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’ (https://undark.org/2020/07/23/cracking-down-on-research-fraud/, https://blog.pubpeer.com/). 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’ (https://retractionwatch.com/) 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!! (https://blogs.bmj.com/bmj/2021/07/05/time-to-assume-that-health-research-is-fraudulent-until-proved-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 (https://peerj.com/articles/11150.pdf) 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 (https://www.preprints.org/manuscript/202109.0162/v1), 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 (https://www.preprints.org/manuscript/202104.0286/v1) 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 (https://apps.who.int/iris/handle/10665/329439), 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 (https://milindwatve.in/2020/07/20/covid-in-india-why-the-strategies-need-to-change/). 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; https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2769235). 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, https://bjsm.bmj.com/content/early/2021/04/07/bjsports-2021-104080). 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, https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008292). 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 (https://apps.who.int/iris/handle/10665/329439).  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 (https://www.preprints.org/manuscript/202104.0286/v1). 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 (https://www.who.int/…/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 (https://bjsm.bmj.com/content/early/2021/04/07/bjsports-2021-104080). 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 (https://pubmed.ncbi.nlm.nih.gov/33754149/). 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.

Covid 19: Kumbh, Election rallies and the second wave surge

This virus does not seem to stop giving surprises. So far so many predictions from people of science, including some of mine, have failed. I did not think, for example, that the second wave will be so sudden and large, although CFR has kept on dropping as I predicted. Even amidst the oxygen crisis and other problems with the patient care system, CFR in this wave is much smaller than last wave. But the total deaths are much greater than expected simply because the transmission has been just too rapid.
But what caused this rapid transmission? Everyone seems to blame the election rallies, the madness called Kumbh and elsewhere normalizing life, traffic, transport and travel. While I feel normalizing life is inevitable and must happen, I won’t count a religious gathering of that scale or election rallies as a necessary part of normal life. If examinations also were postponed, why not elections? If at all elections were necessary, why not restrict campaigns to TV, radio, mobile and other media? These have reached every corner of India. It was too obvious a conclusion for everyone that crowding during election rallies and the Kumbh has caused the terrific surge.
However, as a science teacher, I am not content with simple looking logic, opinions and consensus. I wanted to look at data. Particularly when I see so many people convinced about something without giving actual data, I become restless and I want to see the data myself. So I tried to assess how much is the contribution of Kumbh and election rallies to the second wave.
The surprising results are here. The peak of Kumbh crowd was on 14th April and a couple of adjoining days. If there was a large scale viral transmission during this crowding, people would have disperse back to their homes throughout the country, then after a few days of incubation, they would be sick, and eventually will spread the virus further around. We will see a nation-wide rise over and above the background rate of spread. So the slope of the curve would increase significantly a week or two following Kumbh. But we just don’t see any trace of this. The slope does not increase, in fact it decreases a bit.

At this time, it is likely that testing facility is also saturating, so not everyone gets tested. The cases could be an underestimate. When testing facility becomes limiting, only individuals with symptoms are given preference. So a large number of asymptomatic and mild cases escape detection. This is reflected in an increase in the test positivity rate. There have been various estimates of proportion of asymptomatic cases. I took the largest estimate of 80% positives being asymptomatic. Therefore behind every rise in TPR there should be 4 fold more rise in undetected cases. The data can be corrected for this possibility. Even after making this correction, we do not see any increase in the slope following Kumbh and a reasonable time lag. This means that Kumbh had a negligible effect on incidence on a nation wide scale. One can always argue that if Kumbh wasn’t there the curve would have come down more quickly. This is an untestable statement. You can’t give evidence either way. We can say at the minimum that Kumbh did not seem to have increased the pre-existing rate of transmission.

Figure 1: The time trend in the number of new cases in India before and after the Kumbh event (blue arrow). The slope does not show any increase as expected.

The case with election rallies is a little different. It somewhat coincides with the beginning of the second wave. But if you allow for the lag due to incubation period, the wave would have begun prior to election campaigns but only marginally, which we can ignore. But here we have another means of comparison. Only five states had elections. So we can directly compare the transmission between states with and without elections. That’s what you see in the second graph. The states with election fall within the range of states without elections. Their transmission rates are in no way greater than states without elections.

