This question was triggered by the announcement of a lockdown again in Pune city. The lockdown decision must be based on a set of assumption, which I think are the following
1. During the earlier lockdown the rate of spread of the infection was under “control”, by whatever it means.
2. After the partial lifting of the restrictions on 1st of June, the rate of spread increased.
3. The facilities for accommodating patients in need of hospitalization and critical care are saturating. We can no more accommodate more patients.
4. Another lockdown will reduce the number of patients. Now let us see the actual numbers. Plotted below is the time trend in 5 day running average of daily reported cases on a log scale. So far the number of positives is such a tiny fraction of the total population, that the number of susceptibles is not a limiting factor in the dynamics. So the slope of the log transformed data is straightaway an indication of the rate of spread of the infection. It can be seen easily with the Indian data, Maharashtra data as well as Pune data that just before the lockdown was lifted, the slope had started declining due to some reason. In India as well as in Maharashtra the slope remained lower whereas in Pune the slope increased again to match the earlier slope. In no case the slope increased. This means that there is NO evidence at any scale that the rate of transmission increased after lifting the restrictions. So saying that the infection is spreading more rapidly after relaxing the restrictions is either an indication of not understanding numbers, or some deliberate motive to propagate lies.
In support of the theory that the infection is spreading faster after lifting the lockdown, they give the absolute number of patients which is on the rise. But the absolute numbers would have gone up even with the lockdown. Absolute numbers say nothing about the rate of spread. They are bound to go up even with the rates that were present during the lockdown.
I will be surprised if the politicians and administrators do not understand statistics. But I believe there are people from science and medicine advising them. At least they are expected to understand numbers. I know in Pune city there is a group of scientists, advising PMC. I had an impression that at least they understand science. But may be, I have to change my opinion now.
The cause of worry is said to be the increasing number of patients saturating the available beds. Clearly here the absolute number of patients matter, not the rate of spread. But will a lockdown reduce the numbers? Currently there is no evidence that lockdown reduces the rate of transmission. Earlier lockdown experience indicates that it certainly does not result into a negative trend in numbers. But even if we assume that it will reduce the slope, it still cannot bring down the absolute numbers. They will still be growing may be at a slightly reduced rate. At the best, instead of saturating today, the beds will be saturated after 3-4 days. So lockdown is not at all a solution to the saturation problem.
One of the important expected outcomes of the first lockdowns was that we did not have enough facility to handle a large number of patients in the beginning. The breathing period that the first lockdown gave was meant to boost up our infrastructure, create more wards for Covid patients. Many cities in the world did this quite efficiently but did Pune do it? Very early in the pandemic, there were dozens of mathematical models predicting the possible numbers that hospitals may have to accommodate in the coming months. The actual spread of the infection has been much slower than what most models predicted. And even then if we are running short of beds now, it simply reflects on the inability of the administration and their scientific advisers to read numbers. John Allen Paulos wrote a book in 1988 called “Innumeracy”. He uses the word with a meaning parallel to illiteracy. Educated people learn to read, write and understand letters, words and their meanings. But most people don’t seem to understand numbers. They may at the most be able to read and write them. Understanding numbers is a different ball game. This is ok for laymen. But I wonder, is there nobody in Pune who really understands numbers and so can come out and say, NO! Lockdown is not the solution! It is not going to work, because the problem a lockdown can potentially solve, simply does not exist. The rate of spread of the epidemic has not increased by lifting lockdown, so it is not going to come down by imposing it again.
Last month I posted my hypothesis that the virus responsible for the pandemic is evolving rapidly towards a less virulent virus. I was astonished that it became “viral”. My blog has a limited niche readership which usually counts only to a few hundreds. But last time the original blog post had a readership of over a quarter of a million, which does not include sharing on other social media.
I received a large number of responses and it was impossible to respond to all, so I thought I will wait for more data and return with updates along with my rebuttal to the responses. While most of the responses were positive and appreciative, there was some trolling as well, which I enjoyed. What I consider most important are the responses raising certain issues. There were two main lines of arguments
(i) The death rates are not actually declining, the data or the analysis is fallacious. This might be either because (a) if the rate at which new infections appear is increasing and death happens after a time lag, the ratio of new Covid deaths to new infections would seem to have decreased but that does not mean death rate has decreased (b) the Indian data in which I showed a decreasing death rate are not reliable (c) the number of positives have increased because of more testing and so the death rate only appears to have decreased.
(ii) If the death rates are really coming down it might be owing to better medical care being available, not due to reduced virulence.
Fair enough. Respectfully I studied all the above possibilities and now I am giving more data from more reliable sources, with better analysis, and due consideration to alternative interpretations, all of which together shows that Covid has indeed become much milder throughout the globe. With this rate there is no doubt that it will be just like any other seasonal flue in a few weeks to few months. Moreover this trend is not explained by anything else but evolution of the virus towards reduced virulence.
Before we look at the data, let me add some clarification about the meaning of the ratios that we will use. In an on-going epidemic, the true death rate is difficult to calculate. I used last time and will continue to use below the ratio of the number of confirmed deaths in a day and the number of new cases detected on that day. The term case fatality ratio is commonly used for the ratio over cumulative data. I am using daily data so I will use a new term ND/NC for the ratio of the daily new Covid deaths by the daily new positive cases. I will use one more ratio here, ND/NR that is the ratio of number of deaths in a day to the number of cases declared as recovered on that day. The two indices reflect death rate, no doubt, but they are also affected by a few other factors.
One gift of the pandemic is that almost everyone has by now heard of R or R0, the rate at which the infection spreads. There is most often some time gap between the day of diagnosis and day of death. There is a larger time gap till a person can be declared recovered. Because of the time gaps, in a growing epidemic with R > 1, the ND/NC ratio is less than the death rate and the ND/NR is greater than the death rate. With a downward curve or R < 1, it is the reverse. In either case, the true death rate lies between the two ratios. As long as R remains the same both the ratios are directly proportional to the death rate. So a time trend in either of the ratios reflects time trend in the death rate. If death rate is constant, the ratios remain constant. However, if R changes, the relationship changes transiently. Only if R keeps on changing with time, the two ratios show a time trend even when death rate is constant. If R is decreasing continuously ND/NC ratio will show an increasing trend but ND/NR will show a decreasing trend even when the true death rate is constant. If R is increasing continuously with time, ND/NC ratio will show a decreasing trend but ND/NR will show an increasing trend. If on the other hand death rate itself is changing, then both ND/NC and ND/NR will show a time trend in the same direction. So if both the ratios are decreasing it is sure sign of death rate decreasing, but if one has a decreasing and other has increasing trend, it is because of a changing R. Thus the use of two ratios instead of one gives us greater insights into what is causing an observed time trend.
