Accounting for the long-distance transmission route: An epidemiological model of airborne disease transmission in hospitals
Abstract
Nosocomial transmission of respiratory infections poses a major threat to patient safety, while also affecting healthcare workers’ (HCW) health, generating substantial costs for hospitals. These infections spread through both close-proximity interactions at short distances, and via aerosols that remain suspended in the air, enabling long-range transmission within a room when a susceptible individual is at a distance from an infectious individual. The relative contribution of each transmission route is pathogen-dependent. However, models distinguishing them remain scarce, limiting the design of effective intervention strategies. Here, we propose a novel agent-based stochastic model of respiratory pathogen transmission in a hospital ward that integrates both transmission routes together with contact patterns and individual movements. After informing our model with real close-proximity interaction data collected in two French intensive care units, we simulate a range of combinations of short- and long-range transmission levels to investigate their differences. Selecting parameter values that keep overall ward transmission intensity stable across combinations, the model is used to further evaluate the impact of intervention strategies on incidence risk. We find that the predominance of one route over another has little effect on overall outbreak dynamics, though the impact across individuals varies markedly. Patients are mostly at risk of short-range transmission from HCWs, while HCWs are mostly affected by whichever route is predominant. This directly influences intervention effectiveness. Universal masking emerges as the most effective strategy, reducing both transmission routes. Its stringency can be relaxed with limited loss of effectiveness when combined with ventilation in relevant rooms. Importantly, interventions targeting HCWs, notably ventilation in rooms not accessible to patients, indirectly reduces incidence in patients, with a stronger effect when coupled with relaxed masking interventions. Finally, intervention ranking remains robust across parameter values, as confirmed by a sensitivity analysis. This new model highlights the importance of explicitly considering physical mechanisms of transmission, and the need for interventions that remain effective irrespective of pathogen characteristics and ward organization.
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BibTeX
@article{gaufres2026accounting,
title = {{Accounting for the long-distance transmission route: An epidemiological model of airborne disease transmission in hospitals}},
author = {Olivier Gaufrès and Quentin Leclerc and Julien Derdevet and George Shirreff and Solen Kernéis and Lulla Opatowski and Laura Temime and Maylis Layan},
journal = {PLoS Computational Biology},
year = {2026},
month = aug,
doi = {10.1371/journal.pcbi.1014564},
url = {https://hal.science/hal-05795274v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Accounting for the long-distance transmission route: An epidemiological model of airborne disease transmission in hospitals
AU - Olivier Gaufrès
AU - Quentin Leclerc
AU - Julien Derdevet
AU - George Shirreff
AU - Solen Kernéis
AU - Lulla Opatowski
AU - Laura Temime
AU - Maylis Layan
PY - 2026
DA - 2026/08/03
JO - PLoS Computational Biology
DO - 10.1371/journal.pcbi.1014564
UR - https://hal.science/hal-05795274v1
AB - Nosocomial transmission of respiratory infections poses a major threat to patient safety, while also affecting healthcare workers’ (HCW) health, generating substantial costs for hospitals. These infections spread through both close-proximity interactions at short distances, and via aerosols that remain suspended in the air, enabling long-range transmission within a room when a susceptible individual is at a distance from an infectious individual. The relative contribution of each transmission route is pathogen-dependent. However, models distinguishing them remain scarce, limiting the design of effective intervention strategies. Here, we propose a novel agent-based stochastic model of respiratory pathogen transmission in a hospital ward that integrates both transmission routes together with contact patterns and individual movements. After informing our model with real close-proximity interaction data collected in two French intensive care units, we simulate a range of combinations of short- and long-range transmission levels to investigate their differences. Selecting parameter values that keep overall ward transmission intensity stable across combinations, the model is used to further evaluate the impact of intervention strategies on incidence risk. We find that the predominance of one route over another has little effect on overall outbreak dynamics, though the impact across individuals varies markedly. Patients are mostly at risk of short-range transmission from HCWs, while HCWs are mostly affected by whichever route is predominant. This directly influences intervention effectiveness. Universal masking emerges as the most effective strategy, reducing both transmission routes. Its stringency can be relaxed with limited loss of effectiveness when combined with ventilation in relevant rooms. Importantly, interventions targeting HCWs, notably ventilation in rooms not accessible to patients, indirectly reduces incidence in patients, with a stronger effect when coupled with relaxed masking interventions. Finally, intervention ranking remains robust across parameter values, as confirmed by a sensitivity analysis. This new model highlights the importance of explicitly considering physical mechanisms of transmission, and the need for interventions that remain effective irrespective of pathogen characteristics and ward organization.
SN - 1553-734X
SN - 1553-7358
ER -