Science versus career: a trade-off

Within the last 24 hours I heard two complaints from two different students from very different institutions. I received a call last night from an agriculture student. He was in a tricky situation and wanted my advice. His dissertation work had ‘failed’. He wanted to develop a technology, a machine with some agricultural application, but it didn’t work. He had himself put in tens of thousands of rupees to make a machine, but it failed to work. Now his mentor was asking him to cook up some data and pretend that it worked, without which he said the degree won’t come. The application of the machine being season dependent, if he had to rework with refinement, he would have to wait till next year. The student was reluctant to give false data but simultaneously was terribly upset because not only he would lose the money he had put in, but also lose one full academic year.  

The other case was of a girl having completed her PhD desk work, was awaiting a paper to get accepted and preparing to complete the thesis. Typically at this stage, the post doc applications begin. The girl’s complaint was that during some phase of her work she had a major difference of opinion with her boss which strained their relations. Now she wasn’t sure whether her boss will give her a “good” letter of recommendation, without which she though she couldn’t get a post doc.

In both the cases I could sense that the students were terrified by the thought that their career could be completely destroyed in one stroke by their supervisor. In the first case the boy talked to me directly on phone and I tried to find out how thorough he was at his work. If the experiment failed, did he analyze and try to reason out why it failed? He said he was confident about his data and could say why their earlier thinking was wrong and why things cannot be done that way. I said, “I am ignorant about your field the only thing I would suggest a science student is this. You stand firm on your position and complete your report. Don’t cook up data only to please your boss. An analytical view of a failure is also a contribution to science. Try to convince your boss that you have sufficient work to make a dissertation, but avoid the temptation of cooking up data.”

The girl in the other case was not even ready to discuss her problems personally with anyone in academia. She was afraid that the ‘anyone’ may turn out to be a friend of her boss. If I get any chance to talk with her I will tell her more or less the same thing. If you think you are right, remain firm on your stand and don’t compromise your science for someone’s whim. If it is about post doc, communicate your problems frankly to your potential post doc mentor. It might work. There are alternative ways of doing good science and finding a career.

Frankly speaking I am not sure my solution will protect the careers of the two students, my solution is primarily to protect science from misconduct. Clearly in both the contexts there is an unfortunate tradeoff between doing good science and doing a successful science career. The two are way different and in instances like this, diametrically opposite.

My main concern goes much beyond the two cases. In science, one needs to be careful about experimental designs, accuracy, errors, biases, logical traps etc., but being confident about having done well on this front, it is necessary to stand firm on one’s position. Compromising on it to please someone like the mentor, the thesis examiner, the reviewer of manuscript or some established elite in the field is against the spirit of science. Conformity bias is a major hurdle in the progress of science and in both the cases this is exactly what is getting encouraged.

Now if the students’ fear is true, and complying with what their boss says is the only way of getting on to the career path, then the system is selecting against scientific spirit. My personal advice to the two students would be that if you are afraid of someone ruining your career, you are actually not fit to do science. You should find some other career for you, perhaps better than this. But what actually matters for them is ground reality and not my utopian advice. If taking a firm stand prevents them from getting their degrees or further positions, the system is filtering out at an early stage the right kind of mindset for science. If students are made to compromise their own stand in order to make a successful career, the future of science is bound to be bleak. With this trend academia would become completely devoid of the scientific spirit in no time.

I had related earlier one of my experiences as a reviewer ( In this case I differed from the authors in the interpretation of their experiment. Difference of opinion is fine. It’s a milestone in the path of healthy science. The authors could have counter-argued and defended their interpretation more clearly during revision and rebuttal. But I was the “reviewer god” of that moment. They didn’t agree with me but also didn’t want to displease the reviewer God. So in effect, they muddled the entire argument further and the residual clarity was also lost. I would have been happier to see them say “We beg to differ on this issue and we defend our stand with better arguments in the revised manuscript”. I would have recommend acceptance on such a stand, even if I didn’t agree. But the peer review culture is so degraded that many authors avoid any argument with the reviewer. This tendency makes publications easier but science more difficult.  Today’s science institutions as well as science publishing is taking such a shape that if you want to make a successful career in science then you need to compromise with the spirit of science.

The problem lies with the institutional culture, individual mindset and the norms of career path. Clearly there are individual mentors that encourage students to think and be independent at an early stage. But this is left to individuals. There is nothing in the system to ensure this. Solving individual cases or helping individual victims is not sufficient. The system and the norms of a typical career path need a rethinking.

Crowd-funding appeal to support farmers

using a novel ‘game theory’ based approach:

For the first time in my blog, I am making an appeal to contribute to crowd funding to support the pilot implementation of a novel scientific concept that has a direct application to solve a major problem in human-wildlife conflict. We play here a game with real money which solves a real life problem!!

The problem: In India, with its high population density and rich wildlife, there are large areas of inevitable human-wildlife co-existence. Strengthening agriculture in and around protected areas boosts conservation since it reduces people’s direct dependence on forest resources. Therefore farmer support needs to be a high priority conservation action. A major but largely overlooked problem is crop damage by wild herbivores. While compensating farmers for the damage caused by wild animals is an accepted principle in India, the process of assessing and compensating damage is fraught with a large number of practical difficulties and therefore has been ineffective.

A Novel solution: We came up with an alternative to traditional concept of compensation with a novel game theory based concept called “support cum reward” (SuR). The SuR amount to be paid to farmers is calculated as percentage based on the average loss in productivity over a group of farmers. Each farmer receives SuR as the percentage on his own productivity. Therefore while each farmer is supported by the average, a farmer showing high productivity in spite of the damage gets a higher reward. The data are self reported by farmers and endorsed by neighboring farmers. The unique game theory components ensure that honest self-reporting gets maximum returns. Since honest behavior is the most selfish behavior, there is little chance of anyone defecting from cooperation. This is a new game that is immune to cheating by any variant behavior. As a result the entire process can be automated through farmer friendly mobile apps so that the system becomes community operated with little need for central administration, auditing or policing. The design of the system has several built-in features of cross verifying data, automated detection and prevention of fraud, self-auditing and self-correcting.

A Prior small scale trial: SuR was implemented on a small scale (75 farmers) for three years in which the average farmer’s productivity increased over 2.5 fold. In this trial the SuR inputs of Rs 1.6 million increased the productivity by about Rs. 7 million. This phase of research was funded by Vidarbh Development Board, DeFries Bajpai Foundatin and NAAM foundation. This concept is appreciated in the scientific world as reflected by publication in one of the top journals in the field, and an international award by the Society for Conservation Biology.

Towards scale up: Now, the Forest Department of Maharashtra, has shown interest in conducting a medium scale pilot trial for 8 to 10 villages near Tadoba. We have signed a MoU with the Field Director TATR and the executive director of Tadoba Andhari Tiger Reserve Conservation Foundation. While the foundation takes the responsibility of managing the finance and partially funding the pilot, we still need to raise over Rs. 1.5 crore through crowd funding, CSR and other means. The medium scale implementation will pave the way for the Government to adopt a new policy to handle human-wildlife interaction.

Why citizen science: The nature of research is difficult to fit in the bureaucratic systems of Indian Institutions and universities going by my prior experience. Therefore myself and my team will work independently in collaboration with the farmers on the one hand and the Foundation on the other. This is a unique example of doing high quality science with direct community involvement, establishing a non-institutional model of doing science. Beyond handling people’s problem it is an attempt to raise an alternative model in which farmers including the illiterate ones turn into researchers and address their own problems themselves. It is not about ‘taking’ science to people it is about doing science with people.  Personally for me, this is going to be perhaps my career best science. You can donate starting from a few hundred dollars of few thousand rupees (no upper limit) at the link- Remember to go to the “other” option in the Type of donation window and write “For farmer support cum reward (SuR) fund”.

For more detailed information about SuR see links-शेतकरी-आधार-ईनाम-योजना-farmer-support-cum-reward/

Covid 19: Why can’t they do simple mathematics?

For a student of science like me, whatever is happening with the pandemic and whatever is being said reveals how human mind works, how we perceive science, how we ask questions and what satisfies our questions.