Almost throughout the globe the R value for Covid 19 has been decreasing with time. In other words the days required for doubling have been increasing. So if the death rate was constant, we should have seen an increasing ND/NC ratio. It does show an increase in some countries like Canada that have achieved an accelerated downward epidemic curve. This does not necessarily mean the death rate increased in those countries. But if ND/NC is decreasing in spite of reducing R, then it is almost certain that death rate is decreasing. This is the picture in global data pooled over all the countries. Globally we see that both ND/NC and ND/NR are consistently decreasing since mid April. One more important global pattern is that the proportion of active cases needing critical care has also been decreasing.
One of the objections to my analysis last time was that data are not reliable in many countries either due to lack of transparency such as that suspected in China or data mismanagement that we have witnessed in India recently. But we can always look at data from countries which are democratic and whose data transparency has not been questioned. Illustrated here are patterns from a few representative countries showing increasing, stable or declining infected populations.
Most notable is Sweden, a country which did not forcibly impose lockdown. Although Sweden witnessed a high rate of infection along with high death rate initially, the decrease in ND/NC has been one of the steepest in Sweden. Germany has both downward curve as well as decreasing ND/NC ratio which is a sure shot indicator of decreasing death rate.
India needs a particular mention since last time I illustrated it as an example case of decreasing ND/NC. The decrease that time was very steep and if that rate continued, it appeard that by first week of July it will become just another flu, in terms of its mortality rate. I also said that I suspect the slope will not remain the same. In reality the ND/NC for India increased significantly between 26th May and 11th June and then started declining again. There is an interesting possible reason for the abrupt rise. In India and Maharashtra in particular, from where the largest chunk of deaths come, the infection curve took a very strange turn by the end of May. This was the time when lockdown was lifted and everyone feared that the already upward infection curve will become extremely sharp. But instead it suddenly became flatter, remained flat for quite some time and then increased, but much slower than expected. Which means R underwent a dramatic drop, which is expected to increase ND/NC ratio transiently. That seems to have happened. But ND/NR kept on declining monotonically suggesting that the death rate continued to decrease.
There are problems with the Indian data. By mid June, over 1500 deaths that were declared as non-covid deaths earlier were reclassified as Covid deaths but then they were added to the data not on the day of death but on the day the classification was changed. As a result the time trend is completely messed up. If one tries to correct for this bias, the number of deaths on the left will have to go up by an average 14%. If such a correction is made, both the ratios will decrease more sharply. So although the data are messed up, there are reasons to believe that even in India the death rate has been going down.
What is surprising is that on lifting the lockdown, the curve did not go up more sharply. It actually flattened a little. Most people tend to give the credit for flattening any curve to the quarantine, social distancing, sanitization and other preventive measures. Here you see the reverse. When people’s movement, transport, crowding etc. increased and preventive measures relaxed, R decreased further. If it was not because of better preventive measures, what could be the alternative cause? The proportion of infected individuals is still a tiny fraction of the population, so herd immunity is unlikely to have played any role. Again I suspect, the virus itself had changed by the time the lockdown was lifted.
Can the apparent decrease in ND/NC be just because of increased testing so that we are detecting more of the asymptomatic cases? I have two responses to this. One is to look at whether the testing has disproportionately increased. In India, they started with testing the contacts of known positives and the high risk groups. As the number of positives increase, this number of tests is expected to increase more or less proportionate to the number of positives. If testing had really expanded to go beyond and sample random individuals from the general population, the ratio of number tested and number positive should change substantially. Also since the proportion of positives in the general population is expected to be less than the contacts of known positives, an effort to expand sampling will inevitably result into a decrease in the proportion of positives. Using this indicator we do not see any evidence of testing having increased, because the proportion of positives has marginally increased instead of decreasing.
But even if we assume that the testing has really expanded and we are finding more asymptomatic cases and that is the reason why the ND/NC ratio is coming down, the question remains, from where did so many asymptomatic cases come up? Either they were there in the same proportion right from the beginning or they increased recently. The former means that the death rate was never high. The virus was never as dangerous as we were made to believe. Whether intentionally or not, people have been fooled about the threat of the virus all the time! If we take the latter possibility, if so many asymptomatic cases have come up suddenly, that itself is evidence that the virus is losing virulence and most of its infections do not even make you feel sick.
What if the death rate is truly decreasing, but that is only because better medical facilities are available. This sounds logical but the data look strange. Across countries, the ones with poor infrastructure and absence of good medical care have much lower death rates than developed countries that always had excellent health care. So associating good medical care with lower date rate has problems. There is no effective drug available as yet that would make a large difference. Even the promising clinical trials so far have marginal effect sizes. So how far treatment can change the death rate in the current situation is doubtful. You may say that the high death rate in developed countries is due to having greater proportion of elderly in the society. This is the factor that explains why there is high death rate here. But when the death rate in these countries reduced from over 10 % to less than 3 %, did the proportion of elderly changed this much? Or did any new treatment suddenly became available that made this difference? There is no way treatment can explain the reducing death rates. Even more convincing is the trend in the proportion of patients requiring critical care. That itself has come down substantially. So evolution of the virus to lower virulence is the only sensible explanation for it.
If the virus is really losing virulence, how far are we from calling it just another flu virus. The answer to this question might be different in different countries. Since there is little transmission across countries currently, virus evolution may have taken different speeds in different areas. Globally the proportion of patients requiring critical care has come down and at present, only 1.4 % of active cases are on critical care. Not all of them will die. This means over the next week or two the ND/NR ratio would have come down below 1 %. For seasonal flu the death rate ranges from 0.1 % downwards. Which means Covid is rapidly approaching the status of any ordinary flu.
Another indication comes from CDC data on the causes of death in the US. Influenza-pneumonia together cause 5-7% of total deaths in the US. By mid April, this had gone up to 27% mainly due to Covid. Last week it was back to 6.9 % again which is hardly different from the baseline. CDC report says that last week’s proportion may be somewhat underestimate so do not consider the basal level to be back as yet. But the trend shows that it is almost back.
In India, the average deaths per day are of the order of 50,000 and on an average about 6 % are due to respiratory infections. So the daily respiratory deaths are of the order of 3000. Covid daily deaths are of the order of 300 to 500 per day currently. So we are about 15 % above the average in terms of death. This will increase transiently since the number of new infections is still going up. But with both R and ND/NR ratio decreasing, the number of deaths are unlikely to approach what many mathematical models had predicted earlier. So although we still have time to call Covid just another flu, we are certainly going in that direction.
Now, a million dollar question. If the data are so clear, why is nobody talking about reducing threat from the virus?
