PCOMPBIOL-D-26-00130

Accounting for the long-distance transmission route: an epidemiological model of airborne disease transmission in hospitals

PLOS Computational Biology

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PLOS Computational Biology

Thomas Leitner

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PLOS Computational Biology

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At this stage, the following Authors/Authors require contributions: Julien Derdevet, Olivier Gaufrès, Solen Kernéis, Quentin J. Leclerc, Lulla Opatowski, George Shirreff, Laura Temime, and Maylis Layan. Please ensure that the full contributions of each author are acknowledged in the 'Add/Edit/Remove Authors' section of our submission form.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: This study uses an agent-based modeling framework to evaluate the roles of close-proximity interactions and aerosol-mediated transmission in healthcare-associated infections (HAIs) in ICUs. The authors use real-world proximity data to generate synthetic contact networks and simulate the transmission of a pathogen with characteristics similar to SARS-CoV-2. They further evaluate several interventions and rank their effectiveness under different transmission scenarios. Aerosol-mediated transmission poses a significant challenge for HAI control, so this study addresses an important and timely question. Overall, the technical approach is sound and the findings are clearly presented. Below are several comments and questions that I hope will help further improve the manuscript.

1. In the simulation design, pathogen spread in the ICU is treated as occurring within a closed system by introducing a single infection at the beginning of the simulation. This design overlooks an important component of HAI dynamics—the interaction with the community. This issue is particularly relevant for SARS-CoV-2, which continues to circulate widely in the general population. Healthcare workers (HCWs) interact with family members and other community contacts, and newly admitted patients may introduce infections into the ICU. It seems feasible for the model to incorporate such external introductions without major changes to the model structure.

2. The contact data were collected over a 36-hour period. This short sampling window may miss systematic temporal patterns in hospital operations, such as weekly variations in patient volume (e.g., lower patient numbers during weekends). It would be helpful to discuss whether this limitation could affect the representativeness of the simulated contact networks and the robustness of the results.

3. Some details about the simulation setup are unclear. In particular, what time step was used in the simulations (e.g., minutes, hours, or days)? Close contacts can vary substantially in duration. The choice of time step could influence the calculation of the force of infection. For example, if the model was integrated at an hourly time step, how were short contacts lasting only a few minutes (possibly occurring multiple times within an hour) accounted for? Clarification of this issue would help readers better understand the simulation framework.

4. Intervention parameters were selected from the literature. Masking was assumed to reduce transmission rates by 70%, which may be somewhat optimistic. Under this assumption, universal masking appears capable of controlling nearly all simulated outbreak scenarios. However, real-world experience suggests that masking alone has often been insufficient to fully control SARS-CoV-2 transmission. A sensitivity analysis using lower masking effectiveness would strengthen the robustness of the conclusions.

5. In the simulations, transmission rates were varied to generate different scenarios. It would be helpful to clarify how these transmission rate settings relate to the basic reproduction number (R₀). Providing a mapping between the model parameters and implied transmissibility would help readers better interpret the simulated scenarios.

6. In the generated contact networks, HCWs account for the majority of individuals. In real ICU settings, when HCWs are identified as infected, protocols typically remove them from patient care to reduce risk to patients. While such reactive interventions may be less relevant for pathogens with frequent asymptomatic carriage (e.g., antimicrobial-resistant bacteria), they are relevant for SARS-CoV-2. A discussion of how such policies might influence transmission dynamics in the modeled setting would be valuable.

7. The use of the term “long-range” transmission may not be entirely accurate in the context of the model. Aerosol transmission in the simulations appears to occur within the same room rather than over large spatial distances. Its main effect is to allow transmission among individuals who do not have direct spatiotemporal overlap in close contact. Clarifying the intended meaning of this term would improve precision.

Reviewer #2: Please find my review attached.

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Reviewer #1: No: Interaction data from the Nods-Cov-2 project are not publicly available but can bemade available from the corresponding author on reasonable request. All codes and synthetic contact data necessary to perform the simulation study are available online at https://github.com/MESuRS-Lab/aerosol-epi-model.

Reviewer #2: No: The code was made available but not the data. I recommend including synthetic data in the publication.

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Reviewer #1: No

Reviewer #2: Yes: Thi Mui Pham

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Submitted filename: peer_review.docx

Dear Miss Layan,

We are pleased to inform you that your manuscript 'Accounting for the long-distance transmission route: an epidemiological model of airborne disease transmission in hospitals' has been provisionally accepted for publication in PLOS Computational Biology.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology.

Best regards,

Edward M. Hill

Academic Editor

PLOS Computational Biology

Thomas Leitner

Section Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: My questions have been addressed in the revision.

Reviewer #2: The authors have addressed all of my concerns. I don't have any further comments.

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: None

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: Yes: Thi Mui Pham