Rats were the carriers in the Black Death.
This has been taught for decades but interestingly a recent study show that may be wrong.
http://www.pnas.org/content/early/2015/02/20/1412887112.abstract
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Professor Nils Christian Stenseth and scientists from the University of Oslo say they've compared tree-ring records from Europe and Asia with more than 7,000 outbreaks of the plague. They conclude that weather during much of that period would be too wet for rats to flourish and carry infected fleas across continents.
But he says wet springs succeeded by warm summers would be a honeymoon atmosphere for another species of vermin.
"Such conditions are good for gerbils," Stenseth told the BBC. "It means a high gerbil population across huge areas, and that is good for the plague.
"If we're right," he added, "we'll have to rewrite history."
But he says wet springs succeeded by warm summers would be a honeymoon atmosphere for another species of vermin.
"Such conditions are good for gerbils," Stenseth told the BBC. "It means a high gerbil population across huge areas, and that is good for the plague.
"If we're right," he added, "we'll have to rewrite history."
In the US the reservoir of Yersinia pestis (the bacteria that caused the black plague) is not rats but prairie dogs.
http://guardianlv.com/2014/07/pneumonic-plague-in-colorado-prairie-dogs-fleas/
No definitive answer yet one way or another but it is at least possible that rats have gotten a bad rap over the last several hundred years.
And why even bother posting that since in any case the carrier was some rodent. It is an irrelevant post and noise.
Edit: (I know the reason you think it is relevant, because you are trying to say that fundamental analysis can be wrong, thus we can't predict anything with certainty, But FA being fallible is precisely why I can trust cyclical models which do not incorporate any FA and instead are entirely based in statistical confidence of the repeatability of a cyclical pattern)
The point is that opportunistic mutations and plague spread with higher probability when society abruptly and collectively radically changes its behavior (because this provides a different host environment to which the human immune systems have not adapted and to which new opportunistic pathogens might fit the new modes of transmission).
War and pestilence of economic decline are one such factor.
But the more important point here, is that you doubt anything you can't understand fundamentally. The reason you are grasping at straws trying to judge my and Armstrong's mental state (which is irrelevant to cyclical model analysis!) is because you being a Thinking-Judging Myers-Briggs personality type do not trust anything you can not fully verify from the root of its fundamentals.
Your error is that cyclical models have their fundamentals based in statistical correlation of repeating cycles over long histories. This is not something a human can map in his mind. The computer must test the zillions of possible correlations and narrow it down to those which have reliability repeated for all of recorded history (since Mesopotamia 6000 B.C.).
Cyclical models don't incorporate any fundamental understanding whatsoever, and thus they aren't subject to the human error of confirmation bias and myopic perspective. Cyclical models are only based on testing those patterns of timing that repeat over and over without fail throughout all human history. Armstrong's computer has backtested all the cyclical correlations it found throughout all human history.
Edit: You want to view the problem space from FA, analogously as you wanted to frame Armstrong's weather prediction in terms of hurricanes or some irrelevant aggregate statistic of annual tornadoes for the entire USA, thus you trying to force the analysis into your cherry picked (arbitrary) bias. Whereas, if you broadened your perception, you would see the logic that cyclical analysis doesn't even incorporate any FA and is verified via statistical confidence. You would see that you will make human errors of assumptions that the computer doesn't make when it tests all possible patterns for repeatable statistical confidence, e.g. you wanting to test only the pattern of East coast hurricanes or the aggregate of annual continental tornadoes.
Until you grasp that cyclical models can predict anything that repeats with a cyclical pattern, then you will continue to be lost.
If you deny that pandemics follow a cyclical pattern, then you are saying recorded history is a lie.
http://www.popsci.com/warmer-weather-asia-meant-black-death-europe-15-years-later
If this turns out to be true it would be one instance where a cyclical model (anything that matched up to warms period in Asia) would accurately predict plague outbreaks.
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researchers found that the plague outbreaks were preceded by warmer temperatures in Asia, while there was no correlation between temperature patterns in Europe. Of course travel between Europe and Asia wasn’t nearly as quick as it is today. The temperature spikes in Asia happened 15 years before an outbreak occurred in Europe, a lag that the researchers attribute to the length of the Silk Road trade routes.
They think the pattern went something like this: Warm weather in Asia caused more fleas and small rodents like gerbils to multiply. Then when cooler weather arrived, the rodent populations tumbled, and the bounty of fleas moved on to new hosts, like camels traveling the famous trade route. After a long journey, the flea population encountered rats, another excellent rodent host, in the harbor towns and stowed away on ships bound for Europe.
They think the pattern went something like this: Warm weather in Asia caused more fleas and small rodents like gerbils to multiply. Then when cooler weather arrived, the rodent populations tumbled, and the bounty of fleas moved on to new hosts, like camels traveling the famous trade route. After a long journey, the flea population encountered rats, another excellent rodent host, in the harbor towns and stowed away on ships bound for Europe.
If this turns out to be true it would be one instance where a cyclical model (anything that matched up to warms period in Asia) would accurately predict plague outbreaks.
Again you are trying to find fundamental reasons that certain phenomena could be cyclical, because you believe on the micro level of analysis that the mutation of viruses is random.
But this is a myopic perspective on your part.
Again refer to my math explanation of why Black Swans are not random.:
Again you make me repeat what I wrote to your before, that what appears to be unpredictable to those who haven't model sufficient historical data to establish the long-term cycles, is not random to those who have collected the necessary data.
If you study Taleb's anti-fragility math (which followed his Black Swan work), it is clear that systems overcommit to error because they lack a holistic perspective.
Taleb is pointing out that they are unseen and that is what makes them Black Swans, but he is saying the reason they are unseen and unpredictable is because the system is structured in such a way that makes it incapable of incrementally adjusting to error. He makes two essential equivalent points (they are duals). One is that bottom-up systems have more degrees-of-freedom and the agents are closer to the error and can adjust more readily (this is the simulated annealing point I have been making over and over again since at least 2010). Secondly, implicit in his math is that if there is a historically repeating pattern then this information would not prevent top-down systems from being unable to adjust to the error that the information makes clear.
...
Point is that fundamental analysis (e.g. climatologists) isn't necessary to recognize repeating patterns which be correlated without any fundamental knowledge. And it turns out that fundamental analysis is often very poor at making predictions, because it fails to account for some factors, e.g. Greenspan not accounting for international capital flows.
If you study Taleb's anti-fragility math (which followed his Black Swan work), it is clear that systems overcommit to error because they lack a holistic perspective.
Taleb is pointing out that they are unseen and that is what makes them Black Swans, but he is saying the reason they are unseen and unpredictable is because the system is structured in such a way that makes it incapable of incrementally adjusting to error. He makes two essential equivalent points (they are duals). One is that bottom-up systems have more degrees-of-freedom and the agents are closer to the error and can adjust more readily (this is the simulated annealing point I have been making over and over again since at least 2010). Secondly, implicit in his math is that if there is a historically repeating pattern then this information would not prevent top-down systems from being unable to adjust to the error that the information makes clear.
...
Point is that fundamental analysis (e.g. climatologists) isn't necessary to recognize repeating patterns which be correlated without any fundamental knowledge. And it turns out that fundamental analysis is often very poor at making predictions, because it fails to account for some factors, e.g. Greenspan not accounting for international capital flows.
