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.

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