novel coronavirus – Close Read

On the lab-leak hypothesis

One problem with the debate over the novel coronavirus’s “lab leak” origin hypothesis is a problem I’m starting to see in quite a few other areas of pandemic-related analysis and discussion. It’s that no one will say why others are wrong, even as they insist others are, and go on about why they are right.

Shortly after I read Nicholas Wade’s 10,000-word article on Medium, I pitched a summary to a medical researcher, whose first, and for a long time only, response was one word: “rubbish”. Much later, he told me about how the virus could have evolved and spread naturally. Even if I couldn’t be sure if he was right, having no way to verify the information except to bounce it off a bunch of other experts, I was sure he thought he was right. But how was Wade wrong? I suspect for many people the communication failures surrounding this (or a similar) question may be a sticking point.

(‘Wade’, after the first mention, is shorthand for an author of a detailed, non-trivial article that considers the lab-leak hypothesis, irrespective of what conclusion it reaches. I’m cursorily aware of Wade’s support for ‘scientific racism’, and by using his name, I don’t condone any of his views on these and other matters. Other articles to read on the lab-leak topic include Nicholson Baker’s in Intelligencer and Katherine Eban’s in Vanity Fair.)

We don’t know how the novel coronavirus originated, nor are we able to find out easily. There are apparently two possibilities: zoonotic spillover and lab-leak (both hypotheses even though the qualification has been more prominently attached to the latter).

Quoting two researchers writing in The Conversation:

In March 2020, another article published in Nature Medicine provided a series of scientific arguments in favour of a natural origin. The authors argued: The natural hypothesis is plausible, as it is the usual mechanism of emergence of coronaviruses; the sequence of SARS-CoV-2 is too distantly related from other known coronaviruses to envisage the manufacture of a new virus from available sequences; and its sequence does not show evidence of genetic manipulation in the laboratory.

Proponents of the lab-leak hypothesis (minus the outright-conspiratorial) – rather more broadly the opponents of the ‘zoonotic-spillover’-evangelism – have argued that lab leaks are more common than we think, the novel coronavirus has some features that suggest the presence of a human hand, and a glut of extra-scientific events that point towards suspicious research and communication by members of the Wuhan Institute of Virology.

However, too many counterarguments to Wade’s and others’ articles along similar lines have been to brush the allegations aside, as if they were so easily dismissed – like my interlocutor’s “rubbish”. And it’s an infuriating response. To me at least (as someone who’s been at the receiving end of many such replies), it smacks of an attitude that seems to say (a) “you’re foolish to take this stuff seriously,” (b) “you’re being a bad journalist,” (c) “I doubt you’ll understand the answer,” and (d) “I think you should just trust me”.

I try not to generalise (c) and (d) to maintain my editorial equipoise, so to speak – but it’s been hard. There’s too much of too many scientists going around insisting we should simply listen to them, while making no efforts to ensure non-experts can understand what they’re saying, much less admitting the possibility that they’re kidding themselves (although I do think “science is self-correcting” is a false adage). In fact, proponents of the zoonotic-spillover hypothesis and others like to claim that their idea is more likely, but this is often a crude display of scientism: “it’s more scientific, therefore it must be true”. The arguments in favour of this hypothesis are also being increasingly underrepresented outside the scientific literature, which isn’t a trivial consideration because the disparity could exacerbate the patronising tone of (c) and (d), and render scientists less trustworthy.

Science communication and/or journalism are conspicuous by absence here, but I also think the problem with the scientists’ attitude is broader than that. Short of engaging directly in the activities of groups like DRASTIC, journalists take a hit when scientists behave like pedagogic communication is a waste of time. More scientists should make more of an effort to articulate themselves better. It isn’t wise to dismiss something that so many take seriously – although this is also a slippery slope: apply it as a general rule, and soon you may find yourself having to debunk in great detail a dozen ridiculous claims a day. Perhaps we can make an exception for the zoonotic-spillover v. lab-leak hypotheses contest? Or is there a better heuristic? I certainly think there should be one instead of having none at all.