A number of statements are being made by the mainstream Covid epidemiologists which are actually not supported by data. The same data can be interpreted differently. The different possible interpretations need to be treated as competing hypotheses and tested by making additional differential testable predictions. This is the core method of science. But the human mind has not evolved to follow the scientific method and pursue truth. It has evolved to make stories. The stories need to satisfy some audience. The attempt of an investigator is to satisfy his/her present audience with minimum effort. Different audiences have different satisfaction thresholds. If your story satisfies your audience, you stop there. You stop looking for alternative interpretations. You don’t need to test them. You don’t need to recheck whether your story is consistent enough with evidence. Whether there are any anomalies and whether they are serious enough to raise doubt on your story is no more a concern as long as nobody challenges your story. If someone challenges the story the first line of defence is to say the person is not credible, ignore him or better brand him as ‘anti-science’. Such branding works most of the time, so that you don’t really have to take trouble to pursue scientific method further. Scientific solutions are costlier. Social solutions are cheaper.

Take the example of the new wave which is said to be caused by the variant omicron. Is this a well tested hypothesis? By the methods of science that we teach undergraduates, any hypothesis can be tested against a null hypothesis. Different variants keep on arising and vanishing in a population owing to chance as well as by selection, and selection can happen due to not one but multiple reasons. In viruses, there is selection within a cell, during cell to cell transmission, there is selection on dose of the virus being transmitted, time for which the host remains infective, time for which viral particles survive outside the host, how the virus elicits immune response, whether and how virus evades host immunity and so on. The selection is necessarily multi-level with additive as well as multiplicative components and is really a challenge to selection dynamics. Further there can be trade-offs between any of these. Natural selection on viruses is much more complex than the prevalent naive thinking that a more infectious virus will win the race.

As the wave goes up and down, new variants keep on arising. Many variants increase their abundance while the wave is downwards. So new variants keep on arising during any part of the wave and by chance some variant may happen to ride a rising wave. This should be the null hypothesis, only by rejecting which we can say that a given variant is certainly responsible for a wave. I could not find any such analysis in literature but a story seems to be accepted that the new wave is because of omicron. The increase in proportion of omicron is not always accompanied with increased transmission. For example, in Russia, between 29th November 2021 and 11th Jan 2022, the Omicron proportion increased from negligible to 50 % while mean number of cases per day came down by more than half. Omicron accompanied a downward and not upward trend in total number of cases.

Even in countries where omicron accompanied a rise in the wave, omicron does not account for the rise. In India, for example between Dec 29th and Jan 20th the incidence rose about 40 fold and omicron proportion increased from negligible to 77%. By simple arithmetic, a rise to 77% can explain only about   4 fold increase in total incidence. During this time the delta variant incidence also increased by over 9 fold. If the new wave was caused by omicron, why did we see the delta cases going up 9 fold? This is simple arithmetic that we teach in secondary schools. Perhaps for experts in this field using simple mathematics is too below their dignity. They have to use sophisticated models to impress everyone. Simple arithmetic poses a major anomaly for the hypothesis that the new wave is “caused” by omicron. Data only show that omicron is associated with the new wave, that too in some countries, not everywhere. It is equally likely that the selective environment during the new wave allowed the spread of omicron. So the wave is the cause and omicron the consequence. Alternatively the association is only coincidental and there is no causal association between the two. Unless such alternatives are considered as competing hypotheses and ruled out, ‘omicron caused the new wave’ is not a scientific statement, by the core principles of science. It’s only a story that has convinced most people, therefore it is considered scientific as of today.

This is how science works most of the time. It is a complex social process that sometimes, particularly when convenient, uses the fundamental principles and methods of science. The methods of science is the tool used by people whose prejudices, interests and agendas decide the emerging inferences.

Then what caused the new wave? There are several alternative possible reasons. We showed with a model earlier that the wave pattern is possible by considering immunity as a continuous rather than a binary variable. In reality, immunity IS graded and not binary. By this model, waves arise and wane even without a new variant. So it is very much possible that waves arose as a part of the population dynamics of what we called in the model as ‘small immunity effects’. Alternatively it is also possible that the vaccines give only systemic immunity, but respiratory mucosal immunity has a different set of mechanisms which the vaccine administered by injection does not strengthen much. As a result when the low tide lingers around for sufficiently long period people tend to lose mucosal immunity, but systemic immunity is still there. Therefore a new wave begins with whatever variant is around, but does not lead to serious symptoms among the vaccinated. If this hypothesis is true, we should see increase in cases of all prevalent variants, though not to the same degree because different variants have different competitive abilities. Also even where the incidence is declining, the more competitive variant will become commoner. I am not saying this IS the reason. I am saying that being open to different possibilities and testing them with differential predictions is how science should work, but during Covid times scientists themselves seem to have forgotten the methods of science. I am happy that the Covid epidemiologists are providing a science teacher like me several examples to demonstrate how science should be and should not be done. It is also enriching in me the curious student of social psychology of science. It is interesting to see how the community of scientists actually works besides the principles of science.

Covid 19: Evolutionary interactions between vaccine, immunity and virulence. 

The Covid-19 picture is changing rapidly and if viewed with an unprejudiced mind, keeping several alternative possibilities open, a clear interpretation is emerging. There is a new wave spreading world-wide with unprecedented numbers but at the same time the proportion of hospitalizations and deaths are far less than earlier waves. Why is it so? There are two prevalent explanations which are mutually contradictory. On the one hand this is said to be an epidemic among the unvaccinated; on the other hand the reduced severity of symptoms and lower hospitalization and mortality rates are also credited to the vaccines. Both cannot be simultaneously true, if we do a simple calculation.

The global trend in the ratio of number of deaths and number of cases – a seven day running average.

Take the case of UK. The ratio of new cases to new deaths has come down from an average of above 10% between April and June 2020 to an average of 0.15 % between December 2021 and today. That is two orders of magnitude. If we have to ascribe the credit for reduced severity to vaccines alone, we will have to assume that in the unvaccinated, the severity remains unchanged. Going by this assumption, and given that 70% of the population of UK is fully vaccinated, for bringing down the mortality by over 50 fold it is necessary that vaccinated people are about 20 times more likely to be infected than unvaccinated people. Since this is absurd, we need to accept that the severity of infection in the unvaccinated has also come down substantially. This can either happen because the unvaccinated have also become immune by natural infection, or because the virus has mostly lost its virulence.

In order to differentiate between these two, we can have a look at Australia which also has about 70 % population vaccinated. But Australia had successfully kept the infection away until recently and therefore the unvaccinated are unlikely to be immunized by natural infection. When the number of cases is small, calculation of death rate is subject to large stochastic fluctuations. So we take only the period in which there were more than 100 deaths per week. This was the situation in Aug-Sept 2020 when the ratio in Australia was over 10 %. Then there was a long time in which there were zero or negligible Covid cases and deaths in Australia. So the population had little chance of acquiring immunity by natural infection. This situation changed again only after October 2021, but now the death proportion was much lower, about 0.6 % and between October 2021 and today it further declined to 0.05 %. Similar to UK if this is to be explained by vaccination alone, we will have to assume that vaccinated people are about 70 times more likely to get infected. Since this is unlikely, it is clear that the reduction in severity and mortality is not explained by vaccination and naturally acquired immunity together. The virus has indeed evolved towards reduced virulence. It was round about 20 times more severe than flu in the beginning which has come down to about 2 to 3 times. It is still more severe than common cold and flu but is moving rapidly to become just another.

Evolution will reduce the virulence of the virus was my clear prediction right from May-June 2020. The literature of evolution of virulence is full of mutually contradicting and confusing arguments. But the case with Covid has been very clear. Multiple studies showed that there was poor correlation between severity of symptoms and viral load. Virulence can give a selective advantage to a virus only if it is tightly correlated to the number of virus particles being shed by the host. If correlation with numbers is poor, there is no selective advantage in being virulent. On the contrary, virulent variants are more likely to face quarantine and thereby restrict their transmission. A milder strain allows the host to move around in the population and thereby spread it more widely. The reduction in severity of symptoms, hospitalization rates and mortality was a conspicuous trait in the beginning. But then it appeared to stagnate and had some hick ups for some time. I was perplexed by this trend but a couple of possible reasons soon became apparent.

There is another, more subtle contributor to selection for lower virulence. The immune status of the host exerts a strong selective pressure on pathogen virulence and this has received little appreciation in virulence literature. Immune response is costly in terms of resource allocation as well as the potential damage to tissues through heightened inflammatory and oxidative components of the process. Therefore it is not wise for the body to launch an all out immune response for every pathogen encountered. At times, particularly for milder pathogens, the cost of the immune response might be greater than the cost of being infected. The host therefore should make a judgment of the invisibility or virulence and accordingly optimize the immune response. The response given to a highly virulent pathogen must be of high intensity, but that given to a milder pathogen should be of minimum necessary intensity. There is some evidence that this is what the body actually does. It gives a high intensity immune response if and when a normal inhabitant turns virulent. For opportunistic pathogens living on the body, exposure to the organism was always there, but what changed was the level of invasion. Even in Covid data, the immune response obtained after a severe infection has been shown to be more intense than an asymptomatic one.