I can perceive many reasons. One is a genuine worry that if we say so, people will become even more irresponsible. But this is happening all over the world anyway. There are two possible ways in which people will act in near future. One is that they realize that the virus is much less of a threat than what we believed earlier, so there is no need to worry, but it’s good to be cautious not for one’s own safety but for the elderly and weaker ones. So some responsibilities and care need to continue. The other possible way is that they stop believing in WHO and other health authorities and choose their own ways to go about. The first is obviously more preferable and that will happen if the authorities accept reality, change the policies, take people in confidence and tell them why they need not worry now but still need to be responsible in certain ways. It is also important to be prepared for a second wave if any, or may even be a new virus. So we need to learn from experience and chalk out long term policies. If the authorities do not take such a clear stand and continue to play ostrich, people are bound to choose the other way, the signs of which are already seen in all parts of the world.
There are more reasons why scientists won’t talk about the obvious time trends. Admitting that the virus is milder now is equivalent to losing huge funding opportunities. Huge amount of investment has been made in vaccine and drug development. This I would say is valuable as a back-up. In case the trend changes or there is a second wave we need to be prepared for the worst. But that is not the way it is going. It is going as a big business plan. People in this business will be most reluctant to accept that the virus is becoming milder. So partly for genuine reasons and partly for business scientists and health authorities will refrain from accepting reality.
But common man should. All data are in the public domain. Anyone can look at the original data and ask his/her own questions rather than getting fooled authorities, by media and fall prey to Zohnerism.
I had said in an earlier blog article that the SARS Cov-2 virus responsible for the current pandemic is likely to evolve rapidly towards reduced virulence. The reason why I expect this is that on the one hand, almost all countries are implementing strict quarantine measures for all detected positive cases. But on the other hand, we cannot afford to do mass testing, leading to many undetected asymptomatic cases roaming around and spreading the virus. The virus reaches huge populations and also has a high mutation rate, so all possible variants will keep on arising. A virulent strain is most likely to cause severe infection which will invite testing and ultimately quarantine. A mild variant, on the other hand is more likely to lead to asymptomatic or mild symptomatic infections which are more likely to escape screening followed by quarantine and therefore keep on spreading. In several generations of the virus, which is a short time for us, natural selection will favour the mild variants.
While all research on the virus is engaged in developing vaccine, studying pathogenic mechanisms or suggesting treatments, nobody seems to talk about evolution of the virus. This is for two reasons. One is that people in medicine are never trained to think of evolution. The other is that virulence is difficult to quantify. It is easier to sequence the virus, study its proteins, look for antibodies in the host etc. Researchers typically do what is easy to do rather than what is scientifically more relevant. Since one cannot measure a change in virulence easily, nobody will even talk about any hypothesis related to it. This is what I call “evidence bias” in science. If it is difficult to find evidence to either falsify or support a hypothesis, people will avoid talking about the hypothesis because it cannot make a paper. Whether the hypothesis is relevant to public health is not an important issue, whether you can publish a paper is.
But in the epidemiological trend at the global as well as the Indian scene, there are definite signs of reduction in virulence. Although the infection is growing, the death rate is consistently reducing with time. Look at the patterns. From mid-April, although the total number of new cases per day has been increasing, the total reported deaths per day is decreasing.
The same is happening in India too. In fact, the case fatality rate in India was always low and it is decreasing further, although the absolute number of deaths per day has not started decreasing yet.
I plotted the time trend in the ratio of daily reported positive cases to daily reported deaths starting from the day the new death count exceeded 50. Although there are expected daily chance fluctuations, there is a clear decreasing trend.
Now if we make a simplistic assumption that the linear trend continues, then we can come out with a prediction that in India in about 35 days, Covid 19 will remain only as dangerous as any seasonal flu. The assumption of linearity is of course an oversimplification, the slope may not remain the same throughout. The second caveat is that case fatality rate cannot be exactly equated to mortality rate. In a growing epidemic, case fatality rate is an underestimate or mortality. But that need not affect the trend. The estimate of 35 days might be too optimistic. It may take somewhat longer. But the direction is assuring. Anecdotally I heard from some of my clinician friends that the proportion of patients needing critical care is already low.
The vaccine trial and mass production is going to take many months and may not immediately become available or affordable to the masses. For the huge population of India, acquiring herd immunity is a huge task and will not happen for a year or two. But much before either of the two becomes useful for public health, evolution would have taken care of the deadliness of the virus. We need to continue quarantine and good medical care of symptomatic cases, but not be fussy about the asymptomatic ones. Because they are going to be the saviours. Let us wait for a couple of months to see whether the prophecy turns out to be true qualitatively or quantitatively. If it does, it has a long term lesson for medicine. Virulence management strategies should become an integral part of public health planning. This is not the last time that a new virus arises. This will keep on happening. Understanding of evolutionary dynamics is certainly required to manage public health.
My lifetime goal is to liberate science from science institutions and the SCB award has assured me that yes, it can be done and is worth doing.
In the history of science, a number of major advancements were achieved by someone outside the academic circles. Charles Darwin and Gregor Mendel are the most prominent examples who completely changed our understanding of biology, but who were not academic scholars in any formal capacity. A question worth asking is whether this can happen today. Science is increasingly getting monopolized by Universities and Institutes, so much so that it is almost impossible now to imagine someone doing high quality science outside academia. The monopolization of science is driven by two key processes, namely funding and publication. Most funding agencies simply do not have any norm to fund an individual researcher who has a proven record of high quality output but who is not affiliated to any institution currently. Scientific journals, on the other hand do not have any written rule of not publishing anything from a person without affiliation, but most journals now have huge author charges, so that no one without substantial funding can publish in them.
Science itself is at loss by such monopolization. Although Institutions are built to support science, they simultaneously put limits on doing science. Every institute has a set of bureaucratic rules and procedural routines and they expect science to fit into these routines. Science needs to modify itself to fit into the routines rather than the routines being flexible enough to accommodate the requirements of research. The routines are designed to support certain types of research requirements and researchers doing those types can happily work within the system. But by definition research cannot be bound by routines. There are no routine questions in research, only questionable routines. Institutes can cater for research routines easily and it is extremely difficult to do creative science within those stone walls.
