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James_Miller comments on Rationality Quotes October 2014 - Less Wrong
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Zach Weinersmith (Twitter)
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I feel the Ebola article makes a false comparison. We have highly competent disease control measures that keeps Influenza's death toll bounded around the 50k order of magnitude per year. With Ebola, the curve still looks exponential rather than logistic - if the trend continues we'll have a 6-figure bodycount by January.
A fairer comparison would be Ebola to 1918 Spanish Flu.
(Oh and that isn't even taking into account that the officials have been feeding the media absolute horseshit about the "single patient" with Ebola)
Downvoted for mindless panic.
There are no measures to speak of to control the flu. It goes through the world every year and we just live with it because it's rarely fatal.
The Ebola curve is not exponential in the countries where appropriate measures were taken, Nigeria and Senegal: http://www.usatoday.com/story/news/nation/2014/09/30/ebola-over-in-nigeria/16473339/ Clearly the US can do at least as well.
While Ebola might mutate to become airborne and spread like flu, and there is a real risk of that, there is little indication of it having happened. Until then the comparison with the Spanish Flu is silly. It's not nearly as contagious.
Your linked post in the underground medic is pretty bad. The patient contracted Ebola on Sep 15, most people become contagious 8-10 days later, so the flight passengers on Sep 20 are very likely OK. There is no indication that the official story is grossly misleading. There are bound to be a few more cases showing up in the next week or so, just as there were with SARS, but with the aggressive approach taken now the odds of it spreading wide are negligible, given that Nigeria managed to contain a similar incident.
My guess is that the total number of cases with the Dallas vector will be under a dozen or so, with <40% fatalities. I guess we'll see.
Upvoted for the firm prediction. Confidence level?
I would say 90% or so.
... And it looks like I was right, if unduly pessimistic. Total new cases: 2, total new fatalities: 0. I expected at least some of the patient 0's relatives to get infected, and I did not expect the hospital's protection measures to be so bad. It looks like the strain they got there is not particularly infectious, which saved their asses.
the numbers of ebola cases were no longer exponential since mid Sept. instead they stay almost constant with ~900 new cases per week since Sep.14 This should have been clear to WHO and researchers at least since mid-Oct. Still they publically repeated the "exponential" forecasts , based on papers using old data. Ban ki Moon (on 2014/10/09) and Chan(on 2014/10/14) and Aylward said it. WHO until now puts forward their containment plan based on 5000-10000 new cases in the first week of december. They didn't correct it yet.
according to Fukuda on 2014/10/23, the WHO-committee on 2014/10/22 on the third meeting of the International Health Regulations Emergency Committee regarding the 2014 Ebola outbreak in West Africa stated that there continued to be exponential increase of cases in Guinea,Liberia,Sierra Leone
I'm far from an expert myself but unless, as you say, the experts are feeding us via the media "absolute horseshit" the expected number of U.S. deaths from Ebola is way below 50K.
What countermeasures is that number conditional on being taken?
What we seem to be doing but with significantly more countermeasures if the number of U.S. victims increases. Obama would suffer a massive political hit if > 1000 Americans die from Ebola and I trust that this is a sufficient condition to motivate the executive branch if things start to look like they could get out of control.
Motivation may be necessary but it's not sufficient. The Federal Government is not exactly a shining example of competency.
Will the CDC handle Ebola like FEMA handled Katrina?
That article about the flu "forgets" to mention a rather important fact: the effectiveness of the flu vaccine is only about 60%.
In particular, with this effectiveness there will be no herd immunity even if you vaccinate 100% of the population.
I believe this is incorrect. The required proportion of the population that needs to be immune to get a herd immunity effect depends on how infectious the pathogen is. Measles is really infectious with an R0 (number of secondary infections caused by a typical infectious case in a fully susceptible population) of over 10, so you need 90 or 95% vaccination coverage to stop it speading - and why it didn't much of a drop in vaccination before we saw new outbreaks.
R0 estimates for seasonal influenza are around 1.1 or 1.2. Vaccinating 100% of the population with a vaccine with 60% efficacy would give a very large herd immunity effect (toy SIR model I just ran says starting with 40% immune reduces attack rate from 35% to less than 2% for R0 1.2).