This exerts a differential selective pressure. If the host has a good immune infrastructure and possibly immune memory already existing against the pathogen, a virulent variant will evoke a strong immune response and thereby cause its own destruction. A mild variant on the other hand, may not evoke a strong immune response and thereby may get away causing a mild infection and spreading to a few more individuals. Therefore as the population immunity increases, the pathogen evolves to be milder. This can happen by naturally acquired as well as vaccine induced immunity. Therefore we witness that after a substantial population got vaccinated, the downward trend in virulence got steeper once again.

The concept of optimizing immune response can potentially answer one more question. Why in Covid the vaccine induced immunity appears to be short lived, contrasting with small pox. Corona viruses are seldom that virulent. Most are mild. Pox viruses can be very deadly. It is possible that our systems have evolved to invest less in immunity against Corona viruses and more against poxviruses. This hypothesis is worth exploring further.

But why did the declining virulence appear to stagnate in the middle stages of the pandemic? I think the possible reason is that we blunted natural selection by our own preventive strategies. Selection for milder strain is strong if the severe cases are effectively quarantined and milder ones are allowed to mix in the society. But owing to the contact tracing approach, we tried to quarantine everyone exposed, reducing seriously the selective advantage for the milder varieties. We also reduced our general immunity levels by the extra precautions taken, including masks and sanitizers. There have been reports that in countries where the preventive measures were very successful initially, infections by common endemic and seasonal mild strains of viruses suddenly started needing hospitalization. This is an indication that the preventive measures have actually been creating an immune bankruptcy or what has been called in published literature an immunological debt over time. What can be beneficial in the short run can become counterproductive in the long run. Note that ALL studies showing the beneficial effects of masks are short term studies 3 or at the most 5 months. Nobody has followed the long term effects of regular use of masks. This has never been a part of the mainstream thinking in preventive medicine, but evidence for preventive measures undermining immunity has been published in the context of many different diseases multiple times.

So, the vaccines have been useful in an unexpected way. They don’t seem to have prevented the spread of infection very efficiently. But they appear to have helped in creating a stronger selective pressure for decline in virulence. On the other hand it is quite possible that masks, that were helpful in the short run, may have turned counterproductive by changing the intra-host selective environment. Ultimately Covid is bound to become just another common cold virus and the progress in that direction is clearly visible. There is no other likely fate of the pandemic. The question is whether we could have facilitated the rate of this evolution. My gut feeling is yes, we could have, by implementing better designed and carefully optimized preventive measures. But not only data needed for this optimization is absent, even such a concept is absent, so systematic studies in that direction are not even expected to happen. Evolution today is rich in molecular data but that comes as a cost of deteriorating insights into natural selection. During the Covid saga, there were plenty of talks and huge data about the mutations and the variants and their transmission, but little insights into how selection worked on the mutants being generated. There were some half baked ‘evolutionary’ statements. Someone said that by the preventive restrictions you can keep the viral population limited and thereby minimize mutants arising. But the question whether viral evolution is mutation limited or selection limited was never critically examined. Sound evolutionary thinking needs being open to alternative possibilities, insights into possible selective forces and a keen eye on the patterns in data. If such thinking becomes a part of preventive medicine, I am sure it will make handling of future epidemics more efficient.

The epidemic of scientific misconduct: an innocent question

It’s not a new virus. Various types of frauds have been there throughout the history of science. The fake fossil human skull famous by the name Piltdown man created its own history. Some experimental results by great personalities such as Gregor Mendel and Jacques Monod are said to be “too good to be true”, i.e. so well fitting their hypothesis that such a result is statistically highly unlikely given the inherent biological variability. It is possible therefore that they themselves, or someone obliging them manipulated results or cherry picked only the favorable ones. There can be many more examples that never got exposed. But in spite of such examples the core spirit of science has largely survived and maintained its reputation.

However, the rate at which misconduct is mounting today seems to be unprecedented. There is reproducibility crisis, doubts raised about designs and conduct of clinical trials, statistical analysis twisted to support the favored hypothesis, manipulated images and cooked up data. It is difficult to decide whether the frequency of scientific misconduct has increased or only the rate of getting exposed has. The rate at which image manipulations are getting detected is due to the efforts of groups like ‘pubpeer’ (, Image manipulation is only one type of fraud. Currently most of the flags raised are based on detecting image manipulation. Many other types of frauds may have just escaped because we don’t have tools to detect them. If this is true, the actual frequency of misconduct must be substantially higher. ‘Retraction Watch’ ( makes data on over 30,000 retractions and the reasons behind them accessible to people. Retractions on getting the misconduct exposed include papers by reputed laboratories and mentors. Occasionally the responsible people have to pay the cost of the deed through their nose but many others appear to escape more or less unhurt. The culprits may not pay the cost but science may be paying a big cost in terms of its reputation. Certainly I find it hard to prevent myself from losing faith in published science and I am not the only one. Richard Smith, the former editor of British Medical Journal, remarks that it is time to assume that all health research is fraudulent until proven otherwise!! (

There have been many discussions on how to prevent the scientific frauds of various kinds. People have been debating the pros and cons of better ethics education, institutional mechanisms of vigil , strict action and punishment as deterrent and so on. But I am troubled more by another innocent but unpleasant question that keeps on peeping in my mind in spite of my repeated attempts to suppress.

The high frequency of retractions makes me wonder about how science works today. I thought science works by building on prior knowledge and working the way ahead. In my mental model of science, every piece of evidence, every bit of data, any new concept, model, analysis is crucial for progress. One published piece of work lays the foundation of further work and so on. If this was true, any paper retracted would have made some concept collapse, some paradigms failed, some lines of work given up, some technologies defunct and so on. But I am surprised that following over 3000 retractions per year there is hardly any collapse seen, no fundamental change in the direction of work, no reconsideration of the existing paradigm, no rethinking of any prevalent theory. If retraction of thousands of papers has no major effect on science, it only means that these papers never had any relevance to science. Their being in a state of ‘published’ or ‘retracted’ makes no difference to the field. Perhaps they are being published only for the benefit of the authors in building their CVs and getting better positions. If they really made a difference to the field of science, their retraction should have affected the field quite badly, but that is not the impression one gets after looking at the work of pub-peers and retraction watch. Thousands of papers have been retracted but no scientific theory has collapsed or no technology has been withdrawn.

If papers are irrelevant, so must be the career of a researcher. If in judging the career of a researcher, we go by the number of such publications and weigh by impact factors and so on, we are counting the irrelevant. This way we are building more and more irrelevant science in our institutions. No wonder if the institutions themselves become irrelevant soon. They already have to a large extent, as the apathy of common man, government and media shows.

I still hope and believe that interesting, fundamental, relevant and important science is being pursued somewhere. But it may not be with the big people working in the prime institutions of science and publishing in big journals. It may be happening in some obscure lab somewhere, some teacher with a handful of undergraduate students, some farmer, some uneducated, illiterate, humble, unnoticed individuals in some corner of a third world addressing a basic question. May be that kind of science will count ultimately, some day, some time. Let us wait till them.

What else can we do?  Tell me if you know.

Time to revive science

Now that Covid-19 is increasingly taking the shape of a milder, stable and endemic disease, the panic, fear, paranoia response should be giving way to more scientific and analytical thinking. It is understandable if under the grip of fear open minded and analytical thinking becomes impossible. This certainly appears to have happened throughout the pandemic. Much of the information and advice being spread across official channels was inadequately evidence based, without much scientific foundation and at times inflicting more damage than mitigation. Simultaneously a number of known and previously well demonstrated principles were ignored without reason. Now that the global mind should be in a better condition to sustain balanced and logical thinking, science should be back on the central stage and should replace rhetoric. It may not be able to rectify the damage already done, but rethinking at this stage will enable us to learn from history and avoid repeating the same mistakes in case of a future epidemic.

The loopholes that I could visualize in mainstream science during the intellectual pandemic included being closed to alternative possibilities, forgetting prior knowledge and wisdom, giving some ‘explainawaytions’ for the observed patterns without a proper hypothesis testing approach, projecting a rhetoric as scientific truth, black and white vision i.e. inability to perceive gray shades of reality, getting blinded by partial or inadequate knowledge, forgetting the importance of contextuality and believing in one size fit all, fragmented vision i.e. the inability to put together different currencies of cost benefits in making a public health policy and so on. In the long run, the response of public health authorities to the pandemic should become a text book example of how science should not be done. Here I will describe only one example of how the mainstream has failed to think about alternative possibilities.

