arXiv subjects

C. Raina MacIntyre

Publications and source records attributed to C. Raina MacIntyre.

4 recordsLinked to original sources

arXiv2026

From Ecological Connectivity to Outbreak Risk: A Heterogeneous Graph Network for Epidemiological Reasoning under Sparse Spatiotemporal Data

Estimating population-level prevalence and transmission dynamics of wildlife pathogens can be challenging, partly because surveillance data is sparse, detection-driven, and unevenly sequenced. Using highly pathogenic avian influenza A/H5 clade 2.3.4.4b as a case study, we develop zooNet, a graph-based epidemiological framework that integrates mechanistic transmission simulation, metadata-driven genetic distance imputation, and spatiotemporal graph learning to reconstruct outbreak dynamics from incomplete observations. Applied to wild bird surveillance data from the United States during 2022, zooNet recovered coherent spatiotemporal structure despite intermittent detections, revealing sustained regional circulation across multiple migratory flyways. The framework consistently identified counties with ongoing transmission weeks to months before confirmed detections, including persistent activity in northeastern regions prior to documented re-emergence. These signals were detectable even in areas with sparse sequencing and irregular reporting. These results show that explicitly representing ecological processes and inferred genomic connectivity within a unified graph structure allows persistence and spatial risk structure to be inferred from detection-driven wildlife surveillance data.

q-bio.PE↗

arXiv2025

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

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.

q-bio.PE↗

arXiv2025

Evaluating the Bias in LLMs for Surveying Opinion and Decision Making in Healthcare

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.

cs.CL↗

arXiv2022

Towards realistic simulations of human cough: effect of droplet emission duration and spread angle

Human respiratory events, such as coughing and sneezing, play an important role in the host-to-host airborne transmission of diseases. Thus, there has been a substantial effort in understanding these processes: various analytical or numerical models have been developed to describe them, but their validity has not been fully assessed due to the difficulty of a direct comparison with real human exhalations. In this study, we report a unique comparison between datasets that have both detailed measurements of a real human cough using spirometer and particle tracking velocimetry, and direct numerical simulation at similar conditions. By examining the experimental data, we find that the injection velocity at the mouth is not uni-directional. Instead, the droplets are injected into various directions, with their trajectories forming a cone shape in space. Furthermore, we find that the period of droplet emissions is much shorter than that of the cough: experimental results indicate that the droplets with an initial diameter $\gtrsim 10μ$m are emitted within the first 0.05 s, whereas the cough duration is closer to 1 s. These two features (the spread in the direction of injection velocity and the short duration of droplet emission) are incorporated into our direct numerical simulation, leading to an improved agreement with the experimental measurements. Thus, to have accurate representations of human expulsions in respiratory models, it is imperative to include parametrisation of these two features.

physics.flu-dyn↗