Figure 2: The rise in daily new cases in states with elections (solid lines) and without elections (dotted lines). The ones holding elections did not show a higher transmission rate than the normal range at any time. Since the starting incidence of different states was widely different, they are all normalized by the number of cases they had on 1st Feb, so that their further progression becomes comparable.

So I won’t blame the Kumbh and elections solely for the wildfire of transmission. They had a negligible role seen in data even after correction for biases. So we have to look for alternative causes for it.

Perhaps this is not so surprising because I showed in a previous blog (https://milindwatve.in/2021/02/21/covid-19-did-lockdown-work/) that during the downward phase of the wave, even the Bihar elections and Farmers’ agitation did not affect the downward trend in any way. These patterns, combined with our prior finding (https://www.preprints.org/manuscript/202104.0286/v1 ) that globally, preventive restrictions have only marginally affected the slopes of the curves, provokes a serious rethinking of all that we were told about transmission. It is also being realized that longer distance airborne spread of this virus is much more common than what was believed earlier and that the chances of spread outdoors are considerably smaller than indoors (https://www.nytimes.com/2021/05/07/opinion/coronavirus-airborne-transmission.html?fbclid=IwAR0sld85XEG1v_m-Pp-gVby6qvjBMa0Fst0ETxPS7448aNDb1fdXjwHoqeg ). All the crowding incidents that we are talking about here are under the open sky.
When there is a conflict between our beliefs and data, I prefer to go by data, keeping margins for its biases and inadequacies. Everyone doesn’t. They believe in data when it supports their beliefs. They say data not good when it doesn’t.

But the pandemic hasn’t ended. More surprises might be waiting for us and we should be prepared to see more data, test more, interpret more, learn and unlearn more. The virus, so far, seems to have evaded science in a big way. It’s better we take every definitive statement from health authorities with a pinch of salt.

Peer review quality, acceptance and rejection

Two papers from my group got published this week itself. And I am amazed at the range of peer review quality experienced. I had written earlier about the experience with Current Science. The same manuscript, with marginal refinement was accepted by PeerJ and got published this week (https://peerj.com/articles/11150.pdf). Although acceptance makes the authors happy, the peer review quality was equally disappointing as the Cur Sci experience. Two good things about this journal are that they make the peer review public and they ask authors’ feedback. I wrote a feedback that although the paper was accepted, the peer review quality was disappointing. But our earlier experience with this journal was good. A paper published earlier in this journal had critical and balanced review. It is just too common that there is large variance in the peer review quality of the same journal.

The other experience was diametrically opposite. Our work with farmers near Tadoba got published yesterday in the journal Conservation Biology, a leading journal in this field ( http://doi.org/10.1111/cobi.13746). This piece of work I count among the top 5 of my lifetime. The peer review of this manuscript was one of the most rigorous peer reviews I have seen in my life. The manuscript was difficult to review since it involved game theory, agriculture, wild life, human behaviour and social science. In addition we had done some non-conventional things. We did not pre-plan the methods. Obviously we could not take a prior ethics approval of the institutional committees. Ethics was addressed by field workers and farmers from time to time. We allowed the methods to evolve and the farmer participants contributed their thinking to the evolving methods. The farmers also interpreted the results their own way and we included their thinking in interpreting the results. So farmers were mid way between the subject of research and contributors to research.  Most important, none of us had any formal training in social science research and we did it only using common sense and the need of the time as felt in the field. I could perceive many potential problems of violating research norms. But we kept everything transparent. Did not pretend or hide anything.

Three reviewers responded, each one coming from a different field. So editors had carefully taken care of the multidisciplinary nature of the work. All the three appreciated the central idea as well as our contextually flexible ways as novel and relevant but at the same time raised a number of issues about the details. For addressing all of them three of the authors had to work hard for days on end. This was the most rigorous revision of my life. But everything being of high intellectual quality there was a deep satisfaction. In places they pointed out our weak points in the work. We responded admitting that this side of the work was weak but it remained weak for such and such reasons, which they seemed to accept. They had looked at possibilities beyond this paper and we responded to it from which the path to take the work forward was almost worked out. I want to write about this experience separately and in substantial details. So am reserving the details for now. Let me say here that I am deeply satisfied with this review quality although it was hell of hard work involved in revising.

