Inference and control of the nosocomial transmission of methicillin-resistant Staphylococcus aureus

Pei, Sen; Morone, Flaviano; Liljeros, Fredrik; Makse, Hernán; Shaman, Jeffrey L

Asymptomatic colonization is a major issue hindering the control and elimination of MRSA in hospitals (Cooper et al., 2004a) . Screening can identify colonized patients and evaluate the general colonization burden; however, it is an inefficient and costly measure that wastes resources that otherwise could be used to solve more urgent problems. As shown above, given the heterogeneity of contact among patients, levels of exposure to the hazard of colonization differ substantially. As a result, more efficient intervention strategies can be designed that leverage this individual-level heterogeneity. In Figure 4A , we display the inferred distribution of colonized patients in the Swedish hospitals over time. Colonized patient numbers peak in the middle of the record and decline thereafter. To determine who and where these high-risk individuals reside within the network, we can use the agent-based model to quantify colonization risk at the individual level. The distribution of individual colonization probability at T = 40 (week 160), generated from 10 4 simulations using inferred parameters, is displayed in Figure 4B . A clear heavy-tailed power-law distribution y / x À2:13 emerges in which the colonization probability spans several orders of magnitude (see Figure 4 -figure supplement 1 and Appendix 1 for a rigorous statistical analysis of this distribution) (Clauset et al., 2009; Muchnik et al., 2013) . The complex spatiotemporal interaction patterns within the network give rise to a small number of patients with a disproportionately high risk of colonization. To examine how these individuals distribute among hospitals, we visualize the colonization probability in Figure 4C . High-risk patients tend to appear in densely connected clusters.