Karl Friston: Up to 80% not even susceptible to Covid-19 because of "Immunological Dark Matter"?

Unherd.com has published an article discussing some ideas about COVID-19 with Karl Friston, who is a leading and impactful statistical data scientist at University College London who has made fundamental contributions in neuroimaging, introducing and promoting the idea of "statistical parametric maps" that are widely used by scientists all over the world. Karl was ranked by Science magazine as the most influential statistician in the world to neuroscience.


Although not published in any academic journals, I want to mention and discuss this article given how much respect I have for Karl as a professional, and how unique his perspective is. If true, this would have major implications in how we manage this virus, and what we can expect moving forward.


The article argues that a certain percentage of people have some level of prior immunity to COVID-19 so are never in the group of "susceptible" in the population. They cite a study in leading journal Cell as suggesting 40-60% of people who have not been exposed to SARS-CoV-2 have immune T-cells from other similar coronaviruses that comprise the mish-mash that is collectively called "the common cold". Incidentally, I wrote a blog post on this article back on May 18th. As I highlighted in that post, this does not automatically mean that these individuals are immune to COVID-19, and given how the most severe COVID-19 cases result from intense immune response that somehow does not neutralize the virus but instigates an overactive inflammatory response that leads to COVID-19's most severe and life-threatening complications, it is possible that these individuals are more prone to severe disease. As I mentioned in that blog post, we don't really know yet what that means.


In an interview published in the Guardian, Karl discusses "dynamic causal modeling" (DCM) that has been used in physics, and he has applied to do dynamic causal modeling of the brain. He talks about the use of these models as "generative models" to model the dynamics of SARS-CoV-2 spread. Other generative models include the SEIR (Susceptible-Exposed-Infected-Recovered) models underlying most of the epidemiological models for infectious disease spread. He argues those fail because they don't flexibly enough allow all possible states or assess which ones really matter in explaining the pandemic's trajectory over time. He describes DCM models as including response of individuals and society as part of the modeling process.


In this interview, he also says that what is currently happening in the UK is not likely to have a big effect on triggering a rebound because of "immunity" he believes the public has acquired in the first wave, but he worries about later the "immunity" may wear off. He mentions that antibody tests give us the potential of gaining insights to this moving forward. He emphasizes that we have time to get test-and-trace strategies in place before a potential second wave in the fall and winter.


Personally, I agree with a lot of what he has said here, except I wonder about what we really know about immunity at this point, which leads into his next point.


In discussing why some countries like Germany have experienced far fewer cases and less death than other countries like the UK, he proposes that it has little to do with policies implemented by these countries, and more to do with some natural characteristics that Germans may possess that make them less susceptible to the virus. He states that "There are various explanations, but one that looks increasingly likely is that Germany has more 'immunological dark matter', people who are pervious to infection, perhaps because they are geographically isolated or have some kind of natural resistance." He uses the analogy of "dark matter" in the universe, a theoretical construct that is little understood but is used as a kluge to explain some observations that cannot yet be explained by known mechanisms. He suggests the implications of this is that targeted testing of those at high risk may be a better approach than population level testing. The obvious question that is unanswered is how to identify who these high risk individuals are.


In his interview with Unherd, he suggested that the susceptible population in Germany was never 100%, but more like 50% or even 20%, speculating that 50-80% of the population had this "immunological dark matter" that somehow made them not prone to be infected. He is cautious about attributing the source of this effect, but discusses some type of special natural immunity as one potential explanation. This is how he explains why the epidemiological models predicting 80% infection rate in the population and 0.5%-1.0% death rate in the population did not come to pass -- that these models were "right" for a smaller population, but the actual susceptible population was much smaller than the entire country.


These are interesting ideas, and I would love for Friston to be right, since this would mean we are far closer to herd immunity than we thought, and this would imply maybe the pandemic will not last that long even without an available vaccination.


However, to me it seems like he is inventing this "immunological dark matter" explanation to explain how the realized outcomes did not match the epidemiological models, and how different countries had different outcomes. From what I've seen, there is little evidence that a high proportion of the population has antibodies against SARS-CoV-2 because of exposure to other coronaviruses, maybe 1%, and also as I mentioned above, it is not clear that merely having antibodies is enough to confer immunity.


To me, I think there is a simpler explanation: the models did not account for natural changes in human behavior. Faced with a pandemic, many people do not need government-imposed lockdowns to take precautions to not be exposed to the virus, and so in places like Sweden that never locked down, they still experienced mitigation of viral spread from the behavior of a large subset of the community. Countries in the world, or states in the USA, that have had better outcomes may have had better community compliance with the recommended precautions, and upon opening from lockdowns it may be the regions in which individuals are not carefully following social distance and mask wearing are the ones experiencing upticks.


I think our success or failure in managing and living with this virus in the coming months and years depends strongly on scientific discovery, clear communication, and community behavior. I am optimistic that as we continue to learn more about the virus and this knowledge is clearly communicated, that the public will take the necessary steps to mitigate the spread of the virus ... I sure hope so.








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