Infectious hospital agents: A HAI spreading simulation framework

Infectious Hospital Agents (IHA) is an individual-based simulation framework that is able to model wide range of infection spreading scenarios in the hospital environment. The simulations are agent-based simulations driven by stochastic events, the evolution of the model is tracked in discrete time. Our aim was to build a general, customisable and extensible simulation environment for the domain of Hospital-Associated Infections (HAIs). The system is designed in Object Oriented fashion, and the implementation is in C++. In this paper, the authors describe the motivations and the background of the framework, sketch the conceptual framework, and present a demonstration example. © 2017, Budapest Tech Polytechnical Institution. All rights reserved

Infectious Hospital Agents: an individual-based simulation framework

In this paper we present the plan, motivation, background, and the design of an agent-based simulation framework describing the spread of Hospital-Associated Infections (HAIs). We are developing a general simulation environment that is able to model wide range of pathogen transmission scenarios in hospital environment. The elements of the simulation include among others: admission and discharge patients, pathogen transmission via healthcare workers, colonization and infection, modelling hospital events, scheduling treatments, the interventions against HAI spreading. The evolution of the model is tracked in discrete time, and the simulation is driven by stochastic events sampled from predefined distributions. Our aim is to build a general, customisable and extensible simulation environment for the domain of HAIs, therefore the presented design is in Object-Oriented fashion. We implement the system in R using S4 classes, although the design is general. The results of the simulations are time series and transmission networks

The Core Might Change Anyhow We Define It: The Instability of Key Actors in Longitudinal Social Network Data

Central actors or opinion leaders are in the right structural position to spread relevant information or convince others about adopting an innovation or behaviour change. Who is a central actor or opinion leader might be conceptualised in various ways. Widely accepted centrality measures do not take into account that those in central positions in the social network may change over time. A longitudinal comparison of the set and importance of opinion leaders is problematic with these measures and therefore needs a novel approach. In this study, we investigate ways to compare the stability of the set of central actors over time. Using longitudinal survey data from primary schools (where the members of the social networks do not change much over time) on advice-seeking and friendship networks, we find a relatively poor stability of who is in the central positions anyhow we define centrality. We propose the application of combined indices in order to achieve more efficient targeting results. Our results suggest that because opinion leaders may change over time, researchers should be careful about relying on simple centrality indices from cross-sectional data to gain and interpret information (for example, in the design of prevention programs, network-based interventions or infection control) and must rely on more diverse structural information instead