Identifying asymptomatic spreaders of antimicrobial-resistant pathogens in hospital settings
Sen Pei, Fredrik Liljeros, Jeffrey Shaman
Abstract
Significance Healthcare-associated infections caused by antimicrobial-resistant agents are hard to eliminate in hospitals partly because of the existence of asymptomatic spreaders who unwittingly transmit these pathogens to others. In practice, identifying asymptomatic patients colonized with antimicrobial-resistant agents is challenging, as only a limited number of carriers are typically observed. Here, we develop an efficient, individual-level inference method capable of estimating the colonization probability for each individual in a hospital network. Using real-world patient-to-patient contact networks and sparse observations of colonization, the proposed method identifies carriers of methicillin-resistant Staphylococcus aureus , a prevalent antimicrobial-resistant pathogen, more accurately than competing approaches informed by hospitalization history and contact tracing. In in silica control experiments, the individual-level inference supports improved, targeted interventions against healthcare-associated infections.