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.
First, there was light news of a vaccine against COVID-19 nearing the end of its phase 3 clinical trials with very promising results, accompanied with breezy speculations (often tied to the stock prices of a certain drug-maker) about how it’s going to end the pandemic in six months.
An Indian disease-transmission modeller – of the sort who often purport to be value-free ‘quants’ interested in solving mathematical puzzles that don’t impinge on the real world – reads about the vaccine and begins to tweak his models accordingly. Soon, he has a projection that shines bright in the dense gloom of bad news.
One day, as the world is surely hurtling towards a functional vaccine, it becomes known that some of the world’s richest countries – representing an eighth of the planet’s human population – have secreted more than half of the world’s supply of the vaccine.
Then, a poll finds that over half of all Americans wouldn’t trust a COVID-19 vaccine when it becomes available. The poll hasn’t been conducted in other countries.
A glut of companies around the world have invested heavily in various COVID-19 vaccine candidates, even as the latter are yet to complete phase 3 clinical trials. Should a candidate not clear its trial, a corresponding company could lose its investment without insurance or some form of underwriting by the corresponding government.
Taken together, these scenarios portend a significant delay between a vaccine successfully completing its clinical trials and becoming available to the population, and another delay between general availability and adoption.
The press glosses over these offsets, developing among its readers a distorted impression of the pandemic’s progression – an awkward blend of two images, really: one in which the richer countries are rapidly approaching herd immunity while, in the other, there is a shortage of vaccines.
Sooner or later, a right-wing commentator notices there is a commensurately increasing risk of these poorer countries ‘re-exporting’ the virus around the world. Politicians hear him and further stigmatise these countries, and build support for xenophobic and/or supremacist policies.
Meanwhile, the modeller notices the delays as well. When he revises his model, he finds that as governments relax lockdowns and reopen airports for international travel, differences in screening procedures in different countries could allow the case load to rise and fall around the world in waves – in effect ensuring the pandemic will take longer to end.
His new paper isn’t taken very seriously. It’s near the end of the pandemic, everyone has been told, and he’s being a buzzkill. (It’s also a preprint, and that, a senior scientist in government nearing his retirement remarks, “is all you need to know”.) Distrust of his results morphs slowly into a distrust towards scientists’ predictions, and becomes ground to dismiss most discomfiting findings.
The vaccine is finally available in middle- and low-income countries. But in India, this bigger picture plays out at smaller scales, like a fractal. Neither the modeller nor the head of state included the social realities of Indian society in their plans – but no one noticed because both had conducted science by press release.
As they scratch their heads, they also swat away at people at the outer limits of the country’s caste and class groups clutching at them in desperation. A migrant worker walks past unnoticed. One of them wonders if he needs to privatise healthcare more. The other is examining his paper for arithmetic mistakes.
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 Dbeing 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?
In December 2014, public health researchers and activists gathered at a public forum in Cambridge, Massachusetts, to discuss how our perception of diseases and their causative pathogens influences our ideas of what we can and can’t do to fight them. According to a report published in The Harvard Gazette:
The forum prompted serious reflection about structural inequalities and how public perceptions get shaped, which often leads to how resources are directed. “The cost of believing that something is so lethal and fatal is significant,” [Paul] Farmer said.
[Evelynn] Hammonds drew attention to how perceptions of risk about Ebola had been shaped mostly through the media, while noting that epidemics “pull the covers off” the ways that the poor, vulnerable, and sick are perceived.
These statements highlight the importance of a free press with a spine during a pandemic – instead of one that bends to the state’s will as well as doesn’t respect the demands of good health journalism while purporting to practice it.
We’ve been seeing how pliant journalists, especially on news channels like India Today and Republic and in the newsrooms of digital outlets like Swarajya and OpIndia, try so hard so often to defend the government’s claims about doing a good job of controlling the COVID-19 epidemic in India. As a result, they’ve frequently participated – willingly or otherwise – in creating the impression that a) the virus is deadly, and b) all Muslims are deadly.
