Covid 19 and the End of Science
by Suranya Aiyar
This article was originally published in the September 2020 issue of Seminar magazine under the heading Covid-19: dodgy science, woeful ethics
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 themselves even as you looked at them. Were they defying you to ever
really know the secret of the Universe?
Albert Einstein, Pixabay, Free for Use |
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 misleading 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 their
variables for their equation. So, assumptions will be built into the
epidemiologists’ models, that they are not even aware of.
(Pixabay) Your model is only as good as the theory on which
it is based and epidemiologists have very poor theories,
if at all, behind their models
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 10 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 26,000 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 6 lakh respiratory disease deaths,
a year, that tells you several other things. If I tell you that the USA has 30
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 1.4
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 are 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.
The WHO does not even carry out its own
estimations every year. Mortality estimates are carried with gaps of about two
or three years, and take several years to be finalised. The 2008 estimates, for
example, were updated in 2011, after taking comments from all counties. 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, can 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
really form a 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 looking at it and seeing it for what it is.
We have only been looking at the super-computer-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.
We still don't understand why Covid can clear up in a few days or have you choking to death in 9 days flat. (Pic: Pixabay) |
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 9 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 anti-virals, 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 anti-viral
drugs you can be HIV-positive for years, indeed for decades, without falling ill.
Anti-virals 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 anti-virals 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 anti-viral 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 anti-virals. Poorer
countries in Asia and Africa were the first to use anti-virals, viral
inhibitors and other therapies like hydroxychloroquine, ivermectin, plasma
therapy and faviparivir 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 heath 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 7 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 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 even experimental drugs for it 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 the coming of Covid-19.
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 (7, 9).
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 10 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.
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 anti-viral
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 critically ill patients with the use of
dexamethasone.
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 (14, 15).
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
since 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…” (16).
“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” (17).
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 (18). 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 in 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 (19,
20).
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 anti-virals
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.
In many ways, epidemiologists are like the
blind-folded men trying to guess what the elephant was in Gandhiji’s favourite
fable. As they 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” (21).
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,
which was 40 to 60 percent 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 (22).
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 is
the way in which, while we were focussed on Wuhan early this year, the disease
was already entering countries from many places at once. But 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 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 focussed
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 focussed 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 (23,
24). 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 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 the patterns 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 and 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 super-computers, 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 is trained in
mathematics and law. Her paper, Covid-19: Dodgy Science, Woeful Ethics, is
available on covidlectures.blogspot.com (https://covidlectures.blogspot.com/).
(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) Strategies for mitigating an influenza
pandemic, Ferguson et al., Nature, Vol 442, pg. 448, 27 July 2006. Link:
https://www.nature.com/articles/nature04795
(3) COVID-19 - virtual press conference,
WHO, 30 March 2020. Link: 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 Organisation Writing
Group, Centres for Disease Control and Prevention Vol 12 Number 1, January
2006. Link: https://wwwnc.cdc.gov/eid/article/12/1/05-1370_article.
(5) Ethical considerations of experimental
interventions in the Ebola outbreak, Annette Rid and Ezekiel J Emanuel, The
Lancet, Vol. 384, 22 November 2014. Link: https://www.thelancet.com/pdfs/journals/lancet/PIIS0140-6736(14)61315-5.pdf
(6) Ebola: What it tells us about medical
ethics, Angus J. Dawson, The Journal of Medical Ethics 2015; 41: 107-110; Link:
https://jme.bmj.com/content/41/1/107;
Ebola and ethics: autopsy of a failure, Christian A Gericke, BMJ 2015; 350.
Link: 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. Link: 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. Link: https://www.latimes.com/world/africa/la-fg-three-ebola-experts-release-drugs-20140806-story.html
(9) Ebola is now curable…wired.com, 8
December 2019. Link: https://www.wired.com/story/ebola-is-now-curable-heres-how-the-new-treatments-work/
(10) 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, Sinead Baker, 9 April 2020. Link: https://www.businessinsider.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
(11) Why ventilators may not be working as
well for Covid-19 patients as doctors hoped, Time, 16 April 2020. Link: https://time.com/5820556/ventilators-covid-19/
(12) Management of Covid-19 respiratory
distress, John J. Marini and Luciano Gattinoni, JAMA Insights, Clinical Update,
24 April 2020. Link: https://jamanetwork.com/journals/jama/fullarticle/2765302
(13) Doctors face troubling question: are
they treating coronavirus correctly? The New York Times, 14 April 2020. Link: https://www.youtube.com/watch?v=bp5RMutCNoI.
(14) US Field Hospitals stand down, most
without treating any Covid-19 patients, npr.org, 7 May 2020. Link: 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.theguardian.com/world/2020/may/04/london-nhs-nightingale-hospital-placed-on-standby
(15) Covid-19: Nightingale hospitals set to
shutdown after seeing few patients, BMJ 2020; 369, 7 May 2020. Link: https://www.bmj.com/content/369/bmj.m1860
(16) At the
Epicentre of the Covid-19 Pandemic and Humanitarian Crises in Italy: Changing
Perspectives on Preparation and Mitigation, Nacoti et al., NEJM
Catalyst, 21 March 2020. Link: https://catalyst.nejm.org/doi/full/10.1056/CAT.20.0080.
(17) Hospitals as
health factories and the coronavirus epidemic, Giorgina Barbara Piccoli,
Journal of Nephrology (2020) 33: 189-191, 21 March 2020. Link: https://paperity.org/p/237906528/hospitals-as-health-factories-and-the-coronavirus-epidemic
(18) Unexpected cause of death in younger
Covid-19 patients is related to blood clotting, BioSpace, 28 April 2020. Link: https://www.biospace.com/article/covid-19-increases-risk-of-heart-attacks-and-stroke/?fbclid=IwAR3wum5CgAyBrlCQ2eBwQCy_sU2Evq4iuyV4dqhT7ZP5efdSOVb_KWPkUnw
(19) Clinical course and risk factors for
mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective
cohort study, Zhou et al., The Lancet, Vol 395, 1054, 28 March 2020,
first published on 9 March 2020. Link: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30566-3/fulltext.
(20) Clinical Characteristics of 138
Hospitalized Patients with 2019 Novel Coronavirus-infected Pneumonia in Wuhan,
China, Wang et al., JAMA 2020; 323 (11): 1061-1069, 7 February 2020.
Link: https://jamanetwork.com/journals/jama/fullarticle/2761044.
(21) Report 12: The Global Impact of COVID-19 and Strategies for Mitigation
and Suppression, COVID -19 Response Team, 26 March 2020. Link: https://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/report-12-global-impact-covid-19/
(22) 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 20202 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. Link: https://buffalonews.com/business/local/covid-19-brutal-on-ny-long-term-care-facilities-nationwide-its-worse/article_739b408b-5d34-5b8d-be83-124047368d2b.html
(23) 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. Link: https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf
(24) Italian case onset data can be found
here: https://www.epicentro.iss.it/en/coronavirus/sars-cov-2-dashboard
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