Risk Assessment and Prevention Strategy of Virus Infection in the Context of University Resumption
Round 1
Reviewer 1 Report
Thank you for a very interesting study that I believe will be of interest to the readers of any journal. There are a number of aspects in this paper that I believe need updating which could lead to changes in the conclusions of the paper. See my comments below in no particular order of significance:
1) There are a number of minor errors in the standard of English in the paper please correct these.
2) Your work focuses on the indoor environment however there are a number of studies referenced in the literature review section that are not as relevant to the indoor environment. As the introduction section is quite large please consider revising the size of it to include only relevant studies.
3) Please rephrase the sentence on Page 4, which refers to outbreaks. There have been numerous outbreaks in all parts of the world since the first outbreak, please make your comments more generalizable.
4) More relevant literature that focuses specifically on class sizes or occupancy levels and infection risk is required in this paper. And more work in referencing this in the discussion section is also required. There have been a number of studies which have already looked at class size and ventilation regimes. A table of relevant literature would be more appropriate if introductory sections on urban environments is reduced
5) Please explain and discuss how the campus is considered representative of other campuses nationally and internationally
6) Please explain your assumptions in more detail
7) Please provide a reference to the NetLOGO software
8) Please provide reasons for the use of different inputs to the Wells-Riely model, these will vary depending on activity
9) Please provide a more solid basis for your probabilities, guidance from REHVA would suggest that risk should be based on secondary infectious or event specific reproductive numbers which gives a more clear insight into the suppression or growth of the virus. The values of 25% to 50% in wells riely probability terms would seem quite high based on a recent publication by Kurnitski in Building and Environment. Again like above I would suggest contextualizing your risk thresholds so they make more sense. This could lead to changes in conclusions.
10) Please describe the details of the buildings in more detail. The current or design ventilation rate is not explained, nor is the volume of buildings. In addition to this the rationale for how the maximum occupancy value was determined is needed. More information in absolute terms (L/s or number of people) is needed as these provide practical implications for the results.
11) Please describe why the ventilation rate was changed in percentage terms as opposed to be based on occupants. Typical sizing of ventilation systems use L/s/p to determine the rate. Is there any practical limitation in existing systems. Please state this also. Can the existing system increase its ventilation rate by 200%?
12) Please indicate the accuracy of the GPS trackers and what uncertainty is likely with these and if there are any likely data quality issues.
13) Please describe what is meant by buildings with "narrow space"
Please increase the size of a number of figures as they were very hard to interpret. For example Table 3 requires more annotation and clarity on what is being visualised. No indication as to what size or colour or what is on x or y axes is presented.
Author Response
Dear Editor and Reviewers,
Thank you for your useful comments and suggestions on our manuscript. We have modified the manuscript accordingly and detailed corrections are listed below.
Comment:
Thank you for a very interesting study that I believe will be of interest to the readers of any journal. There are a number of aspects in this paper that I believe need updating which could lead to changes in the conclusions of the paper. See my comments below in no particular order of significance:
Point 1: There are a number of minor errors in the standard of English in the paper please correct these.
Response 1: We have carefully proofread the languages and correct the errors in the expression of English.
Point 2: Your work focuses on the indoor environment however there are a number of studies referenced in the literature review section that are not as relevant to the indoor environment. As the introduction section is quite large please consider revising the size of it to include only relevant studies.
Response 2: This study quantitatively evaluated the effect of two measures as ventilation intervention and occupancy constraint on the decrease of infection risk in the context of university resumption. Based, the section introduction was mainly developed from the three parts: the review of the influence of population controlling on the development of epidemic; the significance of ventilation on the ensure of indoor air clean and indoor occupant safety; the urgency of establishing the prevention and control evaluation system for the resumption of colleges and universities. The relevant references about the influence of enhancing ventilation on the improvement of indoor environment has been added in the third paragraph of the section introduction. And some literature with little correlation have been removed to cut the size of introduction.
Point 3: Please rephrase the sentence on Page 4, which refers to outbreaks. There have been numerous outbreaks in all parts of the world since the first outbreak, please make your comments more generalizable.
