WHAT is epidemiology? It is surprising how few of us know anything about it. In the Age of Science, the one thing that we can be sure of is that wherever we go, most people won’t know much science.

But they have faith in it.

A lot has changed in science from when I was a teenager in the late 1980s. Computers had only just begun to come on the scene. Algorithms were just a type of equation. Science was about dreaming of the day when Stephen Hawking would finally solve the riddle of the Universe with his Grand Unified Theory of Physics. Science was wonder and mystery. It was not so much about what you knew, but about what you did not know. In some types of science, like quantum physics, it was about what you could never know, as you endlessly contemplated those elusive subatomic particles that changed even as you looked at them. Were they defying you to ever really know the secret of the universe?

At that time, we were all familiar with the figure of the Mad Scientist and his twin, the Nutty Professor. We used to laugh at their absentmindedness, their enthusiasm for ideas that defied common sense and their evil designs on us, that only they seemed not to know were doomed to fail. Beneath the hilarity, we were keenly aware of something quite serious. Even as we laughed at the Mad Scientist and Nutty Professor, we remembered Hiroshima and Nagasaki, the Eugenics Movement, the Genocide of World War II, Albert Einstein’s deep angst over the atomic bomb and the threat of nuclear war.

Cut to the 21st century, and things were rather different. Gone was the mystery of science. The science was now ‘Settled’. It had been ‘Settled’ by supercomputers. There was no sign of the Mad Scientist or the Nutty Professor. In this new world, no one laughed at scientists and everyone knew what an algorithm was. Stephen Hawking wrote, what would turn out to be his last book, The Grand Design, in which he said that he had come to the conclusion that there would never be a Grand Unified Theory of Physics. He said this had something to do with the limits of physics as a method and the universe as its subject. But no one noticed because now we had algorithms to tell us ‘Everything’.

We were primed for this. There is no way, otherwise, that we would have fallen for the Grand-Covid-Chicken-Little-End-of-the-World-Venture, as we have done.

 

Let us start with understanding what epidemiologists do. Epidemiologists work with algorithms that are supposed to enable you to calculate the total number or size of a phenomenon by taking into account the variables that affect it. They feed the numbers for different variables into a computer that has been programmed to run calculations using the chosen algorithm. The results of these calculations can be plotted on a graph, to give you the curves with which we have all now become so familiar.

But a computer can only run the variables according to the algorithm. It cannot tell you what those variables should be, and herein lies the rub. Deciding which variables are relevant to the phenomenon you are studying is not a mathematical exercise, but a theoretical one. Ideally, you should have a solid theoretical understanding of the phenomenon under study, on the basis of which you can identify, in a rigorous, stable and complete way, the set of variables that apply. An estimation using mathematical modelling is not merely about putting a number on different elements in your equation. It is, in essence, a theory of what elements to include in the equation, and how they relate to each other.

But epidemiologists are not big on theory; they don’t spend much time thinking about whether they have taken into account all the factors that drive a disease outbreak, or their relative importance. There is no great understanding, in principle, of any disease or any population. They prefer to run with working assumptions, which they keep changing as things unfold in the real world, with whatever disease they are modelling. Since these assumptions are made without much knowledge of the biology of the pathogen; or the population under study; or of the actual practice of medicine, you do not have to be a mathematical modeller to predict that things are likely to go wrong.

 

The writer and mathematician, Cathy O’Neil, puts it in this way in her book, Weapons of Math Destruction: ‘models are by their very nature, simplifications. No model can include all of the real world’s complexity… Inevitably some important information gets left out… to create a model, then, we make choices about what’s important enough to include, simplifying the world in a toy version that can be easily understood and from which we can infer important facts and actions.’

After putting together their back-of-the-envelope variables, epidemiologists then start the exercise of ‘fitting’ their models to the data. As the information and data for the disease comes in, the quantities assigned to different variables in their model are changed so as to produce the outcome that is observed. On this basis, epidemiologists will work backwards to tell you, for instance, the ‘Reproduction Number’ or ‘R’, which is the number of people who can be infected by one ill person; this is a key estimate that epidemiologists use to predict the rate of transmission of a disease. After back-calculating to infer the R, they then use this R value to work forwards to predict the number of cases.

Do you see the circularity of the reasoning? Where this keeps going wrong is that because there is no understanding in principle, of how the virus behaves, or why some people fall ill and others don’t, your prediction based from the inference of present behaviour is only as good as your assumption about if and how the R is going to change over time.

