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.

Comments

Re:wrong is right

[Anonymous Coward]

Well, IF there hadn't been a very robust response, it could easily have been that bad.

Not surpising.

[hey!]

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.

Re:wrong is right

[silentcoder]

This is almost a cliche, it's exactly what happened after Y2K. We saw a potential threat, a huge one, and a way to prevent it. We mustered great resources to prevent it - and succeeded. But unlike in the movies those who prevented the threats were not celebrated - immediately afterwards they were accused of having made up the threat to justify the resources.

It's a fundamentally stupid failure of logic, but it happens over and over. If you manage to prevent a threat from realizing, people claim the threat was never real.