Containing COVID-19 in the US

It is still possible to contain the COVID-19 epidemic in the US, and to safely re-open without risking many hundreds of thousands of avoidable deaths. Achieving this goal is an enormous undertaking, but it is possible. This post explains how it can be done.

Let me start with a graph that shows a projection of COVID-19 cases and deaths in the US:

The graph shows a COVID-19 model that approximates the epidemic until now: a rapid rise to more than 30,000 confirmed cases per day, and more than 2,000 daily death, followed by a very slow decline in cases and death. It then predicts a rapid rise in daily cases for the next months, to almost 50,000 cases a day, as a result of "re-opening". For the rise shown, the model assumes that many social distancing measures remain in place. The reproduction factor R is assumed to rise to 1.4 from the current value just below 1.0; this is still much less to the R of 4 to 5 early in the epidemic.

The striking feature in the graph is the rapid decline in new cases down to about 8,000 per day at the beginning of June. It is followed by a rapid rise back to more than 40,000 daily cases before a second decline, and then a series of similar peaks that get smaller and smaller.

To get the projected rapid decline in cases, the model assumes that almost the entire population of the US is tested for COVID-19 every 10 days, starting at the beginning of June. Anybody with a positive test result is isolated for 14 days. As shown, the model allows for 15% test failures, which could be a combination of people that are not tested, and false negative test results. The model I used is more realistic than the typical SIRD models commonly used: it tracks each infected person by day since the infection happened, and uses "infection-age" specific infectiousness parameters. It also assumed that anyone who was infected just one or two days ago would give a negative PCR result, and therefore not be isolated. This is why the case counts increase rapidly again after the initial drop.

Let us have a quick look at the number of projected total confirmed cases and death:

Overall, the model run predicts about 2.4 million confirmed cases, and about 240,000 deaths. This is roughly the same number that model runs predicted if the current social distancing restrictions would remain in effect until the end of the year,  and about 460,000 fewer deaths than predicted without population-wide testing.

As I said, the graphs above still assume that many social distancing measures remain in place. Currently, most states that allow restaurants to re-open require that they restrict seating to only 25% or 50% of capacity; however, this makes it hard to impossible for most restaurants to survive. Clearly, the goal has to be to re-open without such restrictions; however, this would result in an increase of the reproduction number to a higher level, for example 2.5. Would regular testing allow for this? Let's have a look:

In the model run shown, almost all restrictions were removed at the beginning of July, so that the reproduction number R increased to 2.5. This resulted in a slight increase in the maximum number of cases in each subsequent test period. To avoid this increase, testing could be done more often. For exampl, if tests would be done every 7 days, then the peak numbers would remain nearly constant over time. Another option would be to keep some measure that reduce transmissions in place, so that R increases only to 2.0 instead of 2.5; this would lead to a steady reduction over time.

The big question is if this level of testing can be achieved. Without a doubt, this would require a tremendous effort. Currently, the US is doing up to 300,000 tests per day; testing the entire population every 10 days would require more than 30 million tests per day, a one hundred fold increase. But until March 10, the US did fewer than 3,000 tests per day, so we have already increased testing capacity 100-fold once; I believe that the US can do this again.

For comparison, let us think back at the Human Genome Project - a major success story for science where the US played a leading role. I was just starting my Ph.D. thesis when the Human Genome Project started, and back then, very few scientists believed that sequencing the human genome would be possible in the projected time frame. But the project was completed years ahead of schedule, and led to major technological breakthroughs. These help us to study and understand the COVID-19 epidemic today, since sequencing a viral genome has become a trivial and extremely fast exercise.

A lot of technology that was developed for the Human Genome Project is very similar to what we need to population-level COVID-19 testing. Many biomedical laboratories have the robotics and PCR machines needed for the tests, and plenty of students and researchers who could help to jump-start the testing. There is a tremendous amount of resources that could be dedicated to this effort on a temporary basis. Considering that we are talking about saving hundreds of thousands of lives, the vast majority of laboratories, researchers, and students would certainly be happy to play their part.

If you take an emotionally detached stance and look at how different countries deal with the COVID-19 epidemic, the observations can be quite fascinating. Countries that have been successful in containing the epidemic have played on their cultural strength: China used drastic control measures; Germany has a scientist as a leader who presented a clear message, and a population that is used to follow rules and regulations; and New Zealand used an interesting combination of such aspects, backed by outstanding science.

Many of the measures that worked elsewhere would not work in the US, where a substantial part of the population values individualism and "freedom" more than anything else. However, a large-scale engineering and technological approach that combines existing science resources and private enterprise fully matches the strengths of the United States.

To make it clear, I think that the "population testing" strategy is just a medium-term strategy, with the primary goal to bring the level of active COVID-19 infections down to a level where measures like contact tracing can be effective; at the current level of 30,000 confirmed COVID-19 infections per day, and probably at least 200,000 infections that go undetected, contact tracing would be somewhere between impossible and ineffective.

Realistically, it seems unlikely that the US government would endorse such a large-scale testing approach, and implement it in an effective way. This is somewhat ironic, since a successful containment by population-scale testing is likely to be the fastest way "back to normal". However, the strategy can be implemented on a smaller scale - on state-by-state levels, or even city-wide levels. Would this require resources that exceed the possibilities of cities or states? Currently, COVID-19 tests run at about $150 each. Testing everyone in the US for a month (three times) at this price would cost $150 billion. That looks like a tremendous amount, but it is much smaller than the various "rescue" packages that the congress has already approved. More importantly, though, the true costs for reagents and machinery are substantially lower than $150 per test - a number below $5 per test is more reasonable. For just a small fraction of the $2 trillion "CARES" act, every person in the US could be tested every 10 days for a year.

There are many non-trivial details in the implementation. Increasing testing capacity 100-fold within a month, as the graphs above assumed, seems quite unlikely; however, the initial rollout could concentrate on the most heavily affected cities and counties, where the impact would be largest. Compliance would be another issue, but one that is solvable: virtually everyone who drives a car in the US has accepted that they'll need to get a drivers license. Testing nearly everyone will require a certain level of enforcement,  but I believe that the vast majority of Americans would agree that saving hundreds of thousands of lives would be worth it.

It's possible. Will it happen?
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There is actually an example where this strategy was used successfully on a small scale in the town of Vo in Italy. I started looking into this after reading an article titled "Population-scale testing can suppress the spread of COVID-19".  The article uses a model that in to simplistic, and therefore over-estimates the number of "misses" that the approach allows, but the general idea is a good one.