 

We have, in very recent memory, all been the victims of the virtual reality that balloons out of this kind of feedback logic, viz., the World Financial Crash of 2007-08. Writing about the junk mortgage-backed securities that were okayed by investment banks based on mathematical risk modelling, Cathy O’Neill writes: ‘mathematical models, by their nature, are based on the past, and on the assumption that patterns will repeat.’ The assumption with sub-prime mortgages was that everyone would not default on loans at the same time. But then they did, and it all came crashing down. In O’Neill’s words, ‘The… false assumption was that not many people would default at the same time. This was based on the theory, soon to be disproven, that defaults were largely random and unrelated events… The risk models were assuming that the future would be no different from the past.’

What makes epidemiological modelling even more unreliable is that epidemiologists do not even spend much time identifying their underlying estimates or assumptions when assembling the variables for their equation. So, assumptions will be built into the epidemiologists’ models that they are not even aware of.

What’s wrong here is not the maths, but the science, or rather the lack thereof. It is said of modelling that your prediction is only as good as your data: ‘garbage in, garbage out’. But this really obscures the uncertainty, incompleteness and messiness that is embedded in epidemiological thinking. Your model is really only as good as the theory on which it is based, and epidemiologists have very poor theories, if at all, behind their models. Often it is a case of garbage all the way down.

Modelling may be helpful, but only as an adjunct to a more rigorous theoretical understanding of a disease. The number of cases that we see for a disease are merely its outward manifestation. To truly understand a disease, we need to dig much deeper into the biology of the pathogen itself, as well as the way in which the human body responds to it. If we have a correct understanding of these things, then we can, perhaps, accurately model the disease. But without this knowledge, modelling is a terrible way to evaluate anything. Even if it turns out to be right, it is so only by chance.

 

In the Covid crisis, another problem we have is that although we have so many numbers flying around – case numbers! deaths! case fatality rates! doubling rates! – we have very little information to really make sense of them. In particular, we have little information about other diseases with which to compare the (so-called) Covid data.

This is a problem because a number by itself gives very limited information, and numbers that are very small or very large can be misleading if taken simply by themselves. If I tell you that Iceland has only 100 deaths from infectious disease a year, that tells you one thing. If I tell you that Iceland overall has only 2000 or so deaths a year, that tells you another. If I tell you that India has 31 lakh Covid cases, that tells you one thing. If I tell you that India has over 31 lakh tuberculosis cases a year, that tells you another thing. If I tell you that India has over 57,000 Covid deaths, that tells you one thing. If I tell you that India has 2.7 to 4.0 lakh tuberculosis deaths, 10 lakh diarrhoeal disease deaths and six lakh respiratory disease deaths a year, that tells you several other things.

 

If I tell you that the USA has 57 lakh Covid cases, while its annual tuberculosis and HIV cases are 10 lakh each, that tells you something. When I tell you that the US typically has 60 thousand deaths a year from respiratory infections and the death toll from Covid is 1.7 lakh and counting, that tells you something else. If I tell you that the US has 22 to 24 lakh deaths a year from non-infectious diseases, that tells you yet a third thing. So, in order for us to really speak intelligibly about the Covid numbers, we have to know something about what the numbers are for other diseases.

But here we run into the difficulty that in the normal course, we do not follow disease in real time, counting cases and deaths as they emerge, and estimating severity from there as we have done for Covid-19. We do not have outbreak curves for any other disease because these were never plotted in real time as they were done for Covid-19. So we never had an outbreak curve from another disease with which we could compare the Covid ones.

We also have no actual counting of cases for other diseases. In order to determine the case incidence or mortality rate for any disease epidemiologists need to do estimation. Yes, estimation again. So anything you hear about the number of cases for tuberculosis, AIDS or malaria in any country is not actual counts, rough aggregations or averages of cases. They are modelled estimates, which are, therefore, subject to all the uncertainties and inaccuracies that we just discussed about epidemiological modelling.

This means that we are all punching in the dark when we are trying to figure out exactly what the Covid numbers mean. Even World Health Organization (WHO) mortality data needs to be placed in context in the same way. If you study the notes to the WHO world mortality data, you will discover that it assigns varying degrees of certainty as to the accuracy of deaths reported under different heads of disease:¹ For a given year there may be no data at all, and the figure reported is… you guessed it… ‘estimated’ again. The WHO makes these estimates even for countries with no death registration data for the year under study, or with no information on the cause of death, or with no data for a particular disease. Even the disease to which the deaths are attributed can be done by estimation.