At a later stage we faced a problem with this paper. The journal specifies many norms or writing style one of which was that all results are to be written in the past tense. We had results of modelling along with empirical work. There was no problem in reporting empirical work. Model results are funny. Simulations and parameter specific results can be expressed well in the past tense, but generalizations and predictions lose their meaning in past tense. The language editor helped us substantially in improving grammar, reducing word count without compromising on content etc. But modelling results in past tense was posing a problem. “Two plus two is four” has a meaning which is not captured by “two plus two was four”. The editor obviously having a superior knowledge of English grammar than us, we did not argue much but made suggestions. Ultimately this section became a hybrid of past and present. I suspect some meaning might have been compromised, but hope that the reader is not at complete loss. Barring this, the experience was a life time amazingly good.

Six years ago the same journal had rejected another paper of ours. The experience that time was that two of three reviewers were critical but positive, the third one was not critical about specific issues but perhaps our findings were not convenient for his stand and his beliefs. We could satisfy two reviewers but not the third one. The editor ultimately rejected our paper. That time also, the quality of reviews was mostly good. Overall, I feel reviews are like random samples. At times you get bad quality reviews even in good journal; at times good quality rejections, and at times bad quality acceptances, but very rarely challenging, thoughtful, balanced and rigorous reviews. I feel these rare events are the ones which maintain the quality of science.

Covid 19 and the “must do something” phenomenon

The last blog I wrote was titled “Did lockdowns work?”. That was more of an impressionist picture based on patterns in Indian data. But over the last few weeks, we analyzed global data for evidence of the effects of preventive restrictions (PRs) of all kinds imposed in all countries. Surprisingly, or not so surprisingly, we found that most of the restrictions worldwide failed in arresting transmission of the virus. The most ambitious objective of lockdowns is to “break the cycle”. A true break in the cycle is expected to arrest the spread completely if the lockdown works for a period slightly more than one infection cycle. This goal was rarely, if ever, achieved. A less ambitious but useful goal would be to reduce the rate of spread of the infection. Testing this is rather tricky because the rate can change spontaneously even if no preventive measure is applied. Therefore it is necessary to separate spontaneous change in slope from the PR induced change in slope.

This problem is like the son-daughter problem. Whether the sex ratio at birth differed from 1:1 cannot be inferred from one or a few families. A given couple can have three consecutive daughters by chance alone. We need a large population to reach a conclusion. Similarly in this analysis which country was successful in reducing the rate by imposing a lockdown cannot be ascertained, because it could be mere chance as well. But the overall success rate can be estimated with confidence. Using this approach, we estimated the success rate of PRs in reducing the transmission of the virus. It turned out that only 4.5 % of the total PRs were successful in reducing the transmission significantly. In a large number of cases the transmission actually increased by imposing a restriction. Quite a number of times the transmission decreased after lifting or relaxing a restriction. This means factors other than restrictions were stronger than the effects of the restrictions. The imposed restrictions could explain only 6.1 % of the total ups and downs of the epidemic curves.

This paper is now available as preprint. https://www.preprints.org/manuscript/202104.0286/v1

Anyone interested in technical details of the analysis can refer to that. Since our inferences are most likely to be viewed as politically incorrect, I don’t know how and how long the peer review will go. But the data are in public domain and the analysis is transparent. So anyone can make an opinion. Just showing two poor correlations here. One is between the stringency of restriction and the expected change in transmission rate, which is not significant despite very high sample size. The other is between change in stringency (i.e. either imposing or relaxing restrictions) and change in transmission rate. This is statistically significant but the strength of the relationship is very poor.

The inefficiency of lockdowns is not surprising. Epidemic is a complex system and simple measures may fail to work. What is surprising is the fact that people are made to believe that this is what is going to work. If the infection spreads, it must be because you were irresponsible, you did not take care. If in some area, the cases went down, we say it was well managed. Then what about countries where there was excellent control in one phase and an uncontrolled surge in another? Saying that people followed a restraint in one phase and did not follow it in another is a circular statement, unless there is an independent and well quantified measure of the restraint.

To me what is more important is the psychology behind this. Medicine, public health as well as political administration does not like to say that we can’t do anything. There is a need to pretend that we are doing the right thing and we are doing our best. Lockdown is an ideal demonstration that we did something. Whether it was effective or not, is immaterial.

