A Leap of Faith, Part 2

My wife, infant son, and I moved to Amherst the first weekend of April 1982. A beautiful snow fell on Sunday. Then, early on Monday morning, my new boss Bruce Levin cross-country skied by the old house we were renting, knocked on the door, and asked me when I’d be coming to the lab!

I had much to learn, of course. I remember learning how to use a pipettor from a technician in Bruce’s lab, and how exciting it was to estimate the number of cells in a flask (typically many millions or even billions). That estimation is done not by counting the cells directly, but instead involves precisely diluting small amounts through a series of test tubes, each tube containing a large, known volume of a sterile solution. At the end of the dilution series, one takes a tiny amount from the final tube and spreads it across an agar plate. The plate is then incubated for a day or so, during which time each of the few hundred cells that survived the dilutions grows into a separate colony. A colony is a clump of millions of cells that can be seen with the naked eye, unlike the individual cells that can be seen only by using a microscope. One counts the colonies on the plate and, using that number and the dilutions that one made, one can then back-calculate the density of cells in the original flask.

In my first effort at this most basic procedure, I did three replicates from the same flask. I was thrilled when I counted the colonies on the first two plates, and the numbers differed by only a few percent. The third plate, however, differed by perhaps a factor of two, which meant I had done something wrong—maybe I’d let an air bubble into the pipettor’s tip, displacing some of the liquid—and I realized the importance of attention to details.

A little later, while I was still learning the ropes, Bruce had me perform a more complicated experiment to measure the rate at which a certain virus, called T6, adsorbs to and infects E. coli cells. The experiment required a lot of repetitive dilutions and plating of samples that I had to process quickly and accurately. The basic idea is that free viruses should decline in number over time as more and more of them enter cells. (This decline continues only until the first viruses to infect cells have had enough time to produce the next generation of viruses, hence the need to process the samples quickly.) Alas, my experiment was a total failure. What was I doing wrong? I think Bruce had me repeat the experiment, with the same lousy outcome. Though he never said it, perhaps he would regret hiring me. After all, given my lack of experience, Bruce had also taken a leap of faith.

After my second failure, Bruce checked his notes about the particular strain that we were using. As it turned out, he had given me a strain of E. coli that was resistant to T6! Hence, there were no infections, and that explained my failed experiments. Later on, I was able to use the same protocol to measure the rate at which a different virus, T2, adsorbed to and infected E. coli.

Oh, and what about my experiment to look for evolutionary changes that compensated for the cost of bacterial resistance to infection by viruses? That’s what I had proposed in my letter to Bruce asking about a postdoc. I never got to that experiment while I was in Bruce’s lab. However, it provided the seed for a project that I eventually conducted as an early-career faculty member at the University of California, Irvine.

[Bacterial colonies growing on agar plates. Photo credit: Brian Baer, MSU.]

A Leap of Faith, Part 1

I did my graduate work at the University of North Carolina, Chapel Hill, in what was then called the Department of Zoology. I had several important and formative experiences during those years: clear advice from my advisor, Nelson Hairston (1917-2008), about the value of well-designed experiments in ecology; an eye-opening course on the integration of ecological and evolutionary perspectives, taught by Janis Antonovics (then at Duke University, just a few miles from UNC); an abysmal failure in my own attempt at an experiment with praying mantises; an enlightening collaboration with a fellow grad student, Phil Service; and a dissertation project on the effects of forest clearcutting and competition on beetles in the mountains of southwestern North Carolina.

Although that dissertation project was reasonably successful, I realized it was not a good fit to my skills and interests. Many of my fellow students were excellent naturalists with a love for the organisms they studied. While I enjoy being outdoors, I’m not a naturalist. Instead, I’m intrigued by the conceptual questions that biologists ask about the living world. And as my graduate work moved forward, I realized that questions about evolution, including especially the mechanisms and dynamics of evolution, interested me most. However, the beetles I was studying were not well-suited to those questions. So how could I pursue my interests?

While we were finishing our doctoral projects, Phil and I spent a lot of time discussing potential systems for studying evolution. As he moved on in his career, Phil chose to study evolution using fruit flies, a long-standing model system for studying genetics. I recalled an undergrad course I had taken, where we learned about elegant experiments done with microbes, including one by Salvador Luria and Max Delbrück that showed mutations happen at random, not in response to selection.