 

The estimation is made from various things like projections based on available mortality data for a previous period, or estimating deaths by looking at the demographic profile of a country, ‘interpolation/extrapolation of number of deaths of missing country-years’, ‘scaling of total deaths by age and sex to previously estimated WHO all-cause envelopes’, estimating adult mortality from child mortality and a key called the ‘WHO modified logit life table system’, and so on.

The WHO does not even carry out estimations every year. Mortality estimates are carried out only every two or three years, and take several years to be finalized. The 2008 estimates, for example, were updated in 2011, after taking comments from all countries. For later years, for the moment, all that the WHO seems to have are modelled estimates by the American epidemiological institute called the Institute for Health Metrics and Evaluation (IHME). The WHO estimates from after the year 2008 do not say whether they have been circulated to countries for comments, and there is nothing to indicate that anyone from IHME, which is headquartered in the remote State of Washington in the USA, has ever been to countries like India for which they have done these estimations.

 

The WHO says that you cannot compare the country-wise data, or even the year-wise data. But what do the numbers mean if you can’t compare either year-on-year figures for a country, or country-to-country figures with each other? As I said earlier, garbage all the way down.

So even comparing deaths from one disease to another only takes you so far. It is a quagmire of estimates.

There is no escaping the uncertainties of disease estimation, even with large-scale testing. Even though some countries tried to do real-time testing to assess rates of Covid infection, no country had the resources to test everyone. Even population-wide testing, if it can be done, will only give you a snapshot of the infections at the moment. To keep tabs on disease prevalence via universal testing over a period of time, the entire population would have to be tested periodically.

So what we have here are three levels of missing science and information – the general lack of scientific knowledge in the public; the lack of science in epidemiological modelling; and the absence and impossibility of getting any numerical information about diseases that can form a truly reliable standard against which to judge the Covid numbers with which we are being assaulted, ambushed and disarmed. Talk about death by numbers. It’s a billion! Bam! Now y’er dead.

 

So where does this leave us? Is there a Covid-19 pandemic or is it all a fantasy of the epidemiologists? Covid-19 is not a fantasy, but we have not been seeing it for what it is. We have only been looking at the supercomputer-aided and algorithm-abetted drama performed by the epidemiologists.

The epidemiologists told us that if Covid-19 was allowed to spread unchecked, then billions would be infected, and millions would die. The WHO and public health experts told us that, therefore, we had to have a disease containment strategy that would stop the virus from spreading. Then the epidemiologists and the WHO came together in a rousing jugalbandi exhorting us to ‘flatten the curve!’ The idea, they said, was to bring the number of infections to within manageable levels.

But Covid-19 proved to be unmanageable whether you had one case or a billion. For reasons that we do not, as yet, understand, Covid can be mild and clear up in a few days, or have you choking to death in nine days flat. No amount of flattening the curve can solve this problem. And it is a problem of some significance if you or a loved one is on the curve, however flat it may be.

Why, when antivirals were known to be effective in reducing the severity of viral infections, was the WHO and public health field in general so focused on ‘non-pharmaceutical’ interventions? Because we do have medicines for viral diseases. This is how AIDS was brought under control. With antiviral drugs you can be HIV-positive for years, indeed for decades, without falling ill.

Antivirals and medicines like hydroxychloroquine do not ‘cure’ viral disease, in the sense of eliminating them from the body, but they are well known to reduce the severity of infection, which can also be life saving. In the epidemiological work on pandemic influenza, the assumption is that antivirals can be given for viral infections, both as a preventive and as treatment: ‘prompt treatment with antivirals reduces clinical severity and infectiousness.’² Even the WHO has acknowledged the efficacy of antiviral drugs and medicines like hydroxychloroquine in retarding the progress of viral infections.³

 

We need to turn away from those exponential graphs, and look into why disease containment rather than treatment has become the guiding principle of public health interventions for epidemics, despite the availability of medicines. The formal name for disease containment is ‘Non-Pharmaceutical Measures’. This gives us some hint of what might be going on. The negation implied in this expression is of Pharmaceutical Measures, i.e., medicines. Did the idea of disease containment arise as an alternative to, or perhaps even in opposition to, pharmaceutical measures?

