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  • This video was made possible by CuriosityStream.

  • When you sign up at CuriosityStream.com/Wendover, you'll also get access to Nebulathe streaming

  • site that Wendover is a part of.

  • Covid-19 is a devious disease.

  • It's not deadly enough to kill a large proportion of its hosts, so they go on to spread it to

  • an incredible number of individuals, meaning its fairly low mortality rate has already

  • killed hundreds of thousands.

  • It disguises itself, presenting like a common cold, yet can escalate to something far more

  • worrisome.

  • It also hides, with the possibility of being spread by someone who does not exhibit any

  • symptoms whatsoever.

  • All together, this leads to one simple truth: until there is an effective treatment, a vaccine,

  • or herd immunity, perhaps the most important thing a healthcare system itself can do to

  • minimize the number of cases and deaths is test.

  • Worldwide, diagnostic testing for Covid-19 has ramped up incredibly fast.

  • The first genetic sequence of the virus was published on January 5th, and within days,

  • the first testing protocols were published by researchers.

  • In the second half of January, the world was testing hundreds of individuals per day.

  • By February, they were testing thousands per day, then in March, hundreds of thousands,

  • and now, in July, 2020, millions of Coronavirus tests are performed and processed each and

  • every day.

  • Believe it or not, though, this is not enough.

  • The process has been mired in issues and, to get to the point where testing can actually

  • minimize the damage, rather than just quantify it, we need to rethink the system itself.

  • The core of any Covid test is a laboratory method known as a polymerase chain reaction.

  • This is a commonly-used procedure that copies DNA or, through reverse transcription, RNA,

  • enough so that it can be studied.

  • The various steps in the processdenaturation, annealing, and elongationrequire various

  • specific temperatures, and so performing this process requires a machine called a thermal

  • cycler.

  • These thermal cyclers can reach precise temperatures incredibly quickly and accurately, but it

  • comes at a cost.

  • The cheapest thermal cyclers are priced in the thousands of dollars, but the high-throughput

  • ones used to process COVID tests often cost in the many tens of thousands of dollars.

  • Therefore, at this time, it is not practical for every doctor's office to have one, especially

  • as many less-automated variants require trained technicians to operate.

  • There are some faster point-of-care tests hitting the market, but they tend to be less

  • accurate, more expensive, and can process fewer tests at a time.

  • Therefore, while samples can be collected almost anywhere, processing them is a greater

  • challenge.

  • One of the largest testing providers in the US is a company called Quest Diagnostics.

  • At the time of writing, they have processed about 17% of all tests performed in the US,

  • and have the capacity for 130,000 each day.

  • They carry out this work in one of their twenty-eight labs spread out across the United States.

  • Of course, it's easy to see that there are wide swaths of the country without a lab nearby,

  • so, to get samples from where they're collected to where they're processed, they rely on

  • their own small logistics network.

  • This system involves plenty of ground transportation, but also, in order to quickly transport samples

  • from far-away areas to the lab, they use airplanes.

  • Each night, after the day's samples have been collected around the country, Quest Diagnostic's

  • fleet of airplanes get to sky starting at around 7 pm.

  • These twenty or so aircraft are primarily based in Reading, Pennsylvania and Lawrenceville,

  • Georgia.

  • As an example, on July 16th, 2020, one aircraft, a Pilatus PC-12 registered as N338QD, took

  • off at 8:45 pm from Reading, Pennsylvania and flew to Elmira, Syracuse, Rochester, Niagara

  • Falls, Allegheny County, Manassas, and Baltimore where samples were trucked to the lab a few

  • miles from BWI airport.

  • The aircraft then returned to Reading, Pennsylvania by 3:30 am.

  • On that night, the majority of their aircraft focused their flights on the American south,

  • though.

  • This is a region without many Quest Diagnostics labs and a surge of cases, at that time.

