Rwandan Researchers Have Unique Way To Overcome Limited COVID-19 Testing. Here's How It's Done
Like several nations, Rwanda is finding it impossible to test every citizen for COVID-19 with lack of supplies. However researchers there have formed an approach which is drawing attention outside Africa too, as per a report in AP.??
They are using an algorithm to refine pooled testing, which tests batches of samples from groups and then tests each person only if a batch comes tests positive for COVID-19. This conserves scarce testing materials.?
Rwandas mathematical approach, the researchers say, makes that process more efficient. That's an advantage for developing countries with limited resources, where some people must wait several days for results. Longer waits mean a greater chance of unknowingly spreading the virus.?
Those behind the algorithm have expressed some pride that a potential solution to a dogged problem in the global crisis is coming from Africa. Experts have noticed. Sema Sgaier, a Harvard assistant professor of global health, called the Rwanda approach an example of the incredible solutions in very resource-poor settings that have come out of the continent.?
Some experts, and even the researchers, have noted concerns that the complexity of the approach could deter its widespread use. If you told this to a technician, they would say, What a mess. I want a simple scheme,’ Sigrun Smola, a molecular virologist at Saarland University Medical Center in Germany, told the journal Nature in a recent article on Rwanda’s and other approaches to pooled testing.?
The method developed by Wilfred Ndifon, a mathematical epidemiologist and director of research at the African Institute for Mathematical Sciences Global Network in Rwandas capital, Kigali, is now being turned into software that will guide lab technicians, minimizing human error.?
The technician will continue to do the normal lab work, Ndifon told AP. The software will make it easier to make the calculations. It will tell the lab tech how many samples to put together for testing and, in the end, how many samples are infected.?
The approach is the most efficient in places where virus prevalence is relatively low, the researchers say.?
The algorithm itself is novel, Ndifon said. It reduces the number of required retests when a group sample comes up positive. Rather than retest every sample in the batch individually, the mathematical formula dictates how to create and overlap smaller groups from the batch in a way that allows the positive samples to be identified.?
He described it as arranging samples in a multi-dimensional structure such as a cube or hypercube and testing along the slices.