For example, in order to purchase equipment there is a procedure in any institute, which is designed for some of the equipment to be used within the institute. I had a behavioural intervention based project for farmers in a farmer-wildlife conflict area. I wanted the farmers to install solar fencing, for which funding was available in a research project with me. But I wanted the farmers to decide which equipment they wanted and how would they like to purchase it. This was behaviourally important. If they take a decision themselves, they are responsible for the decision. If the researcher takes a decision for them, they are not. The behavioural consequences and the outcomes would obviously be different. However, since the funding was routed through the institute, the institute said you should follow the institute procedure for purchase. We will invite quotations and we will purchase for the farmers. They cannot have any say in the purchase procedure. There was another caveat, the equipment will belong to the institute and when the project was over, farmers will have to return it. This was just not compatible with the behavioural intervention principles on which the project was based. So either I had to compromise with my science or let go the funding and spend the necessary amount from my pocket. Needless to say, I preferred the latter.
The example is not the only one by which institutes arrest the spirit of science. They have multiple ways of doing it. Since there is a limit to which institutes can pursue good science, we need to look beyond institutes to implement any novel, out of the box, creative and socially useful science. For a large part of my career I worked as a teacher in an undergraduate college, where one is supposed to teach science, without actually “doing” science. I thought this was impossible, and the only way to effectively teach science was to DO science in partnership with the young minds. Later, for 10 years I worked in a leading science institute which was also supposed to teach science. Here I experienced that although there were many good researchers here, the ones having a vision for using it to inculcate the spirit of science were exceptionally few. I think now, that this is an inevitable result of the way we have been “institutionalizing” science. I was convinced even more when I decided to liberate myself from mainstream science organizations and started working in an individual capacity in association with people and without being affiliated to any organization. It was a blessing in disguise that I left “formal” science.
When you start working with people, entirely new avenues of science open up for you. Illiterate people are great mathematicians. They have their own ways of modelling, calculating, judging and making decisions. They have an inbuilt intuitive economics which is different from the economics being taught at universities and often it is more insightful. They make statistical inferences which are based on a set of principles somewhat different from what is taught in University Statistics. For example trained statisticians use a fixed significance level, people appear to vary the significance cut off based on the cost-benefits of the inference, which is much more sensible. But their great strengths become their weaknesses in some contexts. Academia has so far failed to understand the evolved innate mathematical, economic and statistical algorithms of people. Academics have developed methods of statistics, economics and computation without any regard to the evolved and innate statistical, economics and computational methods of people. Now we expect them to understand our methods while we have failed to understand theirs. As a result many of our schemes intended for their development are failures. Understanding principles of people’s behaviour is a highly challenging field of science and is yet in its infancy. So doing science with people is certainly no inferior than doing high tech ivory tower science. The kind of questions you can handle in fields and forests and grasslands cannot even be imagined sitting in those ivory towers.
Understanding human mind and human behaviour is the toughest business and needs very high quality science. Unfortunately classical social sciences are bound by ideologies and isms. The science of people needs to evolve from people, not from ideologies and the only way to do this is liberation again; liberation from ideologies, from traditional mind sets, from what you learnt in textbooks, from organizational routines and from premeditated goals and protocols. I don’t have any tall claims of having discovered something great in my attempts to study people, but can certainly share my experience that it feels great to work with them when you are free from all these handcuffs.
The piece of work, which led to the SCB award is an attempt to establish a novel principle on which the foundation of community management can be laid. This idea evolved from the interaction with people. On the one hand it’s a new game in game theory; much more complex than all the classical games dealt by the theory. Dr. Neelesh Dahanukar, a former student of mine has been a valuable contributor to developing a formal model. But more remarkable is the actual experimental implementation of the game for addressing a real life problem, with real money. I am a pretty bad organizer, so I couldn’t have done the organizational part of the empirical work myself efficiently. But I was fortunate to be backed by a whole team of efficient people including Poorva Joshi of Bioconcepts, Vijay Dethe, Smita Dathe, Shankar Bharade and other volunteers of the NGO: Paryavaran Mitra, and of course all the farmers, who I consider not the subjects of my research but my research collaborators. We could do the experiment with real money owing to the generosity of Vidarbh Development Board, DeFries Bajpei Foundation and NAAM Foundation.
With the only exception of Neelesh, none of my co-workers had any formal background in research, but together we came up with a scientifically novel and socially useful outcome, certainly the first real life, real problem, real money game theory experiment in India and perhaps in the world. I have at least one case to demonstrate now where science, liberated from institutions worked well.
While this is certainly not the first example of people’s science, the tradition needs to be revived and strengthened. The trend of institutional monopolization of science needs to balanced by alternative avenues of science. While we certainly need science institutes on the one hand, an alternative to institutional science is needed to do the kind of science that Institutes and Universities can’t.
It is a common belief that immunity declines in old age. This is particularly relevant today because in the covid 19 pandemic, fatality in the elderly is considerably higher. People believe that this is because of decreased immunity in the old age. While some markers of immunity do go down with age, the question is whether old people are really more susceptible to infections? One needs to differentiate between intermediate markers and end points. Showing that a given treatment reduces cholesterol is different from showing that it actually reduces heart attacks. Similarly showing some antibody titres going down is different from actually showing increased frequency of infections.
A few years ago, we took up this question as a “katta” question. I had 4 summer students then and they were given this exercise of how to test whether markers like declining antibody titres could be equated to increased susceptibility to infection. Does the susceptibility to infection really go up? If yes, whether there are alternative interpretations to it, and how to differentiate between the alternative interpretations? An attractive looking alternative interpretation came up, which was that the microorganisms associated with the body over a long time, evolve to overcome the resistance mechanisms of the individual within the lifetime of an individual host. This concept is not new. Bill Hamilton used it in 1980 to develop his own hypothesis for the origin of sex. Vidhya, one of the summer students took the challenge seriously and not only came up with some testable predictions of the immunity decline versus pathogen evolution hypothesis but dug up some data and tested them. Summer trainees are there for a very short period and starting with development of a new idea they can rarely complete any piece of work. I think for them, the ability to generate a new idea is more rewarding than being able to complete a piece of work.
I like to talk about ideas at all stages of development to as many people as I can. In many institutes there is a culture of keeping your ideas secret until you complete your work and publish them. I think this might be a good strategy in places where there is an overall dearth of ideas. Working with young minds, I never experienced this state, so I talked about what Vidhya did, to many people around. Then Akanksha, who had done a lab rotation with me but had joined another lab that time, thought that it was worth pursuing the idea further. So she and Ulfat, my long time co-worker, did more systematic data search, involved Vidhya once again and started digging more data, analyze and test the alternative hypotheses across a wide diversity of pathogens. All this was not a priority work, so it took almost 5 years to complete and publish the work, which happened just over a few weeks ago.