  • Another PC-12, for example, flew from Lawrenceville, Georgia to Jacksonville, Gainesville, Tampa,

  • Orlando, Valdosta, Lawrenceville, Roanoke, Manassas, and Greensboro, before returning

  • to Lawrenceville, all in one night.

  • Filling in the rest of the routes, you can see just how much ground this small airline

  • covered in one nightstopping at a large number of cities and towns, going to just

  • a few labs.

  • Now, in theory, a system like this, bringing a whole countries worth of samples to a few

  • central sites, should provide economies and efficiencies of scale, and, to an extent,

  • it does, however, other economic concepts have simultaneously decreased its effectiveness.

  • To start with, there's a heavy supply and demand imbalance for crucial equipment.

  • To perform a test, about twenty different consumable supplies are needed including nasal

  • swabs, transport tubes, reagents, and other equipment.

  • Without any of these twenty items, a test cannot be performed, so a bottleneck in any

  • one of those twenty supply chains stops testing.

  • In addition, economic forces are actually decreasing overall efficiency.

  • Particularly in a country like the US, where testing is overwhelmingly conducted by private

  • organizations rather than the government, thousands of providers are each bidding against

  • each other for equipment.

  • In conventional economics, one typically wants to avoid monopolies and oligopolies because

  • a few number of players have too much power.

  • This is why, in countries like the UK, for example, where 75% of the grocery market is

  • cornered by four firms, products like milk are sold for just about what it costs to producefarmers

  • barely turn a profit.

  • That's because, if a farmer can't sell their milk to Tesco, Sainsbury's, Asda,

  • or Morrisons, they don't really have another option, so they'll take any price they can

  • get and meanwhile, there are plenty of farmers, so the grocery stores have plenty of options

  • if one says no to their low price.

  • Meanwhile, in the Covid test supply market, the producers have thousands upon thousands

  • of potential customers, the testing providers, so if one customer won't accept a high price,

  • there are plenty of others and one that most desperately needs the supplies inevitably

  • will.

  • If, for example, the federal government was the only customer in the US, or just the largest

  • one by far, the supply producers would just have to accept what the government paid because

  • there would be no alternatives.

  • Now, resources might not be allocated as efficiently using a centrally-planned system, but it would

  • at least come at a lower cost.

  • In the real world, the larger providers like LabCorp and Quest Diagnostics do have higher

  • buying power, processing more tests per day than most countries, but they still have to

  • compete against the rest of the world in procuring supplies, which increases cost.

  • In recent weeks, Quest Diagnostics, and other major testing providers, have been able to

  • collect far more samples than they have been able to process, and tests now take up to

  • two weeks, or sometimes more, to come back.

  • Tests that take so long to return are of little use since many asymptomatic or mildly symptomatic

  • patients might not quarantine for that full period while waiting for results.

  • Therefore, major focus has been placed on how to increase the efficiency of testing.

  • For this, Quest Diagnostics received the first emergency use authorization in the US from

  • the FDA to implement a technique known as, “pooled testing.”

  • With this, up to four samples will be combined and tested together.

  • If they generate a negative result, then all four tests are considered negative.

  • If the group tests positive, then they are separated and tested individually.

  • Now, if 100 people are tested traditionally, 100 tests will have to be processed.

  • But let's say that 100 people are tested using pooled testing.

  • In the US, on average, about 8% of tests currently come back positive, so with 100 tests, grouped

  • into 25 pools of four, you would expect about 32% of those to come back positive.

  • For those positive pools, you'd then process each test individually, so you will have processed

  • five times for four results.

  • For the 17 pools that came back negative, you only processed once, meaning that altogether,

  • this pooled testing technique used 57 processing slots to generate 100 resultseffectively

  • almost doubling capacity.

  • This pooled testing technique, though, only works if testing is widespread enough that

  • only a small portion of results come back positive.

  • If an average of 20% of results came back positive, as was the case in the US in early-April,

  • the efficiency would diminish quickly.