Meanwhile, the graduate students and faculty at UNC had a seminar in which we discussed recent papers in the field of ecology. One week we read a terrific paper by Lin Chao, Bruce Levin, and Frank Stewart titled “A complex community in a simple habitat: an experimental study with bacteria and phage.” I forget who chose that paper for our seminar, but I owe that person a debt of gratitude. 

Phages are viruses that infect bacteria, and the paper provided an elegant demonstration of the interplay of ecological and evolutionary processes on a time scale of a few weeks. It documented the coevolution of E. coli and a virus, called T7, that can infect and kill the bacteria. The authors showed that the bacteria evolved resistance, then the virus evolved the ability to infect the resistant cells, and finally the bacteria evolved resistance to the viruses with the extended host range. Moreover, they showed that virus-sensitive and virus-resistant host genotypes coexisted because the sensitive types were better competitors for the limiting resources in the environment. That paper and others by Bruce Levin cemented my interest in using microbes to study evolution in action.

In March of 1981, about a year before I defended my dissertation, I wrote Bruce to ask if he would consider me for a postdoctoral position in his lab. I admitted I had no experience working with microbes, but I proposed an experiment. His team’s work showed that bacteria that evolved resistance to phage were outcompeted by their sensitive progenitors when those viruses were not present. I wondered whether the tradeoff was an unavoidable metabolic cost, or whether bacteria could evolve compensatory changes that reduced the cost of resistance. My proposed experiment suggested a way to look for such compensatory changes.

Bruce invited me to visit his lab and give a talk at the University of Massachusetts, Amherst, that spring. I remember him greeting me when I got off a bus at the town square and being surprised by just how young he looked. Although he was 40 years old and a full professor, Bruce could easily have passed for an undergrad. More importantly, I recall our intense discussions over the next two days with Bruce at a chalkboard, writing equations that described the growth of various interacting microbes, and using terms that I barely understood.

Despite my limited experience and knowledge of microbiology, Bruce offered me a postdoctoral position in his lab. I was thrilled, but also worried about doing research in a new field where I lacked experience and knowledge. Nonetheless, I took that leap of faith. And I’m so glad I did.

[Nelson Hairston after his retirement from UNC (left) and Bruce Levin in the mid-1980s (right).]

New Beginnings

Greetings on this winter solstice!  The winter solstice marks a sort of new beginning, as the days become longer for the next half year, before then becoming shorter until the cycle is repeated. 

Every day, the E. coli populations in the long-term evolution experiment (LTEE) experience a cycle of renewed resources and growth followed by depletion of their food and then waiting for the next transfer event. 

On a much longer timescale, the LTEE also experiences cycles as it is passed from one scientific generation to the next. With that in mind, we’ve made a new website that reflects the beginning of the second scientific generation of the LTEE, as the populations and responsibility for their sustenance will soon pass from my lab to that of the new director, Jeff Barrick.

On this website, you can get an introduction and quick overview of the LTEE including how it works, its goals, some of the key findings, and plans for its future.  You can see a timeline of the experiment with some of the milestones and key events in its history.  You can read, watch, and listen to a few of the news stories about the LTEE.  You can find resources including protocols and links to important datasets.  You can search and find links to the publications that report findings from the LTEE itself as well as descendant experiments that have used the LTEE lines. And last, but not least, you can see the talented people who’ve done and are doing the work behind the LTEE, including propagating the populations, performing analyses, analyzing data, and reporting the findings.

We’ve probably missed some papers, and we know that we’re still missing photos for some participants. We’ve also only scratched the surface of reporting past news.  So please let one of us know if you find someone or something LTEE-related that you’d like to see included on this website.  For now, enjoy the new beginnings as seasons and generations continue onward!

Vinyl

Who remembers the old LP record albums?  They were made of vinyl, and music was recorded by etching tiny variations along a spiral groove. You put an LP onto a turntable, and you set the stylus with a fine needle into the groove. As the turntable rotated, the needle vibrated according to those tiny variations along the groove. And by amplifying that analog signal, music emanated from your speakers.    

The LP replaced an earlier format that used shellac instead of vinyl. The older format rotated on the turntable at 78 rpm, and a 12-inch diameter record allowed for only about 5 minutes of music per side. The vinyl LP allowed finer etching along a narrower groove, and these albums turned at 33 and 1/3 rpm. This technology allowed over 20 minutes of music to be recorded on each side of the disc. Hence the acronym LP, which stands for “long play.”