Response 3: The sentences in the text have been carefully corrected to make our comments more generalizable.
Point 4:More relevant literature that focuses specifically on class sizes or occupancy levels and infection risk is required in this paper. And more work in referencing this in the discussion section is also required. There have been a number of studies which have already looked at class size and ventilation regimes. A table of relevant literature would be more appropriate if introductory sections on urban environments is reduced.
Response 4: The relevant literature that focuses specifically on the effect of occupant density on the infection risk have been added in the second paragraph of introduction section as ‘Relevant studies have shown that the spread of the epidemic is closely related to the flow of population in the earliest outbreak site[7]. Based on the exploration of the temporal and spatial transmission mode of the infectious virus, it was found that the epidemic spread from the aggregation of population, and then spread to other sites due to the flow of population[8]. Sari[9] examined the affecting factors of the virus spread in the building and concluded that the occupant density is one of the most important factors in virus transmission. Mokhtari[10] took a university building as case to seek the optimum occupant distribution patterns that account for the lowest number of infected people, and pointed that an population distribution can reduce the number of infected people by up to 56%. Kovesi[11] examined the effect of the population of pupils on respiratory infections in Canadian schools and demonstrated the importance of occupant distribution. Sun[12] proposed a prediction model for infection risk in confined spaces with the consideration of occupant density, ventilation rate and exposure time, the results showed that social distancing is a great approach to decrease the infection risk and also minimizes the required ventilation rate in buildings. ’. Referring to these researches, the relevant discussion has been supplemented from the perspective of occupancy. In addition to the constraint of occupant density, increasing ventilation has proved to be another effective measure to control infection risk in the space. Relevant literature and standards that focuses on the effect of ventilation efficiency on the infection risk have been also added in the third paragraph of introduction section. Besides, since the outbreak of epidemic in the end of 2019, the HAVC associations have developed guidelines for the operation of ventilation system and emphasized the significance of enhancing ventilation efficiency. The concrete guidance have been introduced in the form of table and shown in Table 1.
Point 5: Please explain and discuss how the campus is considered representative of other campuses nationally and internationally.
Response 5: The universality of the case campus has been added in the end of Section 3.3 as ‘Occupants transferred between campus buildings usually for living, studying, meal or other basic activities. The main differences between universities nationally and internationally lie in the climate, teaching characteristic, activity categories, the number of building group and the behavior habits of occupants. The differences between the climate, teaching characteristics, activity categories will not produce an impact on the results of this study. The habit differences are embodied in the occurring time points of typical events. The earliest starting time points, the latest ending time points and the course duration of different universities may vary due to the diversity of sunrise and sunset time in different regions. Influenced by the regional culture, the dining time also varies especially for the dinner time. This paper intends to propose effective prevention and control suggestions for decreasing infection risk of virus based on the occupant distribution characteristics and exposure duration at typical events.Therefore, in the actual process of risk prediction and prevention management, the occurring time points corresponding to above typical events can be moved forward or backward in accordance with the region. The case buildings of this study represent the typical functional buildings in Colleges and universities. The selected case campus could represent the distribution of multiple functional buildings of general university nationally and internationally. ’
Point 6: Please explain your assumptions in more detail
Response 6: This study intends to assess the effect of occupant distribution constraint and building ventilation intervention on the decrease of infection risk, and the effect of other uncertain and uneven factors on research results should be eliminated as much as possible. The location of occupants in the building has randomness, and this will lead to the difference of occupant aggregation degree and indoor air distribution in different areas, however, which are over the research scope of this study. In addition, in order to simplify the research process, it is assumed that the virus in the space will not disappear or die. Therefore, in the process of risk assessment, related assumptions will be made to eliminate the influence of uncertain and uneven factors and create a relatively ideal environment.The detailed explanation of the assumptions have been added in Section 2.1.
Point 7: Please provide a reference to the NetLOGO software
Response 7: The references to the introduction and application about the software Netlogo has been added in the Section 2.2.
