Home >Opinion >Views >India should not be misled by a deceptive fall in covid numbers

The reported number of daily covid infections in India has dropped from over 90,000 to just over 60,000 in the past five weeks. Other related metrics are showing improvement, too. These numbers, however, are catastrophically misleading.

It is widely accepted that reported infection and mortality numbers have always been significantly lower than reality. But the current reports are downright dangerous because of two reasons. First, the gap between the numbers and the reality is wider today than six weeks ago, and is widening. Second, even as the situation worsens, the country is starting to believe that we are past the worst and things are improving. Six weeks ago, while the numbers were an underestimate, they were pointing in the right direction—that the pandemic is escalating. Today, the numbers are pointing in the wrong direction. Even as the pandemic continues to escalate, we are starting to believe that it is receding. What reveals this?

Let us compare the situation in Uttar Pradesh (UP) with that in Karnataka. The 7-day average for the daily infection number for Karnataka has been between 7,000 and 10,000 for the past few weeks, while that of UP has dropped from 5,000-6,000 to about 3,000. The cumulative cases in UP are 2,000 per million population (225 million) and in Karnataka they are 11,500 (68 million).

With UP lagging Karnataka on all parameters of public health and system effectiveness, and its population density being two-and-a-half times that of Karnataka, can UP be miraculously better off than Karnataka on the spread of covid? Even the most credulous would know that cannot be true. This conundrum gets resolved by digging merely one level into the data—it is the testing.

Karnataka has conducted 100,000 tests per million of population, whereas UP has conducted 57,000. Eighty per cent of the tests in Karnataka are molecular (RT-PCR) tests, while in UP 70-80% are antigen (RAT) tests. This matters hugely. False negatives on molecular tests are a negligible proportion; on antigen tests, they range from 20% to 50%. A false negative is when a test misses detecting infection in a sample. During a public interest litigation, the Delhi High Court has recently noted false negatives to be about 40% on antigen tests. UP is testing inadequately and inaccurately. The reality there is much worse than the reported numbers.

The exact proportion of antigen tests in UP is very hard to figure out. Most states are no better. There is no regular, transparent, easily available disclosure of this crucial data. The country has remained at about a million tests per day from end-August, but the proportion of antigen tests are unknown. And that is critical—particularly as antigen testing has ramped up significantly in this period. Karnataka is one of the few exceptions. It has done a creditable job of ramping up molecular testing, and reports detailed data.

Whatever data is available publicly, and some that we have collected from the ground, suggests the proportion of antigen tests is over 50%, rising up to 80% in many states. The positivity rate on antigen tests, as can be expected with its very high false-negative rate, is one-third to half of that of molecular tests. The proportion of antigen testing is much higher in rural districts and the slums of large cities.

Why is this happening? It is a cascading effect of two forces. First, all states have tried to increase testing. Daily targets for testing have been assigned to all levels of the public health system. Second, it is far easier to conduct an antigen test. Antigen test kits give results in a matter of minutes at the site of collection. For a molecular test, though, the collected sample has to be sent under very stringent conditions to the nearest centre with an RT-PCR machine, which can be hours away. And then, given the shortage of RT-PCR capacity in most parts of the country, the results may take anywhere from two to six days. The far-easier antigen test has been embraced to show that testing is rising, ignoring its inherent inaccuracy.

No one on the ground is trying to hide or misrepresent any data. But the design of our data system has ignored this all-important slicing of the data, enabling this perverse development. UP is merely an illustration. Most big states are in a similar situation. Since the pandemic has spread from large urban areas to smaller towns and rural areas—and which is where it is escalating—we are now running blind, given our inadequate and inaccurate testing. The brunt of what follows would be borne by the disadvantaged.

The path out of this morass is fairly simple. Change the data system to report all relevant details on testing. Ramp-up molecular testing (or equally accurate methods) capacity—including in rural districts. Have a very strict mandate on antigen testing—only where it is useful; for example, in high-positivity clusters for quick identification.

The more difficult part is to rein in our desire to declare victory by staying wilfully blind to reality. The most important lesson from the Spanish Flu that crushed the world in 1918 was not an epidemiological one. It was that a pandemic can only be tackled by truth. We seem to be doing the opposite. We have been silently complicit in developing a system that hides the truth.

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