Why am I telling you this? I started the LTEE on February 24, 1988. A year on our planet is about 365.25 days, and so a century is 36,525 days. There have been 12,175 days from February 24, 1988, until today. That’s exactly one third of a century.

The LTEE has now revolved around our sun 33 and 1/3 times!  I think that qualifies as an LP.

An old LP album cover …
even older than the LTEE.

Writing in the lab notebook on the occasion of the LTEE circling the sun 33 and 1/3 times.

The Next Time

As we continue to fight the current covid pandemic caused by the SARS-CoV-2 virus, it’s not too early to begin thinking about the next pandemic.  I’ve been mulling this over for a while, and I was prompted to write this post by a twitter thread from Michael Baym.

Michael wrote about some work that he and Kaylee Mueller started, early in the pandemic, to develop a rapid colorimetric assay for covid.  They decided to curtail their work, however, when the personal risk of continuing to work in the lab seemed too great. But Michael is now wisely looking ahead, thinking about what science can do to respond even more quickly to the next pandemic.

Last February, as most of the world was just waking up to the threat posed by covid, I wrote a post with words of wisdom for pandemic preparedness. The words were written by a former Secretary of the US Department of Health and Human Services, Michael O. Leavitt, in 2007, who at that time was especially concerned about the potential for an influenza pandemic. He said: “Everything we do before a pandemic will seem alarmist. Everything we do after a pandemic will seem inadequate. This is the dilemma we face, but it should not stop us from doing what we can to prepare.”

So congratulations, and thanks, to Michael and all the others who are looking ahead. But really, all of us need to look and think ahead, using our hearts as well as our minds. 

Almost exactly a year ago, I was very worried about how hard this country would be hit by the pandemic.  I wrote:  “I think it is entirely possible, maybe even likely, that Europe will get hit harder by the coronavirus than China has been hit, and the US may get hit even harder than Europe.” 

I suggested that a number of epidemiological and sociopolitical issues would contribute to the United States being especially hard hit by the pandemic.  Among the former, “China’s outbreak started from a single point source in Wuhan … The US, meanwhile, has gotten many independent seeds both from China and from Europe … hundreds or even thousands of smoldering embers at first, most growing unseen and uncontained …”  Among the latter, “here in the US, we have deep social divisions, widespread skepticism of expertise (often fed by those divisions), an extremely complex political landscape with federal, state, & municipal layers of government … and many independent-minded people who are inclined to disregard advice and instructions—a wonderful attitude some of the time, but an exceptionally dangerous attitude during a pandemic.”

My worries about the next pandemic have been leaning to the problems of social division and disregard for evidence.  As terrible as this pandemic has been, the next one could be worse … even much worse.  How will people react if the next pandemic is 10 times more deadly than covid?  What if the next outbreak causes disproportionate mortality in kids or young adults?  Would the (mostly) right-wing denialists still refuse masks? Would anti-vaxxers (on the left & right) still oppose vaccination?

So, Michael Baym is right to be thinking ahead, as was Michael O. Leavitt. As a nation, we need to commit resources to support science (including the basic sciences that lead to breakthroughs in medicine) as well as our often neglected public-health system.  But we also need to find ways to come together as people, to overcome the sometimes willful ignorance, and to discuss things in a meaningful, non-conspiratorial way. 

Science and public-health workers can only do so much. The rest is up to all of us to protect ourselves, our families, and our communities from covid … and from the next pandemic … and from ourselves.

How NOT to Write a Response to Reviewers

Last year I outlined my strategy for writing a response to reviewers.  It was intended primarily for early-career scientists, and the strategy I outlined was most relevant for a paper that had generally positive reviews.

One piece of my advice was to try to view every comment as constructive, even if you disagree with it. Reviewers are often mistaken on some points; indeed, one of the major benefits of the review process is that it calls attention to where we, as authors, have not explained ourselves clearly to the reader.

In my experience as an author and editor, it is pretty rare for a reviewer to say things that are truly hostile or otherwise inappropriate. However, it does occasionally happen that reviewers are unfair. 

I’ve blogged previously about one particularly aggressive and unconstructive review that my coauthors and I received. It was a harsh critique of the very first paper on the long-term evolution experiment with E. coli.  Fortunately, the other reviewer was very positive, and the editor requested a revision.

For some time I’ve thought about posting my response to that negative review. However, I thought the response was perhaps somewhat ill-tempered and overly long. Now, more than 30 years later, if I were advising a young scientist facing a similar review, I’d probably say: “Forget revising it for that journal. Just move on and try again elsewhere.”  But I didn’t do that myself, and I guess it worked out alright in the end.

