Published on: 2020-03-24

SARS-CoV-2, 19 times less likely to infect people under 15

Is a small volume of low quality data sufficient for making meaningful claims about CFR and R0 ?

Epistemic Status: Wild off-the-cuff speculation

Note: This post was written on 24-03-2020 during the srars-cov-2 pandemic

DISCLAIMER !

This is NOT an article claiming that we should stop caring about the covid-19 pandemic, nor is it claiming that the number presented in the title is actually correct, rather, it’s a punny attempt to show the kind of data-driven uncertainty that leads to us not fully understanding the behavior of the disease in terms of spread and frequency of symptoms.

If you wish to crucify me for claiming “Maybe the current measure we are taking are not ideal based on what we know so far”, please do me the favor of actually reading the whole thing first.

NOR is this an article claiming that our current measures are “killing the economy at the cost of saving lives”… I find that position almost absurd and lacking in understanding about this whole “social contract thing” we have going that means we value life over material goods.

The very uncertain hypothesis I am presenting here is that we are engaging in a “panic response” which might be unjustified given current data and could lead to both a higher number of people dying from coivd-19, higher numbers of people dying overall and worse economic damage than the proper response.

Lastly, and very importantly, I am not an MD nor an epidemiologist and taking a though piece as a guide for deciding to isolate yourself or to break quarantine is pretty stupid. If this encourages any action other than “Hmh, interesting, let me try to find some data and look at it”, then I will claim right now said action is misguided.


Are children less likely to be infected by sars-cov-2 ? (No)

It’s fairly hard to get raw data about covid-19 cases, the mos comprehensive dataset of individual cases I could find is compiled by the medical community at DXY.cn (a Chinese medical discussion board):

https://docs.google.com/spreadsheets/d/1jS24DjSPVWa4iuxuD4OAXrE3QeI8c9BC1hSlqr-NMiU/edit#gid=1187587451

I find it curios that out of 397 cases with a reported age in the US, only 4 were under the age of 15.

I say I find this curios because based on the 2018 census data, 18.71% of the US population is under 15 years of age.

This leads me to the clickbaity number in the title (round(0.187/(4/397)) == 19).

However, I did not pick the US specifically to get this high of a number, indeed, China yields an even higher one. I simply thought 19 had a nice ring to it considering the disease’s name… and the whole point of this article is to explain why this observation is blatantly wrong anyway.

While this particular coronavirus is indeed novel, coronaviruses in general are a pretty wide spread species and some of them have symptoms we label under “common cold”, together with the original SARS and the MERS coronavirus, I think enough data to say with a very high certainty:

Even more so, kids are particularly bad at not licking random surface, touching strangers, eating random stuff off the ground and overall doing stuff that is unsafe in terms of not getting infected by almost any given virus or bacteria.

So why are so few kids getting infected ?

Well, you could say:

But I could say:

I think the real explanation is more along the lines of:

However, if this is the case, this reasoning need not extent to children alone. All of the reasons listed above are not specific to children, it just so happens that well functioning immune system are overly represented in children.

There’s plenty of older people with well functioning organisms that could react to an infection similar to how most children could. Although this breaks down past a certain age, since our thymus is slowly replaced by adipose tissue in the morbid, painful and not yet reversible process we call “aging”.

In some ways this article is a “layman’s defense for the layman” of this analysis by Oxford’s center for evidence-based medicine: https://www.cebm.net/global-covid-19-case-fatality-rates/. Which claims CFRs (case fatality rates) way bellow what’s being currently reported and places much more of the blame or comorbidities.

That is to say, this is hopefully an intuition as to why “A lot of people in retirement homes dying in Bergamo” doesn’t translate to “This virus will kill 1-5% of Earth’s population”.

It’s also hopefully an explanation of why R0 (the basic reproductive rate) ranges from 1.4 to 3.9 (which is huge, even compared to the ranges we get for most seasonal influenza strains) and thus it could really be anywhere from “if left unchecked it will spread like a vengeful medieval plague” to “it spreads more or less like the flu”.

Two things that keep me awake at night

However, it does leave me curios as to two things.

I’ve seen the CFR and R0, or various facts derived from them, used by physicians and authorities in terms of communicating information to the public and enacting quarantine measures.

These two things would heavily influence how that information is communicated and how said measures are encated.

If the data for this disease is so poor (e.g. respectable institutions have numbers order of magnitude different for the estimated CFR), why go ahead and make claims based on R0 and CFR to being with ?

I’m not claiming the diseases is not dangerous, on the contrary, I am claiming that it could be potentially MUCH MORE dangerous than we think or MUCH LESS dangerous than we think.

That’s an issue, because that’s how you get to under-reacting and causing millions of deaths or over-reacting and causing millions of death (due to a horrible recession, even though they won’t be in Europe or the USA, they will be in Africa, as people start tightening the belt and donations to charities are among the first inessential things to go away).

Would the wise thing here not be to try and gather more data as aggressively as possible ? Rather than going on TV and making claims around numbers for which no degree of certainty exists.