The core of scientific methods is hypothesis driven and using evidence for testing differential testable predictions of alternative hypotheses should be the core scientific method used. This approach was conspicuously lacking during the entire course of the pandemic, and it was lacking at each of the three steps. First, alternative possibilities need to be recognized; second, ways to differentiate them should be sought and third, attempt to falsify or support one or more of them should be made.

Our earlier paper published in PeerJ was one of the few such attempts ( seen on the stage of science during the pandemic. There are many more examples. Another paper on a slightly different approach to modeling an epidemic just appeared as a preprint (, represents a slightly different way of thinking that raises alternative possible reasons for many of the observed patterns in the pandemic. A predominant and consistent pattern across the globe has been that the peak and subsequent decline of a surge happens at orders of magnitude lower levels than the predicted herd immunity threshold. By the prevalent epidemiological thinking the decline in incidence starts when a threshold proportion of people become immune. This herd immunity threshold depends upon the rate of transmission or R0. By the observed trends in the pandemic, herd immunity was expected to be somewhere between 50 to 80% of the population. But in reality every wave started a downward trend at a very small fraction of the population getting infected. Also after reaching the threshold, the disease is expected to decline rapidly to approach eradication. But we actually saw it stabilizing at a variable and unpredictable level for some time followed by a new surge.

In our model, this is precisely what is expected to happen. Our model differs from the classical model which treats immunity as a binary variable. In classical models any living individual is susceptible, infected or immune at a given time. Our model treats immunity as a continuous variable instead and incorporates small and subtle changes in immunity. Many such small immunity effects (SIEs) have been demonstrated previously. For example respiratory infections by other viruses contribute some cross immunity. When one gets a small exposure to the virus not sufficient to cause active infection, it may cause a small increment in immunity. Repeated subclinical exposures are known to boost immunity as demonstrated with other viruses previously. In a population, while some individuals get doses of the virus large enough to cause a clinical course of infection, many more get a lower dose that fails to cause an obvious infection but the immunity is pushed a little bit upwards. This change may be one or two orders of magnitude smaller than immunity acquired by active infection or vaccination, but this small increment in immunity can bring about a big change in the epidemiological picture. It is long known that the relationship between dose of pathogen and probability of infection is sigmoid. Because of the sigmoid nature, at times a small increment in immunity increases the chance of escaping infection substantially. People with such small increments can escape infection for the time being but the effect being small wanes faster. A small decline in immunity in the absence of repeated exposure is also a known SIE. As a result the first wave is arrested rapidly but then the immunity level of such people can also decline fast as the wave recedes. This creates a part of the population susceptible again and a second wave may begin. This wave pattern does not necessarily need new variants to start a new wave. New variants can nevertheless ride a wave. So the shape of the epidemic is largely decided by the small immunity effects. We only included those effects that have been demonstrated before, at least qualitatively.

Currently we have no tools to monitor the mechanisms of SIE at a population level. But the SIE model is able to reproduce/explain/mimic almost all the patterns seen in the pandemic with or without new variants. A model is not a proof of what happens in reality, but a model can raise new possibilities and if they explain the real life patterns better, they need to be considered seriously.

There are some prevalent explanations for the dwarf peaks and repeated waves which need to be considered as alternative hypotheses. One for the reasons given for dwarf peaks is that the non-pharmaceutical measures applied such as masks and lockdowns could have controlled the transmission effectively. This is testable since it makes predictions that are different from the small peaks caused by SIE effects. One is that whenever lockdowns with different stringencies are applied, the incidence curve should show a decrease in slope and whenever lifted show increase in slope. We have analyzed this previously ( to show that the effect is very weak or marginal. 31 % of the times the slope has actually increased after applying restrictions and 45.4 % of the times it has decreased after lifting restrictions contrary to expectation. Even by a liberal estimate not more than 9 % of the lockdowns have reduced the transmission above what is expected by chance. So masks and lockdowns clearly fail to account for the universality of dwarf peaks. There is another prediction that is yet to be tested rigorously. If the decline is caused by population immunity, either conventional or the SIE, then we observe a symmetry in the shapes of the peaks i.e. a rapid rise is accompanied by a rapid decline, slow rise goes with a slow decline. This is because a rapid rise is accompanied by rapid changes in population immunity which is responsible for the decline. This type of symmetry is not expected if lockdowns arrested the peaks and caused a decline. In fact, factors such as host population density that facilitate a sharper rise will make a steep decline more difficult, taking the shape farther away from symmetry. Analysis of symmetry of peaks can be used to differentiate between whether lockdowns caused the dwarf peaks or small immunity effects. The symmetry analysis is yet to be done elaborately, owning to its statistical complexity, but one can glance through the incidence curves of all countries to realize that over 70% of the peaks are quite symmetrical. So the small immunity effects are more likely to be the cause of dwarf peaks than the lockdowns and masks.

The repeated waves can be because of the small immunity effects and/or new variants evading immunity against prior variants. Here too we can make differential testable predictions. If the virus evades prior immunity then the front line workers that are more exposed to infection should show greater incidence just as in the first wave. On the other hand, if the SIEs are mainly responsible for the second wave, by the model prediction the second wave should disproportionately affect people that are less exposed or more protected. Testing this prediction also needs rigorous retrospective study, but the impression expressed by many doctors and epidemiologists has been that in the second wave more of people protected in the first wave have been affected.

Testing differential predictions is not yet complete but SIE looks to be more likely explanation of the dwarf peaks and repeated waves. The SIE model also expects a surge after majority of the population is vaccinated, which has been a repeated pattern hard to explain. So we need to be open to the SIE effects as an alternative way of thinking, but the small immunity effects have never been a part of mainstream thinking.

The implications of SIE are serious and shake the fundamental assumptions behind public health policies applied during the pandemic. The SIE model shows that by arresting the first wave, the second wave becomes more likely. Particularly in populations that may have a higher background immunity contributed by cross reactivity to other respiratory viruses, masks and lockdowns can be counterproductive in the long run. They can cause a second wave that is more destructive than the first with or without a new variant. The second wave predictions of the model appear to match with that in India in particular. It is likely that the severity of the second wave was because of the protection measures turning counterproductive. Today we do not have convincing evidence either way, but the certainty about masks being always protective is definitely under serious doubt. It is important to realize that all the studies showing the efficacy of masks are short term studies never going beyond 3-5 months. Masks can turn counterproductive because they interfere with the protective small immunity effects but allow small accumulating decline in immunity. The model also shows that masks and vaccines are antagonistic in their net effects. It may be a better policy to contraindicate masks after vaccination. CDC actually recommended this but the reasons for this recommendation were not made clear. Therefore whether masks are good in the long run is an open question. Public health policies without seriously addressing such questions should be called pseudoscience.

Many more flaws in the mainstream beliefs behind public health policies and the science behind it can be pointed out. Although huge amount of money went into research, we know very little about the mode of transmission, which is of central importance for an epidemic. Huge data on mutations and variants was generated but little efforts to relate it to clinical picture. It is impossible that so many mutations did not affect the behavior of the virus, but people handling genome data have no information on disease behavior and clinicians kept on wondering how so much of sequence data would help them. Interestingly the evolution paradigm itself seems to have changed in that everyone talks about mutations now but hardly any one talked about the complexity and subtlety of selection on the virus. Viral evolution during an ongoing pandemic is a multi-level selection problem and interesting data came up during the pandemic which would take the debate on multi-level selection substantially forward, but no evolutionary biologists seem to have used it so far. We do not have reliable methods to quantify ‘infectivity’ and ‘virulence’ because both can alter substantially by SIEs and any estimates without considering SIEs can be completely misleading. But without any reliable methods of estimation everyone kept on talking about more infective and immunity evading variants. Contradicting this, the vaccine group kept on asserting that vaccines are effective against all variants so far. I have seen no attempts to resolve this contradiction. The question whether viral evolution during a pandemic is mainly mutation limited or selection limited has not been even asked, forget addressed. The inadequacies in our understanding of immune mechanisms are exposed even more. Inadequate knowledge is not a crime or a flaw, but denying the inadequacies and pretending or making claims as if everything is known is.  All this could be said to be inevitable under the pressure, panic and paranoia, but now is the time to do more sound science. This is the right time to strengthen real and unbiased science forgetting who was right and who was wrong, welcoming cross questioning and open mindedly addressing alternative possibilities so that we don’t repeat the same mistakes in a future threat of another respiratory virus.