Without further ado, here’s the response to that reviewer. (You can click on the image for each of the 4 pages to enlarge it.)

---

An Engineering Perspective on Accelerating Vaccine Distribution

by Daniel Lenski

Key Points:

• We can continuously prioritize vaccination of the most at-risk populations, and at the same time immediately offer remaining vaccine doses to lower-priority recipients.
• We can plan for optimal inter-dose timing for 2-dose vaccines without holding back half the supply of those vaccines.
• We can build a system to maintain consistent levels of availability and prioritization in all regions of the country for the months and years it will take to produce enough vaccines for near-universal immunization.
• The field of industrial engineering offers well-studied and proven techniques to accomplish this.

---

The rapid design, development, and validation of multiple safe and highly effective vaccines for COVID-19, by scientists in the US and around the world, has been a stunning achievement. Our challenge has now pivoted to the task of inoculation against this pandemic disease.  How quickly can we produce and deliver sufficient vaccines to maximize protection of life and health, offering the hope of returning to more normal lives and economies? 

Vaccine manufacturers AztraZeneca, Pfizer, and Moderna expect to produce enough vaccine to inoculate only one-third of the world’s population in 2021,^[1] and the US expects to receive enough to inoculate only 50 million Americans by the end of March.^[2]  Production will certainly limit the rate at which we can achieve a high level of immunity in our populations, but the slow roll-out of vaccination in the USA underscores how much we must also optimize delivery.

As of January 11, 25.5 million doses have been provided by the US government, but only 9 million doses (35%) have been injected into the arms of willing and available recipients.^[3] Vaccine stockpiles continue to grow, indicating clearly that delivery, not manufacture, is currently the bottleneck. Delays have been ascribed to insufficient guidance and funding for states and cities, limited and confusing schedules for vaccination appointments, and confusion about current prioritization schemes.  In an example of the floundering, one week ago New York governor Andrew Cuomo instituted harsh penalties both for providers who vaccinated ineligible recipients and for providers who allowed doses to expire or otherwise go to waste. These requirements of maximally efficient utilization and strict prioritization are both logical and important, but they are inevitably in tension with each other. In the absence of clear and simple guidance for how to resolve this conflict, it is unsurprising that these dual mandates have led to a very low pace of vaccination in New York.

How should a program of mass vaccination operate amid a deadly pandemic? What should its goal be, day in and day out, from individual vaccine providers to cities and states to the country as a whole? The goal, as I see it, is to get vaccine doses into every willing recipient^[4] while consistently ensuring that the most vulnerable, at-risk groups have prioritized access, and at the same time dispensing vaccines as fast as they can be produced.  A vial of vaccine sitting in a freezer for days or weeks, awaiting the arrival of a high-priority recipient, does no good for anyone. In contrast, vaccinating any human right away will eliminate a vector for the spread of the disease, and move all of us one step closer to ending the pandemic.

The field of industrial engineering can provide us with crucial techniques and tools to sustain a balance between rapid delivery and prioritization for the months and years ahead.  From 2015-2020, I worked with semiconductor factories in the US and around the world providing advice and software.  Our services addressed problems such as: maximizing throughput by identifying and removing bottlenecks in multi-step processes; making efficient use of scarce resources, including time, labor, and raw materials; prioritizing completion of urgently-needed output; and adhering to constraints in the relative timing of critical steps to ensure quality and reliability.

The problems of vaccine delivery are strikingly similar: for maximum efficacy, the Pfizer vaccine’s two doses should be delivered 21-28 days apart^[5]; it’s critical to vaccinate at-risk groups early on^[6]; all currently approved vaccines require storage and transportation in expensive deep freezers, must be thawed in multi-dose batches, and expire wastefully if not dispensed quickly.

The tradeoff between prioritization and maximally efficient use of time and materials, illustrated by Governor Cuomo’s orders, is a glaringly obvious one to industrial engineers. If a strict sequential order is followed, the next recipient may not be available in time to use the next vaccine dose (leading to expiration and waste), while giving the vaccine on a purely first-come-first-served basis will maximize utilization but hinder rapid access for the highest-priority recipients. It is clear, however, that some members of high-priority vulnerable groups are either unable or hesitant to receive COVID-19 vaccination right now, while some members of low-priority populations are willing and eager to receive it immediately, but cannot due to lack of both eligibility and information about availability. Industrial engineering offers simple mechanisms to achieve an efficient and dynamic balance between these competing demands, such as by creating multiple priority queues at each vaccination provider and switching from higher- to lower-priority recipients immediately when the former are not present.