(Typo edit)
So? A 60% reduction in the chances of getting the flu is still orders of magnitude better than a 100% reduction in the chances of getting ebola. Also, herd immunity isn't all-or-nothing. I'd expect giving everyone a 60% effective flu vaccine would still reduce the the probability of getting the flu by significantly more than 60%.
I hear that herd immunity only really works when the percentage of people vaccinated is in the high 90s, but IANAD.
According to the Wikipedia page on herd immunity, it seems to be that it generally has to be at about the 80s. But my point is that it's somewhat of a false dichotomy. Herd immunity is a sliding scale. Someone chose an arbitrary point to say that it happens or it doesn't happen. But there still is an effect at any size. IANAD, but I would expect a 60% reduction would still be enough for a significant amount of the disease to be prevented in the non-immune population. In fact, I wouldn't be surprised if it was higher. If you vaccinate 90% of the population, then herd immunity can't protect more than the remaining 10%.
You can treat herd immunity as a sliding scale, but you can treat it as a hard threshold as well.
In the hard threshold sense it means that if you infect a random individual in the immune herd, the disease does not spread. It might infect a few other people, but it will not spread throughout the entire (non-immunized) herd, it will die out locally without any need for a quarantine.
Mathematically, you need a model that describes how the disease spreads in a given population. Plug in the numbers and calculate the expected number of people infected by a sick person. If it's greater than 1, the disease will spread, if it's less then 1, the disease will die out locally and the herd is immune.
The spreading of deseases sounds like it would be modeled quite well using Percolation Theory, although on the applications page there is mention but no explanation of epidemic spread.
The interesting thing about percolation theory is that in that model both DanielLC and Lumifer would be right: there is a hard cutoff above which there is zero* chance of spreading, and below that cutoff the chance of spreading slowly increases. So if this model is accurate there is both a hard cutoff point where the general population no longer has to worry as well as global benefits from partial vaccination (the reason for this is that people can be ordered geographically, so many people will only get a chance to infect people that were already infected. Therefore treating each new person as an independent source, as in Lumifer's expected newly infected number of people model, will give wrong answers).
*Of course the chance is only zero within the model, the actual chance of an epidemic spread (or anything, for that matter) cannot be 0.
I think percolation theory concerns itself with a different question: is there a path from starting point to the "edge" of the graph, as the size of the graph is taken to infinity. It is easy to see that it is possible to hit infinity while infecting an arbitrarily small fraction of the population.
But there are crazy universality and duality results for random graphs, so there's probably some way to map an epidemic model to a percolation model without losing anything important?
The main question of percolation theory, whether there exists a path from a fixed origin to the "edge" of the graph, is equivalently a statement about the size of the largest connected cluster in a random graph. This can be intuitively seen as the statement: 'If there is no path to the edge, then the origin (and any place that you can reach from the origin, traveling along paths) must be surrounded by a non-crossable boundary'. So without such a path your origin lies in an isolated island. By the randomness of the graph this statement applies to any origin, and the speed with which the probability that a path to the edge exists decreases as the size of the graph increases is a measure (not in the technical sense) of the size of the connected component around your origin.
I am under the impression that the statements '(almost) everybody gets infected' and 'the largest connected cluster of diseased people is of the size of the total population' are good substitutes for eachother.
In something like the Erdös-Rényi random graph, I agree that there is an asymptotic equivalence between the existence of a giant component and paths from a randomly selected points being able to reach the "edge".
On something like an n x n grid with edges just to left/right neighbors, the "edge" is reachable from any starting point, but all the connected components occupy just a 1/n fraction of the vertices. As n gets large, this fraction goes to 0.
Since, at least as a reductio, the details of graph structure (and not just its edge fraction) matters and because percolation theory doesn't capture the idea of time dynamics that are important in understanding epidemics, it's probably better to start from a more appropriate model.
Maybe look at Limit theorems for a random graph epidemic model (Andersson, 1998)?
And indeed the table you mention does shows ranges rather than points. But even the bottom of those ranges are far above 60%.
How do you know there is no phase transition?
Retracted after reading Kyre's comment that what applies to measles doesn't necessarily apply to flu.