Greater the volume of scientific literature, slower the progress of science:

In a recent discussion with someone having spent his entire life in science, and refusing to dilute his commitment even after retirement, an interesting issue came up. The volume of published literature is increasing rapidly making the life of interested readers more and more difficult. A reader has to make a quick decision as to whether to read a paper by quickly looking at the title and may be just first and last line of the abstract. This is the most critical step and a bottleneck in science communication. How does a reader take a decision to spend a little more time on the paper? (forget complete reading). Of course readers vary and there is no one rule fit all. But in our discussion we stumbled upon one such rule that seems to work quite often. Whenever there is something in the title and abstract that sounds contradicting the reader’s logic/thinking/prior knowledge/belief/interest or whatever, it is immediately swept aside. This might be either because there is an inherent resistant to paradigm shifting ideas as Thomas Kuhn says, but also because there have been too many false alarms. There have been too many writers “trying to prove Einstein wrong, prove Fermat’s last theorem or have a method to square a circle etc, so for those who have far too many things to read and too little time, a quick decision has to be made and making a negative decision is statistically correct” (His words, somewhat rearranged). This means that if a genuine breakthrough is being published, it is least likely to be read by anyone.

When I examined my own behavior, I thought I would do the reverse. If there is something deviating from the trodden path towards pre-conceived goals, I would read it with priority and more curiosity. Obviously readers differ in their choices. One (rather oversimplified) classification of readers is that there are explorers versus track racers. Explorers are typically slow, somewhat aimless, more curious, with little preconceived ambitions and aspirations, driven more by curiosity than by the desire to succeed in something. They will be attracted by titles and abstracts sounding off-beat, challenging mainstream thinking, iconoclastic, non-conformist. The other class, the track racers are committed to a small field, have a vision as to what they have to achieve in as little time as possible, they want to be and often are more productive in their published output and so on. They will quickly and efficiently decide what they don’t want to read. They will quickly short list the articles that they can potentially cite. For citing something it is often a waste of time to read it entirely. But they know quite well what to read and what to skip. So they can make best use of their time.

Potentially both the classes have differential importance to the field of science. A good combination of the two would benefit science. But I am afraid, the frequency of explorers is declining as I can see. The reason is the process by which individuals are selected to enter the field of science. They are selected by ‘merit’. At the UG stage, merit is first screened by success in the Board and University exams, only secondarily by interviews etc. At a later stage selection is based on where you graduated, whose lab, whose recommendation you carry, where did you publish, how many papers, with whom and so on. Here to actually read papers of the faculty or post doc candidate is not affordable because there are so many applicants and judging someone by reading his work is just out of question. In this process the track racers almost always outcompete the explorers. As long as the selection procedure does not change, the publication smartness along with some luck will always weigh heavily over exploration, novelty and originality of thinking.  As a result, I suspect, explorers are getting extinct from the field rapidly.

This extinction would further make reading of off-beat papers even rarer. Trodden paths will become even stronger and inescapable. There will be greater and greater career success with lesser and lesser science. Is there a way out?

I am not a pessimist.

The way out as I can see is to do science outside the institutional and academic framework. Strengthen citizen science. It is not difficult to do good science outside the institutional framework. But it is more difficult to publish in the mainstream journals having the prevalent  brahminical culture. The word brahminical here does not refer to caste by birth, religion etc. It refers to monopolizing science by certain institutions; denying science coming from elsewhere and in any other form; refusing to recognize anything as science unless published in peer reviewed journals; the exorbitant author charges that make it impossible for a citizen to publish his science in the mainstream journals. There is one more important difference. Citizen science demands that you read it. In brahminical science, just that it is published in a peer reviewed journal is enough. You don’t have to read it yourself and decide anything. Interestingly, editors and peer reviewers are also readers first and they follow the same culture. They too have to take a quick decision, and not reading it is the quickest way of judging. The review process is opaque. So a paper has gone through peer review can only be called a belief. Anything that is not transparent is not scientific by any standard. But once the label of peer reviewed journal is obtained, it is believed based on the label itself. Throughout this process rarely anyone reads a paper completely. If it is labeled Ganga water, it must be “purer” than your tap water by all means.

There can be exceptions of course. I was told, that late Prof. S. Chandrashekhar of Chicago would read each and every paper submitted to the ‘Astrophysical Journal’ from beginning to the end, during the many decades for which he was the editor. Some individuals of this species may still be there but overall I suspect, this species is near extinction. A culture of reading with curiosity, reading with interest should be central to science. But the huge volume of literature being published in any field is making this impossible. That’s why my feeling is that the greater the volume of literature published, slower will be the conceptual progress of science. It will swell more and more, no doubt. But swelling is not progress, edema is not growth.

My science, my way – for handling third and consequent waves, if any

I am fully aware that nobody would listen to me and change anything at the policy level, but as a science minded citizen I am going to write here what I feel is the most logical and effective plan to deal with a third, fourth or subsequent waves of the pandemic. In some countries, a third (or even a forth) wave has certainly appeared already, in spite of vaccination, but the death rate is substantially lower. Since we have invested a lot in vaccines, it would please us to say that the death rate came down because of vaccination. This might be partly true, but it has been coming down even prior to vaccination, although not without hick ups. Interpreting these hick ups will not only be interesting but important for handling future waves wherever and whenever they appear. My views of successful handling of the third wave depend upon my interpretation of the patterns so far. Therefore I am putting down both simultaneously here.

I have an eight point program to effectively manage the pandemic hence forth, including any surge, if, when and wherever it occurs.

  1. Give up the Don Quixotic attempt to “break the chain”: We have been often hearing phrases such as “break the chain”, without understanding what it means. How are we supposed to break the chain, i.e. interrupt one round of infection cycle? By preventing the transmission from currently infected individuals to susceptible individuals. If we can do this for one complete cycle, i.e. about 3-4 weeks, the epidemic will stop once for all. That is the true meaning of “break the chain”. Nobody has successfully broken the chain anywhere in the world so far except where there were only a handful of cases at that time. Such a situation does not exist anymore in any country with only half a dozen possible exceptions. According to a WHO report (, none of the measures suggested including social distancing, masks, sanitizers, lockdowns, school closure, work place restrictions, entry exit screening individually or collectively have evidently ever stopped transmission. They can at the most reduce transmission rate by a few percent. Even the flawed reports that claimed these measures to be effective in the first phase of Covid did not show efficiency high enough to break the chain. So the call to “break the chain” was Don Quixotic right from day one. But it was promoted as a rhetoric and people believed it with religious faith.

This reminds me of a story. A spiritual healer told people, if you do such and such ritual with full faith, the disease will vanish. Saying such a thing is very safe for the healer. If the disease really vanishes, he can take the credit. If it doesn’t, he can say you didn’t do it with full faith, therefore it didn’t work. The same applies to breaking the chain by social distancing. If by chance, the incidence goes down, it is because of that. If it doesn’t, it is because people did not follow the rules properly.  Therefore the health authority are safe in advocating it. Going by such a logic the concept that preventive restrictions can arrest transmission and break the chain can never be proved wrong. It has no falsifiability and therefore by Karl Popper’s definition is not science at all.

In order to arrest transmission, we should first know how the virus is transmitted. Even after one and half years there is no agreement on the relative importance of droplets, aerosols and surface transmission. What is a safe distance has never been determined. Some studies say two meters some say ten meters. So even theoretically what measures can be expected to prevent transmission is uncertain.

Instead of trying to arrest transmission of respiratory virus, which is hardly in our control as we were made to believe, we need to focus on something that is in our hand. Good patient care and vaccination is certainly in our hand. Both are not magic solutions but they can certainly save many deaths and that is evident in data.

In the history of infectious diseases, out of the several dozen viral diseases caused by hundreds of variants, only small pox has been successfully eradicated and polio might have been. Human efforts have not been able to get rid of any other virus so far. But we are not afraid of most other viruses anymore, because we managed them successfully in some other way. They are still there but they no more kill as many people. That is the most likely fate of SARS-Cov-2 as well. How to achieve this as fast as possible should be the prime R & D and health policy question. But such a question is hardly being addressed anywhere. If we are still talking about breaking the chain, getting rid of the virus, keeping it away forever or eradicating it, we are the reincarnation of Don Quixote.

2. Change the testing strategy: tests to detect active infection serves three possible purposes (i) help in deciding the course of treatment (ii) contact tracing and isolation of potential spreaders for arresting transmission (iii) give useful epidemiological data. A careful look of patterns over the last 15 months through most of the world shows that testing, as it is being done, has not served any of the three purposes. The course of treatment of a patient is mainly decided by the symptoms. Being or not being Covid positive has little role in deciding the course of treatment most of the times. If, in a rare case, the treating physician thinks he needs to know the test report to decide the treatment, a test facility should be available. But to do millions of tests is unnecessary to serve any clinical purpose.

