Specialists revealed that flaws in computer modelling led to apocalyptic forecasts of how the deadly Ebola virus would spread in West Africa. Many of the models were off-the-shelf software that failed to take into account complexities and uncertainties in the way the disease spread in Guinea, Sierra Leone and Liberia, they said on Tuesday.
"In the early days of the Ebola outbreak, a lot of people got into the forecasting business," said Aaron King of the University of Michigan, who led a probe into why so many predictions turned out to be wildly wrong. "They did it using appealingly simple mathematical models, and the result was a series of warnings that alerted the world, quite rightly to the seriousness of the situation. But in the end, most of those predictions turned out to be overstated."
Last September, the US Center for Disease Control and Prevention (CDC), using computer modelling, said that Liberia and Sierra Leone could see up to 1.4 million Ebola cases by January 2015 without a major intervention. An international aid operation cranked into higher gear, and the relentless growth of Ebola cases stopped.
The outbreak has killed 10,400 people in the three countries since it began in Guinea in December 2013, according to the UN's World Health Organization (WHO).
"Those predictions proved to be wrong, and it was not only because of the successful intervention in West Africa," King said. "It's also because the methods people were using to make the forecasts were inappropriate."
The probe, published in the British scientific journal Proceedings of the Royal Society B
, pointed the finger at so-called deterministic models. Unlike rival software, known as stochastic models, these do not take into account random elements and uncertainty, including variables in how the virus is transmitted from person to person.
Many Ebola forecasters used these deterministic models to crunch out forecasts, using as a baseline the number of cases that had accumulated since the start of the outbreak. The result was that predictions were overblown, and the world was not told of the uncertainties in them, the study said.
"Deterministic models are easier and faster to work with, and the results look pretty good," King said in a press release issued by his university. "But when you use them, it's a double whammy. Not only are you wrong, you are very sure that you are right."