Neither of course is true. But while political journalists, who in India have generally been quite influential, have helped disabuse people of the latter notion, the former has attracted fewer rebuttals principally because the few good health journalists and the vocal scientists operating in the country are already overworked thanks to the government’s decoy acts on other fronts.
As things stand, beware anyone who says the novel coronavirus is deadly if only because a) all signs indicate that it’s far less damaging to human society than tuberculosis is every year, and b) it’s an awfully powerful excuse that allows the government to give up and simply blame the virus for a devastation that – oddly enough – seems to affect the poor, the disabled and the marginalised too far more than the law of large numbers can account for.
I got to this article about Stephen Wolfram’s most recent attempt to “revolutionise” fundamental physics quite late, and sorry for it because I had no idea Wolfram was the kind of guy who could be a windbag. I haven’t ever had cause to interact with his work or his software company (which produced Wolfram Mathematica and Wolfram Alpha), so I didn’t know really know much about him to begin with. But I expected him, for reasons I can’t explain, to be more modest than he comes across as in the article.
The article was prompted in the first place by a preprint paper Wolfram and a colleague published earlier this year in which they claimed they had plotted a route to a fundamental theory of everything. Physics currently explains the universe with a combination of multiple theories that don’t really fit together. A ‘theory of everything’ is the colloquial name of a universal theory that many physicists argue exist and which could explain everything about the universe in a self-consistent manner.
Wolfram’s preprint paper was startling as things go not because of its substance but because a) he made no attempts to engage with the wider community of physicists that has been working on the same problem for decades, and b) for Wolfram’s insistence that those dismissing its conclusions are simply out to dismiss him. Consider the following portions:
“I do fault myself for not having done this 20 years ago,” the physicist turned software entrepreneur says. “To be fair, I also fault some people in the physics community for trying to prevent it happening 20 years ago. They were successful” [emphasis added].
“The experimental predictions of [quantum physics and general relativity] have been confirmed to many decimal places—in some cases, to a precision of one part in [10 billion],” says Daniel Harlow, a physicist at the Massachusetts Institute of Technology. “So far I see no indication that this could be done using the simple kinds of [computational rules] advocated by Wolfram. The successes he claims are, at best, qualitative.” …
“Certainly there’s no reason that Wolfram and his colleagues should be able to bypass formal peer review,” Katie Mack says. “And they definitely have a much better chance of getting useful feedback from the physics community if they publish their results in a format we actually have the tools to deal with.”
Reading of this attitude brought to mind an episode from six or seven weeks ago, after a pair of physicists had published a preprint paper modelling the evolution of the COVID-19 epidemic in India and predicting that multiple lockdowns instead of just one would work better. The paper was one of many that began to show up around that time, each set of authors fiddling with different parameters according to their sense of the world to reach markedly different conclusions (a bit of ambulance-chasing if you ask me).
The one by the two physicists was singled out for bristling criticism by other physicists because – quite like the complaints against Wolfram – their paper allegedly described a model that seemed to be able to reach any conclusion if you tweaked its parameters enough, and because the duo hadn’t clarified this and other important caveats in their interviews to journalists.
Aside 1 – In physics at least, it’s important for theories to be provable in some domains and falsifiable in others; if a theory of the world is non-falsifiable, it’s not considered legitimate. In Wolfgang Pauli’s famous words, it becomes ‘not even wrong’.
Aside 2 – Incidentally, Harlow – quoted above from the article – was one of the physicists defending physicists’ freedom to model what they will but agreed with the objection that they also need to be honest with journalists about their assumptions and caveats.
In a lengthy Facebook discussion that followed this brouhaha, someone referred to a Reddit post created three days earlier in which a physicist appealed to his peers to stop trying to model the pandemic – in his words, to “cut that shit out” – because a) no physicist could hope to do a better job than any other trained epidemiologist, and b) every model a physicist attempted could actually harm lives if it wasn’t done right (and there was a good chance it was at least incomplete).