Proving the absence is harder than proving the presence of something, and that’s why everyone might be talking about why they’re right. However, in the process, many of these people seem to forget that what they haven’t denied is still firmly in the realm of the possible. Actually, they don’t just forget it but entirely shut down the idea. This is why I agree with Dr Vinay Prasad’s words in MedPage Today:

If it escaped due to a wet market, I would strongly suggest we clean up wet markets and improve safety in BSL laboratories because a future virus could come from either. And, if it was a lab leak, I would strongly suggest we clean up wet markets and improve safety in BSL 3 and 4 … you get the idea. Both vulnerabilities must be fixed, no matter which was the culprit in this case, because either could be the culprit next time.

His words provide an important counterweight of sorts to a tendency from the zoonotic-spillover quarter to treat articles about the lab-leak possibility as a monolithic allegation instead of as a collection of independent allegations that aren’t equally unlikely. For example, the Vanity Fair, Newsweek and Wade’s articles have all also called into question safety levels at BSL 3 and 4 labs, whether their pathogen-handling protocols sufficiently justify the sort of research we think is okay to conduct, and allegations that various parties have sought to suppress information about the activities at such facilities housed in the Wuhan Institute.

I don’t buy the lab-leak hypothesis and I don’t buy the zoonotic-spillover hypothesis; in fact, I don’t personally care for the answer because I have other things to worry about, but I do buy that the “scientific illiberalism” that Dr Prasad talks about is real. And it’s tied to other issues doing the rounds now as well. For example, Newsweek‘s profile of DRASTIC’s work has been a hit in India thanks to the work of ‘The Seeker’, the pseudonym for a person in their 20s living in “Eastern India”, who uncovered some key documents that cast suspicion on Wuhan Institute’s Shi Zhengli’s claims vis-à-vis SARS-CoV-2. And two common responses to the profile (on Twitter) have been:

“In 2020, when people told me about the lab-leak hypothesis, I dismissed them and argued that they shouldn’t take WhatsApp forwards seriously.”
“Journalism is redundant.”

(1) is said as if it’s no longer true – but it is. The difference between the WhatsApp forwards of February-April 2020 and the articles and papers of 2021 is the body of evidence each set of claims was based on. Luc Montagnier was wrong when he spoke against the zoonotic-spillover hypothesis last year simply because his reasoning was wrong. The reasons and the evidence matter; otherwise, you’re no better than a broken clock. Facile WhatsApp forwards and right-wingers’ ramblings continue to deserve to be treated with extreme scepticism.

Just because a conspiracy theory is later proven to have merit doesn’t make it not a conspiracy theory; their defining trait is belief in the absence of evidence. The most useful response, here, is not to get sucked into the right-wing fever swamps, but to isolate legitimate questions, and try and report out the answers.
Columbia Journalism Review, April 15, 2020

The second point is obviously harder to fight back, considering it doesn’t stake a new position as much as reinforces one that certain groups of people have harboured for many years now. It’s one star aligning out of many, so its falling out of place won’t change believers’ minds, and because the believers’ minds will be unchanged, it will promptly fall back in place. This said, apart from the numerous other considerations, I’ll say investigations aren’t the preserve of journalists, and one story that was investigated to a greater extent by non-journalists – especially towards a conclusion that you probably wish to be true – has little necessarily to do with journalism.

In addition, the picture is complicated by the fact that when people find that they’re wrong, they almost never admit it – especially if other valuable things, like their academic or political careers, are tied up with their reputation. On occasion, some turn to increasingly more technical arguments, or close ranks and advertise a false ‘scientific consensus’ (insofar as such consensus can exist as the result of any exercise less laborious than the one vis-à-vis anthropogenic global warming), or both. ‘Isolating the legitimate questions’ here apart – from both sides, mind you – needs painstaking work that only journalists can and will do.

Featured image credit: Ethan Medrano/Pexels.

COVID-19, AMR and India

Maybe it’s not a coincidence that India is today the site of the world’s largest COVID-19 outbreak and the world’s most prominent source of antimicrobial resistant (AMR) pathogens, a.k.a. ‘superbugs’. The former fiasco is the product of failures on multiple fronts – including policy, infrastructure, logistics, politics and even ideology, before we need to consider faster-spreading variants of the novel coronavirus. I’m not sure of all the factors that have contributed to AMR’s burgeoning in India; some of them are irrational use of broad-spectrum antibiotics, poor public hygiene, laws that disprivilege ecological health and subpar regulation of hospital practices.

