Panoramic view of the John Radcliffe Hospital infection networks

Within a hospital, infectious disease can potentially pass from person-to-person through close contact. Without comprehensive data on actual contacts between different patients (contact ‘networks’), it is reasonable to assume that there is an increased chance of transmission of an infection between patients on the same ward, and that this chance would be even higher if patients are in beds next to each other. The probability of person-to-person transmission may also be influenced by multiple factors such as length of contact time, health of individual, age, comorbidities and other factors. The aim of this analysis is to look at the history of which wards and beds (where available) patients have been in within the OUH Trust in order to generate a longitudinal network model. These network models are most famous from applications to Facebook and other social networks, but can also be used with other types of data like this. Adding this time and space data to information about what infections patients had and their clinical records will let us investigate how infections move around hospitals. They also provide a basis for computer simulations that can help explore how different types of prevention measures might work.