Point 8: Please provide reasons for the use of different inputs to the Wells-Riely model, these will vary depending on activity
Response 8: The expression of Wells-Riely model has been explained in the text. The input items respiratory ventilation p and production rate q of occupants are the fixed value for the virus COVID-19. While, the input items infected occupant number I and exposure duration t are changeable under different situation. When considering the influence of elements occupant distribution constraint and building ventilation on infection risk, the basis for Wells-Riley model modification has also been explained in the section 2.3 as ‘The denominator as shown in Equation (9) in the brackets indicates the influence of building volume and building ventilation efficiency on the infection risk in the space, and the molecular in the brackets indicates the influence of the distribution of infected and uninfected occupants and the aggregation degree of occupants on the infection risk. Equation (9) regards all occupants in the space as a whole, and the situation without any prevention measures can be defined as the base scenario. When considering the effect of occupant distribution constraint on the decrease of infection risk, the molecular in the brackets will be corrected by occupancy rate . When considering the effect of ventilation intervention on the decrease of infection risk, the denominator in the brackets will be corrected by the increasing times of ventilation rate, which will be multiplied by the original design ventilation rate to n’. When the two measures are considered simultaneously, the risk of virus infection can be evaluated as Equation (10).
Point 9: Please provide a more solid basis for your probabilities, guidance from REHVA would suggest that risk should be based on secondary infectious or event specific reproductive numbers which gives a more clear insight into the suppression or growth of the virus. The values of 25% to 50% in wells riely probability terms would seem quite high based on a recent publication by Kurnitski in Building and Environment. Again like above I would suggest contextualizing your risk thresholds so they make more sense. This could lead to changes in conclusions.
Response 9: REHVA recommends that the assessment of infection risk will be based on the basic regeneration index R0 and real-time regeneration index Rt. The regeneration index refers to the average number of new infections caused by a case. R0 is usually used to evaluate the transmission intensity of virus during the early outbreak of epidemic, and Rt is usually used to evaluate the transmission intensity in the stage of middle or late development of epidemic. When Rt is less than 1, it indicates that the epidemic could be controlled based on the current prevention measures. When Rt is greater than 1, it indicates that the epidemic will continue to spread, and the optimized and strengthened prevention measures are need. The method of risk assessment is based on the scope of community, city or country, which is used to predict and evaluate the risk trend to adjust the corresponding prevention measures. The corresponding division of low-risk, medium-risk and high-risk areas are based on currently cumulative case and the epidemic situation during a period of time. The definition is different from the risk level divided in this study. Each individual is taken as research object to evaluate the average probability of being infected by the aerosol transmission, contact transmission and other means when there appeared infections in the space. When other conditions are unchanged, the effect of the two measures of occupant constraint and ventilation intervention on the infection probability will be evaluated and compared. The evaluation system fuzzifies the input items of Wells-Riley model and determines the fuzzy subset of each input item, which is expressed by language variables. After determining the fuzzy subset, the fuzzy output item is obtained by introducing the quantization function, and then the fuzzy output will be mapped to the practical significance through the defuzzification process. The domain of the quantified input item are as: input item ‘occupancy’ is [0,1], input item ’the times of design ventilation rate’ is [1,2], output item ‘infection risk’ is [0,1]. The risk levels proposed in the text are obtained by corresponding the domains to the language variables. The division of the risk level depends on the quantitative distribution characteristics of infection risk, rather than the definition of risk level recommended in REHVA.