Writing in 2006 about non-pharmaceutical interventions for pandemic influenza, the WHO Writing Group rejects the feasibility of pharmaceutical interventions saying that the availability of antiviral agents is ‘insufficient’ and that while pandemic preparedness ‘ideally would include pharmaceutical countermeasures (vaccine and antiviral drugs), but for the foreseeable future, such measures will not be available for the global population [of more than] 6 billion’.⁴

 

But this was clearly a huge underestimation of the capacity of countries to deploy antivirals. Poorer countries in Asia and Africa were the first to use antivirals, viral inhibitors and other therapies like hydroxychloroquine, ivermectin, plasma therapy and favi-parivir for Covid-19 treatment. It was Bangladesh that led the way with ivermectin and doxycycline. If anything, it was rich countries with their, in the case of Continental and Nordic Europe, total lack of innovation in pharmaceuticals, and, in the case of the USA, cumbersome clinical trials that lagged behind in finding pharmaceutical interventions for Covid-19.

While it may be impossible to produce drugs for all the 7 or 8 billion people in the world in a short period of time, this is not the way any disease progresses. You will not have all these people falling ill at once; and given the vast numbers of mild cases for Covid-19, not even all those who do fall ill will need pharmaceutical intervention.

If you think about it, this was surely something that the WHO knew very well already. Could the truth lie in the fact that no one wanted to encourage the idea of drugs when this might have meant footing the bill (or giving up patents) for drugs for infectious diseases which, until Covid-19, were really only a problem for low-income countries in Africa? High income countries have a tiny disease burden from infectious disease compared with non-infectious disease (cancers and heart disease), and middle income countries show epidemiological transition, with a reducing burden of infectious disease, and an increasing one under the head of non-infectious disease.

For many years, there has been something dysfunctional in the western approach to drugs for infectious diseases. A particularly sordid episode occurred during the Ebola outbreak of 2014-16 in West Africa. Some European and American health workers who caught Ebola there, were flown back home for treatment. Most of them were cured after being given a cutting-edge medication called ‘ZMapp’. There was outrage in West Africa where people had been told for decades that Ebola had no cure.

 

Initially it was claimed in America that just seven doses of ZMapp were available, which had all been used up, and so nothing remained to be sent to West Africa. But there was widespread speculation that ZMapp was still being sent to Spain and other places for repatriated European health workers. The governments of Nigeria and Liberia immediately requested for the medicine to be sent to them, even while western commentators were delivering sermons on the indispensability of clinical trials.

The WHO stepped in to say that given the emergency situation, experimental use of the drug should be allowed in West Africa. This led to an outcry from academics in the UK and Australia against the use of medicines for Ebola. They made arguments such as that looking at medicines merely to cure a few patients was ‘individualistic’ or that this somehow betrayed the purported wider community good of disease-containment measures.^5,6

Luckily, common sense prevailed over these academic fulminations, and ZMapp was sent to Liberia and Nigeria. So much for the claim that ‘only ⁷’ doses were available.7 Old hands at the Ebola game in West Africa, Peter Piot and David Heymann, stepped in to say that given the severity of Ebola, they themselves would have been happy to try experimental drugs had they contracted this disease. They also said, pointedly, that if Ebola had broken out in the West then it was ‘highly likely’ that the authorities would have speeded up the testing of experimental drugs for it.⁸ This turned out to be prescient going by the promptness with which remedisivir was put to trial in the USA after Covid-19 came to its shores.

 

The West owes a debt to Ebola and West Africa. It was only after West Africans insisted on access to experimental drugs that attention was finally given to working on Ebola drugs, and it is in the course of this work that remedisivir was developed.^9,10

Even though the world went into an unprecedented lockdown that was supposed to stop the spread of Covid-19, the cases relentlessly grew and grew. India went into lockdown at some 500 cases, today it is at over 31 lakh cases. Epidemiologists and the public health establishment justify their disease containment approach by saying that ‘flattening the curve’ reduced the number of people who would have been struck by the disease had there been no lockdown. But this is no answer. Lockdown, other disease containment measures, and the atmosphere of dread cultivated by them have also caused massive damage to life and health owing to the consequent repression of social and economic activity. The adoption of containment measures was presented by epidemiologists and doctors as a false choice between saving lives and saving the economy. The migrant labour crisis is among the many examples that show us that disease containment kills too.

