Mathematician Arni SR Srinivasa Rao has been trying to understand the real magnitude of the pandemic. To do this, Rao, Director of the Laboratory for Theory and Mathematical Modelling at August University’s Medical College of Georgia, in the US, along with his peer Steven Krantz, professor of mathematics at Washington University, has been trying to mathematically ascertain the number of unreported cases of Covid-19 in many countries, including China, Italy, Spain and the US, where the infection wreaked havoc. In their study, recently published in the journal Infection Control and Hospital Epidemiology, they visualised the disparities between reported cases and what they projected using what is called a Meyer wavelet. The higher the wave, the higher the under-reporting; lowering the wave means improved reporting. Based on the model-based predictions, when we look at the data from India, by the end of March almost 1 in 4 Covid-19 cases were identified. But the other three who were not been tested could be spreading or might be taking precautions — we never know accurately. The testing strategy by ICMR is in the correct direction because unnecessarily increasing the number tested randomly will have no gains if ICMR has pieces of evidence that they are predominantly negative people. Statistically, it is good to show that the positivity rate is low by testing many, but that could lead to a waste of resources. However, the advantage of large scale random testing is that it could catch people who are asymptomatic. But in any large country like India, it is not easy to conduct random testing in such a short time. According to the government, the ICMR has the capacity to conduct 10,000 tests daily. By comparison France conducts 10,000 per week, UK with 16,000 tests for the same period, United States with 26,000, Germany doing 42,000, Italy doing 52,000 and South Korea doing 80,000 every week, Bhargava informed.