There are examples in medicine other than lockdown ddemonstrating the “must do something” phenomenon. Remdesivir, hydroxychloroquine and convalescent plasma completely failed in clinical trials. The trials were conducted by reputed organizations and published in flagship journals. But in spite of completely failing in clinical trials, remdesivir is sold as the leading drug everywhere. In my own city, the stocks are nearly exhausted, people are mad after getting it and it is being sold at a high price. This is because, as long as there is no effective antiviral drug, we need to pretend that there is something that works and need to show we are doing our best. Control of blood sugar to arrest diabetic complications (in the case of type 2), blood pressure control to avoid stroke, cholesterol lowering to avoid heart attack have all performed poorly in randomized clinical trials, but these are the most widely sold drugs.

The “must do something” phenomenon is not restricted to medicine. We see it in so many examples including state administration, crime control, business crisis, parental behavior, child behavior and so on. It must be giving a social advantage to the individuals or agencies in control. This advantage predominates and overcomes actual concern. The concern and the criteria of success itself are then shifted. Rather than avoiding complications or deaths, reducing sugar or cholesterol itself becomes a measure of success. This is an interesting phenomenon and I am sure people doing this do it honestly and often with good intensions. They don’t want to be aware that what they do does not work in reality, because working in reality is no more the concern. I did something is the feeling giving satisfaction. This is understandable as a social phenomenon, but my worry is that all this is being sold under the name of science. People are never made aware that the “something” did not work, or worked very poorly, hasn’t worked so far and if it works in your case it might be nothing else but chance. I wish that at least people of science should be aware of this and avoid the trap.

विज्ञान, वैद्यक आणि ब्राह्मण्यवाद

सुरुवातीलाच स्पष्ट करतो की ब्राह्मण्यवाद या शब्दाचा जातीनं-जन्मानं ब्राह्मण असण्याशी काहीही संबंध नाही. ज्ञानावर विशिष्ट समाजाची मक्तेदारी आणि बाहेरच्या समाजाला त्यापासून वंचित ठेवणे म्हणजे ब्राह्मण्यवाद. इतिहासात काही काळ ब्राह्मणांनी हे केले. पण पुढे ज्ञानावरची मक्तेदारी झुगारून ज्ञानगंगा सामान्य लोकांसाठी खुली करण्यासाठी ज्यांनी प्रयत्न केले त्यात ब्राह्मणही होते. त्यामुळे आज तरी ब्राह्मण आणि ब्राह्मण्यवाद यांचा पूर्वीसारखा संबंध राहिलेला नाही. ज्ञान सर्वांसाठी खुलं असलं पाहिजे हा आजच्या विज्ञान युगाचा मंत्र आहे. पण तरीही मागल्या दाराने विज्ञानाच्या क्षेत्रातही ब्राह्मण्यवाद शिरत राहतो. सामान्य माणसाने आणि वैज्ञानिकांनी सुद्धा जागरूक राहून त्याला दूर ठेवणे आवश्यक आहे.

वैद्यकीय क्षेत्रात एक वेगळा ब्राह्मण्यवाद आहे. आज सामान्य माणूस नेटवर अनेक गोष्टी वाचून येतो. डॉक्टरांनी जे सांगितलं त्यावर शंका घेतो. डॉक्टरांवर पहिल्यासारखा निःशंक विश्वास टाकत नाही.  रुग्णाच्या विश्वासाची काही प्रमाणार तरी बरे वाटायला मदतच होते. पण माहिती अधिकाराच्या युगात आता ही गोष्ट अवधड होत जाणार आहे. चुकीची किंवा अर्धवट माहिती मिळाल्याने गोंधळ वाढू शकतो हे खरं. पण यावर कुणी माहिती वाचूच नये अथवा शंका घेऊच नये असा उपाय बदलत्या जमान्यात चालणार नाही. त्यापेक्षा लोकांपर्यंत सर्व माहिती, सर्व संशोधन, सर्व नव्या घडामोडी पारदर्शकपणे, सोप्या भाषेत पोचतील याची त्या त्या क्षेत्रातल्या लोकांनी काळजी घ्यायला पाहिजे.