 

The ‘flatten the curve’ strategy was unhelpful in many ways. It assumed that saving lives was a matter of providing Covid-19 patients with ventilators and critical care. But ventilators left the picture in Europe and the USA almost as quickly as they had entered it in March. By early April, doctors there began to report that ventilators were not helping all Covid-19 patients, and might even be harming them.^11-14 The focus expanded from ventilator-care to other treatments, as doctors began to understand the way in which Sars-Cov-2 attacked the lungs and how the body’s immune system responded to it. The shift in attention from ventilators to the way the disease progressed in the body, opened up investigation into antivirals and enzyme inhibitors to block those aspects of the body’s natural immune response that were exacerbating the damage caused by the pathogen itself.

 

In China, Japan, India, Bangladesh and other countries in Asia and Africa, doctors immediately, as early as February and March when Covid-19 was first detected in their borders, began to use drugs like hydroxychloroquine, azithromycin, doxycycline and various antiviral prescriptions like lopinavir, ritonavir, ivermectin and faviparivir for treatment and prevention of Covid-19. The Bangladeshis announced excellent results with a combination of the antiviral ivermectin with the antibiotic doxycycline, and India’s Council of Scientific and Industrial Research began looking into the re-purposing of 25 drugs, including faviparivir, for Covid-19 treatment. These are only some examples from Asia and Africa of the immediate work that started with different therapies to help Covid-19 patients. By late June the USA had put remedisivir on the market and UK scientists announced some success with the use of dexamethasone for critically ill patients.

What you thus have is a very different picture of treatment than the one envisaged in the ‘flatten the curve’ model, where everything hinged on ICUs and ventilators.

By mid-May, ICU facilities that had been ‘surged’ by rich western countries, as frantically recommended by their epidemiologists, were being shut down, many without having seen any patients.^15,16

 

A fraction of unlucky patients who might become critically ill may require full ICU intervention, but there are many more options for the rest that the epidemiologists clearly had no idea about. For example, oxygen supplementation can be done at home with hired equipment and without the need for oxygen cylinders, as they concentrate the oxygen from the air.

The WHO and public health thinking in general works with fixed ideas of wealth and hospital resources in evaluating health issues. But what is health and what are resources? Covid-19 reduced to nothing the resources of the world’s richest and most technologically advanced countries. We have to ask ourselves what was the worth of all these resources when looking at the ravages of Covid-19 in countries like the UK and Italy. These are countries that have made public health services into a defining socio-political project from the middle of the last century.

 

If you follow the discussion amongst doctors in developed western countries, what comes through starkly is the lack of experience in dealing with infectious disease – a condition itself brought about by wealth. Doctors at the epicentre of the Covid outbreak in Northern Italy were quick to intuit the misalignment of their current medical practice with the exigencies of a highly contagious disease like Covid-19: ‘Coronavirus is the Ebola of the rich… The more medicalized and centralized the society, the more widespread the virus…¹⁷

‘The Coronavirus epidemics should indeed lead to a number of reflections on the organization of healthcare and the way contemporary medicine has lost sight of some diseases, such as infectious ones, that were, probably prematurely, seen as diseases of the past… We have definitely not won the fight against infectious diseases, but we have probably forgotten about them too soon. In a high-technology setting, it is all too easy to forget the overwhelming, often dark power of nature.’¹⁸

Some of the effects of severe Covid-19, such as blood clotting, noticed as new and atypical by western doctors, are similar to those observed in patients in the final stages of any illness when they are headed to sepsis and septic shock.¹⁹ Some of the worse cases of Covid-19 sound similar to patients in the last stages of Ebola in West Africa, or dengue in India. A lot of the issues raised by Italian and American doctors in March and April, when they were first hit by Covid, about being careful of lung damage from intubation, keeping patients ‘dry’, i.e. being conservative on fluid replacement as this can cause further lung damage, and on the timing of intubation for patients showing severe respiratory distress, are covered as a routine matter in the Indian National Clinical Management Guidelines for Covid-19. This may well be the case for other Asian and African countries as well.