Wolfram is guilty of the same thing: his preprint paper won’t harm lives, but the mortal threat is the only thing missing from his story; it’s otherwise rife with the same problems. His hubristic remark in the article’s denouement – that he deserves “better” questions than the ones other physicists were asking him in response to his “revolutionary” paper – indicates Wolfram thinks he’s done a great job but it’s impossible to see people like him as anything more than windbags convinced of their intellectual superiority and ability to singlehandedly wrestle hideously intractable problems to the ground. I, and likely other editors as well, have glimpsed this attitude on the part of some authors who dismiss criticism of their pieces as criticism of anything but their unclear writing, and some others who refuse to be disabused of a conviction that their conclusion is particularly fascinating.
I’d like to ask Wolfram what I’d like to ask these people as well: What have you hit on that you think others haven’t in all this time, and why do you think all of them missed it? Granted, everyone is allowed their ‘eureka’ moment, but anyone who claims it on the condition that he not be criticised is not likely to be taken seriously. More importantly, he may not even deserve to be taken seriously if only because, to adapt Mack’s line of reasoning, he undermines the very thing on which modern science is founded, the science he claims to be improving: processes, not outcomes; involving communities, not individuals.
There’s a virus out there among many, many viruses that’s caught the world’s attention. This virus came into existence somewhere else, it doesn’t matter where, and developed a mutation at some point that allowed it to do what it needs to do inside the body of one specific kind of animal: Homo sapiens. And once it enters one Homo sapiens, it takes advantage of its new surroundings to produce more copies of itself. Then, its offspring wait for the animal to cough or sneeze – acts originally designed to expel irritating substances – to exit their current home and hopefully enter a new one. There, these viruses go through the same cycle of reproduction and expulsion, and so forth.
This way, the virus has infected over 210,000 people in the last hundred days or so. Some people’s bodies have been so invaded by the virus that their immune systems weren’t able to fight it off, and they – nearly 9,000 of them – succumbed to it.
Thus far, the virus has reportedly invaded the bodies of at least 282 people in India. There’s no telling how the virus will dissipate through the rest of the population – if it needs to – except by catching people who have the virus early, separating them from the rest of the population for long enough to ensure they don’t have and/or transmit the virus or, if they do, providing additional treatment, and finally reintegrating them with the general population.
But as the virus spreads among more and more people, it’s going to become harder and harder to tell how every single new patient got their particular infection. Ultimately, a situation is going to arise wherein too many people have the virus for public-health officials to be able to say how exactly the virus got to them. The WHO calls this phase ‘community transmission’.
India is a country of over 1.3 billion people, and is currently on the cusp of what the Indian Council of Medical Research (ICMR) has called ‘stage 3’ – the advent of community transmission. It’s impossible to expect a developing country as big and as densely populated as India to begin testing all 1.3 billion Indians for the virus as soon as there is news of the virus having entered the national border because the resource cost required to undertake such an exercise is extremely high, well beyond what India can generally afford. However, this doesn’t mean Indians are screwed.
Instead of testing every Indian, ICMR took a different route. Consider the following example: there’s a population of red flecks randomly interspersed with yellow flecks. You need to choose a small subset of flecks from this grid (shown below) such that checking for the number of yellow flecks in the subset gives you a reliable idea of the number of yellow flecks overall.
The ideal subset would be the whole set, of course, so there is one more catch: you have a fixed amount of money to figure out the correct answer (as well as for a bunch of other activities), so it’s in your best interests to keep the subset as small as possible. In effect, you need to balance the tension between two important demands: getting to a more accurate answer while spending less.
Similarly, ICMR assumed that the virus is randomly distributed in the Indian population, and decided to divide the population into different groups, for example by their relative proximity to a testing centre. That is, each testing centre would correspond to the group of all people who live closer to that testing centre than any other. Then, ICMR would pick a certain number of people from each group, collect their nasal and throat samples and send it to the corresponding labs for tests.
Say group size equals 100. For a Bernoulli random variable with unknown probability p, if no events occur in n independent trials, the maximum value of p (at 95% confidence) is approximately 3/n. In our case, n = 100 and p at 95% confidence is 3/100, which is 3%. Since this is the upper bound, it means less than 3% of the population has the ‘event’ which didn’t occur in n trials – which in our case is the event of ‘testing positive’. Do note, this is what is safe to say; it’s not what may actually be happening on the ground. So by increasing the sample size n as much as possible, ICMR can ascertain with higher and higher confidence as to whether the corresponding group has community transmission or not.
