Since the Centers for Disease Control and Prevention confirmed on Oct. 12 that a Texas health care worker tested positive for Ebola, media outlets have reported that health officials are now “scrambling” to find out how she contracted the disease despite wearing protective gear. According to the head of the CDC, the infection was caused by a “breach in protocol” that officials are working to identify.

Public Policy professor John Villasenor argues in an article published on Forbes.com that while working to identify weak links in protocol is important, blaming the health worker for breaching protocol ignores the fact that, statistically, having multiple contacts with Ebola patients will lead to an inevitable “limited number of transmissions to health workers.”

He writes:

“This is because if you do something once that has a very low probability of a very negative consequence, your risks of harm are low. But if you repeat that activity many times, the laws of probability—or more specifically, a formula called the “binomial distribution”—will eventually catch up with you.

For example, consider an activity that, each time you do it, has a 1% chance of exposing you to a highly dangerous chemical. If you do it once, you have a 1% chance of exposure. If you do it twice, your chances of at least one exposure are slightly under 2%. After 20 times, you have an 18% chance of at least one exposure, and after 69 times the exposure probability crosses above 50%. After 250 times, the odds of exposure are about 92%. And the exposure odds top 99% after about 460 times.

In other words, even if the probabilities are strongly stacked in your favor if you do the activity only once, with repetition the probabilities flip against you.”

Villasenor ends his article by offering three recommendations for how to analyze this situation, including avoiding assumptions. You can read the full article here.

In another piece published in Slate on Oct. 15, Villasenor asserts that big data should be used as a “core component of the strategy” to protect health workers from Ebola exposure. Big data and statistical methods are vital in analyzing how Ebola can spread and shouldn’t be treated as an afterthought, he says.

Villasenor urges health officials to collect data about interactions between health workers and Ebola patients, and develop protocol for simulations so that health workers can practice using and removing protective gear.

He concludes:

“Big data and statistics alone aren’t going to keep health workers safe from Ebola. But they can certainly help. If we are going to ask health workers to repeatedly step into rooms with patients contagious with a virus that now appears to have a fatality rate of about 70 percent, we have the obligation to do everything possible to minimize the chances that they might be exposed. And today, we’re not doing nearly enough.”