Using Statistical Modeling to Control Staph Outbreaks


MRSA – which stands for methicillin-resistant Staphylococcus aureus — is a type of staph infection that’s resistant to antibiotics. It’s easily spread by human contact, or by touching an object that has the bacteria on its surface.

Some people who’ve been “colonized” by the bacteria are asymptomatic. In other words, they carry MRSA without getting sick, but they can transmit it to others. If these carriers bring MRSA from the community into healthcare settings, the germs can quickly spread. Healthcare workers can also unwittingly transmit the bacteria from one patient to another.

A recent study published in eLife showed that computer modeling “can help identify the role (that) MRSA infections from the community play in hospital outbreaks and test ways to control them.” Authors included Flaviano Morone and Professor Hernan Makse of The City College of New York and The Graduate Center, CUNY, along with colleagues at Stockholm University and Columbia University. Lead author Sen Pei is at Columbia’s Mailman School of Public Health.

The researchers analyzed data from MRSA outbreaks at 66 Swedish hospitals over six years. Statistical modeling inferred key features of the outbreak over time, estimating the number of MRSA infections that occurred within the hospital, versus the number of those who acquired it in the community but brought it into the hospital.

The model simulations were then checked against what actually occurred. In the real world, bacteria “gradually invaded the hospital system from the community” in the first year. As the number of infected and colonized patients rose, so did in-hospital infections. Discharging asymptomatically colonized patients may have led to further transmission outside hospitals, spurring yet another round of colonization and infection. The computer simulations turned out to be “highly correlated” with what really happened.

The study used computer modeling to test what would have happened if “high-risk individuals had received interventions” to prevent the in-hospital spread of MRSA. The study found that targeting fewer than 1% of all patients using this system could avert up to 38% of infections.

The authors cautioned, however, that the strategy “must first be tested in isolated, real-world settings to verify they work before they can be deployed broadly.”

Beyond SUM

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Hernan A. Makse (Professor, Physics) | Profile 1 | Profile 2
Flaviano Morone (Theoretical Physicist) | Profile 1