Too cheap to meter?: Innovating Regular testing

Published in
6 min readMay 25, 2020


In a previous article, I outlined the importance of sequencing our innovations to get out of COVID19 hell. That post can be found here, and provides essential context to this series:

COVID19, as the first post-internet pandemic, has showcased a core problem with modern medicine. As Dr. Tiffany Vora explains, currently, healthcare works like this: “you wait until you have a symptom, and that sends you to your healthcare provider.” She and Dr. Daniel Kraft have named this process “sick care” and, while sick care made sense when the causes of diseases were unknown or the technology for constant monitoring was expensive, it is now the wrong approach to medical interventions.

Of all the data-driven approaches to modern medicine, the quantified-self movement is, by far, the most well-organized community of individuals trying to change the way they engage with their biological information. By observing that community we can glimpse the future of data-driven medicine: Individuals taking control of their health by continually monitoring (often using wearable tech) as many data points as possible: posture, heart rate, blood pressure, cholesterol, gait, cortisol, etc.

A glucometer is a critical piece of technology that has improved dramatically the quality of life of people with diabetes. Today, reliable glucometers can be bought for less than 7 bucks.

But even if you never heard about the quantified self-movement, you probably already know somebody following a data-driven approach testing regime if you, a loved one or a friend has diabetes. The capacity to measure glucose levels at home affordably and rapidly has been revolutionary for people with diabetes and insulin resistance.

Imagine that all portable glucometers ceased to work. People with diabetes would need to go to laboratories to get a report, so the amount of reliable testing data would go down by orders of magnitude. They would probably wait until symptoms make it clear that something is wrong, and, at that point, it could be too late given that if left uncontrolled for long periods of time, high glucose levels can produce permanent and irreversible damage. That is what sick care looks like, and this is what we have been doing with COVID19. While COVID19 tests are scarce even in the best of scenarios that involve driving to a test center, glucose measurement is abundant.

To resume social gathering activities safely, we need an abundance of testing like we have never seen before for respiratory diseases; something akin to diabetes screening.

Innovating Covid19 testing to produce an abundance of testing is a national security priority for this and future pandemics and, frankly, for the future of healthcare. How much testing do we need? Imagine this scenario:

“Honey, we are late for dinner with your parents,” yelled Martin to his wife.

“Well, it would go a lot faster if you helped me finish testing the children before we leave, “ she replied. “Liam has not yet taken the test, and if he doesn’t do it now, the results will not be ready before we arrive at their home. I am not getting inside of the house without it.” Annoyingly, Martin removed his coat and started chasing the toddler around the house. It was already damn hard enough to have to chase the kid around the house to dress him up, and now he always also fought the COVID19 test they had to take before leaving the house.

In this future world where COVID19 tests are abundant, you would test yourself in the morning before going to work (and maybe, as a dual layer of security, your company could also establish a policy to test you before letting you enter the building), restaurants could test before assigning a table to their customers, airports could perform tests before allowing passengers to board a plane and schools could ask parents to perform those tests at home before getting ready for the day.

AI-powered temperature screening in Singapore. Source:

In fact, we have some examples of how the logistics of constant testing would look like as nations with effective contact tracing and testing policies have deployed temperature screening checkpoints in all of the places I used as examples in the preceding paragraph. Unfortunately, though, temperature detection is a weak biological data point to collect given the number of COVID19 patients that do not develop a fever and the long incubation period of the virus. This is why it is essential to replicate the same operation but capturing the right biomarker: the actual presence of the virus.

The core idea here is that testing should become so abundant that it becomes too cheap to meter.”

Today, thermometers and glucometers make testing for fever or glucose already too cheap to meter, but COVID19 tests are not like that. That needs to change.

For example, STOPCovid is a one-step test developed by MIT that works very similarly to a pregnancy test. Developed using CRISPR, “the STOPCovid test combines CRISPR enzymes, programmed to recognize signatures of the SARS-CoV-2 virus, with complementary amplification reagents. This combination allows detection of as few as 100 copies of SARS-CoV-2 virus in a sample. As a result, the STOPCovid test allows for rapid, accurate, and highly sensitive detection of Covid-19 that can be conducted outside clinical laboratory settings.”

STOPCovid testing strips. Source:

While a response time of an hour is still too slow for usage in a “checkpoint” setting (with the exception of airports), its simplicity could make something like STOPCovid a great tool to make testing abundant.

Non-Pharma interventions like “stay at home” orders were meant to buy us time to build robust testing and social tracing protocols. They were the first stepping stone, not the final destination. While the US did not use that time wisely when compared to other industrialized nations that had better results at “flattening the curve,” the US has one core advantage: its innovation industrial capacity is the most developed of all countries. America is built to innovate and government leadership in the United States should make innovation management for testing, treatment, and vaccination its number one priority.

While serendipity cannot be predicted, creating innovation-friendly policies is something that can be done, and the US government knows how to do it. A DARPA approach to pharma would mean well-coordinated programs to incentivize inventors and entrepreneurs to make testing so abundant it becomes “too cheap to meter.”

Until we have so much testing available that we don’t even think about it (imagine having some testing strips in your car, your wallet, your medicine cabinet and you are tested when you go to places, when you walk around, maybe even when you use a toilet) our pandemic response is a shot in the dark. We just don’t know who has the disease and who does not.

Remember when Pres. Kennedy said: “We choose to go to the moon in this decade and do the other things, not because they are easy, but because they are hard, because that goal will serve to organize and measure the best of our energies and skills”?

Do you know what followed? Budget appropriations for more than 77 billion dollars in today’s money. Bureaucracies being created and programs being funded. The “moonshot” was a master class on innovation management.

To build our COVID19 testing capabilities, to build in five months what would take five years, we needed a moonshot. The non-pharma interventions were the stepping stone to give us time to build that testing infrastructure.

If tests become so abundant that they become “too cheap to meter” we can transition from the blunt and damaging instrument that are “stay at home” orders to more surgical and targeted interventions without risking the lives of hundreds of thousands of people. A tested child can hug her grandmother without any risk, professors and teachers can go back to work without fear and, with the appropriate “checkpoint” policy, even big public gatherings could take place again. produced this fantastic video on the test positivity rate. A successful regime of testing that is “too cheap to meter” would bring that number really, really close to 0.0%.

Read more content like this at: