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Computer Modeling Failed During the Ebola Outbreak
the_newsbeagle writes: Last fall, the trajectory of the Ebola outbreak looked downright terrifying: Computational epidemiologists at Virginia Tech predicted 175,000 cases in Liberia by the end of 2014, while the CDC predicted 1.4 million cases in Liberia and Sierra Leone. They were way off. The actual tally as of January 2015: A total of 20,712 cases in Guinea, Liberia, and Sierra Leone combined, and in all three countries, the epidemic was dying down. But the modelers argue that this really wasn't a failure, because their predictions served as worst-case scenarios that mobilized international efforts.
I've been involved in contracts that had public health modeling components. Being "way off" is not necessarily a proof the model is no good when you're modeling a chaotic process which depends on future parameters that aren't predictable. In our case it was the exact timing of future rainfall. In their case it probably had to do with human behavior. A small thing, like an unseasonable rainstorm, or an infected person showing up in an unexpected place, can have immense consequences.
You look at all the data you have, and you think, "Hey, this is a lot of data, I should be able to predict stuff from it," but the truth is while it looks like a lot of data it's a tiny fraction of all the data that's out there in the world -- and not even a representative sample. So you have to guess "plausible" values, and if they're wrong you might not see the kind of result that eventually happens, even after many model runs.
So in most cases you can't expect a computer model to have the power to predict specific future events. It can do other things, like generate research questions. One of our models suggested that having a lot of infected mosquitoes early in the season reduced human transmission of a certain mosquito borne disease later in the season, which was a surprising result. When we looked at it, it turned out that the reason was that the epidemic peaked in the animal population early in the season before people were out doing summer stuff and getting bit. Does that actually happen? We had no idea, but it sounded plausible. The model didn't give us any answers, it generated an interesting question.
Post may contain irony: discontinue use if experiencing mood swings, nausea or elevated blood pressure.
To quote my beloved Bryan Caplan (http://econlog.econlib.org/archives/2015/06/the_sum_of_all.html):
"I didn't think there was anything more to say about infamous doomsayer Paul Ehrlich. Until he decided to justify his career to the New York Times. Background:
'No one was more influential -- or more terrifying, some would say -- than Paul R. Ehrlich, a Stanford University biologist... He later went on to forecast that hundreds of millions would starve to death in the 1970s, that 65 million of them would be Americans, that crowded India was essentially doomed, that odds were fair "England will not exist in the year 2000." Dr. Ehrlich was so sure of himself that he warned in 1970 that "sometime in the next 15 years, the end will come." By "the end," he meant "an utter breakdown of the capacity of the planet to support humanity."'
Okay, here's Ehrlich's side of the story:
After the passage of 47 years, Dr. Ehrlich offers little in the way of a mea culpa. Quite the contrary. Timetables for disaster like those he once offered have no significance, he told Retro Report, because to someone in his field they mean something "very, very different" from what they do to the average person.
In the video interview, Ehrlich elaborates:
'I was recently criticized because I had said many years ago, that I would bet that England wouldn't exist in the year 2000. Well, England did exist in the year 2000. But that was only 14 years ago... One of the things that people don't understand is that timing to an ecologist is very very different than timing to an average person.'"
There could have been incorrect accounting for the effectiveness of education or news efforts. The medical personnel may have improved faster than predicted.
The educational effort was far more effective than expected. Medical personnel had very little to do with it. Ebola was stopped by convincing people that they should wash their hands with soap. That turned out to be easier than expected, in spite of low literacy rates.