But all this said, both the second COVID-19 wave and the rise of AMR have benefited from being able to linger in the national population for longer. The longer the novel coronavirus keeps circulating in the population, the more opportunities there are for new variants to appear; the longer pathogens are exposed repeatedly to antimicrobial agents in different environments, the more opportunities they have to develop resistance. And once these things happen, their effects on their respective crises are exacerbated by the less-than-ideal social, political and economic contexts in which they manifest.

Again, I should emphasise that if these afflictions have been assailing India for such a long time and in increasingly stronger ways, it’s because of many distinct, and some overlapping, forces – but I think it’s also true that the resulting permission for pathogens to persist, at scale to boot, makes India more vulnerable than other countries might be to problems of the emergent variety. And given the failures that give rise to this vulnerability, this can be one hell of a vicious cycle.

The constructionist hypothesis and expertise during the pandemic

Now that COVID-19 cases are rising again in the country, the trash talk against journalists has been rising in tandem. The Indian government was unprepared and hapless last year, and it is this year as well, if only in different ways. In this environment, journalists have come under criticism along two equally unreasonable lines. First, many people, typically supporters of the establishment, either don’t or can’t see the difference between good journalism and contrarianism, and don’t or can’t acknowledge the need for expertise in the practise of journalism.

Second, the recognition of expertise itself has been sorely lacking across the board. Just like last year, when lots of scientists dropped what they were doing and started churning out disease transmission models each one more ridiculous than the last, this time — in response to a more complex ‘playing field’ involving new and more variants, intricate immunity-related mechanisms and labyrinthine clinical trial protocols — too many people have been shouting their mouths off, and getting most of it wrong. All of these misfires have reminded us of two things: again and again that expertise matters, and that unless you’re an expert on something, you’re unlikely to know how deep it runs. The latter isn’t trivial.

There’s what you know you don’t know, and what you don’t know you don’t know. The former is the birthplace of learning. It’s the perfect place from which to ask questions and fill gaps in your knowledge. The latter is the verge of presumptuousness — a very good place from which to make a fool of yourself. Of course, this depends on your attitude: you can always be mindful of the Great Unknown, such as it is, and keep quiet.

As these tropes have played out in the last few months, I have been reminded of an article written by the physicist Philip Warren Anderson, called ‘More is Different’, and published in 1972. His idea here is simple: that the statement “if everything obeys the same fundamental laws, then the only scientists who are studying anything really fundamental are those who are working on those laws” is false. He goes on to explain:

“The main fallacy in this kind of thinking is that the reductionist hypothesis does not by any means imply a ‘constructionist’ one: The ability to reduce everything to simple fundamental laws does not imply the ability to start from those laws and reconstruct the universe. … The constructionist hypothesis breaks down when confronted with the twin difficulties of scale and complexity. The behaviour of large and complex aggregates of elementary particles, it turns out, is not to be understood in terms of a simple extrapolation of the properties of a few particles. Instead, at each level of complexity entirely new properties appear, and the understanding of the new behaviours requires research which I think is as fundamental in its nature as any other.”

The seemingly endless intricacies that beset the interaction of a virus, a human body and a vaccine are proof enough that the “twin difficulties of scale and complexity” are present in epidemiology, immunology and biochemistry as well – and testament to the foolishness of any claims that the laws of conservation, thermodynamics or motion can help us say, for example, whether a particular variant infects people ‘better’ because it escapes the immune system better or because the immune system’s protection is fading.

But closer to my point: not even all epidemiologists, immunologists and/or biochemists can meaningfully comment on every form or type of these interactions at all times. I’m not 100% certain, but at least from what I’ve learnt reporting topics in physics (and conceding happily that covering biology seems more complex), scale and complexity work not just across but within fields as well. A cardiologist may be able to comment meaningfully on COVID-19’s effects on the heart in some patients, or a neurologist on the brain, but they may not know how the infection got there even if all these organs are part of the same body. A structural biologist may have deciphered why different mutations change the virus’s spike protein the way they do, but she can’t be expected to comment meaningfully on how epidemiological models will have to be modified for each variant.

To people who don’t know better, a doctor is a doctor and a scientist is a scientist, but as journalists plumb the deeper, more involved depths of a new yet specific disease, we bear from time to time a secret responsibility to be constructive and not reductive, and this is difficult. It becomes crucial for us to draw on the wisdom of the right experts, who wield the right expertise, so that we’re moving as much and as often as possible away from the position of what we don’t know we don’t know even as we ensure we’re not caught in the traps of what experts don’t know they don’t know. The march away from complete uncertainty and towards the names of uncertainty is precarious.

