COVID-19 testing with an algorithmic approach
In order to successfully improve the number of patients examined per day, this article proposes a binary search strategy for testing probable COVID-19 patients in India.
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With a population of over 1.3 billion people, the number of potential coronavirus carriers is growing at an exponential rate every day.
How does the Algo works ?
A proposed binary search strategy for testing a group of 100 persons using fewer than 100 testing kits is presented below.
Consider a group of 100 people, 5 of whom are contaminated (current situation in India: 5 persons test positive for every 100 people tested).
Let's imagine we can combine the samples of a hundred people into a single huge sample (say, sample X) and test it for the virus. If it comes out negative, we can say that no one in the group is contaminated. If the sample is positive, we can assume that at least one individual in the group is infected.
Summed Up
Many non-programmer doctors may be unaware of such algorithms, and if our existing apparatus can be changed to perform pool testing as described above, we will be able to run a far higher number of tests per day.
Researchers in Germany have already begun to use such methods and have been successful in drastically boosting their COVID-19 testing capacities.