Organization is a central concept in systems. In this paper an ant algorithm for detecting organizations is presented. In a discrete-time context, at each time-step, an organization corresponds to a set of closely interacting entities in a system. This system is mapped to a graph where nodes represent entities and edges represent interrelations. Several colonies of ants compete, and inside each colony, ants collaborate in order to colonize the graph. Detected organizations emerge from the global behavior of the ants. The proposed approach is compared to other methods on a graph where the organizations are already known. It is then tested on two real world graphs studied in the related literature.

Keywords: Organization, community, dynamic graph, dynamic network, ant algorithm.

Infections caused by antibiotic-resistant pathogens are a global public health problem. Numerous individual- and population-level factors contribute to the emergence and spread of these pathogens. An individual-based model (IBM), formulated as a system of stochastically determined events, was developed to describe the complexities of the transmission dynamics of antibiotic-resistant bacteria. To simplify the interpretation and application of the model's conclusions, a corresponding deterministic model was created, which describes the average behavior of the IBM over a large number of simulations. The integration of these two model systems provides a quantitative analysis of the emergence and spread of antibiotic-resistant bacteria, and demonstrates that early initiation of treatment and minimization of its duration mitigates antibiotic resistance epidemics in hospitals.

Keywords: Antibiotic-resistant bacteria, Individual-based model, Differential equation model, Basic reproduction number