Thus far, ICMR has said there is no community transmission in India based on these calculations. Independent experts have been reluctant to take its word, however, because while ICMR has publicised what the sample size and the number of positives are, there is very little information about two other things.
First: we don’t know how ICMR selected the samples that it did for testing. While the virus’s distribution in the population can be considered to be random, especially if community transmission is said to have commenced, the selection of samples needs to have an underlying logic. What is that logic?
Second: we don’t know the group sizes. It’s important for the sample size to be proportionate to the group size. So without knowing what the group size underlying each sample is, it becomes impossible to tell if ICMR is doing its job right.
On March 17, one ICMR scientist said that some testing centres had admitted fewer people with COVID-19-like symptoms and the source of whose infections was unknown (i.e. community transmission) than the size of the sample chosen from their corresponding group. She was suggesting that ICMR’s choice of samples from each group was large enough to not overlook community transmission. To translate in terms of the example above: she was saying ICMR’s subset size was big enough to catch at least one yellow fleck – and didn’t.
As it happens, on March 20, ICMR announced that it would begin testing for a potential type of community-transmission cases even though its sampling exercise had produced 1,020 negative results in 1,020 samples (distributed across 51 testing centres).
The reasons for this are yet unclear but suggests that ICMR suspects there is community transmission of the virus in the country even though its methods – which ICMR has always stood by – haven’t found evidence of such transmission. This in turn prompts the following question: why not test for all types of community transmission? The answer is the same as before: ICMR has limited resources but at the same time has been tasked with discovering how many yellow flecks are there in the total population.
The virus is not an intelligent creature. In fact, it’s extremely primitive. Each virus is in its essence a packet of chemical reactions, and when each reaction happens depends on a combination of internal and external conditions. Other than this, the virus does not harbour any intentions or aspirations. It simply responds to stimuli that it cannot manipulate or affect in any way.
The overarching implication is that beyond how good the virus is at spreading from person to person, a pandemic is what it is because of human interactions, and because of human adaptation and mitigation systems. And as more and more people get infected, and their groups verge towards the WHO’s definition of ‘community transmission’, the virus’s path through the population becomes less and less obvious, but at the same time a greater depth of transmission opens the path to better epidemiological modelling.
When such transmission happens in a country like India, the body responsible for keeping the people safe – whether the Union health ministry, ICMR or any other entity – faces the same challenge that ICMR did. This is also why direct comparisons of India’s and South Korea’s testing strategies are difficult to justify, especially of the number of people tested per million: India has nearly 26-times as many people but spends 11.5-times less on healthcare per capita.
At the same time, ICMR isn’t making it easy for anyone – least of all itself – when it doesn’t communicate properly, and leaves itself open to criticism, which in turn chips away at its authority and trustworthiness in a time as testing as this. Demonetisation taught us very well that a strategy is only as good as its implementation.
But on the flip side, it wouldn’t be amiss to make a distinction here: between testing enough to get a sense of the virus’s prevalence in the population – in order to guide further action and policy – and the fact that the low expenditure on public healthcare is always going to incentivise India to skew towards a sampling strategy instead of an alternative that requires mass-testing. ICMR and the Union health ministry haven’t inspired confidence on the first count but it’s important to ensure criticism of the former doesn’t spillover into criticism of the latter as well.
Anyway, the corresponding sampling strategy is going to have to be based on a logic. Why? Because while the resources for the virus to spread exist abundantly in nature (in the form of humans), the human response to containing the spread requires resources that humans find hard to get. Against the background of this disparity, sampling, testing and treatment logics – such as Italy’s brutal triaging policy – help us choose better sampling strategies; predict approximately how many people will need to be quarantined in the near future; prepare our medical supplies; recruit the requisite number of health workers; stockpile important drugs; prepare for economic losses; issue rules of social conduct for the people; and so forth.