 

By mid-April there was a recognition even in the West that the blood clotting, and other ‘atypical’ reactions they were observing in Covid-19 patients, might be part of the general deterioration into sepsis as is seen with other severe viral diseases, and there was talk of including anti-coagulants like heparin for critically ill patients. But in India, heparin had been included right at the start for critically ill patients in the National Clinical Management Guidelines for Covid-19. Chinese doctors reporting the clinical course of illness in hundreds of patients in Wuhan hospitals in January, had emphasized the observation of thrombosis (blood clotting) in critical cases and noted that elevated levels of a substance called d-dimers correlated with cases that proceeded to become severe.^20,21

While the WHO made the case for disease containment by invoking grim portents for the ‘poor’ and ‘dense’ populations of developing countries, it was these countries that led the charge for finding therapies for Covid-19. The Americans and Europeans were slower off the mark with antivirals and other drugs than the Asians, Russians and Africans. This may be partly because doctors in Asia and Africa who regularly treat tuberculosis, meningitis, diarrhoeal diseases, dengue and malaria, among other infectious diseases, are more experienced with these drugs than western doctors.

 

Epidemiologists are like the blindfolded men trying to guess what the elephant was in Gandhiji’s favourite fable. As the men groped at one or other part of the elephant, they kept making the wrong guess at what it was. The one who caught its trunk called it a snake, the one who felt its tusk called it a weapon, and so on. Epidemiologists get things wrong in the same way. A telling example is how the most famous epidemiologists of them all, the Neil Ferguson-led Covid-19 Response Team which included the Imperial College of London and the WHO Centre for Infectious Disease Modelling, assessed the impact of age on their Covid case predictions.

In a report dated 26 March 2020 called ‘The Global Impact of Covid-19 and Strategies for Mitigation and Suppression’ they say: ‘The average size of households that have a resident over the age of 65 years is substantially higher in countries with lower income compared with middle- and high-income countries… Contact patterns between age groups also differ by country; in high-income settings contact patterns tend to decline steeply with age. This effect is more moderate in middle-income settings and disappears in low-income settings… indicating that elderly individuals in these settings [lower-income and middle-income countries] maintain higher contact rates with a wide range of age-groups compared to elderly individuals in high-income countries.’²²

 

Based on this the Covid-19 Response Team claimed that the elderly were less vulnerable to infection in high-income settings. They were completely wrong, as they failed to account for the increased exposure of the elderly to infection in the communal setting of the care home, whose deaths tragically constituted 40 to 60% of the Covid deaths in high-income countries in Europe and North America. In Canada and some US states, deaths of care home residents were over 80% of the total Covid deaths.²³

This is only one example of the ways in which epidemiological modelling failed to tell us things that might have been useful in mitigating the worst impact of Covid-19. Another example is the way in which, while we were focused on Wuhan early this year, the disease was already entering countries from many places at once. If you follow the first cases in different countries, you see a pattern of disease importation from people with no travel history to China. In fact, on nearly every continent, more countries had their first imported cases from Italy than from China. In France, the first major outbreak was in early March from a Church gathering in Mulhouse in Haut Rhin where, till date, no cases appear to have been connected to Wuhan.

 

In Kenya, the first case (mid-March) was of someone returning from the USA via London. In Iceland, early cases included an import from Austria. In Italy, early cases included imports from Romania and Norway. In Pakistan and India, early cases were imported from Iran. In India’s first hotspot of Mumbai, early cases mostly came from the USA. This should not surprise us as the whole idea of the pandemic is that the connectedness of the world is the main risk and driver of such outbreaks. Yet, when the pandemic actually hit us everyone reacted in a very un-pandemic way by focusing only on China.

In Sweden, early contact tracing focused on people with a travel history to Italy owing to some early cases having been connected to travel there. But Swedish officials later announced that while they were focused on Italy, cases were being imported ‘below the radar’ from many other countries. This is a very clear example of how contact tracing and other containment measures can be misleading in giving the early impression of the infection coming from just one or other place.

Another pattern to which we are failing to give proper attention is the highly clustered nature of Covid-19 outbreaks, with successive disease epicentres losing severity as they emerge. A phenomenon that cannot be fully explained by lockdown and containment measures, as this is a pattern that holds consistently in different countries, despite differences in the timing and quality of intervention measures. There are many anomalies in Covid transmission that are crying out for attention if only we could snap out of our epidemiology-induced hypnosis. The case onset data from China and Italy (other countries are yet to tabulate this) show that their measures actually came into force at or near the time that infections peaked and plateaued.^24,25 Given the 14-day incubation period for this disease, lockdown and other measure do not explain this trajectory. Another anomaly is the way in which outbreaks have not been traced to busy bus or metro routes in big cities with Covid outbreaks, or to shopping malls or crowded vegetable mandis. This indicates that the sharing of public spaces may not in and of itself result in significant transmission, and a more intense, intimate and prolonged interaction is required for transmission to occur. If this is true, then the whole idea of stopping public movement to contain Covid transmission is questionable.

