A computer-based model could help hospitals control outbreaks of methicillin-resistant Staphylococcus aureus and identify MRSA patients who don't show symptoms, according to a study published in eLife.
The research team, led by scientists at the Columbia University Mailman School of Public Health in New York City, created a computer model of MRSA outbreaks using over 2 million admission records from 66 hospitals in Sweden. The records represent a six-year period.
The model recreated outbreaks of the most prevalent MRSA strain, UK EMRSA-15, which is present in 16 countries, the U.S. included. Harnessing statistical techniques used in weather forecasting, the model simulates infection transmission within hospitals and infections imported from the community.
The model estimated as many as 400 asymptomatic MRSA cases per month in the Swedish hospitals and found up to 61 percent of MRSA infections diagnosed in the hospital setting came from the community.
The MRSA simulation also calculates a patient's chances of being infected. The researchers tested the value of these chances by simulating a treatment intervention for high-risk patients.
The researchers' targeted intervention was better at controlling an outbreak than current practices, which involve either treating patients who have spent the most time in hospital, treating patients with the most contacts in the hospital, or using contact-tracing to treat patients exposed to another patient with the infection.
"Compared with traditional intervention strategies that may overlook a considerable number of invisible colonized patients, this new model-inference system can identify a pivotal group for treatment, namely individuals who may otherwise transmit MRSA asymptomatically," said researcher Sen Pei.
The researchers plan to apply their system to other antimicrobial-resistant pathogens and settings with a higher burden of disease.