अनेकांचा असा आग्रह असतो की विज्ञानातील ज्या गोष्टी वादातीत आहेत त्याच फक्त सामान्य लोकांसमोर मांडल्या गेल्या पाहिजेत. पण माहिती युगात हा दुराग्रहच ठरणार आहे. कारण जी गोष्ट जशी असेल तशी समजणं हा आता मूलभूत अधिकार होतो आहे. ते योग्यही आहे आणि अपरिहार्यही. उदाहरणार्थ मीठ खाल्ल्याचा रक्तदाबाशी नक्की काय संबंध आहे, अंडी खाल्यामुळे रक्तातील कोलेस्टेरॉल वाढतं की नाही, कोलेस्टेरॉल कमी केल्यास हृदयरोग टाळता येतो की नाही, औषधाने रक्तदाब कमी केल्यास रक्तदाबाचे दुष्परिणाम खरंच कमी होतात की नाही, औषधांनी रक्तातील साखर नियंत्रणात आणल्यास मधुमेहाचे सर्व प्रकारचे दुष्परिणाम टाळता येतात की नाही याबद्दल विज्ञानाच्या कसोट्यांना चोख उतरतील असे कुठलेच अंतिम निष्कर्ष निघालेले नाहीत. ज्या क्षेत्रात अशी परिस्थिती आहे त्या क्षेत्रात विज्ञानमान्यतेचा दावा करणं, संशोधकांमध्ये असलेले मतभेद लपवून ठेवणं, एखादा उपचार प्रभावी आहे हे सिद्ध झालेलं नसताना तो झाल्याचा दावा करणं ही लोकांची उघड उघड फसवणूक आहे.

आपल्या आयुष्यातल्या सगळ्याच गोष्टी विज्ञानावर आधारित नसतात आणि प्रत्येक गोष्टीला विज्ञानाचे निकष लावून काम करणं व्यवहार्य असेलच असं नाही. अशावेळी आपण त्या क्षेत्रात मुरलेल्या व्यक्तींचे अनुभव, चालत आलेल्या प्रथा किंवा कधीकधी निव्वळ अंदाजाने निर्णय घेत असतो. असं करणं चूक नाही. पण असे निर्णय विज्ञानाच्या मुखवट्यानी पुढे आणणं बरोबर नाही. सामान्य माणसाला यात विज्ञान नक्की कुठे आहे, किती आहे आणि ते कुठे संपतं हे समजण्याचा अधिकार असायला हवा. मग तो वापरायचा की नाही हे त्या त्या व्यक्तीनी ठरवावं. अनेक जण तो न वापरता विश्वासावरच चालतील आणि ते ठीकच आहे. फक्त विज्ञानाच्या कसोट्यांवर न उतरलेल्या गोष्टींना त्या वैज्ञानिक आहेत असं भासवणं अनैतिक मानले पाहिजे.  

अशी प्रच्छन्न अनैतिकता वैद्यकीय क्षेत्रात अजिबातच दुर्मिळ नाही. उदाहरणार्थ प्रत्येक औषधाची, उपचाराची एक मर्यादा असते. पद्धतशीरपणे केलेल्या वैद्यकीय चाचण्यांनी ही मर्यादा स्पष्ट केलेली असते. पण ही माहिती रुग्णांपर्यंत पोचतच नाही. उदाहरणार्थ सुमारे ११००० मधुमेहींना घेऊन केल्या गेलेल्या ADVANCE नावाच्या चाचणीमधे एका गटाला अगदी काटेकोर ग्लुकोज नियन्त्रणाखाली ठेवण्यात आलं, दुस-या गटात ढिसाळ नियंत्रण होतं. पाच वर्षांनंतर ढिसाळ नियंत्रण गटात २० % लोकांना या ना त्या स्वरूपाचे मधुमेहाचे दुष्परिणाम (diabetic complications) दिसून आले. काटेकोर नियंत्रणाखाली असलेल्या गटामधे १८.१ % लोकांना. काटेकोर नियंत्रणाचा फायदा एवढाच. वेगळ्या भाषेत सांगायचं झालं तर मधुमेहाचा एक दुष्परिणाम टाळण्यासाठी २५० व्यक्ती-वर्षे उपचार लागतात. म्हणजे १० मधुमेही व्यक्तींनी प्रत्येकी २५ वर्षे आपली साखर काटेकोरपणे नियंत्रणात ठेवली तर त्यापैकी फक्त एका व्यक्तीचा फक्त एक दुष्परिणाम कमी होईल. आणि ते करताना साइड इफेक्ट म्हणून काही वेगळाच दुष्परिणाम दिसणार नाही याची हमी नाही. एवढाच साखर नियंत्रणाचा फायदा आहे आणि अनेक वैद्यकीय चाचण्यांनी या उपचाराच्या मर्यादा स्पष्ट केल्या आहेत.