Equally importantly, at this time, to make our own jobs that much easier, or at least less acerbic, it’s important for everyone else to know this as well – that more is vastly different.

Super-spreading, mobility and crowding

I still see quite a few journalists in India refer to “super-spreaders” vis-à-vis the novel coronavirus – implying that some individuals might be to blame for ‘seeding’ lots of new infections in the community – instead of accommodating the fact that simply breathing out a lot of viruses doesn’t suffice to infect tens or hundreds of others: you also need the social conditions that will enable all these viral particles to easily find human hosts.

In fact, going a step ahead, a super-spreading event can happen if there are no super-spreading individuals but there are enabling environmental conditions that do nothing to slow the virus’s transmission across different communities. These conditions include lack of basic amenities (or access to them) such as clean water, nutritious meals and physical space.

A new study published by a group of researchers from the US adds to this view. According to their paper’s abstract, “Our model predicts higher infection rates among disadvantaged racial and socioeconomic groups solely from differences in mobility: we find that disadvantaged groups have not been able to reduce mobility as sharply, and that the POIs [points of interest] they visit are more crowded and therefore higher-risk.”

And what they suggest by way of amelioration – to reduce the maximum occupancy at each POI, like a restaurant – applies to a mobility-centric strategy the same way reducing inequality applies to a strategy centred on social justice. In effect, disadvantaged groups of people – which currently include people forced to live in slums, share toilets, ration water, etc. in India’s cities – should have access to the same quality of life that everyone else does at that point of time, including in the limited case of housing.

This study is also interesting because the authors’ model was composed with mobility data from 98 million cellphones – providing an empirical foundation that obviates the need for assumptions about how people move and where. In the early days of India’s COVID-19 epidemic, faulty assumptions on just this count gave rise to predictions about how the situation would evolve in different areas that in hindsight were found to be outlandish – and in some cases in ways that could have been anticipated.

Some modellers denoted people as dots on a screen and assumed that each dot would be able to move a certain distance before it ‘met’ another dot, as well as that all the dots would have a certain total area in which to move around. But as two mathematicians wrote for Politically Math in April this year, our cities look nothing like this:

According to this report, “India’s top 1% bag 73% of the country’s wealth”. Let us say, the physical space in our simulation represents not the ‘physical space’ in real terms, but the ‘space of opportunities’ that exist. In this specific situation of a country under complete lockdown because of the pandemic, this might mean who gets to order ‘contactless’ food online while being ‘quarantined’ at home, and who doesn’t. In our segregated simulation space therefore, the top chamber must occupy 73% of the total space, and the bottom chamber 27%. Also, 1% of the total number of dots occupy the airy top chamber, while the remaining 99% of the dots occupy the bottom chamber.

As a result, and notwithstanding any caveats about the data-taking exercises, researchers reported that Dharavi in Mumbai had a seroprevalence of more than 50% by late July while three wards in non-slum areas had a seroprevalence of only 16%.

The flawed models still can’t claim they could have been right if Mumbai’s slum and non-slum areas were treated as distinct entities. As T. Jacob John wrote for The Wire Science in October, one of the reasons (non-vaccine) herd immunity as a concept breaks when applied to humans is that humans are social animals, and their populations regularly mix such that ‘closed societies’ are rendered practically impossible.

So instead of mucking about with nationwide lockdowns and other restrictions that apply to entire populations at once, the state could simply do two things. First, in the short-term, prevent crowding in places where it’s likely to happen – including public toilets that residents of slums are forced to share, ration shops where beneficiaries of the PDS system are required to queue up, workplaces where workers are crammed too many to a room, etc.

Obviously, I don’t suggest that the government should have been aware of all these features of the epidemic’s progression in different areas from the beginning. But from the moment these issues became clear, and from the moment a government became able to reorient its COVID-19 response strategy but didn’t, it has effectively been in the dock.

This brings us to the second and longer term thing we should do: with the novel coronavirus’s transmission characteristics as a guide, we must refashion policies and strategies to reduce inequality and improve access to those resources required to suppress ‘super-spreading’ conditions at the same time.

