Quantitative Risk Assessment and Tiered Classification of Indoor Airborne Infection Based on the REHVA Model: Application to Multiple Real-World Scenarios

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The study establishes a dual-metric quantitative risk assessment framework based on the modified Wells-Riley model—the REHVA model—incorporating both the probability of infection and the event reproduction number to overcome the limitations of traditional models. The scope of the research is substantial, with detailed elaboration on the model theory, comprehensive data setup for multiple scenarios, and thorough analysis and discussion of the results. The findings offer a clear framework and practical tools for dynamic, science-based management of airborne infection risks in indoor environments in the post-pandemic era. This work holds significant practical implications for guiding public health policies, optimizing building ventilation design, and implementing precision facility management. However, the paper has several areas that could be improved and refined. The main points are as follows:
1. It is suggested to add a transitional paragraph at the end of Section 2 or the beginning of Section 3 to more explicitly articulate why SIR/SEIR models have limitations when assessing risks in specific indoor spaces.
2. The study proposes a practical five-tier risk classification system. To better highlight its novelty, it is recommended to include a brief comparison in the Introduction or Related Works section between the proposed system and any existing risk classification guidelines issued by public health organizations.
3. The five-tier risk classification thresholds in Table 1 are a core contribution of this study. The paper states that these thresholds are “based on existing literature” but lacks detailed justification for their specific values.
4. For the real-world cases (S1-S5), while data such as space volume, number of occupants, and exposure duration are relatively clear, key environmental parameters like the ventilation rate are often missing from the original reports.
5. The Discussion in Section 6 provides a good summary of the research findings, but it could be more in-depth.
6. The Conclusion mentions future research directions, such as multi-infector scenarios and viral variants. It is suggested that this outlook be made more specific.
7. The paper sets default mask efficiency values of 0.5 for infectious persons and 0.3 for susceptible persons. What is the source of these values?

Author Response

Response to Reviewer 1 Comments

 

Thank you very much for valuable comments from reviewers. We are very grateful for the much improved quality of the revised manuscript based on the comments you have given, and I would like to respond to the comments as follows.

 

<Point 1> It is suggested to add a transitional paragraph at the end of Section 2 or the beginning of Section 3 to more explicitly articulate why SIR/SEIR models have limitations when assessing risks in specific indoor spaces.

Response 1: The limitations of the SIR/SEIR model in assessing infection risk within specific indoor environments have been more clearly articulated, and the following statement has been added immediately before Fig. 1 in Section 2 (Related Works).

 

“In particular, critical environmental parameters such as room volume, ventilation rate, and occupant density are not explicitly incorporated into the model. Moreover, it assumes a homogeneous population, thereby failing to account for spatial heterogeneity such as seating arrangements or airflow patterns, and it has limited capacity to precisely reflect dynamic environmental factors, including temporal variations in ventilation and changes in mask-wearing compliance.”

 

<Point 2> The study proposes a practical five-tier risk classification system. To better highlight its novelty, it is recommended to include a brief comparison in the Introduction or Related Works section between the proposed system and any existing risk classification guidelines issued by public health organizations.

Response 2: At the end of the introduction, the following statement was added to highlight the distinctiveness of this study in providing a quantitative indoor risk assessment compared to existing guidelines.

 

“Existing guidelines primarily focus on epidemic phases (e.g., Alert, Warning, Severe) or population-level risk classifications, and therefore do not provide detailed quantitative indicators at the level of specific indoor spaces. In contrast, the five-tier system proposed in this study utilizes the Probability of Infection and R_event values, offering a distinctive approach in quantitatively assessing indoor risk.”

 

<Point 3> The five-tier risk classification thresholds in Table 1 are a core contribution of this study. The paper states that these thresholds are “based on existing literature” but lacks detailed justification for their specific values.

Response 3: mmediately before Table 1, the following statement was added to provide a more detailed explanation of the criteria-setting process.

 

“The five-tier risk classification thresholds proposed in this study were determined with reference to the allowable risk ranges presented in the REHVA guidelines, and by comparing them with the infection probability intervals reported in studies based on the Wells–Riley model. In addition, the calculated results from the empirical cases analyzed in Chapter 4 (Scenario Design) were applied to adjust each threshold range to a more realistic level.”