What the data essentially show is this (see figure). With age the incidence of all opportunistic pathogens increases substantially, often 10 to 30 fold. But the incidence of externally acquired infections, that is infections by pathogens which do not remain associated with the body over a long time, does not go up; on the contrary reduces with age. If there was a decline in general immunity, the incidence of all types of pathogens should have gone up, may not be to the same extent, but the direction should have been the same. But some infections appear to become more common and some substantially less common, and there is a clear demarcation. Those by opportunistic pathogens go up. Those by externally acquired pathogens go down or don’t change. This pattern is compatible with the pathogen evolution hypothesis and not very supportive of general decline in immunity.
There was another interesting pattern observed in the data. Although old people do not get outside infections more commonly, once infected, they are likely to collapse faster. This we think, is because of a general physiological decline, rather than an immunity decline. If it were immunity decline, it should have affected incidence as well as morbidity. But it does not increase incidence. Selectively increases morbidity and mortality among the infected ones. This indicates that the physiological ability to cope with infections declines with age, not the immunity. If this is true, what should matter more than age is the overall physiological fitness, not the number called age. In those data, we couldn’t test this because no index of general physiological fitness was available.
When we did this work, the current coronavirus pandemic was not even on the horizon. So obviously our paper does not have any data on it. Right from the beginning of the epidemic, people have been talking about old people being more susceptible. Now we have data on age specific incidence in India till date and it shows the same pattern. The incidence actually comes down in the older age classes. One possible reason might be reduced exposure but there is another interesting possibility which I am surprised, no one seems to be talking about. Old people, who have experienced a large diversity of viruses in their life time are more likely to have cross-immunity to a new virus. So old people are less likely to be affected by a new virus. But on the other hand, the case fatality does increase substantially with age, but that too in individuals who already show markers of physiological decline such as hypertension, diabetes, loss of cardiorespiratory fitness etc. This matches perfectly with our hypothesis.
Looking at patterns in data and making a hypothesis to explain that is one thing. And developing a hypothesis based on theoretical considerations first, raising alternative hypotheses simultaneously and then systematically looking at data to compare the alternative hypotheses is another. The level of scientific rigor in the latter is substantially higher. In published data, it is difficult to know the path. People may actually have followed the former but pretend the latter. But in this case we already published our hypothesis, along with supportive data. And now a new global picture emerged which complies with our hypothesis very well. This is heartening. The take home lesson is that for old age care, fitness matters more than actual age for so many communicable as well as non-communicable diseases. The exception is opportunistic pathogens. We cannot stop them evolving. So that effect of age is inevitable and so are those infections. But otherwise fitness is the key to overcome aging and age related diseases. In the long run, caring for the fitness of the elderly is a better approach than trying to protect them from external sources of infection.
I published close to a hundred research papers, mostly in peer reviewed mainstream journals but at times also in some so called predatory journals. For me, particularly at this stage, all are interesting experiences in different ways. A simple thing I am going to do now is to look at the ‘objective’ measures namely citations per year and the impact factors of the journals in which they were published on the one hand; and my own perception and subjective judgment of the importance of that work on the other. Do the three have anything to do with each other?
Here are the scatter diagrams of the three. I must state first that there were two outliers which are not included in these diagrams. My most cited paper, one with close to 1000 citations was published in 2001, making citations per year close to 50. It was published in a journals that has a an impact factor of 1.8 today and it has remained more or less the same over many years. The other extreme is another paper published in 1997 in a journal that has an impact factor of 59 today. But in 23 years, it was cited only 17 times. So the two extremes are actually upside down of the expected relationship, but let us leave them out being outliers. The rest of the papers make scatters that look like this. Needless to say they do not show any correlation with each other.
A typical researcher thinks first of the highest impact journal in which a given manuscript is likely to get published, if it gets rejected there, he or she goes for a little lower one and so on until it finally gets published somewhere. I did the same at times, but not every time. Life is an opportunity to experiment and I experimented with my own papers in a variety of ways. This included publishing in some so called predatory journals too, and my experience there, particularly about the sensibility of reviewers’ comments, was not very different from high impact mainstream journals. No doubt, the degree of sophistication was different.
My own subjective ranking of my own papers is based on the novelty of the central idea, it’s relevance to fundamental science, its non-obviousness, logical soundness and clarity of the evidence. I made a conscious attempt not to factor in the importance given by others, the extent to which it helped my own career or that of my students. The lack of correlation with the number of citation perhaps indicates that what I thought was good science, others may not have found worth citing. On the other hand, when I looked at the papers that had cited ours, exceptionally few of them had cited them for the central argument in the paper. Often they were cited for supporting a statement that was never made in our paper. On a few occasions our paper was cited in support of exactly opposite view than the central argument of our paper.
About the number of citations received, in general, papers that more or less supported the beliefs prevalent in a field were highly cited. The ones that opposed the prevalent beliefs were not cited. This is important. There was no counter-argument ever. There was no debate, no criticism. People only seemed to ignore what they did not like. The quality of argument and evidence did not seem to matter much. This has been quantitatively the commonest response. But science does not progress by the response of the masses, I mean the researcher masses. It progresses by the outliers. Rarely someone sees a subtle point in what you wrote and appreciates, rethinks, introspects, at times attacks back, but everything in a very lively way. This is very rare, but science progresses by these rare interactions. I did experience such instances too and these are the true rewards in a scientific life. A rare, critical, agreeing or disagreeing but curious and interested response is what science progresses by, and this is not countable!! This is the reason why numerical indices might be useful in the business of a science career, but have nothing to do with science itself.
There has been a long held belief that pathogens, in the long run evolve to become mild, lose or reduce their virulence. This is believed to be because rapidly killing their host is against their own interest. While there have been remarkable and well documented examples where a virus, new to a host population spreads like wildfire and kills a large proportion of the population, but soon loses much of its virulence. This is certainly the story of the Myxoma virus that was introduced to control the population of rabbits in Australia in 1950.
The myxoma virus was said to lose its virulence due to natural selection because viruses that allowed their host to live longer had a better chance to spread in the population than the ones that killed the host very fast. This is not as easy as said, because it is a multilevel selection problem. Within a host a virulent variant will grow more vigorously than a mild one, but across host individuals the ones infected by the less virulent ones will live longer and keep on spreading the virus. Multilevel selection, unfortunately, has been a contentious issue in evolutionary biology, fought more with beliefs and rhetorics than with sound, realistic and context based models. But the most sensible argument has been that under one set of conditions, the within host selection becomes stronger than the between host selection and higher virulence evolves. Under a different set of conditions the reverse may happen and virulence is progressively lost. In reality we have examples of myxomatosis in which virulence was demonstrably lost, but we also have examples like small-pox virus which ultimately became extinct but never lost its virulence.