A logic could even help anticipate (or perpetuate, depending on your appetite for cynicism) ‘leakages’ arising due to, say, caste or class issues. Think of it like trying to draw a circle with only straight lines of a fixed length: with 200 strokes, you could technically draw a polygon with 200 sides that looks approximately like a circle – but it will still have some discernible edges and vertices that won’t exactly map on a circle, leaving a small part of the latter out. Similarly, using a properly designed technique that can predict which person might get infected and who might not can still catch a large number of people – but the technique won’t catch all of them.
One obvious way to significantly improve the technique’s efficacy as it stands is to account for the fact that more than half of all Indians are treated at private hospitals whereas you can be tested for COVID-19 only at a government facility, and not all VRDLs receive samples from all private hospitals in their respective areas.
Ultimately, the officials who devise the logics must be expected to justify how the combination of all logics can – even if only on paper – uncover most, if not all, cases of the virus’s infection in India.
The contents of this blog post should have come out earlier (in a different form) but better late than never, eh? The Ebola outbreak has been more threatening than ever of going out of control (even as whether we’re really in control now is doubtful). As doctors and healthcare workers grappled with containment in West Africa, Michael Osterholm, the director of the Center for Infectious Diseases Research and Policy, University of Minnesota, wrote an alarmist opinion piece in The New York Times on September 11 that was more panic-mongering than instigatory. The thrust of Osterholm’s argument was:
The second possibility is one that virologists are loath to discuss openly but are definitely considering in private: that an Ebola virus could mutate to become transmissible through the air. … If certain mutations occurred, it would mean that just breathing would put one at risk of contracting Ebola. Infections could spread quickly to every part of the globe, as the H1N1 influenza virus did in 2009, after its birth in Mexico.
Sometime soon after, I spoke to a virologist at Columbia University, Dr. Vincent Racaniello, about Osterholm’s statements. I picked out Dr. Racaniello after stumbling on his virology blog (bookmark it, it’s very insightful) which at the time appeared to be one of the few voices of reason advocating caution in the face of the outbreak and pushing against the notion of an airborne Ebola virus with some crucial facts. Below, I reproduce parts of our conversation that address the nature of such facts and how they should guide us.
Note: For the TL;DR version, scroll right to the bottom.
What we know about Ebola based on what we’ve learnt from studying viruses
Some viruses are studied more than others because of their impact on human health. HIV, influenza, the herpes viruses… Herpes viruses infect almost every person on the Earth; influenza infects hundreds of thousands every year; HIV has infected millions and millions of people – so those get most of the attention, so people work on them a lot. Some of the things you find may be generalizable, such as the general need of a virus to get inside of a cell, replicate its genome. But each virus has specifics. Each is very different, the genome is different, the way the genome is encased is different, the way it gets into cells is different, and the ways they spread from person to person are often very different.
For example, if you study transmission of the influenza virus in an animal model, you may learn what controls the transmission of those viruses through the air, but you can’t assume that’s going to be the same for the Ebola virus. So people make the mistake of saying “Because this virus does this, then that virus must do the same thing”. That’s not correct. Unfortunately, it makes it complicated because every virus needs to be studied on its own. We can’t study influenza and hope to prevent Ebola.
How viruses evolve to become deadlier
From what we have seen, if you gain a function, you typically lose something else. When humans impose genetic changes on viruses, they’re doing so from their point of view as opposed to the way it happens in nature, where evolution does the job. When a virus in nature somehow evolves and becomes transmissible in some species, it’s because the virus with the right genome has been selected as opposed to in the lab where a human puts one or two mutations in a virus and gets a phenotype. We don’t know how to achieve gain-of-function in viruses in the lab. We have a lot of hubris, we think we can do anything with viruses. We introduce an amino acid change but who knows what it’s doing to the virus.
What we’ve observed over the years is that when you introduce changes in the virus in the laboratory to get a new property that you want, you lose something else. In terms of transmission, there haven’t been that many transmission experiments done with viruses to understand what controls transmission. H5N1 – avian influenza – ferrets is really the only one – and there, the gain of aerosol transmission caused the loss of virulence. It’s probably because you need other changes to compensate what you’ve done but we’re only looking at transmission.