Contact tracing can be effective only when there is a small number of cases and only a small proportion of asymptomatic and undetected cases. Above a threshold incidence, it simply stops working ( At this stage of the pandemic with a large number of undetected cases, contact tracing to prevent transmission is equally Quixotic. So continuing this effort is not worth.

On the third purpose, the way the tests were being done so far, the data generated has been highly biased and quite unreliable for any useful epidemiological purpose. So tests have not served any of the three intended purposes.

So I would suggest the following strategy for tests. (a) For clinical purposes, only when a test is likely to influence the course of treatment, as decided by the physician it should be done. (b) For generating good epidemiological data, instead of doing millions of tests haphazardly, let a cluster of institutes be given a mandate and adequate funding to do well-designed sample surveys which will generate useful epidemiological data. What is important for epidemiology is not the total number of cases, (which nobody is likely to know anyway, all numbers given so far are wrong!!) but faithful representation of the trends. This needs well thought out sampling design, not simply more massive testing. Attempt to detect every infected person by a test is also a quixotic effort and needs to be given up as fast as possible. A well designed sampling strategy will be immensely more useful, simultaneously saving cost as well as panic and confusion in the minds of people. Better data will be available to scientists and all the data mismanagement can be eliminated.

3. Lock the lockdowns: Detailed and careful global analysis gives little evidence that lockdowns help reducing the transmission of the virus substantially, making them worth paying all the economic, educational and social costs. The distribution of the costs over the society is highly unequal. The decision makers themselves pay hardly any cost. Focused protection of the vulnerable groups and normalizing all other life needs to be the priority. At the most large avoidable gatherings can be restricted. So far there is no convincing and consistent evidence that such gatherings have increased the transmission of the virus, but one may do that if the events are not perceived essential.

Travel restrictions have led to another strange problem. As we reduce substantially the transmission across countries and regions, the epidemic curves take different shapes in different regions. When the prevalence declines in one area, it is on the rise in another. These non-synchronous surges ensure better long term survival of the virus. So it is quite likely that travel restrictions actually help the virus more than us. Also the virus will evolve differently in different parts of the world adding to the uncertainty of the course of the pandemic.

4. Invest in people oriented patient care practices: A good practice to face a calamity is to hope for the best but simultaneously prepare for the worst. Going by the worst of all mathematical predictions, it is necessary to make patient care facility available at every corner of the world. The lockdowns did two kinds of damages to prevent this. One is to give a false assurance that masks and social distancing will prevent the spread, so less attention was given to development of patient care facilities. The second was that the lockdowns made the economy collapse in some areas so badly that investment in the facility and availing the facility also became difficult.

Furthermore, at least some of the patient care practices of the first and second wave were counter-productive. Over the last week I have been traveling in some tribal interiors and talking to people about their Covid experience. My impression is that the wave swept across even the remote areas and almost everyone was sick during the peak period. But people were reluctant to get tested and admitted to hospitals for multiple reasons. One is that the government hospitals were full and private ones were unaffordable. After losing jobs in the lockdown, even the cheapest of the hospitals and transport to the hospitals was not affordable to many. Other, even more important was our patient quarantine practice. Quite often a person admitted to a Covid ward was not seen by its relatives again, not even the dead body. Culturally and emotionally, death rituals are extremely important. Denying people even a sight of their dear ones even after death, led to increased reluctance to admitting and even testing for Covid. Seriously infected patients remained at home spreading the infection further. The secretive funerals may have been intended as an extra precaution to prevent spread, but in reality it might have been counterproductive. This illustrates the need to understand people and make the health care practices people oriented.

5. Directing evolution of the virus: It is very clear that the virus is evolving very rapidly. We have lots of data on mutations but almost no insights into what these mutations mean clinically. But we see simultaneously that death rates have decreased although apparent infectivity may have gone up. There is a set of conditions under which as the virus evolves in a host population its virulence goes down although infectivity may go up. In the context of Covid, two conditions are crucial for evolutionary changes in virulence. (i) If virulence is related to viral replication and transmission, the virus has a selective advantage in being virulent. But in Covid 19, the correlation between disease severity and respiratory viral load is very poor as shown by multiple studies (DOI 10.1016/s2213-2600(20)30354-4; DOI 10.1101/2020.07.20.20157792; This means that there is only a marginal advantage to the virus in being more virulent. (ii) On this background, if the serious cases are effectively quarantined and the mild and asymptomatic cases remain intermingled with the population, the milder variants will have better chance to spread. This will eventually lead to loss of virulence. The rate of this loss will depend upon how we handle the epidemic. (a) If we attempt efficient contact tracing so that all cases including asymptomatic ones are always diagnosed and detected, we are not giving any selective advantage to the milder forms. In such a case a more virulent virus will evolve because there is at least a weak correlation of virulence with viral replication. (b) If we allow the mild and asymptomatic cases to mix with the population, but effectively quarantine the serious cases, we give a selective advantage to the milder variants. (c) If, there is no effective isolation at all, then again higher virulence is more likely to evolve. By carefully managing and differentiating between cases we can boost evolution of the virus towards loss of virulence. This might be bad in the short run but good in the long run and I suspect, the only strategy that has high chances of effectively ending the epidemic.

Interestingly nobody in the mainstream even talks about evolution of virulence. On a few occasions responsible people talked about it and they were immediately trolled so heavily that evolution of virulence became a taboo in this field. But death rates reflected in different indices have been coming down, with some transient trend reversals. The trend reversals also can be interpreted logically. In India, the death rate at the beginning of the second wave was only one fifth of that in the first wave. But it gradually increased as the second wave peaked. I can see two possible reasons for it. One is the saturation of hospitals and patient care facility which clearly increased the death rate. The other is more subtle. As explained above, in remote rural India, the funerals under isolation, often without even informing relatives, led to increased reluctance to admitting and even testing for Covid. Many serious cases remained and died at home. In the absence of isolation of severely infected individuals, the virulent strains must have had a good chance of spread. Thus cremation under strict isolation may be intended to prevent transmission, but it actually ended up increasing transmission, that too of the more virulent forms.

Strengthening host immunity also affects the evolution of the virus in subtle ways. Immune response is costly for the host physiology. The immune response has evolved to see that the cost of immune response should not be greater than the cost of invasion itself. Therefore the body does not give an all out immune response to all pathogens all the time. The investment in an immune response is carefully weighed against the intensity of invasion. A virulent virus will evoke an all out immune response and thereby destroy itself in an immune-competent host. A mild virus is more likely to escape intense immune response and thereby survive better. So as the average immunity of the population goes up, the virus evolves to become progressively milder. Vaccination drive serves this purpose the best, rather than the proposed quixotic eradication of the disease, or preventing infection or preventing subsequent waves which it hasn’t. Signs of third or fourth wave are clearly seen in countries with good vaccination coverage as well. Directing viral evolution is going to be the truly useful role of vaccination. Otherwise we have seen that the vaccines neither prevent subsequent waves nor stop new variants from arising.  In fact greater the proportion of vaccinated, greater the selective force for new variants.

Given the small genome and huge global population of the virus, the evolution of the virus is most unlikely to be mutation limited. It is expected to be selection limited and therefore it is possible to manage selective pressures on the virus so that it rapidly becomes milder and remains just another coronavirus.  

6. Fitness drive: Global data and multiple published studies have shown that physical and physiological fitness, independent of age, has been keeping serious symptoms and death away. There is consistent global data on fitness relating to Covid survival (see for example, Sedentary lifestyle is a high risk factor. During the first and second waves we forced the gyms, playgrounds, jogging parks and swimming pools to shut down. This is very likely to have increased the susceptibility of the population. Promoting rather than closing down fitness activities would be the right strategy if there is yet another wave. Fitness is the most reliable way of surviving the infection. Closing down fitness activities was the biggest mistake that should not be repeated in any case.

7. Make up for the loss in education? There is no need for school and college closure. That is the age group least affected by the virus. There is little evidence that closures helped. But even if we reopen now, the hardest thing to do is to recover the educational loss already incurred. Recovering from the economic loss might be easier than recovering from the educational loss of an entire generation. How to recover is one of the toughest questions and needs a lot of thinking to go in. Perhaps there may not be a single solution to all levels, all schools and all subjects since details matter a lot. But certainly a focused special drive is needed for making up the lapses in education.

