Yes, losing 2x+ the annual number in one quarter should be noticed. But, maybe the virus evolved additional transmissibility over time, or maybe it evolved to be a little more deadly.
It simply seems less likely that this virus was first noticed in November, by China, yet it didn't hit major urban areas in the rest of the world for another four months. We're too interconnected as a species; nearly half a million people came to the US from China alone, after COVID-19 was classified there [1]. It feels more likely that people have been getting it, and dying from it, well before March; these cases were just simply miscategorized as the flu.
[1] https://www.nytimes.com/2020/04/04/us/coronavirus-china-trav...
To make the numbers work for the "weird cold" in early February case, I guess we have to work backwards. Assume that when we went into lockdown was the actual peak, 8 million people in New York City had COVID-19. That means that there is one death for every 500 cases. Then we have to pick a reproduction rate, which I have no idea how to pick, so I'll say that it increases by 1.5x every day (so on day one you have x cases, then on day 2 you have x + 1.5x, then on day 3 you have x + 1.5x + (1.5)^2x, etc. Going back a month from March 20 (which is approximately 30 days, and when a lot of people report their "weird cold"), that would mean we'd expect around 8 million / 383500 = 20 cases on Feb. 20. With 1 death per 500 cases, you'd have 0 deaths at that level.
I know I've pulled these numbers out of my nether region and so they are likely very wrong. But with some back-of-the-envelope math, I think we can have some sick people in February without a lot of deaths.
Obviously my 8 million total cases in NYC number is too high, and the 20 cases on 2/20 is too low for me and my friends to be those cases. But that exponential can be tweaked to make something plausible. If we make it 1.2 instead of 1.5, then we should have had about 7000 cases on 2/20, and that means around 14 deaths. That seems quite plausible to me. So I dunno. There was a weird cold. It's weird. It could mean anything.
I signed up to get an antibody test. 1 test is not data, but it will be very interesting to see the results.
> I know I've pulled these numbers out of my nether region and so they are likely very wrong. But with some back-of-the-envelope math, I think we can have some sick people in February without a lot of deaths.
Yeah, in two ways: That's not how the growth rate math works, but if we went by your math instead of the number, that's about 4-5x faster than what we were seeing in March.
A growth rate of 1.5 means if we had X cases on day 1, we'd have X(1.5^1) on day 2, and X(1.5^2) on day 3. This virus's growth rate at the beginning of the pandemic stage was around 1.4 (or to use your math, 0.4? I'm not sure what you meant by "(1.5)^2x", is that a typo of "(1.5^2)x" or did you mean 1.5^(2x)"? The second one is straight wrong).
Which is to say, you can't just look at the population death rate and really appreciate just how dangerous this is for older populations. The CFR for over sixty is a staggering 15% in WA. That is ridiculously high and completely masked if you look at all cases.
If they're not taking any precautions (because there's no knowledge of the virus in the community) then it can spread to those types of facilities very easily. In New Zealand where only 1,500 people have have COVID-19 (likely to be very accurate, 200,000 tests have been conducted) there's already been two outbreaks in nursing homes.
I just think the total IFR actually undersells how dangerous this is.
She's 29 and otherwise healthy. I'm sure we would have noticed this disease without the high mortality rate among the elderly.
Yes, it can do damage and is very dangerous for an at risk group. No, we don't know who that is, yet. Age clearly proxies for a risk factor. But which one?
Unless you are wanting to claim that no children have gotten this. Which, seems highly unlikely. (Or is the risk whether it will provoke an immune response?)
But I also had a truly "weird cold" go through my workplace early this year in SFBA, so I still have to wonder what that was.
[0] https://www.wolframalpha.com/input/?i=us+death+rate+*+421+pe...
Source? I can only get a rate that high by taking the highest death count I can find (which involves extrapolations to attribute deaths to Covid-19) for New York State and dividing it by the population of New York City (which is clearly invalid).
In anycase, even a rate of ~.1% would be absurd given ~20% of people are testing positive by antibody tests.
I subsequently found what I believe you are working from here: https://www1.nyc.gov/site/doh/covid/covid-19-data.page which does specify New York City (as opposed to New York State) so both the numerator and denominator refer to the same thing.
Interestingly, they appear to be using a larger denominator than your 8.5 million (NYC metro vs. NYC proper perhaps?) and get a rate of 0.175% = 175.66/100,000, eg in the "citywide total" line of https://github.com/nychealth/coronavirus-data/blob/master/by...).
But this is at least closer to your figure than I was able to get previously.
https://www1.nyc.gov/assets/doh/downloads/pdf/imm/covid-19-d...
Specifically has numbers for NYC. You can sum the boroughs or use the total listed below. Using confirmed by pcr cases you get ~15k/8.6 = ~.175% as you said.
Using probable excess deaths adds an extra 5k deaths, hence ~.25%
But, wouldn't lengthening the incubation period also be a successful evolutionary strategy regardless of lethality?
It seems to me that there are many possible strategies that a mutating virus might gain an advantage and we shouldn't just assume that the only one that they would use would be to become more mild.
Luckily, sars-cov-2 seems relatively stable.
> Trade-offs between different components of parasite fitness provide the dominant conceptual framework for understanding the adaptive evolution of virulence (Alizon et al. 2009).
...
> By far, the most widely studied trade-off involves transmission and virulence (Anderson and May, 1982; Frank, 1996; Alizon et al. 2009). Transmission and virulence are linked by within-host replication: increasing parasite abundance increases the likelihood of transmission, but also increases the likelihood of host death; mathematically, this assumption can be formalized by making transmission rate β an increasing function of parasite-induced mortality rate ν. Nearly all of the literature we summarize below assumes this trade-off. However, another potential trade-off suggested by an examination of R0 involves virulence and recovery rate (Anderson and May, 1982; Frank, 1996). This trade-off is also mediated by replication rate, with high abundance increasing the likelihood of host death, but also decreasing the likelihood of the host clearing the infection (Antia et al. 1994); mathematically, this assumption makes recovery rate γ a decreasing function of parasite-induced mortality rate ν.
It can very well go the other way as with 1918 'spanish' flu. Or it can change in ways that won't affect overall mortality much.
It was in the middle of world war 1. Soldiers were infected, and the ones that became the most ill were sent home to recuperate, or die. Either way they spread the virus better than the ones who weren't as ill, and who stayed in the field.
So the usual evolutionary pressure was upside down here.
If it's truly random it wouldn't be directional, yeah?