We have one more interesting case in which virulence was apparently constant for over thousand years. Then something happened and within 3-4 decades virulence was lost almost completely. This happened with leprosy. Descriptions of leprosy have been there in old literature from which it seems that it has been with us almost unchanged for several centuries. Then between 1960s and 1990s, the pictured changed dramatically. The proportion of lepromatous cases, which is the most severe form of the disease, started coming down monotonically all over the world. Milder forms of leprosy were still there for a few decades, but then they also started disappearing rapidly. This was brought about by a combination of social stigma associated with the disease and discovery of effective drugs against leprosy. One of them in the absence of the other would not have succeeded. Owing to the attached social stigma leprosy patients were not being treated in general hospitals. There would be separate asylums or at least separate wards. As a result going to these wards was to advertise that I am a leper, which nobody liked. But patients with severe form of the disease had to do that. Those with milder forms could hide their disease for a longer time and move around normally in the society. As a result the more virulent forms were selectively killed by the drug, and the less virulent ones allowed to live longer and thereby spread more. This differential selection was so strong that leprosy ceased to a problem in four decades. Effective drugs were there for staphylococci as well. But they, instead of losing virulence, developed drug resistant varieties. This could be mainly because a systemic infection as well as a small boil was being treated with the same antibiotic. In a more severe form of the disease, the germs would have replicated much more before facing the antibiotic and thereby have a better chance to spread than a milder form of infection. Here selection would favour the more virulent ones and eventually antibiotic resistant ones. This means that our practices of preventing and treating the disease as well as the social beliefs with which we view the disease influence the evolutionary course of the pathogen.
This certainly would apply to the rapidly spreading and, needless to say, continuously evolving coronavirus Covid 19. The entire world has taken unprecedented steps to prevent, or at least slow down its spread. What is rarely addressed is how would the social distancing and lockdown affect further evolution of the virus?
If we do large scale testing, try to detect and isolate every positive individual, there is a small chance that we might drive the virus to extinction by completely preventing transmission to other individuals. But this is unlikely and does not seem to be working that way, although it may have slowed down the spread. But what could natural selection be doing under this scenario? If every positive case is in quarantine, more virulent and less virulent strains are both being isolated with the same probability. Let’s assume that every propagule generated from the quarantined individuals has a very very small chance of escaping isolation and infecting someone. Just because the virulent ones make more propagules, they have a greater chance of escaping than the milder varieties. This means we are giving a selective advantage to the more virulent ones over the milder ones and as a result virus will evolve for increased virulence.
On the other hand if testing kits are so limited that you reserve them for individuals showing considerable symptoms, the milder cases will escape detection and will live and spread. Some of the more virulent ones are also likely to escape if they happen to be in partially immune individual who can keep the symptoms suppressed. But on an average the virulent ones are more likely to be isolated than the mild ones. As the milder ones spread more, they make the population immune, preventing the spread of the virulent ones further. This, by evolutionary logic, is expected to lead to gradual loss of virulence along with increasing herd immunity.
The logic goes very similar for the lockdown. If the lockdown is complete, and every case is effectively isolated, only the virus varieties making more propagules will survive. If on the other hand people with marginal symptoms or mild symptoms go around and mix with the population, the milder variants have a better chance to spread and immunize the population at the same time.
But all these arguments are based on probabilistic logic. Not a deterministic one, and there lies the problem. Although on an average a relaxed isolation practice will spread the milder variants more than the virulent ones, transiently more individuals would certainly be infected with the virulent ones and suffer more.
Here lies the most basic ethical problem of medicine. If there is a conflict between what is good for an individual versus what is good for the population, or what is good in the short run versus what is good in the long run, what do I do? If we just let go the virus wild, ultimately the virulence will come down as well as herd immunity will build up. But during the process many individuals will suffer. Who will take responsibility for their suffering?
There is no simple answer to this question particularly when we have little data on the actual transmission dynamics to make quantitative evolutionary predictions. Making a good evolutionary model needs greater details on the dynamics of mutant segregation during the transmission process since multilevel selection crucially depends upon that. What I am most surprised to see is that among the multitude of arguments supporting and opposing the lockdown, there is hardly any evolutionary argument. Evolution in viruses is surprisingly fast, particularly when they are spreading on a global scale. What will be the future course of the pandemic will be decided to a large extent by how the virus evolves. But there are hardly any attempts to study the infection dynamics from the functional evolution point of view. There are some molecular evolution studies though. I don’t have final answers right away, but I am sure incorporating evolutionary thinking in the designing of strategies to manage the pandemic would make substantial difference. But the facts remains that it is not only a problem of prediction and management it is an ethical dilemma at the same time.
What do we know about the covid-19 or more popularly the coronavirus that has left the whole world panic struck at the moment. Well, we know everything! We know its complete genome, we know every molecule that makes it. We know its complete structure. Isn’t that enough?
But we know nothing! We don’t know how and where all it will spread. What will be the overall mortality rate? How many people will get killed? When will it reside? Will it completely go sometime or remain endemic somewhere in some population? May be some other animal? How do we control it? Why are the efforts to control it largely failing in so many countries, particularly the technologically most advanced countries?
The pandemic has brought a very important lesson for science, for biology in particular, in case we want to learn from it. Science should advance by relevance, be driven by questions, be funded according to the importance of the underlying question. But that is not how science progresses in real life. Researchers go by what is easy to work on, what is trendy, what is more prestigious, where things are ready made, paths are well laid out, what will give quick ‘success’. Success, by the way, is not success in resolving the underlying problems, it is success in publishing in high profile journals, getting patents, getting a quick name and fame, making big money, ensuring further funding. Today knowing about the genome, transcriptome, proteins and other molecules has become a routine. What was not easy a few decades ago has now become a routine that anyone can follow and get something in hand. This, in itself, is a big achievement. I have no doubt about it. But does it give us the insights that we need most badly? Well, knowing the molecules of a virus can perhaps expedite vaccine development, but still it will take months or even years to undergo all necessary trials and testing before coming in use. What do we do in the meanwhile?
What we hardly know about the virus is its ecology, its interaction with the host individual as well as the host population. The population level outcomes, how variance within the population affects the virus propagation, infectivity, virulence, asymptomatic infections, interaction with comorbidities, convalescent state, carrier state if any and so on. Does it affect any other animals? Can it remain dormant in any other species? Did it come from any other animal, if yes why didn’t it come earlier? How would the virus evolve now on and how would our prevention and treatment strategies shape its evolution? These questions are about its biotic ecology. The abiotic ecology might appear simpler to study. Its survival in air, on skin, on other surfaces, its susceptibility to natural and man-made conditions, how it varies with ambient conditions, seasonal fluctuations and so on. But looks like, as of now we don’t even know its abiotic ecology sufficiently well. Forget about complex biotic interactions. The result is that we can make no reliable predictions about what course the pandemic will take. Studying all this is orders of magnitude more difficult and there is no routine protocol that will give us all necessary answers. The only thing we are sure of is that at least at the moment knowing every molecule that makes the virus is not helping us much.
