose with statistics can be the death knell for a career. As a result it is prudent to be very certain before publishing. Public health officials, particularly those in charge of approving vaccines, dread the possibility that a vaccine that will be given to millions of healthy people, often children, to prevent diseases where death is rare, which could harbor some flaw that causes a hundred avoidable deaths; they seek the highest standards of proof of safety and efficacy before approving such a vaccine.

But a pandemic is not a search for truth, and a COVID vaccine administered in the midst of a pandemic is very different than a measles vaccine administered to 2-year-olds. The pandemic makes these decisions for FDF or for vaccine approvals into clinical decisions, where health professionals should be balancing the certain benefit of reducing the thousands of daily deaths against the uncertain cost of the possibilities of harmful side-effects and uncertain details of efficacy (when does immunity kick in, how long does it last, how valuable is a booster) that additional months of testing and trials would reveal more clearly.

Public health researchers, academics for the most part, lack the ability (and courage) to make the sort of cost/benefit analysis with necessarily limited data that clinical physicians make every day in examination rooms. Any good clinician, faced with the citizenry of a country as their patient, would have opted for FDF, the AZ vaccine, and quite likely reduced doses by the start of the year. Because we are stuck with academics and administrators as our decision makes, unable to see beyond their usual routine of searching for the truth and protecting their reputations, thousands more will die.

Profile of Youyang Gu, data scientist

In mid-April, while he was living with his parents in Santa Clara, Calif., Gu spent a week building his own Covid death predictor and a website to display the morbid information. Before long, his model started producing more accurate results than those cooked up by institutions with hundreds of millions of dollars in funding and decades of experience.

“His model was the only one that seemed sane,” says Jeremy Howard, a renowned data expert and research scientist at the University of San Francisco. “The other models were shown to be nonsense time and again, and yet there was no introspection from the people publishing the forecasts or the journalists reporting on them. Peoples’ lives were depending on these things, and Youyang was the one person actually looking at the data and doing it properly.”

The forecasting model that Gu built was, in some ways, simple. He had first considered examining the relationship among Covid tests, hospitalizations, and other factors but found that such data was being reported inconsistently by states and the federal government. The most reliable figures appeared to be the daily death counts. “Other models used more data sources, but I decided to rely on past deaths to predict future deaths,” Gu says. “Having that as the only input helped filter the signal from the noise.”

The novel, sophisticated twist of Gu’s model came from his use of machine learning algorithms to hone his figures.

Here is the full Bloomberg piece by Ashlee Vance, I am especially pleased because Youyang was an Emergent Ventures winner.  Here is Youyang Gu on Twitter.

The economic geography of global warming

By Jose Cruz Alvarez and Esteban Rossi-Hansberg:

Our baseline results show welfare losses as large as 15% in parts of Africa and Latin America but also high heterogeneity across locations, with northern regions in Siberia, Canada, and Alaska experiencing gains. Our results indicate large uncertainty about average welfare effects and point to migration and, to a lesser extent, innovation as important adaptation mechanisms.

A few points:

1. Average global welfare loss is about six percent.  The discount rate is 0.9%, and yes those are welfare losses.  Losses as a percent of gdp are somewhat lower, because amenity costs are a factor with global warming.

2. About half of the global losses stem from the negative effects on productivity.  For Africa, amenities losses are especially important.  Overall the biggest losers are Central America, India, Brazil, and Africa.  Many colder regions are better off.

3. The model allows for many margins of adjustment, including migration.  Cheaper/freer migration is a good way of limiting the costs from global warming.

4. Subsidies to green energy don’t help very much, because of Jevons-like effects, namely that energy becomes cheaper and total energy use rises.

5. A carbon tax postpones fossil fuel use but in the long run it doesn’t help much, unless the delay in fossil fuel extraction is used to buy effective abatement measures.

Of course the assumptions in such papers always can be challenged, but this is one case where the authors think like economists throughout the entire exercise.  It seems to be one of the best such studies.

My net conclusion (not theirs) is that the paper shows why serious action has been so slow in coming.  The world as a whole might lose about two years worth of economic growth, with most of the losses concentrated in countries that are not calling the shots.  Let’s say a poor country loses fifteen percent of welfare and about ten percent of gdp.  Isn’t that somewhat similar to many of the losses caused by the current pandemic?  Circa early 2021, how many vaccines are we sending to those places?

I do fully agree that we should be more cosmopolitan, but first to fix the malady we must understand it.

The Big Push: A Plan to Accelerate V-Day

In the Washington Post I have an extensive piece on accelerating progress to V-day, Vaccine or Victory day, the day everyone who wants a vaccine has gotten one. I cover themes that will be familiar to MR readers, including First Doses First, Fractional Dosing, Approving More Vaccines and DePrioritization to Expand Delivery. I won’t belabor these points here but the piece is useful at collecting all the arguments in one place and there are lots of authoritative links.

One point I do want to make is that all the pieces of the “Tabarrok plan,” if  you will, fit together. Namely, use First Doses First to make a big push to get as many people vaccinated with first doses as possible in the next 90 days. Approve more vaccines including Johnson & Johnson, AstraZeneca and others and make them available to anyone, anywhere–that is possible because these vaccines don’t require significant cold storage, J&J is a single shot and AZ is better with a second shot at 12 weeks or later all of which eases distribution.

…some people argue that adding a third (or fourth) vaccine might not help because of persistent delivery logjams at the state and local levels. But we know there is unused distributional capacity, even for the supply we do have. The United States is currently administering about 1.5 million coronavirus vaccine shots per day. While that sounds like a lot, for comparison consider that in September — during the pandemic, when social distancing measures were in full effect — we vaccinated for the seasonal flu in some weeks at the rate of 3 million people a day.

There are two main reasons the rollout has been so slow. First, the Moderna and especially the Pfizer vaccines require ultracold storage. (The Johnson & Johnson and AstraZeneca doses can be stored at ordinary refrigerator temperatures.) Second, we have tried to prioritize vaccinations using a confusing mishmash of age, health conditions and essential-worker status that differs by state and sometimes even by county. “Confirming such criteria is complicated at best, and it’s probably not even feasible to try under conditions of duress,” as Baylor’s Hotez puts it.

Arguments continue about prioritization lists, and the idea of tossing them entirely would cause a political fight. But there is a compromise at hand: Quickly approve the Johnson & Johnson and AstraZeneca vaccines and make them — and only them — available to anyone, anywhere. Keeping things simple is a sure way to increase total vaccinations. With no cold-storage requirement, the new vaccines could be administered by any of the 300,000 pharmacists and more than 1 million physicians in the United States authorized to deliver vaccines, most of whom are not now giving Pfizer or Moderna shots.