C. Raina MacIntyre

Haley Stone, C. Raina MacIntyre, Mohana Kunasekaran et al.

Understanding infectious disease transmission in institutional settings requires models that capture how contacts arise from structured routines, roles, and spatial constraints. In aged care facilities, interactions are driven by care delivery, staff scheduling, and resident mobility, producing patterns that differ from those assumed in population-level models. This study develops an agent-based framework to generate high-resolution contact matrices by simulating task-driven behaviour, staff workflows, and movement through shared spaces. Rather than prescribing contacts, interactions emerge from scheduled activities and proximity during task execution. The model is parameterised using activity-diary data from aged care workers and separates behavioural logic from physical layout, enabling adaptation to different facility designs without altering core mechanisms. Results show strong heterogeneity in contact patterns across care levels and staff shifts. Low and medium care residents had higher contact frequencies than high care residents, while day and afternoon staff shifts accounted for most resident-staff interactions. Contacts clustered around daily routines such as meals and communal activities. Incorporating a proximity-based airborne transmission component showed that risk was concentrated during high-contact shifts and among more mobile residents. Vaccination scenarios substantially reduced predicted transmission, with the greatest impact when both staff and residents were vaccinated. By linking organisational processes to emergent contact structure, this framework provides a reproducible approach to contact matrix generation for institutional settings, supporting more realistic transmission modelling and evaluation of targeted infection control strategies.

Yonchanok Khaokaew, Flora D. Salim, Andreas Züfle et al.

Generative agents have been increasingly used to simulate human behaviour in silico, driven by large language models (LLMs). These simulacra serve as sandboxes for studying human behaviour without compromising privacy or safety. However, it remains unclear whether such agents can truly represent real individuals. This work compares survey data from the Understanding America Study (UAS) on healthcare decision-making with simulated responses from generative agents. Using demographic-based prompt engineering, we create digital twins of survey respondents and analyse how well different LLMs reproduce real-world behaviours. Our findings show that some LLMs fail to reflect realistic decision-making, such as predicting universal vaccine acceptance. However, Llama 3 captures variations across race and Income more accurately but also introduces biases not present in the UAS data. This study highlights the potential of generative agents for behavioural research while underscoring the risks of bias from both LLMs and prompting strategies.