Is the field of science going to learn a broader lesson after the panic subsides? Today all biology is being viewed only in terms of molecules. There is no doubt that molecules are important and we gt to study them. But understanding life is not understanding molecules. It is much beyond molecules. But studying molecules is the current wave on the fashion street.
Are we going to go by the relevance of questions or continue to follow the fashion street? In the current situation fields like disease ecology, epidemiological modelling do exist and there are brilliant brains working there. But there is a big gap between people who do the molecular biology of the virus and those trying to understand its ecology. People on the two ends don’t even talk to each other, if they do, they may not understand each other’s language and concerns. This gap is not created by the limitations of science. It is created by the culture and social behaviour in the field. Of particular importance is the picture of biology that we project for students. Certain fields have a bigger craze among students which again is a societal creation than anything in science itself. Biology curricula are highly biased and these biases follow trends in frontline research. Funding, on the other hand, goes more by the cultural definition of success over real scientific achievements. This course of science is natural since it is simply driven by the evolved behavioural instincts of researchers. But humans have a unique capacity to exceed the instincts and behave thoughtfully. We expect that at least people of science should use this capacity sufficiently frequently.
Myself and Sonali Shinde wrote a reply to Nature (an abridged version of the following article) immediately after publication of the article. After two and half months they declined to publish it, on the grounds that they received many responses “making overlapping points” and that they will publish representative ones. Now let us wait and watch whether what we say below is represented in the set of responses they finally publish. I won’t be surprised if it is not because what we suggest here is what the science publishing community is deliberately avoiding to do for quite some time.
Intelligent Martians had been doing research on human behaviour for quite some time. Once a PhD student doing her observations through a superpower telescope, saw a mob of people doing something exciting. She called her mentor,
“Look, what these humans are doing there on earth.”
“Who are they? Men or women?”
“How would I know? They are not wearing any clothes!!”
This is precisely how a committee comprising 43 researchers, publishers, funders and policy makers from 10 countries that met in Ottawa, Canada last April is looking at the problem of predatory journals after two days of brain-storming. They want to identify predatory journals not by what they are but by what clothes they wear. The outcome of this meeting is published as an article in nature (Grudneiwicz et al 2019). The definition of predatory journals they have proposed is so subjective and open to interpretation that with a tighter strain, most mainstream journals can be labeled predatory and with slightly coarse one, none would be filtered out. The definition may not matter so much in practice but the diagnostic criteria would, since that is how one would identify a predatory journal in practice.
What should be the diagnostic criteria for a predatory journal? Charging the authors is a common practice now in so many flagship journals that it cannot be a differentiating criterion. Advertising in some form or the other is also practiced by some mainstream publishers and there is nothing unethical in it. The committee has listed certain other trivial criteria for diagnosis as a predatory journal. These criteria are either difficult to know before submitting (which the article itself acknowledges) or are so superficial that the journals can easily improve upon them and still remain predatory. For example, they list “an unprofessional looking webpage, spelling or grammar mistakes or irrelevant text” as markers of predatory journals. The act of identifying these as diagnostic markers would immediately make them improve on it, but that will not change the predatory nature of these journals. The biggest surprise decision of the committee is to leave out the quality of peer review as the defining or diagnostic marker. The only difference that can exist between mainstream journals and predatory journals is the quality of peer review. But they say “At the moment, journal quality, adequacy of peer review and deceit are too subjective to include……(as diagnostic criteria)”
This is precisely equivalent to identifying someone from the clothes and not from the being that he or she is.
It can be perceived without much difficulty that the main problem does not lie with the predatory journals, it lies with the mainstream journals. There isn’t sufficient transparency in the mainstream journals. The unnecessary confidentiality of the peer review process is the root cause of the problem. One of the reasons there is resistance to transparency is that the quality of peer reviews of the mainstream journals itself is often, if not always, questionable. Whenever an attempt to investigate has been made, biases and unprofessional behaviour of reviewers and editors of the mainstream journals has been found. This is well documented and published independently by several research groups (Campanario 1998; Bornmann et al 2010; Phillips 2011; Tomkins et al. 2017; Haffar et al. 2019; Kuehn 2017; Lee et al. 2013, Silbiger and Stubler 2019, Elson et al 2020). Even a subtle biases can permit persistence of a wrong paradigm and prevent acceptance of truth (Akerlof and Michaillat 2018). So no doubt is left that the peer reviews of mainstream journals are frequently of bad quality and are a major obstacle in the progress of science. Reviewers and editors can easily get away with bad quality reviews simply because they are never exposed. Systematic enquiries in the peer review process are also limited by the confidentiality (Couzin-Frankel 2013). It is most ridiculous that the main pillar of science, which is publishing, is not available for scientific inquiry. Making peer reviews public will certainly make editors and reviewers more responsible.
The threat of predatory journals is unlikely to disappear as long as the review process of mainstream journals remains confidential. Let there be dozens of committees like the Ottawa committee; let there be dozens of attempts to isolate and banish the so called predatory journals; the threat of predatory journals will not vanish as long as the mainstream journals do not themselves improve. If the presence of predatory journal really induces an introspection process in mainstream science publishing, I would say predatory journals is a boon to science.
But mainstream science is reluctant to take up the hard work. They think making superficial efforts like the Ottawa committee will work. They think they can diagnose and isolate predatory journals and then everything will be alright. This is not going to happen. In fact making lists of predatory journals is a dangerous solution since very soon it will become a business opportunity similar to the impact factor business. On the other hand, in the absence of clear and legally valid definition the agencies making such lists can be easily sued for defamation. So the entire attempt to list predatory journals and warn authors not to publish in them is a dubious affair.
The only long term solution to predatory journals is that mainstream journals make their editorial and review process completely transparent, independent of acceptance or rejection. This can be done using pre-print services and steps towards this goal are already underway (Brainard 2019). The unfortunate but not unexpected fact is that only 17 journals have subscribed to the scheme of open and transparent peer reviews so far. There are other means of making things transparent as well (Watve 2019). The reluctance to make things transparent makes one suspect that something fishy could be going on behind the curtain of confidentiality.
“If corruption is a disease, transparency is a central part of its treatment.” — Kofi Annan.
“A lack of transparency results in distrust and a deep sense of insecurity.” — Dalai Lama
If transparency becomes the norm of the peer review process, the entire reader community is free to judge the review quality. Then the so called predatory journals will either have to improve their review process, i.e. essentially cease to be predatory, or perish automatically. No formal committees and actions will be needed against them. Thus the definition of predatory journals is very simple- based on peer review quality and the only effective solution is also quite straightforward and that is transparency. But by evading a clear definition as well as the most logical solution, the scientific community is unnecessarily making the matters more complex.