 

By following the epidemiologist’s approach of ‘flattening the curve’, we were all focused on overall numbers. Success in combatting the virus was judged in terms of how many infections we have prevented. But this is a fact over which we can only speculate, while in some places, as in care homes in Europe and North America, we failed to notice and disperse chains of transmission as they emerged. Even today, so many months down the line, we are unable to remove the blinkers that the epidemiologists have put over our eyes to actually look at the virus and take into account the totality of its behaviour and peculiarities. What emerges is the limited importance of mathematical modelling and the degree to which thinking in these terms only serves to further confuse and confound us when confronted with a novel disease.

We talk of control, and even after the virus has raced around the world, infecting hundreds of thousands in the richest and most scientifically advanced places, we still think we know and can control it. In this we show the smallness not just of epidemiology, but even of science. At least of the kind of science that we are doing today. Even if we are willing to gamble on defying nature, we have to first be ready to accept that science in its current state knows very little about Sars-Cov-2. Before we can control it, we have to understand it.

Perhaps we can start with this idea – Sars-Cov-2 is part of nature, as are we all. When we went to war against it, the ravages of lockdown have shown us that we ended up hurting ourselves. Disease sits in people. Our war on disease became a war on the people. Whether we like it or not, we and this virus are connected. If we try to understand the nature of this connection, maybe we can reach the happy ending to which science has, so far, always led us. In order for this to happen we need to ditch the epidemiologists and the supercomputers, and encourage the real scientists to go ahead with a sober and uninhibited scientific exploration of Covid-19.

Until science comes to the rescue, we have no choice but take things philosophically. If this is the end, then at least let us go with dignity.

 

* Suranya Aiyar’s paper, ‘Covid-19: Dodgy Science, Woeful Ethics’, is available on covidlectures.blogspot.com (https://covid-lectures.blogspot.com/).

Footnotes:

1. See, for instance, https://www.who.int/healthinfo/global_burden_disease/estimates_country_2004_2008/en/ and ‘Mortality and Burden of Disease Estimates for WHO Member States’ issued by WHO’s Department of Measurement and Health Information and ‘WHO Methods and Data Sources for Country-Level Causes of Death 2000-2016’, dated 2018.

2. Ferguson et al., ‘Strategies for Mitigating an Influenza Pandemic’, Nature 442, July 2006, p. 448, 27. https://www.nature.com/ articles/nature04795

3. COVID-19 – virtual press conference, WHO, 30 March 2020. https://www.who.int/docs/default-source/coronaviruse/transcripts/who-audio-emergencies-coronavirus-press-conference-full-30mar2020.pdf?sfvrsn= 6b68bc4a_2

4. ‘Nonpharmaceutical Interventions for Pandemic Influenza, International Measures’, World Health Organization Writing Group, Centres for Disease Control and Prevention Vol 12, Number 1, January 2006. https://www.nc.cdc.gov/eid/article/12/1/05-1370_article.

5. Annette Rid and Ezekiel J. Emanuel, ‘Ethical Considerations of Experimental Interventions in the Ebola Outbreak, The Lancet 384, 22 November 2014. https://www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736(14)61315-5.pdf

6. Angus J. Dawson, ‘Ebola: What it Tells us About Medical Ethics’, The Journal of Medical Ethics 41, 2015, pp. 107-110. https://jme.bmj.com/content/41/1/107; Christian A. Gericke, ‘Ebola and Ethics: Autopsy of a Failure, BMJ, 2015, p. 350. https://www.bmj.com/content/350/bmj.h2105

7. ‘Trial of Ebola Drug ZMapp Launches in Liberia’, US, Centre for Disease Research & Policy, 27 February 2015. https://www.cidrap.umn.edu/news-perspective/2015/02/trial-ebola-drug-zmapp-launches-liberia-us

8. ‘Africans, Three Ebola Experts Call for Access to Trial Drug’, Los Angeles Times, 6 August 2014. https://www.latimes.com/world/africa/la-fg-three-ebola-experts-release-drugs-20140806-story.html

9. Op. cit., fn. 7.

10. ‘Ebola is Now Curable…’ wired.com, 8 December 2019. https://www.wired.com/story/ebola-is-now-curable-heres-how-the-new-treatments-work/