8. Research questions to focus on: The pandemic has exposed the inadequacies of the way research community works. We have failed to identify and address the most important questions, recognized alternative possibilities, making specific local context based policies, cross question and recheck earlier conclusions, challenge rhetoric and keep on improving our understanding of the disease. While we did see some good science during this time, many important questions were almost completely left out. The precise mode of transmission of the virus is still not clear including the importance and dynamics of aerosols versus other modes of transmission and their context specificity. Realistic evaluation of the performance of various preventive restrictions in the context of different localities has been seriously lacking. There were some early attempts that were seriously flawed. Later there seems to be nothing published. It is possible that public health policies that work in the short run may fail to work or even become counterproductive in the long run (see for example, But we have been emphasizing on the same preventive measures, for the success of which we have no reliable evidence. There is need to reassess the efficiency of all the measures periodically, rethink and redesign the policies accordingly. Substantial amount of research needs to go in such an effort in every country because the success will depend upon local conditions which are different in different countries/regions/cities. What works in one city may not work the same way in another. Assuming what worked in New Zealand will work in India is being superstitious.

The epidemiological models have largely proved themselves inadequate, to put it mildly. The reason is that they are oversimplifying the population processes. For example almost all models treat immunity as a binary variable. An individual is either susceptible or immune, which is far removed from reality. This binary perception of immunity has led the illusionary herd immunity concept. Cross immunity offered by common cold and other milder viruses, immunity by repeated subclinical exposure to the virus are not considered at all by the models. These can potentially be factors chat change the course of the epidemic even qualitatively. Building such models need not be too tough, but needs an appreciation that current models are failing and we need to incorporate at least some of the complexities of the immune mechanisms in the model by making immunity a continuous and perhaps multidimensional variable. I haven’t come across attempts to build such a model. Not because it is difficult but because the vision is surprisingly absent.

Most important, we need a lot of realistic research on human behavior. The success of any public health policy depends upon whether it is behaviorally sound, how people would interpret it, what will be the consequent perceptional and behavioral changes in the short and in the long run, whether people will follow the advice, will it become a ritual and lose its original meaning and so on. Study of human behavior needs to be an intrinsic part of policy making in every field. For public health it makes lot more sense.

Boosting research in the right direction is also a complex behavioral problem and needs to be perceived that way. This is a more fundamental question of research culture itself about which I have written many times and will keep on writing. So I won’t expand on it here. But I would certainly hold the research community ultimately responsible, if the governments are following any strategy without sufficient evidence base, without repeatedly getting evaluated and in turn causing more harm to people than the disease itself.

All the pseudoscience of Covid 19:

When a definitive statement is made without sufficient evidence by someone in a responsible position where people view the person as a scientist, it certainly amounts to pseudoscience. In science it is often inevitable that you go ahead with hypotheses that have not been rigorously tested as yet. Having uncertainty does not make it pseudoscience, but projecting it to the public as an established truth and hiding the uncertainty is what I call pseudoscience. During the Covid 19 pandemic we have witnessed umpteen examples of it.

The early assertion that the virus is not a lab leak, when there was no definitive evidence either way is not the only example. For a number of other things that are being projected as scientific truths, the evidence is actually quite questionable. Projecting them as truth has possibly led to devastating consequences.

The so called non-pharmaceutical interventions (NPIs) such as mass use of masks, social distancing, school closures, lockdowns have been projected as scientific ways of combating the infection and raising any questions about them is trolled as anti-science. But what is the evidence that they prevent spread of the infection, and to what extent?

In 2019, just a few months prior to the beginning of the pandemic, WHO published an official report on the evidence and recommendations for NPIs for respiratory diseases. Since Covid 19 was not yet on the horizon, the focus is on influenza. But it is important to see what the report says (  The team writing the report undertook a detailed systematic review of literature on all the NPIs, available at that time and their conclusions are based on a meta-analysis of all relevant literature. They say, “The evidence base on the effectiveness of NPIs in community settings is limited, and the overall quality of evidence was very low for most interventions. There have been a number of high-quality randomized controlled trials (RCTs) demonstrating that personal protective measures such as hand hygiene and face masks have, at best, a small effect…”

Specifically they have to say this regarding individual NPI components:

Hand hygiene: However, there is insufficient scientific evidence from RCTs to support the efficacy of hand hygiene alone to reduce influenza transmission in influenza epidemics and pandemics.

Entry exit screening and border closure: There is sufficient evidence on the lack of effectiveness of entry and exit screening to justify not recommending these measures in influenza pandemics and epidemics. Border closures may be considered only by small island nations in severe pandemics and epidemics, but must be weighed against potentially serious economic consequences.

Face maks: There is a moderate overall quality of evidence that face masks do not have a substantial effect on transmission of influenza.

Surface cleaning: There is a low overall quality of evidence that cleaning of surfaces and objects does not have a substantial effect on transmission of respiratory disease.

Contact tracing: There is a very low overall quality of evidence that contact tracing has an unknown effect on the transmission of influenza.

Isolation of patients: There is a very low overall quality of evidence that isolation of sick individuals has a substantial effect on transmission of influenza except in closed settings.

Quarantine of exposed individuals: There is a very low overall quality of evidence that quarantine of exposed individuals has an effect on transmission…

School and work place closure: There is a very low overall quality of evidence, and the studies that have been published reported or predicted that school measures and closures have a variable effect on transmission of influenza…… There is a very low overall quality of evidence that workplace measures and closures reduce influenza transmission.

Avoiding crowding: There is a very low overall quality of evidence on whether avoiding crowding can reduce transmission…

Travel restrictions: The quality of evidence cannot be judged because no study was identified.

All the above are copy pasted verbatim from the 2019 WHO official report. The report also recognizes that many of the NPIs can be highly disruptive and can cause substantial losses to the society. Therefore the report very sensibly recommends the measures with low costs but does not recommend the ones with high social and economic costs. Accordingly it clearly states in section 2 of the report that contact tracing, quarantine of exposed individuals, entry-exit screening and border closure are NOT recommended. Face masks, school closure or avoiding crowding is only conditionally recommended. Imposing these measures without studying the relevant conditions in the local context amounts to pseudoscience in my view.

The report has further wisdom: It says in settings where multiple exposures occur, removing one mode of transmission may not be sufficient to reduce overall transmission. This makes sense in the current scenario where we know that it is impossible to close all modes of transmission. We have a set of rather arbitrary restrictions which may at the most reduce some of the modes of transmission, but certainly not all. It is common sense that that this cannot “break the chain” that it is supposed to do.

This report has made very honest, logical and sensible statements and has weighed evidence very carefully. However, as the paranoia of the pandemic set in, all this wisdom was forgotten in moments. It looks like this report saw the dustbin immediately and all the ineffective measures were imposed as if they were well proven miracle solutions to save the earth. Were there any studies in between publication of this report and the global NPI recommendations with the beginning of the pandemic? The answer is no. After the NPIs were recommended, within the next few months there was a mushrooming of studies, published by all high impact journals, most of which said that the NPIs are effective. How is it that what was ineffective, doubtful or marginally effective for respiratory infections just a few months ago suddenly became effective? No comparative study and justification for the miracle has been given. A careful look at those studies reveals that they have all kinds of typical well known flaws that a clinical study can but should not have. Most of them compare a set of arbitrarily chosen countries with no justification of why they chose only these countries. If comparison across countries is a valid method, we will also have to agree that healthcare kills, because countries with better health care have greater death rates. The need for consideration of other confounding variables is apparently not felt by these studies. Most important, it is long known that epidemics take wave forms, which is true for the current pandemic as well. In a wave form, the rate of transmission changes on its own, even without any intervention. This natural change in the rate should make the null hypothesis for examining the effect of an intervention. Surprisingly not a single study has an appropriate null hypothesis incorporating natural changes in the transmission rates. The only study that tries to incorporate a null hypothesis is ours and that finds little effect of NPIs during the pandemic ( Therefore even after over a year of data on the pandemic, we do not have any agreement along with sound evidence on the effectiveness of the NPIs. But still the NPIs are being promoted with definitive statements. For example, at the peak of the second wave in India Anthony Fauci said that India needs a lockdown. This is nothing else but pseudoscience.

Contextuality being ignored in a pseudoscience regime is no surprise. The Mumbai slums, for example, have a population density of 270,000 per square Km. If everyone stays at home the mean neighboring individual distance turns out to be less than the recommended social distancing. In normal life a substantial part of the population is out for livelihood activities. Many have night duties as well. So on working days, the slums have a moderate density. By lockdowns the density of people in an indoor congested environment actually increases. The nature of housing is such that a table fan in one house quickly carries aerosols to neighboring houses. Would ‘stay at home’ advice work in such a setting? But details like this matter only for reality based genuine science. Why should they matter for pseudoscience? Only pseudoscience can recommend context independent magical solutions.

