About two-thirds of the 2,387 people who’d contracted the disease before this year’s outbreak died worldwide. The fatality rate of more than 14,000 people who have been infected with Ebola in West Africa this year is 71 percent. But eight of the nine people who have been treated for the disease in the U.S. have recovered and been released from the hospital. One person has died.
The question addressed in the article is then: how to aggregate this information?
Bayesian statistics seemed like a promising option for estimating U.S. mortality, because it provides a framework for updating prior informed belief (mortality rate in prior outbreaks in the U.S. and elsewhere) with new information (the lower mortality rate in the U.S. in this outbreak).
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Tony O’Hagan, emeritus professor of statistics at the University of Sheffield in the U.K., (...) started with the assumption that the Ebola mortality rate in the U.S. would be 30 percent, about half that in Africa — peppered with a liberal amount of uncertainty because it was essentially an educated guess. Then once he factored in the eight recoveries in nine U.S. cases, his estimate was of a mortality rate of 17 percent.
Waller offered some ideas for how to refine the analysis. What we want is a model that takes into account the individual attributes of each case when estimating the likelihood of death. Those attributes include age and health of the patient, time from first symptoms to start of treatment, training of medical staff and treatments used. What factors led to lower mortality in the U.S., and which can be replicated in the West African countries with climbing caseloads?
Building such a model would require detailed data not just on the nine U.S. patients, but on as many Ebola patients worldwide as possible. That data isn’t always collected and compiled in a usable way, though — especially in an emergency treatment setting.
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