The simultaneity is important. For example, simply increasing the average house size from 4 sq. m, say, to 8 sq. m won’t cut it. Instead, buildings have to be designed to allow ample ventilation (with fresh air) and access to sunlight (depending on its natural availability). As researchers from IDFC Institute, a think-tank in Mumbai, noted in another article:

Dharavi’s buildings and paths are irregularly laid out, with few straight routes. Based on calculations with OpenStreetMap routes and Google Earth imagery, it appears 68% of pathways and roads are less than 2 m wide. Such a dimension offers little space for air circulation, and reduces airflow relative to other, properly planned areas, and admits fewer air currents that could help break up the concentration of viral particles.

Mitigating such conditions could also impinge on India’s climate commitments. For example, with reference to our present time in history as the hottest on record, and many countries including India experiencing periods in which the ambient temperature in some regions exceeds thresholds deemed safe for human metabolism, science writer Leigh Phillips wrote for Jacobin that air-conditions must be a human right:

What would it mean to have a right to air-conditioning? Precisely, the right should be to have free or cheap, reliable access to the thermal conditions optimal for human metabolism (air temperatures of between 18 degrees C and 24 degrees C, according to the WHO). Neither too hot nor too cold. The right to Goldilocks’s porridge, if you will. New buildings must come with A/C as part of any “Green New Deal”. The aim of any programme of publicly subsidised mass retrofitting of old buildings shouldn’t be just to fuel-switch away from gas heating and improve insulation, but also to install quiet, efficient air-conditioning systems. At the scale of the electricity grid, this demand must also include the requirement that A/C run on cheap, clean electricity.

So really, none of what’s going on is simple – and when governments respond by offering solutions that assume the problem is simple are avoiding dealing with the real causes. For example, ‘super-spreading’ is neither a choice nor an event – it’s a condition – so solutions that address it as a choice or event are bound to fail. Seen the other way, a community with a high prevalence of a viral infection may be much less responsible for its predicament than the simple interaction of their social conditions with a highly contagious virus.

But this doesn’t mean no solution except a grand, city-scale one can be feasible either – only that all solutions must converge, by being targeted to that effect, on eliminating inequalities.

Powerful microscopy technique brings proteins into focus

Cryo-electron microscopy (cryo-EM) as a technology has become more important because the field that it revolutionised – structural biology – has become more important. The international scientific community had this rise in fortunes, so to speak, acknowledged when the Nobel Prize for chemistry was awarded to three people in 2017 for perfecting its use to study important biomolecules and molecular processes.

(Who received the prize is immaterial, considering more than just three people are likely to have contributed to the development of cryo-EM; however, the prize-giving committee’s choice of field to spotlight is a direction worth following.)

In 2015, two separate groups of scientists used cryo-EM to image objects 2.8 Å and 2.2 Å (1 nm is one-billionth of a metre; 1 Å is one-tenth of this) wide. These distances are considered to be atomic because they represent the ability to image features about as big as a smallish atom, comparable to that of, say, sodium. Before cryo-EM, scientists could image such distances only with X-ray crystallography, which requires the samples to be studied to be crystallised first. This isn’t always possible.

But though cryo-EM didn’t require specimens to be crystallised, they had to be placed in a vacuum first. In vacuum, water evaporates, and when water evaporates from biological objects like tissue, the specimen could lose its structural integrity and collapse or deform. The trio that won the chemistry prize in 2017 developed multiple workarounds for this and other problems. Taken together, their innovations allowed scientists to find cryo-EM to be more and more valuable for research.

One of the laureates, Joachim Frank, developed computational techniques in the 1970s and 1980s to enhance, correct and in other ways modify images obtained with cryo-EM. And one of these techniques in turn was particularly important.

An object will reflect a wave if the object’s size is comparable to the wave’s wavelength. Humans see a chair or table because the chair or table reflects visible light, and our eyes detect the reflected electromagnetic waves. A cryo-EM ‘sees’ its samples using electrons, which have a smaller wavelength than photons and can thus reveal even smaller objects.

However, there’s a catch. The more energetic an electron is, the lower its wavelength is, and the smaller the feature it can resolve – but a high-energy electron can also damage the specimen altogether. Frank’s contributions allowed scientists to reduce the number of electrons or their energy to obtain equally good images of their specimens, leading to resolutions of 2.2 Å.

Today, structural biology continues to be important, but its demands have become more exacting. To elucidate the structures of smaller and smaller molecules, scientists need cryo-EM and other tools to be able to resolve smaller and smaller features, but come up against significant physical barriers.