Experience from manufacturing can also clarify the problem of delivering second doses with optimal timing. Available quantities of Pfizer and Moderna vaccines in the USA were effectively halved by the initial plan to reserve a second dose of vaccine for each patient as soon as their first dose is administered. Some experts have recently suggested distributing all available 2-dose vaccines as first doses,^[7] reasoning that rapidly dispensing single doses will save more lives than a predictable but slow pace of second doses, while virologists warn that the reduced efficacy of single doses could have grave consequences in the longer term, by allowing vaccine-resistant variants of the SARS-CoV-2 virus to evolve and spread. In fact, neither reserving second doses nor abandoning their correct timing is necessary. Because future delivery of vaccine supplies to the USA is relatively predictable (at least in terms of the lower bound), an optimal steady-state solution is for providers to limit the rate at which they dispense first doses to half the rate at which they expect to receive future doses, which will leave them with sufficient supplies to consistently vaccinate patients returning for their second doses during the optimal time window.^[8]

Beyond the failure to balance between rapidly dispensing available vaccines and prioritizing them, along with a sub-optimal approach to reserving second doses, vaccine distribution in the USA appears gummed up by a pernicious combination of insufficient information about when and where COVID-19 vaccines are available, and complex paperwork and administrative requirements.^[9]

If the incoming Biden administration were to ask me to design a plan for rapid distribution of COVID-19 vaccine, my proposal would include the following elements:

• A national database to track vaccine inventory and rates of dispensation at the level of each provider, in near real-time. This will be crucial for determining the appropriate rates at which to resupply providers with more vaccine doses, so as to sustain and maintain inventory of vaccines across the country without developing geographical and temporal imbalances in inventory.
• First-come-first-served vaccine dispensation at the level of individual providers, with the crucial addition of multiple queues for patient intake, so that the most vulnerable can always receive the vaccine before others, no matter when they decide to get it.
• Training for all vaccination providers to implement the queuing system uniformly and consistently, along with minimal and consistent administrative requirements.
• A website to track wait times for each queue, at each provider, in near real-time. The availability of wait times at nearby locations will likely be crucial to motivate a continuous high rate of vaccine delivery, by allowing many Americans to seek out the vaccine on short notice when wait times are short for their eligibility cohorts.

Ending the COVID-19 pandemic through mass vaccination will present an extraordinary range of challenges for physicians, public health officials, scientists, politicians, and society at large. The tools of industrial engineering certainly cannot help with many of these challenges; however, they can help us achieve and sustain one crucial goal at all scales: getting vaccine doses into every available, willing human being as fast as they can be produced, while continuously ensuring that the most vulnerable people have the most rapid and streamlined access to the vaccine. I know that President-Elect Joe Biden’s COVID-19 task force will include epidemiologists, physicians, and virologists.^[10] I would encourage him also to appoint experts in industrial engineering and operations research, who can provide strategic guidance and tactical advice to speed up and smooth out nationwide vaccine distribution.

---

Appendix: A Specific Proposal

If the incoming Biden administration were to ask me to design a national vaccination program with the above goal of dispensing vaccines as rapidly as they are manufactured, while also continuously guaranteeing preferential access to prioritized populations, here’s what I’d propose. To simplify, I’ll assume that our present vaccine distribution bottlenecks are indeed overwhelmingly a “last mile” problem,^[11] and that there are no major logistical impediments to reliably delivering vaccine supplies to providers anywhere in the country within timescales of 1-2 weeks.

First, establish a national database of vaccine-dispensing providers, and a mechanism to log daily inventory for each provider. Apportion newly-manufactured vaccine among the states and territories, and from there down to the level of individual providers. The first round of apportionment will take some guesswork; in the interests of speed and simplicity, my strong inclination would be to apportion the first round simply by population. Subsequent rounds should be adjusted up and down based on past demand and current inventory, in order to prevent geographical and temporal imbalances in inventory.