In nature, perhaps that would be taken care of, so that’s why I say when you change something in a virus you lose something else. But this is not to say that this is always going to be the case. You can’t predict in viruses – you can’t predict in science, often – what’s going to happen. But what we can do is use what we know and use that to inform our thinking. For example, in nature, influenza viruses are very nicely transmitted, but they’re not all that virulent. They don’t have a 90% case-fatality ratio like Ebola, so I think there’s something there that tells us that aerosol transmission is a difficult thing to achieve. But we don’t know what will happen.
An Ebola virus virion. Image: CDC/Wikimedia Commons
About what other evolutionary pathways Ebola has at its disposal
Viruses can be transmitted in a number of ways. They can be transmitted through the air, they can be transmitted by close contact of various sorts, they can be transmitted by body fluids, they can be transmitted by sexual contact, intravenous drug use, mother to child during birth, they can be transmitted by insect vectors, and of course some can be transmitted in our DNA – 8% of our genome is a virus. We have never seen a human virus change the way it’s transmitted. Once a virus has already been in people, we have never seen it change.
We’ve been studying viruses for just over a 100 years which is admittedly not a long time – viruses have probably been around since the beginning of the Earth, billions of years – but we go based on what we know, and we’ve never seen a virus change it’s mode of transmission. I’m not particularly worried about Ebola changing its routes of transmission. Right now, it’s spreading by close contact from person to person via body fluids and I think it’s going to stay that way. I don’t think we need to worry about it being picked up by a mosquito for example – that’s very difficult to do because then the virus would have to replicate in the mosquito and that’s a big challenge. And who knows, if it acquired that, what other property would be compromised.
What, according to Dr. Racaniello, we need to focus on
I think we need to really bear down on stopping transmission. It can be done, it’s not going to be easy, but it’s going to require other countries helping out because these West African countries can’t do it themselves. They don’t have a lot of resources and they’re losing a lot of their healthcare people from the epidemic itself. I don’t see what worrying about aerosol transmission would do. I don’t see it changing the way we treat the outbreak at all. I think right now we need to get vaccines and antivirals approved, so that we can get in there and use them. In the meantime, we need to try and interrupt transmission. In past outbreaks, interrupting it has been the way to stop the outbreaks. Admittedly, they’ve been a lot smaller, easier to contain. But SARS infected 10,000 people globally and it was contained by very stringent measures. That was a virus that did transmit by aerosol. So it can be done – it’s just a matter of getting everyone cooperating to do it.
If a virus can become more transmissible after infecting a human population
If you saw the movie ‘Contagion’ – in this movie, the virus mutated and increased its reproductive index, which I thought was one of the weaknesses of the movie. We’ve never seen that happen in nature, which is not to say that it hasn’t. When a virus starts circulating in people, it has everything it needs to circulate effectively. Often, people will bring the 1918 influenza virus which seemed to get more virulent as the outbreak continued but back then we hadn’t even isolated the influenza virus. It wasn’t isolated until 1933. So there’s just no way we can make definitive statements about what did or didn’t happen, but people speculate all the time.
I wish we could go back in time and sample all the viruses that have been out there but we’re going to have to see it happen. For that same reason, no virus has ever changed its transmission route in people. If it had, we could have taken the virus before and after the change and sequence it and say, “Aha! This is what’s important for this kind of transmission!” We don’t have that information so we depend on animals for this.
TL;DR:
We can’t study influenza and hope to prevent Ebola.
When you introduce changes in the virus in the laboratory to get a new property that you want, you lose something else.
In nature, influenza viruses are very nicely transmitted, but they’re not all that virulent. I think there’s something there that tells us that aerosol transmission is a difficult thing to achieve.
No virus has ever changed its transmission route in people.
SARS infected 10,000 people globally and it was contained by very stringent measures. That was a virus that did transmit by aerosol. So it can be done – it’s just a matter of getting everyone cooperating to do it.
