BENGALURU — The COVID-19 pandemic has forced us all to get comfortable with an array of numbers and glut of statistical methods. But of the three numbers that governments and the media use to convey the extent of the virus' spread — the total number of cases, the number of active cases, and the number of new cases daily — it is the first number that grabs most headlines.

Last week, on July 16, India's total tally of COVID-19 cases hit the 1 million mark. But since 0.63 million of those are cured, the total number of active cases is 343,000.

So why do we speak about the total number of cases? One reason is that the total number of COVID-19 infections is a way to understand the spread of the virus through the total (susceptible) population until a specific point of time.

If epidemiologists, based on the virus' reproductive ratio (R0), have a broad sense of how many people in the country will eventually get infected, then the total number of cases can tell us — at a glance — how close or far we are from it. To be sure, this is an inexact science. Predictions for India range from 10 million to 150 million total infections by the time the pandemic ends.

Since the rising total number of cases counts as bad news, the government in India has focused its "good news" messaging on the total number of recovered patients, which is also rising. But this absolute number (or even the percentage of total cases that are cured as of now) tells us nothing about when the tide was turned, since active cases continue to rise.

Because an epidemic officially peaks when the number of new cases starts to fall, the day-wise gap between the number of new active cases and the number of recoveries can convey a better sense of when India will begin to see light at the end of the tunnel.

Since an epidemic officially peaks when the number of new cases starts to fall, the day-wise gap between the number of new active cases and the number of recoveries can convey a better sense of when India will begin to see light at the end of the tunnel.

Data from the Union health ministry makes this point clearly:

India_data_covid_graph_1

The gap will effectively close when India hits its peak, i.e. when the blue line of new daily cases starts to dip and then intersects a (by then) more steeply inclined green line of new daily recoveries. But as the data shows, the gap has only widened since the country's four-phase lockdown concluded.

To get a better sense of the disease's all-India march at the current time, therefore, this graph is a better indication of the road that lies ahead than the total number of cases and total number of recoveries. But like the all-India total, this graph does not capture the spatial variation so crucial to the spread of the virus.

Why? Because different parts of the country are at markedly different stages of their respective epidemics. Thus, the total (and daily) number of cases, recoveries, active cases and new infections can give us a misleading picture when we look at regional, state-level data.

For example, now that India's COVID-19 epidemic is nearing sub-national status, India reaching 1 million cases doesn't reveal the hyper-localized addition of new cases, and separately recoveries. Put another way, the forest of cases is now too large for us to discern the health and wellbeing of different patches based on forest-level data.

But then why should the gap between the number of new cases on a day-to-day basis versus the number of new recoveries be any different? It isn't, except in one important way: The total number of cases includes those who have already recovered or died, whereas the gap denotes only the present, so to speak.

That is, while the total number of cases continuously becomes a poorer reflection of India's exact situation in both space and time, the gap becomes so only in space.

To understand the present situation, and the future to a limited extent, the national daily gap between new infections and new recoveries is more useful. It won't tell us how a specific state is doing but it will be more specific to the current status of healthcare, R0, mortality, etc.

In any case, we have enough data to plot the daily gap of new infections and recoveries for each state. As we can see below, Delhi is doing better than Karnataka.

India_data_covid_graph_2

india_the_wire_data_covid_graph_3

On the other hand, the total number of cases tells us neither how specific states might be doing nor how India is doing right now, since the metric is skewed by past data. Put another way, the gap is more sensitive to the prevailing situation than the total number of cases.

In the last half decade, India must have had about 10 million tuberculosis cases, but such a number doesn't feature in today's medical and healthcare narratives.

And when the total number of cases is an order of magnitude higher than the number of active cases, and two orders higher than the number of new cases daily, its sensitivity to the present is bound to be quite weak. That is, the addition of 30,000 new cases will increase India's total number of cases from 1 million to 1.03 million, an entirely marginal difference.

A healthcare worker collects swabs for COVID-19 tests at a screening center in Guwahati, Assam, India — Photo: David Talukdar

This is also why epidemics that have gone on for longer — such as those of tuberculosis and HIV — no longer care for the total number of cases. For example, in the last half decade, India must have had about 10 million tuberculosis cases, but such a number doesn't feature in today's medical and health care narratives. Diarrhea (due to various causes) is another example. India is likely to have had a few billion cases in the last decade, but nobody cares because this figure is meaningless.

The scale of India's COVID-19 epidemic on many counts pales in comparison to India's tuberculosis epidemic, but then again the COVID-19 epidemic has become, or is approaching, sub-nationality — the way tuberculosis has been for many years now. So let's keep an eye on the total number of cases, but have a closer watch on the width of the gap.

For example, it will be hard to challenge the central government if it decides to impose one more lockdown now and emerges from it later claiming the total number of cases increased only marginally in percentage terms, and therefore the lockdown was a success. But the gap — free from the normalizing pressures of historical trends — could prove the government quickly wrong.


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