For example, while Frank’s techniques allowed scientists to reduce the number of electrons required to obtain the image of a sample, using fewer probe particles also meant a lower signal-to-noise ratio (SNR). So the need for new techniques, new solutions, to these old problems has become apparent.

In a paper published online on October 21, a group of scientists from Belgium, the Netherlands and the UK describe “three technological developments that further increase the SNR of cryo-EM images”. These are a new kind of electron source, a new energy filter and a new electron camera.

The electron source is something the authors call a cold field emission electron gun (CFEG). Some electron microscopes use field emission guns (FEGs) to shoot sharply focused, coherent beams of electrons optimised to have energies that will produce a bright image. A CFEG is a FEG that reduces the brightness in favour of reducing the average difference in energies between electrons. The higher this difference – or the energy spread – is, the more blur there will be in the image.

The authors’ pitch is that FEGs help produce brighter but more blurred images than CFEGs, and that CFEGs help produce significantly better images when the goal is to image features smaller than 2 Å. Specifically, they write, the SNR increases 2.5x at a resolution of 1.5 Å and 9.5x at 1.2 Å.

The second improvement has to do with the choice of electrons used to compose the final image. The electrons fired by the gun (CFEG or otherwise) go on to have one of two types of collisions with the specimen. In an elastic collision, the electron’s kinetic energy doesn’t change – i.e. it doesn’t impart its kinetic energy to the specimen. In an inelastic collision, the electron’s kinetic energy changes because the electron has passed on some of it to the specimen itself. This energy transfer can produce noise, lower the SNR and distort the final image.

The authors propose using a filter that removes electrons that have undergone inelastic collisions from the final assessment. In simple terms, the filter comprises a slit through which only electrons of a certain energy can pass and a prism that bends their path towards a detector. This said, they do acknowledge that it will be interesting to explore in future whether inelastically scattered electrons can be be better accounted for instead of being eliminated altogether – akin to silencing a classroom by expelling unruly children versus retaining them and teaching them to keep quiet.

The final improvement is to use the “next-generation” Falcon 4 direct-electron detector. This is the latest iteration in a line of products developed by Thermo Fisher Scientific, to count the number of electrons impinging on a surface as accurately as possible, their relative location and at a desirable exposure. The Falcon 4 has a square detection area 14 µm to a side, a sampling frequency of 248 Hz and a “sub-pixel accuracy” (according to the authors) that allows the device to not lose track of electrons even if they impinge close to each other on the detector.

A schematic overview of the experimental setup. Credit: https://doi.org/10.1038/s41586-020-2829-0

Combining all three improvements, the authors write that they were able to image a human membrane protein called ß3 GABA_A R with a resolution of 1.7 Å and mouse apoferritin at 1.22 Å. (The protein called ferritin binds to iron and stores/releases it; apoferritin is ferritin sans iron.)

A reconstructed image of GABA_A R. The red blobs are water molecules. NAG is N-acetyl glucosamine. Credit: https://doi.org/10.1038/s41586-020-2829-0

“The increased SNR of cryo-EM images enabled by the technology described here,” the authors conclude, “will expand [the technique] to more difficult samples, including membrane proteins in lipid bilayers, small proteins and structurally heterogeneous macromolecular complexes.”

At these resolutions, scientists are closing in on images not just of macromolecules of biological importance but of parts of these molecules – and can in effect elucidate the structures that correspond to specific functions or processes. This is somewhat like going from knowing that viruses infect cells to determining the specific parts of a virus and a cell implicated in the infiltration process.

A very germane example is that of the novel coronavirus. In April this year, a group of researchers from France and the US reported the cryo-EM structure of the virus’s spike glycoprotein, which binds to the ACE2 protein on the surface of some cells to gain entry. By knowing this structure, other researchers can design the more perfect inhibitors to disrupt the glycoprotein’s function, as well as vaccines that mimic its presence to provoke the desired immune response.

In this regard, a resolution of 1-2 Å corresponds to the dimensions of individual covalent bonds. So by extending the cryo-EM’s ability to decipher smaller and smaller features, researchers can strike at smaller, more precise molecular mechanisms to produce more efficient, perhaps more closely controlled and finely targeted, effects.