याच महिन्यात ऑक्सफर्ड विद्यापीठातून प्रसिद्ध झालेला एक शोधनिबंध असे सुचवतो की उच्च रक्तदाब औषधाने कमी केल्याचा मेंदूला तोटाच होतो आणि त्याने स्मृतिभ्रंश होण्याची शक्यता वाढते. यापूर्वीही संशोधकांनी असं दाखवलं आहे की मेंदूला रक्ताचा पुरवठा कमी पडतो तेंव्हा मेंदू रक्तदाब वाढवून तो सुरळीत करण्याचा प्रयत्न करतो. अशावेळी बळाने रक्तदाब कमी केला तर मेंदूला तोटाच होतो. हा मुद्दा वादाचा असू शकेल. पण असा वाद आहे हे पेशंटला कळू देऊ नका अशी भूमिका घेणं हा ब्राह्मण्यवाद झाला. कोव्हीडच्या उपचारांमध्ये दिली जाणारी अनेक औषधेही वैद्यकीय चाचण्यांमधे प्रभावहीन ठरली असूनही सर्रास दिली जात आहेत आणि महागड्या किमतीला विकली जात आहेत. कारण वैद्यकीय चाचण्यांमध्ये काय दिसलं ही माहिती सगळ्यांपर्यंत पोचू दिली जात नाही.

जेंव्हा उपचाराच्या मर्यादा स्पष्ट होतात तेंव्हा दोन प्रकारच्या भूमिका घेता येतील. एक म्हणजे उपचारांचा फक्त संभाव्य आणि अत्यल्प फायदा जरी दिसत असेल तरी उपचार केले पाहिजेत. दुसरी भूमिका अशी की फायद्याची मर्यादा एकीकडे आणि येणारा खर्च, असुविधा आणि साइड इफेक्टची शक्यता दुसरीकडे याचा विचार करता हा उपचार नाकारणंच योग्य ठरेल. या दोन्हीपैकी कुठल्याच भूमिकेला तत्वतः चुकीचं म्हणता येत नाही. पण दोन्हीपैकी कुठली भूमिका घ्यायची हे ठरविण्याचा अधिकार पेशंटला असायला हवा. तो अधिकार वापरण्यासाठी लागणारी माहिती त्याला न देणं हा ब्राह्मण्यवाद झाला. आज मधुमेह, उच्च रक्तदाब, वाढलेलं कोलेस्टेरॉल अशा गोष्टींवर केल्या जाणाऱ्या सर्व उपचारांचा फायदा अत्यंत मर्यादित आहे आणि तो किती मर्यादित आहे हे उपचार घेणाऱ्यांपैकी बहुतेकांना माहीतच नाही ही खरी समस्या आहे. ही माहिती सामान्य माणसापर्यंत पोचू नये असं वैद्यक क्षेत्रातील अनेकांना वाटतं. हा ब्राह्मण्यवाद आहे आणि त्याचं संपूर्ण निराकरण करायला हवं. थोडक्यात संशोधनाची पारदर्शकता, संशोधकांनी स्वतः सामान्य माणसासाठी सोप्या भाषेत लिहिणं अशा गोष्टी तर आवश्यक आहेतच पण अन्न व औषध प्रशासनासारख्या व्यवस्थांनी प्रत्येक औषधाच्या मर्यादा औषधाच्या वेष्टणावरच छापण्याची सक्ती करणं आवश्यक आहे. वैद्यकीय व्यावसायिकांनी आणि ग्राहक संघटनांनी तसा आग्रह धरायला हवा. उद्याच्या पेशंटचं आणि वैद्यकीय व्यवसायाचंही हित अशा पारदर्शकतेतच असणार आहे, छुप्या ब्राह्मण्यवादात किंवा दिशाभूल करणाऱ्या अर्धवट माहितीच्या प्रसारात नाही.