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Drowning in Coronavirus Research, Part 42

By July 18, 2020Commentary

If you step back for a moment, it is astounding how much scientific and medical attention is focussed on coronavirus.  While a lot of useful knowledge is produced, this concentrated effort may also contribute to the perception that this is a more dreadful epidemic than it actually is.  In any event, it becomes difficult to sift through everything published and understand how all the results fit into a bigger picture.  And I also am trying to do some actual data analysis and present that.  By tomorrow I hope to be in position to update some of the typical analytics I look at.  But for now, still trying to keep up with summarizing at least semi-relevant papers.

The lockdowns scared people into missing health care, and in some cases, providers were told not to deliver so-called elective care in order to be able to care for any potential coronavirus patients.  This inevitably led to worsening health and even death for many patients.  The failure to weigh these consequences is one of the most disgraceful aspects of the political response to the epidemic.  This paper from the United Kingdom explores changes in cardiovascular deaths, which include heart attack, stroke, heart failure and pulmonary embolism, among adults from 2014 to 2020, with a focus on the period after March 2, 2020.  (Medrxiv Paper)   After this date there were 22,820 acute cardiovascular disease deaths, of which 5.7% apparently had a coronavirus diagnosis as well.   Compared to expected deaths based on prior years, there were 1752 excess deaths, or 8% of the total.  Long-term care facilities experienced 1065 excess deaths, or a 40% rise, there were 1728 excess deaths at home, for a 34% increase, while there were fewer deaths than expected in hospitals.  It is apparent, as the authors point out, that people clearly avoided seeking care when symptoms were present of serious acute exacerbations of underlying disease.

Another paper reflects on what level of cases in the population leads to a generalized immunity that substantially eliminates transmission.   (Medrxiv Paper)   If I go back to forever ago, I am still amazed by the failure of the epidemiological experts and modelers to actually think about the characteristics of this epidemic in terms of  basic concepts like susceptibility, infectiousness and outcomes.  That failure led to pathetic models which alarmed politicians and created these excessive lockdowns which so damaged and will continue to damage our society.  Among other bad assumptions was one that 60%, 70% or even more of the population would have to become infected before population immunity occurred.  Fortunately, several more innovative thinkers have produced models based on variability in susceptibility and infectiousness which estimate much lower percentages for such suppression of transmission.  In this paper the authors assumed that a certain percentage of the population was not susceptible due to innate immunity or cross-reactivity from other coronavirus infections.  The used a variation on the contact matrix which allowed different levels of mixing of the susceptible and non-susceptible groups.  Depending on the transmissibility of the virus, the initial level of resistance to infection, and the contacts between groups, population immunity could be achieved at levels as low as 25% of the population being infected.  The more mixing, the lower the level of infections needed for effective suppression.   My only concern with this alternative assumption, which is supported by an increasing body of research, is that some antibody surveys are finding very high levels of prevalence, implying high susceptibility levels.  I reported on one yesterday from Argentina with a 50% plus prevalence.  It could be that some surveys are missing large number of cases because the assays aren’t sensitive enough.  It could be that some are actually picking up antibodies from seasonal coronavirus infections.  No one is surveying T cells, which may be more prominent in the adaptive immune response.  And it may be that prior exposure to coronavirus infection is widely variable.

This paper examined population immunity in Sweden using a model focused on social clustering.  (Medrxiv Paper)  While a number of people have theorized that Sweden may be approaching population immunity, antibody surveys suggest that if it is happening, it isn’t at the level typically estimated, as far less than 50% of the population has likely been exposed.  Meanwhile, the epidemic in Sweden has basically disappeared, particularly in level of hospitalizations and deaths.  The authors used a pre-existing model of social dynamics to examine the effect on epidemic spreading.  The basic notion is the commonsense one that a pathogen may easily transmit between members of a group, whether family, work or socially based, but has more trouble crossing between groups.  So an epidemic in this model almost has a tree-like structure, wherein when the pathogen gets to a certain branch all the leaves are exposed but crossing to new branches is more difficult.  The researchers use a complex mathematical approach to model these dynamics.  This clustering may slow attainment of population immunity.

This paper has a somewhat scary hypothesis, which is that the virus is evolving to be better at evading detection by the immune system.  The adaptive immune system works by having a large library of protein fragments which it monitors.  If one is presented to it by a cell in the body, and it recognizes it as a dangerous foreign substance, the immune system will respond with antibodies, killer T cells and other immune components.  If a virus evolves under immune system or other pressure (think antibiotic resistance due to drugs) it will have modified proteins that aren’t recognized by our memory immune system.  (Medrxiv Paper)   The authors claim to have detected this in regard to the current strain of coronavirus.  They looked at over  10,000 CV genomes to detect various fragments that might be recognized by B cells.  Their analysis showed that substantial variability was beginning to occur in the spike and nucleocapsid proteins.  There was less variability in regard to T cell fragment recognition.  This may be consistent with a more prominent role for T cells in responding to coronavirus infection.  In any event, there is a danger that too active an effort to suppress any pathogen, causes it to become more infectious and more deadly.

Given that three coronaviruses have emerged in the last 20 years to infect humans, it would be good if any vaccine works against all coronaviruses.  And to the extent that there is a cross-reactive response, that may limit susceptibility to the current strain.  These researchers examined common sequences across strains, with the primary purpose of guiding vaccine development efforts.  (Medrxiv Paper)   One notable finding was that T cell cross-reactivity is likely more important than antibody ones.

And with all the gnashing of teeth and wailing over potentially weak, short-lived antibody responses, this paper again demonstrates that in fact people are generating strong neutralizing responses that last.  As usual, please ignore media hype.  (Medrxiv Paper)   The study was done in New York City among 19,860 with mild to moderate disease.  The results are extremely positive.  Almost all individuals had strong responses for up to 3 months, suggesting they will last.  And the responses were to the spike protein, where neutralization of infectivity occurs.

This study examines the changes in antibody responses over time.  (Medrxiv Paper)   About 675 people were tested for antibodies against both the spike protein, where binding to cells is initiated, and the nucleocapsid.  Both sets were about equal during the active infection phase.  Over time, however, the nucleocapsid antibodies waned while the ones against the spike remained strong.  Since the antibodies against the spike are neutralizing, i.e., prevent infection of a cell, they are more important.  As the authors note, testing only for nucleocapsid protein antibodies is likely underestimating prevalence.


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