  • In that case, you'd expect to have to re-test 20 of the 25 pools meaning that, overall,

  • you'd use 105 processing slots to generate 100 resultsmore than if you just tested

  • them all individually.

  • Therefore, this technique does not work as well for testing in hotspots, where positivity

  • rates are far higher.

  • Conversely, though, if positivity rates are quite low, the efficiency of pooled testing

  • can increase as one could efficiently group 8 or 12 or 16 or more tests together.

  • Lower positivity rates can be achieved through collecting more samples, so pooled sampling

  • can increase testing capacity, therefore increasing its own efficiency.

  • But no matter how fast PCR testing itself is, it is always a lagging indicator.

  • That is because it takes a number of days for people to develop symptoms after exposure,

  • then more days for them to decide to get tested, then more days for the test to come back.

  • That means that, realistically, it will take weeks for significant upticks in transmission

  • to show in the data.

  • Therefore, some focus has been placed on developing a more real-time indicator for COVID transmission.

  • The solution for that is sewage.

  • Sewage is naturally aggregated into a few processing plants, so by testing it for the

  • presence of COVID's genetic signature, one can gain a more accurate picture of how a

  • whole area is doing, without the potential sampling biases of clinical testing.

  • This technique can show if an area has coronavirus transmission, but it can also give a big-picture

  • view of how much.

  • Not only that, but in research tests, sewage testing indicates spikes about a week before

  • clinical testing, meaning it can help tell governments if they should increase restrictions

  • before it's potentially too late to make an impact.

  • But let's go back to the pooled testing.

  • Using sewage testing, you can know the relative chance of a single person from a given area

  • of having Coronavirus, so imagine if you could combine the sewage testing and clinical pooled

  • testing into one cohesive system.

  • This technique was proposed by a group of researchers from Berkley.

  • If you know that, say, there was a 25% chance of a test from Florida coming back positive,

  • you would know that it would be more efficient to test that on its own, however, if you knew

  • that there was a 1% chance that a test from North Dakota came back positive, you could

  • pool that test that with a large group of other low-risk tests and save processing slots.

  • You could also, for example, know that a sample from a healthcare or retail worker would have

  • a much higher chance of coming back positive than a test of an businessperson working from

  • home.

  • The Berkley research proposes a system of machine learning to scale pooled testing sizes

  • based on risk factors.

  • Low-risk samples would be grouped into large pools, and high-risk samples would be grouped

  • into smaller pools or tested alone so that pooled testing never leads to an efficiency

  • disadvantage, no longer the positivity rate.

  • The true genius of pooled testing is that efficiency increases as test numbers increase,

  • so it only takes twice the resources to test a population once daily as it does to test

  • once monthly.

  • Hypothetically, a pooled testing system designed by machine learning could lower the cost of

  • a test to just $3 to $5, meaning the frequency of testing could increase dramatically.

  • Now, we already know when the crunch time will be for testingFall, 2020.

  • Sometime between October and December, each year, there begins a massive increase in the

  • number of people catching the flu, which is highly seasonal.

  • Covid-19's symptoms closely mirror those of the flu, therefore there will be a massive

  • increase in people exhibiting Covid symptoms.

  • Those people will want to get tested, to rule out the possibility of Coronavirus, which

  • means demand will be higher than ever for testing, even if real infection numbers are

  • lower.

  • All the supplies for testing produced now are being used now, there is no stash being

  • built up, which means testing providers and healthcare systems worldwide have a hefty

  • task at hand if there is to be any hope of testing capacity matching demand in the coming

  • winter.

  • Like so many things going on right now, Covid testing logistics and its issues are strongly

  • rooted in economic theory, and there's plenty more I could talk about, but this video was

  • getting a little too long to still appease the YouTube algorithm, so I've posted an

  • extended cut of this video on Nebula including discussion of exactly that.

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B1 US testing test nebula covid diagnostics efficiency

The Logistics of Covid-19 Testing

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    joey joey posted on 2021/06/09
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