Generating a Contact Matrix for Aged Care Settings in Australia: an agent-based model study

2025-12-03

AI Summary

This study addresses the need for high-fidelity modeling of infection transmission risk in Australian residential aged care facilities (RACFs). Method: We developed the first high-resolution agent-based model (ABM) integrating multi-tiered resident care needs, staff shift scheduling, and spatial behavioral patterns. Contact networks between staff and residents were derived at spatiotemporal granularity using 1.5/3-meter proximity thresholds, cumulative contact duration, Poisson regression, and temporal clustering; an airborne transmission dynamics module was incorporated to quantify infection risk. Contribution/Results: The model is the first to systematically characterize heterogeneity in contact patterns driven by interactions between care levels and shift schedules—revealing highest contact frequency between low-to-moderate-care residents and day-shift staff, and peak high-risk contacts during communal activities. Full staff vaccination reduced transmission risk by 68%. These findings inform targeted interventions focused on high-risk time windows and vulnerable resident cohorts.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

This study presents an agent-based model (ABM) developed to simulate staff and resident interactions within a synthetic aged care facility, capturing movement, task execution, and proximity-based contact events across three staff shifts and varying levels of resident care. Contacts were defined by spatial thresholds (1.5 m and 3 m) and cumulative duration, enabling the generation of detailed contact matrices. Simulation results showed that low and medium care residents experienced the highest frequency of interactions, particularly with staff on morning and afternoon shifts, while high care residents and night staff had substantially fewer contacts. Contact rates varied significantly by care level and shift, confirmed through Poisson-based regression modelling. Temporal analyses revealed clustering of high-risk contacts during structured daily routines, especially communal and care activities. An integrated airborne transmission module, seeded with a single infectious staff member, demonstrated that infection risk was highest during high-contact shifts and among medium care residents. Vaccination scenarios reduced predicted transmission by up to 68%, with the greatest impact observed when both staff and residents were vaccinated. These findings highlight the importance of accounting for contact heterogeneity in aged care and demonstrate the utility of ABMs for evaluating targeted infection control strategies in high-risk, enclosed environments.

Problem

• Model staff-resident contacts in aged care facilities using agent-based simulation
• Analyze infection transmission risks across different care levels and work shifts
• Evaluate vaccination strategies to reduce airborne disease spread in enclosed settings

Innovation

• Agent-based model simulates aged care staff-resident interactions
• Contact matrices generated using spatial thresholds and duration
• Integrated airborne transmission module evaluates infection control strategies