Akerlof G. A. and Michaillat P. (2018) Persistence of false paradigms in low power sciences. PNAS, 115, 13228-33.
Bornmann L, Mutz R, Daniel H-D (2010) A Reliability-Generalization Study of Journal Peer Reviews: A Multilevel Meta-Analysis of Inter-Rater Reliability and Its Determinants. PLoS ONE 5(12): e14331. doi:10.1371/journal.pone.0014331
Campanario, J. M.: (1998) Peer Review for Journals as it Stands Today. part 1 and 2. In: Science Communication 19(3) pp. 181–211 and 19(4) pp. 277–306.
Couzin-Frankel J. (2013) Secretive and subjective, peer review proves resistant to study. Science, 341, 1331.
Elson M., Huff M. and Utz S. (2020) Metascience on peer review: testing the effects of a study’s originality and statistical significance in a field experiment. Adv. Methods Practices Psy. Sci. https://doi.org/10.1177/2515245919895419
A person on ground has access to many details of what exists on earth, but a drone picture or a satellite image reveals something that you can’t perceive from ground. The drone’s or satellite’s view actually misses so many details but still gives some realizations that one can’t get on the ground. This is applicable to the field of research as well. Some researchers are obsessed by details, their research reveals more and more details and keep on adding to the data. In biology the details are often so intricate that getting lost in details is a commonplace. Having lost thus, the researchers themselves do not know where the research is going. In order to make sense often one has to leave the ground and take a perspective. This comes at a cost of resolution. The details are no more visible but the path which was obscured by the details might be revealed.
Take the example of cancer. We now know so many molecular details about it that understanding the fundamentals of cancer has become almost impossible. In the history of cancer research, some important insights about cancer were obtained before molecular biology came into existence. Cancer was identified as a multi-stage process by mathematical and statistical analysis of population patterns itself. Then it was realized that a series of mutations are required to transform a normal cell into cancer cell. Later came the tools of molecular biology which revealed that what mathematical models had suspected was actually true. Then on molecular biology kept on adding a huge volume of details, accompanied by much smaller increments in the fundamental understanding.
Cancers arise by a series of mutational and functional changes in some or the other stem cells of the body. These changes are quite like evolution, which happens by a process of random mutations accompanied by natural selection on the mutants. Cancer is nothing else but evolution of normal cells into cancer cells by a similar process of mutations and selection. Cancer researchers, so far have focused their attention on the details of mutational and other molecular changes in cells. Unlike rest of the evolutionary biology, cancer evolutionary biology has paid inadequate attention to selection on the mutants.
Recent research is now converging to show that the rate limiting process in most cancers is not mutation, but selection. Cancer causing mutations can arise in any individual at any time, but most people do not get cancer because their internal environment does not support the mutants. Cancer causing mutations do not have an all-time growth advantage over normal cells. Cancer cells need to compete with normal cells to survive. They can survive and outcompete normal cells only under a certain set of conditions. These conditions are provided by the body’s internal environment. The importance of the tissue microenvironment in the development of cancer is being increasingly recognized only over the last decade, but it was not incorporated adequately in the cancer evolution models.
And now a mathematical model of the cancer evolution process built by us shows that the known population patterns in cancers can be explained only by incorporating different selective forces in different individuals. If the internal environments of all individuals were similar, by all probability almost everyone would get cancer by a threshold age. The mutation probability alone does not explain why only some individuals get cancer. The population patterns of cancer are matched only when the model considers that every individual has a different tissue environment and thereby different selection on the mutants. Many other known patterns in cancer biology cannot be explained without taking individual differences in the selective environment of the mutant cells. So our concept that cancer is limited by the process of selection rather than mutation is supported by multiple lines of evidence.
Can cancers be prevented?
In 2017, a paper appeared in the journal Science claiming that cancer is shear bad luck. This implied that perhaps nothing can be done to prevent cancers. The paper immediately attracted substantial criticism because the methods used for analysis were not sound. This paper delighted me since I used to teach a preliminary course in bio-statistics and I got a real life example of how not to use statistics. I could warn my students, beware!! If you use wrong statistics you are likely to get a paper published in Science!!
Now using the same data but analyzing it more carefully, and using novel mathematical approaches we infer exactly the other way. That cancer is only marginally bad luck and potentially largely preventable by maintaining a healthy internal environment. If the internal environment is healthy, mutants may still arise but are unlikely to outcompete normal cells. This paper is published in Nature publication’s Scientific Reports on 6th March 2020. (https://www.nature.com/articles/s41598-020-61046-7)
Future research in cancer should focus on which factors of the internal environment govern the competition between cells and what regulates these factors. Experiments so far have indicated that the levels of different hormones, expression of a class of molecules called growth factors and the properties of the matrix in which cells of a tissue are embedded are critical components of the selective environment. If maintained at their natural healthy level the tissue environment can prevent cancer causing mutants form growing into a fully evolved cancer cell. Many lifestyle and behavioural factors modulate the tissue microenvironment but our understanding of the links between lifestyle, behaviour and microenvironment is still quite primitive. This understanding will be the key to prevent cancers and future research should mainly focus on this question.
This goes very well with my thinking about the behavioural origins of many of the modern disorders. We evolved for stone age life and a number of behaviours evolved with us to fine tune with that life. Every behaviour is linked to a set of neuro-endocrine pathways and all pathways form a complex network structure. So when we give up certain behaviours for which our body evolved, the entire network changes; the body’s internal environment changes and at least some of these changes set the stage favourable for the cancer driver mutants.
About 10 years ago an interesting paper that appeared in Cell has interesting implications for this theory. In this experiment one group of mice was kept in the conventional caged environment and another group was given a behaviourally rich environment. Both groups were implanted with xenografts and observed for several weeks. In the ones with a behaviourally rich environment the tumours regressed but in those with a monotonous caged environment they grew bigger. Todays human lifestyle is like the monotonous caged mice. We need to gain back the behaviourally rich natural life to get rid of cancers.
Now again, a number of molecular details should follow the new line of thinking. How life-style and behaviours modulate the tissue microenvironment, which microenvironmental factors alter the selective environment for cancer driver mutants, how mutations accumulate and how cancer grows given the selective conditions will make multiple fascinating molecular stories. But the details make sense only when you have the low resolution picture. Unfortunately in today’s science molecular details have become prestigious and perspective taking is not. Both are equally important but not equally prestigious. What you value depends upon whether you want good science or prestigious science.