Featured image: Scientists using a 300-kV cryo-EM at the Max Planck Institute of Molecular Physiology, Dortmund. Credit: MPI Dortmund.

Spray and pray – the COVID-19 version

Kiran Mazumdar-Shaw is the head of Biocon, a company headquartered in Bengaluru and which has repurposed a drug called itolizumab – already approved to help manage severe chronic psoriasis in different markets – to manage cytokine release syndrome (CRS) in COVID-19 patients. Setting aside CRS’s relevance in the COVID-19 pathology (considering it is currently in dispute), Mazumdar-Shaw and a specific coterie of Biocon employees have been aggressively marketing itolizumab despite the fact that its phase II clinical trial seems by all accounts to have been a joke. (I recommend this account.)

Funnily enough, The Print published an article by Mazumdar-Shaw on September 1, in which she describes her experience of the infection (she’s one of The Print‘s funders). Two portions of the article are striking. One is the following paragraph about her treatment, which tacitly implicates a host of drugs and devices in her recovery without providing any additional information of their respective usefulness:

Dr Murli Mohan from Narayana Health, Bengaluru and Dr Shashank Joshi from Lilavati hospital, Mumbai, were my key medical supervisors. I was put on a course of Favipiravir, azithromycin and paracetamol. Apart from this, I continued with my daily dose of Vitamin C, Vitamin D, Zinc, baby aspirin and chyavanprash. Not to mention my twice a week 200mg dose of HCQ. Day two and three were uneventful. I was measuring my oxygen saturation levels six times a day, which were all between 96-98 per cent even after a brisk six-minute walk. My temperature was normal but late evening on Day three, I felt fluish and it extended to Day four and five. No measurable temperature but frequent bouts of sweating, which suggested that my body was fighting the virus. I was also tracking my Cytokine levels.

Reading this brought to mind a terrible period in early 2010, when I had malaria and jaundice together with an unusually strong spate of migraines. I can’t remember the exact drugs and diet that got me feeling better. But after reading what Mazumdar-Shaw went through, I’m inclined to attribute my recovery also to the mug of Bournvita I had every night before bed.

The other striking portion is a list of suggestions that subtly make the case to pay more attention to CRS and treat it with the drugs available in the market for it: “Doctors should not just treat clinical symptoms but rather the cause of the symptoms. If SpO2 (oxygen saturation) reduces, just increasing oxygen flow is not the answer. Treating inflammation caused by cytokines is the answer.” Wonder why researchers don’t yet have consensus… But the Drug Controller General of India has approved two drugs to treat CRS due to COVID-19 in India (through a highly criticised approval process) – and Kiran Mazudar-Shaw’s Biocon’s itolizumab is one of them.

The list is also prefaced by the following statement, among others: “… avoid TV and social media as negative news is bad for fighting Covid-19.” I wonder if this refers to criticism against hydroxychloroquine (HCQ), favipiravir, azithromycin and purported Ayurvedic remedies as well.

India’s missing research papers

If you’re looking for a quantification (although you shouldn’t) of the extent to which science is being conducted by press releases in India at the moment, consider the following list of studies. The papers for none of them have been published – as preprints or ‘post-prints’ – even as the people behind them, including many government officials and corporate honchos, have issued press releases about the respective findings, which some sections of the media have publicised without question and which have quite likely gone on to inform government decisions about suitable control and mitigation strategies. The collective danger of this failure is only amplified by a deafening silence from many quarters, especially from the wider community of doctors and medical researchers – almost as if it’s normal to conduct studies and publish press releases in a hurry and take an inordinate amount of time upload a preprint manuscript or conduct peer review, instead of the other way around. By the way, did you know India has three science academies?

ICMR’s first seroprevalence survey (99% sure it isn’t out yet, but if I’m wrong, please let me know and link me to the paper?)
Mumbai’s TIFR-NITI seroprevalence survey (100% sure. I asked TIFR when they plan to upload the paper, they said: “We are bound by BMC rules with respect to sharing data and hence we cannot give the raw data to anyone at least [until] we publish the paper. We will upload the preprint version soon.”)
Biocon’s phase II Itolizumab trial (100% sure. More about irregularities here.)
Delhi’s first seroprevalence survey (95% sure. Vinod Paul of NITI Aayog discussed the results but no paper has pinged my radar.)
Delhi’s second seroprevalence survey (100% sure. Indian Express reported on August 8 that it has just wrapped up and the results will be available in 10 days. It didn’t mention a paper, however.)
Bharat Biotech’s COVAXIN preclinical trials (90% sure)
Papers of well-designed, well-powered studies establishing that HCQ, remdesivir, favipiravir and tocilizumab are efficacious against COVID-19 🙂

