The Car Crash Analogy.
Many things determine how serious a car crash will be, including speed, and what the car crashes into. With Covid-19 the equivalents could be considered to be how large was the viral load, and what parts of the body became infected. While just as and elderly and more fragile person is more susceptible in both cases, luck, circumstances and precaution taken can all play a role in the severity.
The SARS-Cov2 virus Covid-19 spreads through airborne particles, and may people who inhale some of these particles have no noticeable health symptoms, or would test positive to Covid-19. At the other extreme, with a strong enough infection, even young healthy adults can succumb.
Only 0.7% of car accidents are fatal, and for people under 50, Covid-19 could have even lower stats. But far more people have time in hospital, and just as I don’t want to try my luck with a car accident every 3 months, I don’t want to try my luck with a Copvid-19 infection either, especially if the levels of virus around in the community rises to the level hospital staff have experienced.
Positive/Negative Tests Don’t The Full Story.
We are all too familiar now with the concept of a test for Covid-19. There are two possible results: Positive or Negative.
The implication is, that you either have Covid-19, or you do not. A binary outcome.
However, just like an academic test where it can be considered that you pass or fail, not all who pass are the same, and not all who fail are the same. The academic test is to measure knowledge of a particular subject, and there is far more variation in knowledge between those tested than two groups than is revealed by “passed” or “failed”.
, where despite having an actual mark, perhaps out of 100, the result can be reduced to ‘pass or fail’, perhaps it is not really only a binary outcome.
What if with Covid-19 the same concept of a ‘mark’ can apply, only the higher the actual ‘positive’, the worse things are. Evidence suggests the presentation of Covid-19 as either “you have been infected or not”, rather than considering, “this person contracted a very significant infection” vs “this other person was only mildly infected”, plays a key role in mismanagement of risks presented by the virus.
The key observation is that:
- With a sufficiently mild case of Covid-19, despite testing positive, most of even elderly people, even those with ‘co-morbidities’ will survive Covid-19.
- With a sufficiently severe case of Covid-19, even a young perfectly healthy person could become a casualty.
Surely this suggests that all cases of Covid-19 are not equally severe.
It is not just a case of, ‘you have it or not’, but also of ‘how badly do you have it?’
Why such a range of outcomes?
It makes sense that if you already suffering from ill health, that you would be more at risk of death from any life threatening disease. There is a well known set of risk factors that identify those most at risk from Covid-19.
Consider only people who are under 45, and have no identified risk factors.
Within this group, for the most common level of initial infection, almost nobody requires any medical intervention at all. In fact most people do not even detect any symptoms at all. But then, some people, within this same group, get such a severe case of Covid-19, a disease where most similar people do not even notice symptoms, that they can die.
For these younger people with no identified risk factors who become so ill they can dies must either:
- Have some unidentified risk factor risk about their health making them more vulnerable to Covid-19 are than other otherwise similar people
- Fall victim of some unidentified ‘Russian-roulette’ style mechanism of Covid-19 that randomly makes a small fraction of cases incredibly more severe than other cases
- Have in some way contracted a more severe case of Covid-19 than the vast majority of their peers.
Explanation 1 is possibly explained by genetics if such cases appear distributed in such a way as to be able to explained by genetics, and explanation 2 require the existence of something ‘unidentified’. Explanation 3 still requires the existence of something unproven, but given some data that seems to rule out option 1 as an explanation for cases I am highlighting, I suggest number 3 is the most likely.
Microbial dosage: Infection by a small number of viral particles may manifest in an asymptomatic or mild illness, while a large quantity of particles is likely to do more damage to the immune system, resulting in a more serious illness.
Genetics: Surface proteins on host cells often act as a portal for viruses. Since they are unique to each person, someone without particular surface proteins, for example, may have resistance to SARS-CoV-2 infection.
Infection route: The path of virus entry—whether via nose or mouth through aerosolized droplets from a cough or sneeze, or through touching a contaminated surface and then touching one’s face—could affect disease outcomes, due to differing immune defense responses.
John Hopkins: Bloomberg school of public health.
There are two other factors quoted in the article:
- the virus being different in different areas
- personal immune status
These 2 factors will also potentially vary outcomes, but data can be narrowed to eliminate situations where variations of these factors are present, and still the wide variation in outcomes remains. This again says that while not the only factors, either explanation 1 (by genetics) or explanation 3 (by Microbial dosage or Infection Route) must also play a role.
Health Care Workers and Severe Cases.
I was shocked in February 2020, when I first heard of the death of ‘Hero who told the truth‘, the Chinese doctor Li Wenliang who was censored trying to alert the world of the risks, and ultimately died of the virus. At the time I thought, “are reports are people his age never die of the virus and it is not like he would not have access to the best medical care”. How could he die. Friends even joked “but are we sure he really died of the virus?”. But then, it became clear that while the rule was thought to be people his age do not die of the virus, medical staff seemed to be exempt from such protection.
Country after to country, health care workers appear over represented in the first young people to have severe Covid-19.
Which factor, microbial dosage, genetics, or infection route? Unless people are genetically disposed to become health workers, it seems like either microbial dosage or infection route.
Link: Environment and Mortality?
I am still exploring this. But it does seem that once an outbreak reaches either a certain scale, or a certain maturity, things get worse. I will research further and update this.
It is obvious the more of the virus circulating within a community, the greater chance of being infected. But it also seems that as circulation of the virus increases, the number of otherwise totally health young people infected rises as a percentage of those infected.
Whether Microbial dosage or Infection Route or both are risk factors, the more people who infect a given person, the higher the initial Microbial dosage and the higher the risk of multiple Infection Routes.
As has happened with even young and healthy health care workers, a sufficiently severe infection from an environment with many infected people can kill anyone, of any age, any level of health, regardless of available medical care.
All of this suggests that the possibility as case numbers amongst a community rise, the severity of cases may also rise. A common pattern in several outbreaks seems to be that initially only the very elderly have severe cases, and there are zero deaths below certain age thresholds. But as outbreaks become more severe, and there are 2x and then 5x more cases, the statistic shift from being not 2x or 5x the cases for each age group, but also a spread of cases into younger age groups. It seems the more Sars-COV2 virus in a given community, the less anyone of any age is safe.
Conclusion: Case Numbers are not the whole story.
Statistics divide test results into ‘negative’ or ‘positive’, when it is not really that simple. Just recording deaths without any acknowledgement of the other significant health outcomes can distort views of the challenges faced, assuming that every case under every circumstance with have the same risk profile may also be misleading.
Without testing every citizen, it is very difficult to determine actual case number. There appears to be proof that almost every estimate of case numbers is an underestimate, which makes comparing case numbers between any two areas with a different testing setup pointless.
Perhaps just case numbers alone is not the whole story anyway, as case severity could increase even with the same case numbers.
It also appears that even if we do consider cases within the same location using the same testing, increasing case numbers may present a greater problem than numbers alone suggest. For example, a doubling of case numbers could quite possibly also correspond to an increase in the percentage of more serious cases.
In conclusion, it is advisable to be cautious with ‘worst case’ projections.
