Testing is an essential component of effective outbreak responses. Without widespread testing, we cannot know whether a disease is spreading nor take measures to appropriately respond to it. “All countries should be able to test all suspected cases, they cannot fight this pandemic blindfolded, they should know where the cases are, and that is how they can take decisions,” said Dr. Tedros, WHO Director-General.
But to ramp up testing to a much-needed scale, you need the laboratory space as well as enough, and the right kind of, machines. You need the right reagents — highly specific substances used to extract the virus’s genetic material and to make it easier to study. You need staff to take the swabs from patients’ noses or throats, and staff in labs to process the tests. And you need the logistics in place to get samples from patients to labs. We’re talking about diagnostic tests to find out if you have the virus here — ones that involve a nose or throat swab that has to be sent off to a lab. All this could be difficult to mobilize for some developing nations. India has increased its coronavirus testing capacity to 10,000 samples per day. A senior Health Ministry official stated in a press conference in New Delhi that the government has a maximum capacity to test around 70,000 samples per week.
This is the time to act fast and smart.
Here’s a way to exponentially ramp up a country’s testing capacity without throwing a lot of resources at it. This method is borrowed from Computer science algorithms, more specifically called “divide and conquer.” Here is how we can use this technique to solve the problem at our hands.
Please note: This method requires taking at least 4 samples from each person.
COVID testing Algorithm
- The first step is to form a batch of samples. A batch size of 8 would be ideal. This basically means to mix the samples of 8 people into a single sample.
- Now let’s look at the possible batches if we mix potential COVID positives and negatives. Let’s assign labels to them (It’s for a better understanding of technique and not needed for actual testing).
3. The sorting is done based on the number of tests required to identify all the positives and negatives in a batch.
4. Once the samples are batched, apply a technique called a Binary Search while testing each batch. Our objective is to find all the positives and negatives in a batch of 8 samples. For example, if a batch result after testing gives a negative, we are done as all are negative. But when if it ends up positive, split the batch into 2 smaller batches of 4 samples and repeat the test. This goes on until we find all the positives up until the last one in the batch. Let’s see a visual example of how binary search applies to classes B, E and F batches.
5. Each arrow represents a test that could end up being positive or negative. Based on the number and position of positives in the batch, the number of tests required vary. Here is a table that represents the best kind of batches to worst kinda batches.
6. With all the above information, let’s talk about some scenarios.
Take Indian testing capacity in our example which is 10000 samples per day. The positive rate when 10000 samples are tested is 300 approx.
Let’s consider this rate and the worst-case batch class F in our example(consider it as really bad day). So to identify all 300 positives, it would take 300/4 = 75 worst class F batches. And each of such batch takes 15 tests to identify 4 positives and 4 negatives which takes 75*15 = 1125 tests. These 1125 tests would identify 300 positives and 300 negatives.
Rest 9400 negatives automatically fall into our good Class A which only takes 1 test for 8 samples. So it would take 9400/8 = 1175 batches *1 = 1175 tests.
So a total of 1125+1175 = 2300 tests are enough to identify positives and negatives in 10000 samples. And this is considering the worst-case batches which are highly unlikely. Real numbers are gonna be even better than this.
Summarizing all this, countries with less testing capacity can adopt this technique and could ramp up the capacity by 4 to 8 times on average. Hoping this article would stir up some minds who are the decision-makers in changing testing strategies. This could be the difference between winning over COVID or losing loved ones to it.
If India could adopt this technique, the capacity would boost from 70,000 per week to more than 4,00,000 per week which would change the direction of this war against COVID.