Aside from this, there have been many disease-transmission models whose results have been played up without discussing the specifics as well as numerous claims about transmission dynamics that have been largely inseparable from the steady stream of pseudoscience, obfuscation and carelessness. In one particularly egregious case, the Indian Council of Medical Research announced in a press release in May that Ahmedabad-based Zydus Cadila had manufactured an ELISA test kit for COVID-19 for ICMR’s use that was 100% specific and 98% sensitive. However, the paper describing the kit’s validation, published later, said it was 97.9% specific and 92.37% sensitive. If you know what these numbers mean, you’ll also know what a big difference this is, between the press release and the paper. After an investigation by Priyanka Pulla followed by multiple questions to different government officials, ICMR admitted it had made a booboo in the press release. I think this is a fair representation of how much the methods of science – which bridge first principles with the results – matter in India during the 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.

Questions we should be asking more often

1. Okay, but where’s the money coming from?

In a lecture at the Asian College of Journalism, where I was in the audience as a student, P. Sainath told us that if we needed one rule following which we’d be able to produce good stories, it’s “follow the money”. It’s remarkable how often this suggestion has been borne out (in the right contexts, of course) – and it’s even more remarkable how many people don’t follow it. Asking where the money is coming from also serves to enlighten people about why journalism works the way it does. I’m often asked by aspiring science journalists why a journalistic magazine devoted to, say, astronomy, physics or genomics doesn’t exist in India. I’ve always had the same answer: tell me how you’re going to make money (as in profits, not just revenues).

2. Okay, but what’s the power source?

The next time you receive a WhatsApp forward about a newfangled device that can do remarkable things, ask yourself where it could be getting its power – especially the requisite amount of electric power. Very few claims of amazing feats survive this check, especially as they pertain to very small objects like chips or transmitters being embedded in things and beaming signals to satellites. Depending on the medium through which they’re transmitting – air, soil, water, stone, etc. – and the distance to which they need to transmit, you can get a fair idea of the device’s power needs, and then set about figuring where the power is coming from. This question is analogous to ‘follow the money’; the currency here is energy.

3. Okay, but who’s behind the camera?

We seldom stop to think about the person behind the camera, especially if the picture is striking in some way. This goes for photos and videos about terrifying events like natural disasters, objects deep underwater and strange things in space. Pictures purporting to show something amazing but are actually fake are often taken from impossible vantage points, with a resolution that should be impossible to achieve with the device in use, with an impossible spatial scale, at locations that should have been impossible to reach at that time or by a cameraperson whose presence at the scene defies explanation. At other times, the photos appear as if they could only have been captured by specific people, and that in turn may impose some limitations on their public availability. For example, images captured by fighter-jet pilots shouldn’t be easily available – while those captured by policemen during riots could have been planted.

4. Okay, but who said so?

Ad hominem makes for bad arguments – but it’s very useful in fact-checking. It’s important who makes a certain claim so you can check their expertise and if they’re qualified to make the statement they did. If you’re looking for problems with Darwin’s theory of evolution, listen to an evolutionary biologist, not a geologist – not even if they’re a Nobel-Prize-winner. Asking for the source also helps push back on ‘data supremacy’, the tendency to defer to data just because it’s data and without checking for its provenance or quality, and on a general laziness to ascertain that a claim has been traced to its first-hand source, instead of feeding off of second-hand, third-hand, etc. sources.

5. Okay, but how many things had to fall in place?

The idea of the Occam’s razor has captured the imagination of many a rookie analyst, so much so that some of them over-apply its prescriptions to draw reductive conclusions. In their view, only the likeliest event happens all the time; when something unlikely happens, they smell something rotten – like conspiracy theorists do with the novel coronavirus. However, the mathematics of probability allows unlikely events to happen more often than you think, often because they were only seemingly unlikely to begin with. For example, the novel coronavirus was quietly evolving through other ‘forms’ in the wild before it became the strain adapted to infecting humans – the most widespread animal species on the planet. Even now, there may be other strains circulating in the wild, but we remain fixated on the one infecting us.

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?