  Methicillin-resistant Staphylococcus aureus (MRSA) is a difficult-to-treat
infection that only in the European Union affects about 150,000 patients and
causes extra costs of 380 million Euros annually to the health-care systems.
Increasing efforts have been taken to mitigate the epidemics and to avoid
potential outbreaks in low endemic settings. Understanding the population
dynamics of MRSA through modeling is essential to identify the causal
mechanisms driving the epidemics and to generalize conclusions to different
contexts. We develop an innovative high-resolution spatiotemporal contact
network model of interactions between patients to reproduce the hospital
population in the context of the Stockholm County in Sweden and simulate the
spread of MRSA within this population. Our model captures the spatial and
temporal heterogeneity caused by human behavior and by the dynamics of mobility
within wards and hospitals. We estimate that in this population the epidemic
threshold is at about 0.008. We also identify that these heterogeneous contact
patterns cause the emergence of super-spreader patients and a polynomial growth
of the epidemic curve. We finally study the effect of standard intervention
control strategies and identify that screening is more effective than improved
hygienic in order to cause smaller or null outbreaks.
