Targeting MRSA

Computer-modeling helps trace antibiotic-resistant infections in the hospital.
Digest
  • Views 163
  • Annotations

Modeling the risk of carrying MRSA (blue: low risk, red: high risk). Image credit: Pei et al. 2018 (CC BY 4.0)

Antibiotic-resistant bacteria like the Methicillin-resistant Staphylococcus aureus (MRSA) can live in people for many years without making them sick. During this time, the bacteria can spread to others who come in contact with the MRSA-infected person. The number of people with stealth MRSA infections living in the community has been increasing. As a result, hospitals may not only be dealing with MRSA infections that originated onsite, but also cases imported from the community. That makes tracking and controlling MRSA infections in hospitals difficult.

Now, Pei et al. show that computer modeling can help identify the role MRSA infections from the community play in hospital outbreaks and test ways to control them. In the experiments, data from an MRSA outbreak that occurred at 66 Swedish hospitals over 6 years were analyzed using statistical methods and computer modeling. This helped to identify patients who were likely colonized with MRSA within the hospital and those who had acquired it in the community. Next, Pei et al. used computer modeling to test what would have happened if these high-risk individuals had received interventions to prevent them from spreading MRSA in the hospital. This showed that targeting individuals at high-risk of a MRSA infection could reduce the spread of MRSA in the hospital.

The computer models developed by Pei et al. may help researchers, clinicians and public health officials working to control the spread of antibiotic resistant bacteria. The model can improve our understanding of how antibiotic resistant bacteria spread in healthcare facilities and may enable the development of more effective strategies to control these pathogens. Infection-control strategies created with this system must first be tested in isolated, real-world settings to verify they work before they can be deployed broadly.