Tag: novel coronavirus pandemic

Super-spreads exist, but do super-spreaders?

What does the term ‘super-spreader’ mean? According to an article in the MIT Tech Review on June 15, “The word is a generic term for an unusually contagious individual who’s been infected with disease. In the context of the coronavirus, scientists haven’t narrowed down how many infections someone needs to cause to qualify as a superspreader, but generally speaking it far exceeds the two to three individuals researchers initially estimated the average infected patient could infect.”

The label of ‘super-spreader’ seems to foist the responsibility of not infecting others on an individual, whereas a ‘super-spreader’ can arise only by dint of an individual and her environment together. Consider the recent example of two hair-stylists in Springfield, Missouri, who both had COVID-19 (but didn’t know it) even as they attended to 139 clients over more than a week. Later, researchers found that none of the 139 had contracted COVID-19 because they all wore masks, washed hands, etc.

Hair-styling is obviously a high-contact profession but just this fact doesn’t suffice to render a hair-stylist a ‘super-spreader’. In this happy-making example, the two hair-stylists didn’t become super-spreaders because a) they maintained personal hygiene and wore masks, and b) so did the people in their immediate environment.

While I couldn’t find a fixed definition of the term ‘super-spreader’ on the WHO website, a quick search revealed a description from 2003, when the SARS epidemic was underway. Here, the organisation acknowledges that ‘super-spreading’ in itself is “not a recognised medical condition” (although the definition may have been updated since, but I doubt it), and that it arises as a result of safety protocols breaking down.

“… [in] the early days of the outbreak …, when SARS was just becoming known as a severe new disease, many patients were thought to be suffering from atypical pneumonia having another cause, and were therefore not treated as cases requiring special precautions of isolation and infection control. As a result, stringent infection control measures were not in place. In the absence of protective measures, many health care workers, relatives, and hospital visitors were exposed to the SARS virus and subsequently developed SARS. Since infection control measures have been put in place, the number of new cases of SARS arising from a single SARS source case has been significantly reduced. When investigating current chains of continuing transmission, it is important to look for points in the history of case detection and patient management when procedures for infection control may have broken down.”

This view reaffirms the importance of addressing ‘super-spreads’ not as a consequence of individual action or offence but as the product of a set of circumstances that facilitate the rapid transmission of an infectious disease.

In another example, on July 21, the Indian Express reported that the city of Ahmedabad had tested 17,000 ‘super-spreaders’, of which 122 tested positive. The article was also headlined ‘Phase 2 of surveillance: 122 super-spreaders test positive in Ahmedabad’.

According to the article’s author, those tested included “staff of hair cutting-salons as well as vendors of vegetables, fruits, grocery, milk and medicines”. The people employed in all these professions in India are typically middle-class (economically) at best, and as such enjoy far fewer social, educational and healthcare protections than the economic upper class, and live in markedly more crowded areas with uneven access to transportation and clean water.

Given these hard-to-escape circumstances, identifying the people who were tested as ‘super-spreaders’ seems not only unjust but also an attempt by the press in this case as well as city officials to force them to take responsibility for their city’s epidemic status and preparedness – which is just ridiculous because it criminalises their profession (assuming, reasonably I’d think, that wilfully endangering the health of others around you during a pandemic is a crime).

The Indian Express also reported that the city was testing people and then issuing them health cards – which presumably note that the card-holder has been tested together with the test result. Although I’m inclined to believe the wrong use of the term ‘super-spreader’ here originated not with the newspaper reporter but with the city administration, it’s also frustratingly ridiculous that the people were designated ‘super-spreaders’ at the time of testing, before the results were known – i.e. super-spreader until proven innocent? Or is this a case of officials and journalists unknowingly using two non-interchangeable terms interchangeably?

Or did this dangerous mix-up arise because most places and governments in India don’t have reason to believe ‘high-contact’ is different from ‘super-spreader’?

But be personal and interpersonal hygiene as they may, officials’ use of one term instead of the other also allows them to continue to believe there needn’t or shouldn’t be a difference either. And that’s a big problem because even as the economically middle- and lower-classes may not be able to access better living conditions and amenities, thinking there’s no difference between ‘high-contact’ and ‘super-spreader’ allows those in charge to excuse themselves from their responsibilities to effect that difference.

The number of deaths averted

What are epidemiological models for? You can use models to inform policy and other decision-making. But you can’t use them to manufacture a number that you can advertise in order to draw praise. That’s what the government’s excuse appears to be vis-à-vis the number of deaths averted by India’s nationwide lockdown.

When the government says 37,000 deaths were averted, how can we know if this figure was right or wrong? A bunch of scientists complained that the model wasn’t transparent, so its output had to be taken with a cupful of salt. But as an article published in The Wire yesterday noted, these scientists were asking the wrong questions – that the number of deaths averted is only a decoy.

So say the model had been completely transparent. I don’t see why we should still care about the number of deaths averted. First, such a model is trying to determine the consequences of an action that was not performed, i.e. the number of people who might have died had the lockdown not been imposed.

This scenario is reminiscent of a trope in many time-travel stories. If you went back in time and caused someone to do Y instead of X, would your reality change or stay the same considering it’s in the consequent future of Y instead of X? Or as Ronald Bailey wrote in Reason, “If people change their behaviour in response to new information unrelated to … anti-contagion policies, this could reduce infection growth rates as well, thus causing the researchers to overstate the effectiveness of anti-contagion policies.”

Second, a model to estimate the number of deaths averted by the lockdown will in effect attempt to isolate a vanishingly narrow strip of the lockdown’s consequences to cheer about. This would be nothing but extreme cherry-picking.

A lockdown has many effects, including movement restrictions, stay-at-home orders, disrupted supply of essential goods, closing of businesses, etc. Most, if not all, of them are bound to exact a toll on one’s health. So the number of deaths the lockdown averted should be ‘adjusted’ against, say, the number of people who couldn’t get life-saving surgeries, the number of migrant labourers who died of heat exhaustion, the number of TB patients who developed MDR-TB because they couldn’t get their medicines on time, even the number of daily-wage earners’ children who died of hunger because their parents had no income.

So the only models that can hope to estimate a meaningful number of deaths averted by the lockdown will also have simplified the context so much that the mathematical form of the lockdown will be shorn of all practical applicability or relevance – a quantitative catch-22.

Third, the virtue of the number of deaths averted is a foregone conclusion. That is, whatever its value is, it can only be a good thing. So as an indisputable – and therefore unfalsifiable – entity, there is nothing to be gained or lost by interrogating it, except perhaps to elicit a clearer view of the model’s innards (if possible, and only relative to the outputs of other models).

Finally, the lockdown will by design avert some deaths – i.e. D > 0 – but D being greater than zero wouldn’t mean the lockdown was a success as much D‘s value, whatever it is, being a self-fulfilling prophecy. And since no one knows what the value of D is or what it ought to be, even less what it could have been, a model can at best come up with a way to estimate D – but not claim a victory of any kind.

So it would seem the ‘number of deaths averted’ metric is a ploy disguised as a legitimate mathematical problem whose real purpose is to lure the ‘quants’ towards something they think challenges their abilities without realising they’re also being lured away from the more important question they should be asking: why solve this problem at all?