11. Sinead Baker, ‘80% of New York’s Coronavirus Patients Who Are Put On Ventilators Ultimately Die, and Some Doctors Are Trying to Stop Using Them’, Business Insider, 9 April 2020. https://www.business insider.in/science/news/80-of-new-yorks-coronavirus-patients-who-are-put-on-ventilators-ultimately-die-and-some-doctors-are-trying-to-stop-using-them/articleshow/75065623.cms

12. ‘Why Ventilators May Not Be Working As Well for Covid-19 Patients As Doctors Hoped’, Time, 16 April 2020. https://time.com/5820556/ventilators-covid-19/

13. John J. Marini and Luciano Gattinoni, ‘Management of Covid-19 Respiratory Distress’, JAMA Insights, Clinical Update, 24 April 2020. https://jamanetwork.com/journals/jama/fullarticle/2765302

14. ‘Doctors Face Troubling Question: Are They Treating Coronavirus Correctly?’ The New York Times,14 April 2020. https://www.youtube.com/watch?v=bp5 RMutCNoI

15. ‘US Field Hospitals Stand Down, Most Without Treating Any Covid-19 Patients’, npr.org, 7 May 2020. https://www.npr.org/2020/05/07/851712311/u-s-field-hospitals-stand-down-most-without-treating-any-covid-19-patients; ‘London NHS Nightingale Hospital Will Shut Next Week’, The Guardian, 4 May 2020. Link: https://www.the guardian.com/world/2020/may/04/london-nhs-nightingale-hospital-placed-on-standby

16. ‘Covid-19: Nightingale Hospitals Set to Shutdown After Seeing Few Patients’, BMJ 369, 7 May 2020. https://www.bmj.com/content/369/bmj.m1860

17. Nacoti et al., ‘At the Epicentre of the Covid-19 Pandemic and Humanitarian Crises in Italy: Changing Perspectives on Preparation and Mitigation’, NEJM Catalyst, 21 March 2020. https://catalyst.nejm.org/doi/full/10.1056/CAT.20.0080

18. Giorgina Barbara Piccoli, ‘Hospitals as Health Factories and the Coronavirus Epidemic’, Journal of Nephrology 33, 21 March 2020, pp. 189-191. https://paperity.org/p/237906528/hospitals-as-health-factories-and-the-coronavirus-epidemic

19. ‘Unexpected Cause of Death in Younger Covid-19 Patients is Related to Blood Clotting’, BioSpace, 28 April 2020. https://www.biospace.com/article/covid-19-increases-risk-of-heart-attacks-and-stroke/?fbclid=IwAR3wum5CgAyBrlCQ2eBwQCy_sU2Evq4iuyV4dqhT7ZP5efdSOVb_KWPkUnw

20. Zhou et al., ‘Clinical Course and Risk Factors for Mortality of Adult Inpatients With COVID-19 in Wuhan, China: A Retrospective Cohort Study’, The Lancet 395, 1054, 28 March 2020, first published on 9 March 2020. https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30566-3/fulltext

21. Wang et al., ‘Clinical Characteristics of 138 Hospitalized Patients with 2019 Novel Coronavirus-infected Pneumonia in Wuhan, China’, JAMA 323(11), 7 February 2020, pp.1061-1069. https://jamanetwork.com/journals/jama/fullarticle/2761044

22. Report 12: The Global Impact of COVID-19 and Strategies for Mitigation and Suppression, COVID-19 Response Team, 26 March 2020. https://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/report-12-global-impact-covid-19/

23. International Long Term Care Policy Network, ‘Mortality Associated With COVID Among People Who Use Long Term Care’, updates of 21 May 2020 and 26 June 2020. Link to 26 June 2020 update here: https://ltccovid.org/wp-content/uploads/2020/06/Mortality-associated-with-COVID-among-people-who-use-long-term-care-26-June-1.pdf; ‘State-wise data for the USA from Covid-19 brutal on NY long-term care facilities’, The Buffalo Post quoting Kaiser Family Foundation data, 26 May 2020. https://buffalonews.com/business/local/covid-19-brutal-on-ny-long-term-care-facilities-nationwide-its-worse/article_739b408b-5d34-5b8d-be83-124047368d2b.html

24. Chinese case onset data can be found at Figure 2 on Page 6 of the Report of the WHO-China Joint Mission on Coronavirus Disease 2019, published on 28 February 2020. https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf

25. Italian case onset data can be found at: https://www.epicentro.iss.it/en/coronavirus/sars-cov-2-dashboard