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

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

We next applied the inference system to the UK EMRSA-15 incidence data binned every 4 weeks. Because the UK EMRSA-15 transmission parameters are unlikely to remain constant over the entire 6-year outbreak cycle, we inferred model parameters year by year (52 weeks). Beginning with the first year, we ran the IF inference sequentially through each year. Between 2 consecutive years, the inferred results from the previous year were used to initialize the inference system in the next (see Figure 3 -figure supplement 1). In Figure 3A , we present the distributions of the key parameters b, I 0 and C 0 for each year, generated from 100 independent realizations of the IF algorithm. The parameter estimates together with the associated 95% CIs are reported in Table 2 . All parameter values increased in the first 3 or 4 years, and then gradually decreased thereafter. The inferred parameters can be plugged back into the model to run simulations and obtain information addressing our questions of interest (see Video 1 for an example). For instance, we performed 1000 model simulations using the inferred mean parameter values, and generated distributions of incidence from the stochastic agent-based model. These distributions are compared to observations in Figure 3B . All observations fall within the whisker range of Tukey boxplots (see more analyses in Figure 3 -figure supplement 2). To further explore whether some of the key observed statistics can be reproduced using the inferred parameters, we display the distribution of the number of infected wards in Figure 3C . The observed number lies at the peak of the simulated distribution (vertical dash line). The spatial distribution of infections among different wards can be characterized by the distribution of wards with a certain number of infections in an outbreak. In Figure 3D , we compare this distribution obtained from 1000 simulations with what we observed in the data (red diamonds): the observed distribution agrees well with the The following source data is available for simulated distributions. This close matching indicates that the model structure and inferred parameters can reliably reproduce the observed outbreak pattern in both space and time (see also Figure 3-figure supplement 2). In addition to generating a good model fit, the inference system also discriminates the burdens of nosocomial transmission and infection importation. Nosocomial and imported infections are distinguished by the location of MRSA colonization: if patients acquire MRSA in hospital, they are classified as nosocomial transmission cases; otherwise they are imported cases. Figure 3E compares the distributions of both types of infections generated from 1000 simulations: a substantial number of infections are inferred as importations. In clinical practice, the number of days between hospital admission and infection is usually used to distinguish hospital-acquired from community-acquired infections, typically with 48 hr used as the threshold. We performed this classification and compared the findings with our inference result. As shown in Figure 3 -figure supplement 3, the number of imported and nosocomial cases obtained from inference generally matches the classification result using days from admission to infection.