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

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

The transmission dynamics generated using the agent-based model are intrinsically stochastic, that is, the observed record of UK EMRSA-15 infections is just one realization among an ensemble of all possible outcomes of an underlying highly stochastic process. In order to evaluate the general risk of MRSA transmission, key epidemiological parameters were inferred from the single observed realization. Previous studies have developed methods to infer transmission risk factors and reconstruct transmission paths using individual-level infection data for diseases such as H1N1 and MERS-CoV (Cauchemez et al., 2011; Cauchemez and Ferguson, 2012; Cauchemez et al., 2016) . In particular, Bayesian data augmentation approaches have been applied to MRSA models (Forrester et al., 2007; Kypraios et al., 2010) ; however, these approaches are not readily applicable to our dataset. The data assimilation scheme we developed here enables estimation of epidemiological parameters and key transmission information using aggregated incidence data. As demonstrated in the retrospective control experiment, assessment of individual colonization risk using aggregated data can be quite useful for preventing future MRSA transmission, especially when stealth importations are frequent.