If the current data indicates that kids and most likely healthy young adults are not at risk, while old people and people with comorbidities are at very high risk. Why not act along those lines:

Allow young people that want to break quarantine to do so, monitor them closely and test them in order to figure out what the consequences of spreading in a young demographic is… after all, it seems that they aren’t very significant based on current numbers.

If the consequences seem worrisome, revert the measure before hospitals get overwhelmed.

If the consequences seem on par with a bad strain of rhinovirus or influenza, progressively advise more and more people that they can break isolation (but make sure to inform them that the risk of death exists and that they will be assuming it), thus getting the economy partially back on track and increasing the cohort you are monitoring.

On the other hand, if we are petty certain that old people and people with certain comorbidities (e.g. diabetes and CVD) are at very high risk. Why not target those people directly in terms of aid, quarantine measures and messaging.

Why do we have broad sweeping messages about “stay at home”, which seems to be mainly causing a panic in young well-educated adults (aka the kind of healthy demographic that is prone to leading an overly-healthy lifestyle which could lead them to not even noticing an infection) whilst not at all disturbing grandma and grandpa (aka the kind of people you really really really want to stay at home) ?

Wouldn’t a functioning economy better allow for long-term quarantine of those at risk ?

I’d personally be pretty glad to go around shopping for a few old people in my local community and pass on the supplies to a guy wearing a hazmat suit that will disinfect them to the best of his abilities and then hand them over.

I feel like this kind of “allow young volunteers to boost herd immunity, keep the economy going and provide data, at least until our data tells us otherwise” approach would be preferable to the progressively worsening quarantine that is not discriminating based on age or comorbidities.

But

Again, I don’t think based on current data I am qualified in terms of proposing the kind of measure we should enact.

I do however strongly think that any measures should revolve around “how do we gather better quality data and making it more widely available for people to build models and hypothesis based on”.

I also think that throwing anyone in the same bucket in terms of risk and thus throwing them in the same bucket in terms of the measures we enact is silly.

To me it seems that young healthy people are:

Whilst old or unhealthy people are:

Obviously, “young” and “old” or “healthy” and “unhealthy” are pretty vague terms, but we can pick some arbitrary limits, e.g. you are “young and healthy” if you are bellow 25, exercise daily, have no diagnosis of metabolic diseases, CVD, CVD risk factor or respiratory disease… start from there, and broaden or tighten the definition based on the data we gather.

I think some people might read some form of “moral hazard”, after all out of those people a few might still get severe symptoms or even die from the disease… but it’s not like the current approach has stopped deaths and it’s not like there’s an end in sight. We can’t just quarantine people forever and we can’t hope that the kind of quarantine measures we enact are going to completely empty all human and (potential) animal reservoirs.

At most, what we are doing right now is stalling for time in order to get a cure… but what is a cure ?

Antivirals seldom have the efficacy we’d be looking for.

A vaccine seems to be 12 months away under the most optimistic estimates and is far from a guarantee.

The two things that everyone agrees would help are:

Those are the very things the policies in the US and in most European countries are not going to help us with.

DISCLAIMER !

This is NOT an article claiming that we should stop caring about the covid-19 pandemic, nor is it claiming that the number presented in the title is actually correct, rather, it’s a punny attempt to show the kind of data-driven uncertainty that leads to us not fully understanding the behavior of the disease in terms of spread and frequency of symptoms.

If you wish to crucify me for claiming “Maybe the current measure we are taking are not ideal based on what we know so far”, please do me the favor of actually reading the whole thing first.

NOR is this an article claiming that our current measures are “killing the economy at the cost of saving lives”… I find that position almost absurd and lacking in understanding about this whole “social contract thing” we have going that means we value life over material goods.

The very uncertain hypothesis I am presenting here is that we are engaging in a “panic response” which might be unjustified given current data and could lead to both a higher number of people dying from coivd-19, higher numbers of people dying overall and worse economic damage than the proper response.

Lastly, and very importantly, I am not an MD nor an epidemiologist and taking a though piece as a guide for deciding to isolate yourself or to break quarantine is pretty stupid. If this encourages any action other than “Hmh, interesting, let me try to find some data and look at it”, then I will claim right now said action is misguided.


Main references

The main references used for getting the numbers and factual information claimed in this article are (in order of importance):

General information about covid-19 (the disease), cov-sars-2 (the virus) and the measures proposed against the pandemic:

https://idpjournal.biomedcentral.com/articles/10.1186/s40249-020-00646-x

https://www.elsevier.com/connect/coronavirus-information-center

https://peterattiamd.com/peterhotez2/

https://peterattiamd.com/peterhotez/

The study on which I’m basing my claims for age and comorbidities being the main driver of death and severe symptoms:

https://www.cebm.net/global-covid-19-case-fatality-rates/

Information about the COVID-19 test:

https://www.nature.com/articles/d41587-020-00010-2

Information about DXY & DXY dataset:

https://docs.google.com/spreadsheets/d/1jS24DjSPVWa4iuxuD4OAXrE3QeI8c9BC1hSlqr-NMiU/edit#gid=1187587451

http://www.dxy.cn/

Demographic data used to extract the original numbers:

https://www.statista.com/statistics/270000/age-distribution-in-the-united-states/

https://www.statista.com/statistics/251524/population-distribution-by-age-group-in-china/






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