Second, each provider should dispense vaccines on a first-come-first-served basis, but with multiple priority queues with extremely simple selection criteria. Age is the simplest and most easily documented criterion, and so I have used only that below. Other criteria, such as health-risk factors, occupation, and race or ethnicity have been proposed. However, more complex prioritization runs the risk of slowing down the process for everyone^[12], by turning “eligibility determination” into the rate-limiting step. Something like the following:

• Monday-Wednesday: 6 queues. One for recipients over 80 years age, one for 70+, one for 60+, one for 50+, one for 40+, and one for everyone else.
• Thursday: 5 queues. 80+, 70+, 60+, 50+, everyone else.
• Friday: 4 queues. 80+, 70+, 60+, everyone else.
• Saturday: 3 queues. 80+, 70+, everyone else
• Sunday: 2 queues. 80+, everyone else.

These queues should literally be lines that people who want the vaccine wait in, clearly marked according to the age criteria. During operating hours, patients should be free to join the appropriate queue at any time. Providers should accept and vaccinate all available patients from higher-priority queues before accepting any from lower-priority queues, but should immediately switch over to lower-priority queues if a higher-priority queue is empty. Example: it’s Tuesday, and there are 10 people in the 80+ queue, 30 in the 70+ queue, and 100 in the everyone-else queue. Providers should vaccinate all of the 80+ patients, then immediately start vaccinating all of the 70+ patients, then immediately start vaccinating “everyone else.” If two more 80+ patients arrive after that initial queue has emptied, they would be accepted and vaccinated immediately. Available vaccine doses should be logged daily to the national database. Acceptance of patients from each queue should be logged in real-time so that it’s possible to publish intake rates in real-time for each and every provider.^[13]

This scheme is intended to achieve the following results:

1. No matter when a higher-priority person decides to get vaccinated, they’ll be able to get it with less waiting than all lower-priority individuals.
2. Lower-priority individuals will not have to wait to receive the vaccine unless higher-priority recipients are waiting for it right now.
3. Wait times will be relatively measurable and predictable, encouraging people to drop in and get vaccinated when lines are short, and stay home when lines are long for their priority groups.
4. Vaccine will be dispensed continuously during the operating hours of each provider, ensuring minimal wasted or expiring doses. (Round-the-clock operation should be able to eliminate this entirely.)
5. This weekly cycle is intended to prevent overcrowding of lower-priority patients if there’s sustained high demand from higher-priority groups. For example, given the above prioritization scheme, few 35 year-olds will want to line up on Tuesdays. However, those who do will probably have very good reasons to endure a long wait for the possibility of vaccination, e.g. an immune-compromised family member. By later in the weekly cycle, the wait times and intake rates for the younger age groups should be more predictable based on previous days.

---

^[1]    https://www.nature.com/articles/d41586-020-03370-6

^[2]    https://www.nytimes.com/live/2020/12/15/world/covid-19-coronavirus

^[3]    https://www.nytimes.com/interactive/2020/us/covid-19-vaccine-doses.html

^[4]    Appropriately spaced when 2 doses are required, and excepting those with contraindications.

^[5]    https://www.biopharma-reporter.com/Article/2021/01/07/WHO-weighs-in-on-COVID-19-vaccine-second-dose-delay

^[6]    Modeling from Israel indicates that vaccinating the most vulnerable 7.5% of the population would reduce overall death rates by 75%. https://twitter.com/dwallacewells/status/1340397154683269123

^[7]    https://www.washingtonpost.com/opinions/2021/01/03/its-time-consider-delaying-second-dose-coronavirus-vaccine/

^[8]    This problem gets more complex when the rate of future availability is unpredictable, or when there’s a large build-up of current inventory.

^[9]    https://www.newsweek.com/senior-citizens-wanting-covid-vaccine-face-51-step-online-registration-process-1560622

^[10] https://www.forbes.com/sites/judystone/2020/11/09/president-elect-biden-names-new-covid-19-task-force–whats-the-enthusiasm-about/?sh=723ade8a458f

^[11]   https://www.reuters.com/article/health-coronavirus-vaccine-challenges-tr/analysis-covid-19-vaccines-raise-hope-but-the-last-mile-challenge-looms-idUSKBN28P124

^[12] https://www.wsj.com/articles/vaccination-by-age-is-the-way-to-go-11610476439?mod=hp_opin_pos_3

^[13]  Let’s say it’s Monday at 10 am. I should be able to pull up a page for the pharmacy at the corner of 10th & Elm street, and see that in the last hour:
80+ queue: 12 patients accepted, est. 2 currently in line (→ ~10 minute wait time)
everyone-else queue: 24 patients accepted, est. 20 currently in line (→ ~50 minute wait time)

---