Variant watch: Time to rethink Covid metrics, open up India data

To look at it differently, if Delta variant caused 100 infections in, say, a week and led to four hospitalisations, Omicron would likely cause 300 infections with six hospitalisations in the same period.

To recap the impressive speed at which scientists responded to the Omicron variant, there are three critical things to know.

Omicron is a threat because of the speed at which it spreads even though it is likely to cause milder disease.(AFP)

First, it spreads faster than any Sars-CoV-2 variant yet – a UK study by Imperial College London estimated that for every infection the Delta variant led to, Omicron led to three.

Second, it is highly resistant to antibodies from a past infection or vaccination, but only as far as symptomatic disease was concerned. The risk of reinfection with Omicron rose 5.4-fold compared to Delta.

But, and the third important point, is that it is significantly less likely to cause severe disease. Some of it could be due to its inherent trait – Imperial College researchers estimate a 0-30% reduction in risk for unvaccinated, previously uninfected people needing to go to a hospital. But most of it, as estimated by South African researchers, is likely due to a past infection or vaccination. On average, the UK study found the risk of needing hospital care to be almost halved.

There are some associated insights too, like the two studies by researchers in Hong Kong, Cambridge and Japan that found the Omicron variant replicating more in the bronchus and nasal airways, but was significantly less potent in infecting the lungs.

The import, then, is that Omicron is a threat because of the speed at which it spreads even though it is likely to cause milder disease.

To look at it differently, if Delta variant caused 100 infections in, say, a week and led to four hospitalisations, Omicron would likely cause 300 infections with six hospitalisations in the same period. In the second week, given what we know of the transmissibility, this number will grow to eight Delta hospitalisations (from 200 infections) and 18 Omicron hospitalisations (from 900 infections).

At a population scale, these numbers can be much larger with more confounders. In a population with more past immunity or a more recent vaccination surge, Omicron may cause much fewer hospitalisations. Conversely, a region not exposed to past waves or one where vaccines have waned, the Omicron wave may be more severe.

Reorienting perspectives

The uncertainties make the case for an outbreak to now be assessed more on hospitalisation and fatality rates instead of case rates, experts say.

“We have to do a shift. For two years, infections always preceded hospitalisations which preceded deaths… it was largely the unvaccinated people getting infections then. Omicron changes that. We are moving through a phase where large populations that are vaccinated gets infected, be unwell, and bounce back after a few days. I no longer think that infections should be the major metric – sure, we should continue tracking it, especially in unvaccinated people – but we must focus on hospitalisation and deaths now,” said Ashish Jha, the dean of the Brown University School of Public Health, in an interview with ABC News on Sunday.

In India, there is no official figure on hospitalisations. The Union health ministry issues a daily bulletin with cases, active cases, and testing bulletin, but offers no figures on how many people with Covid-19 were admitted to a medical care facility. In terms of states, only some, like Delhi, give hospitalisation data in their state-specific daily bulletin.

A second expert too recently said this was problematic. “The fact that we are not tracking hospitalisations is going to become very critical in coming months. For example, if Omicron, which is highly transmissible, truly leads to lot of mild infections and we have nothing to worry about, we need to know that,” Bhramar Mukherjee, head of biostatistics at University of Michigan and an epidemiologist who has modelled India’s outbreak, in an interview with The Wire.

Mukherjee added that with the lack of this data, restrictions based simply on surging cases can turn into overkill or unvaccinated people may not be aware of the higher risk they will be at.

India’s data hurdle

The Indian-origin University of Michigan epidemiologist said predicting India’s outbreak is difficult because of “data denial, data opacity and data paucity”. “What is the breakdown of cases, hospitalisation and deaths, across age, sex and now vaccination status? I cannot find it,” she said during the interview.

Last week, the routine press briefing on Covid-19 offered a brief illustration how misrepresented data could be problematic. One of the figures shared by the officials in a slide show claimed 91% of the known cases of Omicron were in fully vaccinated people – without accounting for significant caveats in the fine print. The fine print said vaccination details of 73 of the 183 samples were not available, which meant that in reality, only 47% were know to be fully vaccinated.

But the main figures were reported as is, leading at least one respected civil society member to share photographs of a newspaper headline to falsely claim the “vaccinated were more likely to get infected”.

Even if it were indeed 91%, a second major caveat was mentioned, either by the government or in the particular report: the large proportion of vaccinated people is simply a testing bias artefact. All international travellers, who are mostly likely to be vaccinated, are tested even if they are asymptomatic, and all positive samples are sequenced for their genome.

Omicron changes the epidemic equation significantly. To understand that, all elements of the equation must first be reported and reported faithfully.