Proximity-based modelling of cross-contamination through agent-based simulation: a feasibility study

Abstract

Proximity-based modelling methodology enables mathematical representation of a real system that is characterised by the existence of entities that come into physical contact. Healthcare systems can benefit from this methodology since physical proximity between entities (e.g., patients, clinical items like surgical equipment and blood units) can result in the spread of infectious diseases and cross-contamination. The existing analytical techniques, which are mainly based on differential equations, are unsuitable for representing the fine-grained, micro-world view of the entity interactions that we intend to model. We therefore extend Agent-Based Simulation (ABS) by enabling individual agents to be aware of the physical location of the other agents being modelled in a 3-dimensional space – this is a perquisite for our proximity-based modelling methodology. To demonstrate the feasibility of our approach, we experiment with a scenario wherein boxes of degradable clinical items, modelled as agents, are stored in close proximity. We use Cutting and Packing Optimisation (CPO) algorithms from literature to define the arrangement of these agents in the 3-D space and to make the individual agents ‘location-aware’. An ABS model then simulates cross-contamination by modelling the spread of contaminants among the agents confined in the well-defined space. Our approach can be used to model analogous situations wherein physical proximity between entities in the underlying system is a necessary condition for entity interactions.

Keywords:

• agents
• agent-based simulation
• container loading algorithm
• cutting and packing optimisation
• cross-contamination
• proximity modelling