There are other examples too.

  • Why were play grounds, jogging parks and swimming pools closed during the lockdown? WHO has an official statement saying the virus does not spread in swimming pools (…/advice-for-public/myth-busters). There is substantial evidence on the other hand that exercise and fitness has protective effects against the complications of Covid ( The decision of closure of swimming pool and other fitness activities was taken by someone without looking at evidence on the contrary. This is certainly anti-science.
  • More and more studies are now revealing that after recovering from infection, immunity lasts long. However, for travel and many other purposes, having recovered from Covid is not taken to be equivalent to vaccination. There is no justification given. For a recovered person why two doses of vaccination are still required? There is no science behind such a recommendation.
  • The virus is supposed to spread through coughing, sneezing and may even by talking and breathing out. A dead body doesn’t do any of these. So how does dead body spread a respiratory virus? But funerals of Covid deaths are done by selected people with PPE (salute to their dedicated service, but was it really essential?) and the relatives and friends are strictly kept away (of course with the exception of political leaders).

This is likely to have an unintended but expected effect on people, particularly from villages and remote areas.  For them, often a person admitted to a hospital is never seen again. This is comparable to the Baloch agitation where they say so many people just disappeared in state supported terrorism. What do we do if people compare the two situations? Spending the last moments in the company of relatives and friends is of important cultural and emotional value. Since people started missing this, there was a growing reluctance to admit a sick person, even test for Covid in rural areas. I believe this is one of the main reasons why in the second wave, a large proportion of cases as well as deaths remained unreported. People just preferred deaths over pseudoscience supported terrorism. But this is a social and psychological issue, which health researchers will not cover. In my view it is the unnecessary isolation of the dead without any evidence for transmission from the dead, and the natural social response to it fueled the second wave to a substantial extent. At present this is only anecdotal. This needs to be studied as a hypothesis. Reluctant to even consider alternative possibilities as testable hypotheses is another marker of pseudoscience.

  • The scanty literature including experiments on the benefits of mask are all short term experiments. Something that is useful in the short run can become counterproductive in the long run. It was said in the first phase of the epidemic that the surprisingly low death rate in India and other crowded south Asian countries was owing to cross immunity through other corona-viruses. Such short term cross immunity along with the possible mechanisms has been demonstrated as well ( A possible cause for the rapid onset and higher deaths in the second wave is that owing to masks and social distancing the incidence of other milder respiratory viruses could have come down. Since such immunity is short lived, the population lost this protection on long term use of masks. The only strength that India had in the first wave could have been lost by masks and social distancing. Is this causality proved? The answer is no, but that is because nobody studied it. This hypothesis can make testable predictions. It is possible to take it up as a retrospective study and test it. Such a study would be highly relevant for future policy. Science is certainly interested in alternative hypotheses and testable predictions. Pseudoscience will only ignore alternatives.

We had enough of pseudoscience by now. People should demand science over pseudoscience. Question the rhetoric, cross question the authorities, demand evidence. Good science is also being done no doubt. But it suffocates under heaps of pseudoscience. It is quite possible that this pseudoscience is actually killing more people than the disease itself. Therefore, not being open to evidence and to alternative possibilities, needs to be considered as criminal.

Science and politics: The thin line

My differentiation criterion between science and politics is simple and clear. What is right and what is wrong is a scientific debate. Who is right and who is wrong is a political one.

Democracy is the leading norm in politics today although it is yet far from being universal. The structure of politics in democracy is such that although there can be public debates on various issues and two or more possible stands taken on any issue, what we ultimately vote is not any particular stand but an individual leader or a political party. This is inevitable because there are multiple issues and only one election. The election is between parties and leaders, not between alternative policies on every issue. It is possible that you agree with a given leader’s stand on certain issues but not on others. Then you need to prioritize, give more importance to certain issues and decide your vote accordingly. Here, we can say that it is because of the structure of the democratic process that who is right takes an upper hand over what is right.

By the principles of science, all debates should be about what is right and what is wrong. They are supposed to be driven by logic, mathematics, evidence, experiments, data and so on. But that happens surprisingly rarely. What we see in reality is that any debate quickly takes the form of who is right and who is wrong. It becomes my hypothesis versus yours. Science is not formally driven by voting. But at some level it takes the form of voting. The community ultimately accepts, rejects or ignores a given hypothesis, possibility, opinion, interpretation and the like. This form of voting is worse than political voting because in a democratic process every vote has the same weighting. In science the elite vote is orders of magnitude heavier than a student’s or young researcher’s vote. Often there are experiments, opinions or interpretations that could be logically correct, supported by evidence but have only a minority of followers. Such pieces, even if published are often ignored by the community rather than debating on it. Of late, even the cancel culture seems to have entered science. So a minority stand is not allowed to be expressed, or is quickly labeled ‘anti-science’ or ‘mis-information’.  It is almost impossible to publish a non-conformist hypothesis, experimental result or data analysis in a peer reviewed journal. And the ways of reaching people directly through social media are also closed owing to the increased pressures on social media to sensor ‘misinformation’.

On this background Dr. Anthony Fauci saying, “Attack on me is attack on science” is a 100% political statement. Given that he was responding to political attacks, he might be fair in saying this. But this demonstrates how quickly science becomes politics. Fauci’s is a dangerous statement because the line between science and politics is very thin. Having a scientific opinion against the mainstream Covid preaching is not an attack on science. Disagreeing with the mainstream is never an attack on science. In fact, cross questioning is an essential exercise in science. Without alternative thinking, without competing hypothesis and without challenging mainstream beliefs there remains only pseudoscience. During the Covid pandemic we have seen the tendency to suppress, ridicule and cancel the non-mainstream opinions very frequently. From health authorities and people of mainstream science, public statements and assertions were being made without adequate evidence. The clean chit against the lab leak hypothesis, the insistence on social distancing and use of masks, the imposed lockdowns were all without sufficient data support. In emergency, you may not be able to wait for support. You may have to start doing something with a belief. This is fair, but it shouldn’t have been projected as the word of ‘science’. As soon as data accumulate, all the hypotheses and measures taken based on it need to be reexamined and policies changed accordingly. It is likely that what is seen to be working over a short term might become counterproductive in the long run. So, even tested and published results need to be rechecked in the altered context. But such possibilities are not even being discussed.

By human nature once you advocate something, it gets associated with your ego. A challenge to a policy becomes a challenge personally to you, and then it s not easy to be open to any change. This is in no way restricted to Covid related issues. It has been common throughout the history of science. It became much more serious after peer reviews became the mandate in the 1970s, because peer reviews became an authenticated tool to reject inconvenient evidence or interpretations and impact factors became the convenient tool to ignore them even after being published.  

I believe that the cause of the problem lies in the evolved human nature. Human reasoning has evolved to judge humans and to take sides, not to make unbiased judgments on issues. The question who is right and who is wrong is central to the evolved human mind. What is right may be the appropriate scientific question, but in no time we slip on to the ‘who’ question without even being aware of having done so.

In politics where there are several issues you may agree with someone on some of the issues but not on others. Potentially this can be a very complex process. But in reality, it turns out to be surprisingly simple. Very few people appear to be confused about whom to vote, because they agree on certain issues and disagree on others. For most people, the decision comes quickly and clearly. This is because when you like a leader or a party, you tend to agree with most of their stands. This also seems to be driven by basic human nature. Often the opinion about a person is not made based on issues, the opinions about issues is made based on the person.

This is the reason why the pursuit of science without bringing in politics is so difficult. It may not be impossible but it doesn’t come naturally. You need continued conscious efforts to see that you do not slip on to personal judgments, lobbies, individual cost-benefits or power games in supporting or opposing a stand. The tendency to selectively ignore or cherry pick evidence also comes from making it my hypothesis versus yours. There might be a positive side to it. It is possible that mine versus yours adds more spirit and interest to the debate, but I think the negative side far outweighs the possible benefit. The spirit of focusing on ‘what is right’ and weeding out ‘who is right’ should be a part of basic education and training in science. Training in science today is almost entirely training in tools of science. We rarely talk about the methods of science, the appropriate mindset for science. Scientific studies on the process of research have been very primitive and limited to a few mundane questions. I think possible solutions to the problem of frequent slipping into politics  may lie in the undergraduate classes.