Rajasthan: Ashok Gehlot’s Implausible Data Conjurations

A year-and-a-half of suffering the Wuhan virus has taught us to identify patterns, in the progression and regression of each wave of the epidemic.

The most apparent one is the most fearful — the time-lag between the commencement of a wave, and the consequent rise in numbers, of those who succumb to the Chinese contagion.

The daily death counts begin to rise about a fortnight or so after the case counts start to spike, and start to ebb only a week or a fortnight after the daily cases and test positivity crest.

This is a logical pattern of causality between infection and mortality, seen in cities, districts and states across the country; even in states like Kerala, where the process of reporting epidemic-related deaths has been under a cloud from the start.

But not in Rajasthan.

As this piece will show, the epidemic data released by Chief Minister Ashok Gehlot’s administration appears to have broken with scientific reason, and inverted causality: absurdly, and irreconcilably, the Rajasthan data reveals that the daily death counts peak before the case counts do, and enter into decline in tandem with case counts, instead of after a time lag.

There are three key points to be noted from the chart above.

First, the daily case curve (red line) peaks only on 9 May, but the daily death curve (black line) peaks abruptly on 29 April — 12 whole days in advance.

Both dates are marked by yellow arrows on their respective curves, to highlight the discrepancy. This is like saying that there were no casualties even before an accident occurs.

Second, wild fluctuations are periodically seen on the test positivity ratio curve (TPR; green line). On occasion, the fluctuations are so intense that the TPR values rise by about 15 per cent in a single day, and then drop by about 20 per cent the next day.

Now, the TPR is an extremely sensitive parameter, and does fluctuate, but such wide variations occurring so often, so many times repeatedly, for so long, have not been observed in any other state in the Union.

The reason for these unacceptably high TPR variations is our third observation, which is directly linked to both epidemic management and death counts: inconsistent and pathetically inadequate testing levels.

Rajasthan state epidemic data shows that at no point during the second wave did testing cross one lakh samples a day.

This core failing is highlighted by the fact that Rajasthan, the sixth largest state in the country, has only logged 1.2 crore tests till date, while smaller states like Tamil Nadu, Gujarat, Karnataka and Andhra Pradesh have logged twice or thrice more tests with far greater consistency.

The inferences are painful: that the Gehlot administration’s approach to containing the epidemic is marked by persistent slothfulness; and, that their data reporting has thrown up a massive incongruity, of death rates reducing before case rates do.

To investigate this incongruity more thoroughly, Swarajya analysed Rajasthan epidemic data of the 14 worst-affected districts.

These account for about three-fourths of the state’s reported cases and deaths, of which, the Jaipur and Jodhpur clusters make up a third of the total.

The findings are depressingly revelatory:

In Jaipur, deaths start rising only about a month after the second wave rose, but the death count started declining the same week as cases did — within days, in fact.

These two events are marked by yellow arrows in the chart above, and the same scheme will be used to highlight key dates in subsequent charts for other districts.

As Chart 3 above shows, the same discrepancy manifests itself in Jodhpur too, where Mr. Gehlot contests elections from.

In Alwar, the discrepancy grows: while death rates start rising only about a month after the second wave, they, in fact, start curiously mirroring the daily case rates from week to week without the standard time lag (marked by multiple yellow arrows in chart above).

In Kota, the death rates actually peak and decline into a long plateau before the case counts do.

It is the same impossible state of affairs in Udaipur, where death counts enter into a distinct, consistent decline a week before the case counts even peak.

The list goes on: in Ajmer, the death rates start declining even while the case rates remain in a long plateau for the first half of May 2021. In Sikar, both the daily death and case counts peak just a day apart.

In Pali, it is even more incongruous; cases and deaths peak just a day apart in late April, following which, the death curve parallels the cases curve through spikes and troughs without a time lag to mid-May.

For objectivity, it must be said that standard patterns are observed in Bikaner and Bhilwara, but in the absence of district-level testing data, which stopped being reported when the second wave began, there is little further necessary analysis which may be done to validate these two data sets.

Besides, readers would remember that Bhilwara was touted as a ‘model’, so, that probably prevented a break with scientific causality seen in so many other districts of Rajasthan.

Now, Mr. Rahul Gandhi of the Congress party recently released a ‘white paper’, in which he bemoaned the undercounting of epidemic deaths. Perhaps, he can set an example, by asking his Chief Minister in Rajasthan to scientifically explain how and why deaths declined in that state before cases did.

And as for Mr. Gehlot, he made his entry into politics by doing magic tricks for the Gandhi scions. Will it be that his exit from politics was on account of conjurations essayed with epidemic data?

In conclusion, the epidemic data of Rajasthan reveals an implausibility at both state and district levels, of death rates peaking and declining before case counts do, and of the death rates mirroring case rates without a time lag.

An effect cannot precede its cause.

All data from Covid19india.org