 

<Point 4> For the real-world cases (S1-S5), while data such as space volume, number of occupants, and exposure duration are relatively clear, key environmental parameters like the ventilation rate are often missing from the original reports.

Response 4: Prior to Table 2, the following statement was included to describe the estimation method used when ventilation rate values were not explicitly provided.

 

“In some cases, the ventilation rate was not specified in the original report; in such instances, a conservative estimate was made by referencing the legally required minimum ventilation rates for the given building type and citing ventilation data from facilities of the same type.”

 

 

<Point 5> The Discussion in Section 6 provides a good summary of the research findings, but it could be more in-depth.

Response 5: It was determined that an in-depth analysis had already been provided; therefore, only the following statement was added. (The preceding content had already considered factors such as room size, population, floor area, and occupant density in a comprehensive and in-depth manner.)

 

“The proposed five-tier system can quantitatively capture subtle differences in environmental conditions, thereby serving as a more practical reference for infection control decision-making compared to conventional qualitative assessments.”

 

<Point 6> The Conclusion mentions future research directions, such as multi-infector scenarios and viral variants. It is suggested that this outlook be made more specific.

Response 6: The future research directions were further specified in the conclusion as follows.

 

“The future research will consider multi-source infection scenarios by analyzing, in conjunction with CFD simulations, the changes in risk distribution according to the location and number of infection sources within the same space. Furthermore, a robust assessment framework will be developed to address temporal, seasonal, and regional variations by incorporating quanta emission rates that reflect differences in transmission characteristics among virus variants. In addition, an integrated system will be created by linking IoT sensors with an AI-based prediction platform to enable real-time risk level estimation, thereby allowing direct application to infection control measures and facility operations.”

 

<Point 7> The paper sets default mask efficiency values of 0.5 for infectious persons and 0.3 for susceptible persons. What is the source of these values?

Response 7: . If no mask is worn, the value is set to 0, while the default value for a standard mask is 0.5 (Kurnitski et al., 2021).

Reviewer 2 Report

Comments and Suggestions for Authors

Please find attached.

Comments for author File: Comments.pdf

Author Response

Response to Reviewer 2 Comments

 

<Point 1> How is the “Accuracy” reported in Table 3 defined? Please provide a clear definition and methodology for its calculation.

Response 1: The definition of “Accuracy” is described in Table 3. As it is a standard accuracy measure, it is not expected to cause confusion for the reader.

 

“The “Accuracy” reported in Table 3 refers to a relative error–based accuracy, calculated by comparing the observed number of infections with the REHVA model predictions.”

 

<Point 2> In the ten scenarios listed in Table 2, the total number is up to 400. I am curious how robust the model is when it is applied to a much bigger group (e.g., total number above 1000)? I believe a discussion or additional simulation would strengthen the generalizability of the findings

 

Response 2: Scenarios S1–S5 in Table 2 are a comparative analysis of actual events in which infections occurred. While applying the model to large-scale cases, such as those involving 1,000 people, would be a very interesting research topic, it is considered somewhat beyond the scope of the present study. Moreover, in large-scale facilities accommodating around 1,000 people, much stricter and more specialized infection control measures are likely to be implemented compared to standard building-level protocols. Research on such large-scale populations will be carried out in future work.

 

<Point 3> As the authors mention the limitations of some other models including Wells–Riley-based models, SIR model, SEIR model, I suggest authors to apply at least one of these models to the ten scenarios listed in Table 2, and compare the accuracy of these models with the REHVA model. I believe this comparative analysis would provide a stronger basis for the superiority of the proposed framework.

Response 3: A comparison with the Wells–Riley–based model is also a very interesting topic; however, the limitations of the Wells–Riley approach are already well recognized. In the related works section, we have discussed these limitations, and given that they are quite evident, a direct comparison would have limited significance. This, too, will be considered for inclusion in future research.

 

<Point 4> Some typographical and notational errors: In eq (4), there is a misspelling of“Average Concentration”. On the left-hand side of eq (8), it should be “R_event”.

Response 4: The terminology and notation throughout the manuscript have been revised, including the issues pointed out.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The revised manuscript can be accepted.

Reviewer 2 Report

Comments and Suggestions for Authors

I am satisfied with the reply to my previous comments.