When using Wells-Riley model to evaluate the infection probability of susceptible population, Kurnitski assumed the initial regeneration index R0=0.5. Based on the assumption, in the case of 20 and more persons, much lower individual probabilities have led to less than one infection because of high number of susceptible persons. In addition, the smaller capacity of the maximum occupant number in the space will cause higher infection probability, which is consistent with the research conclusion of B1 in our study. They didn’t consider the scenarios that the number of infected occupant number is over than 1, thus the probability of infection is no more than 50%. The conclusion is also consistent with our results. For the spaces with large population capacity, when assuming only one infection in the space, the infection probability of susceptible occupants is less than 50% even less than 25%. However, when assuming a higher proportion of infections in the space, the infection probability of susceptible occupants will inevitably increase in the case of larger population capacity. the maximum number of the cases proposed by Kurnitski is 154, while the maximum number of B4 proposed in our study is more than 300. Once infection appears, the infection probability of susceptible occupants in the same space will increase. In addition, we don’t assume the regeneration index, instead, we assume the probability of being infected for each individual is equal, causing the average level of infection probability is relatively high. The assumption is not only applicable to the situation with lower regeneration index resulting from the higher vaccination rate in the stage of later epidemic, but also applicable to the situation with higher regeneration index in the early outbreak of other influenza.
Point 10: Please describe the details of the buildings in more detail. The current or design ventilation rate is not explained, nor is the volume of buildings. In addition to this the rationale for how the maximum occupancy value was determined is needed. More information in absolute terms (L/s or number of people) is needed as these provide practical implications for the results.
Response 10: The volume and original ventilation rate of buildings have been added in Section 3.1 as ‘The design ventilation rate of buildings B1~B5 are 4 times/h, 6 times/h, 6 times/h, 10 times/h and 10 times/h, respectively. The volume of each room of buildings B1~B5 are 35 m2, 200 m2, 100m2, 1200m2, 320m2, respectively. ’ In addition, the maximum value of occupant number of different buildings have been added and listed in Table 2. It was determined based on the maximum number of the fixed stations in the room. The value of occupancy is just the ratio of the actual occupant number to the maximum capacity of occupant number. It is a dimensionless constant without absolute terms.
Point 11: Please describe why the ventilation rate was changed in percentage terms as opposed to be based on occupants. Typical sizing of ventilation systems use L/s/p to determine the rate. Is there any practical limitation in existing systems. Please state this also. Can the existing system increase its ventilation rate by 200%?
Response 11: Both the elements as occupancy and ventilation do make the interactive influence on the infection risk. In order to quantify and compare the effect of two measures as occupancy constraint and ventilation intervention on the decrease of infection risk, this paper assume the impact of the two measures are relatively independent. When studying the single effect of building occupancy constraint, the design ventilation rate will be adopted. When studying single effect of building ventilation enhancement, the predicted occupancy rate will be adopted. When studying the multiple effect of two measurements on the decrease of infection risk, the way of scenario assumption and interaction will be adopted. The research shows that there is a large difference in the occupancy rate between the adjacent typical events. If the ventilation rate is designed to be changed based on the occupancy, there will be certainty a delay in time to realize the indoor air fully mixed and diluted for some spacious space. Such as during the dining period in the canteen building, there will exit the phenomenon that the dining event has ended before the indoor air has been fully mixed and diluted. Therefore, if the purpose of decreasing infection risk is aimed to achieve, either adjust the volume of ventilation system in advance or keep the maximum volume of ventilation system will be effective. Obviously, the later method is more reliable and safe, although this method will be bound to cause a more huge waste of energy. Although the occupancy rate during typical events of different buildings can be predicted, it is impossible to exclude the influence of some special activities on the fluctuation of occupancy rate due to the great randomness and uncertainty of occupant movement and behavior. Therefore, for the sake of safe and reliability, this study assumes that the two prevention measures are independent of each other, and adopts the way of scenario hypothesis and interaction to study and compare the effect of single measure and multiple measures on the decrease of infection risk.
However, this study only aimed to quantitatively evaluate the effects of two measures on the infection risk. Based on the proposed times of design ventilation rate, the required minimum per capita of fresh air quantity can be determined, which will be the references for the future epidemic prevention and control management. The relevant discussion section in our paper have been conducted from the perspective of minimum per capita of fresh air quantity, which will make our conclusion more generalizable. For some scenarios assumed in the text such the dormitory rooms, where occupants are more easily exposed to high risk, fresh air ventilation system can be planned to install in each dormitory room in the design stage to make the ventilation rate reach to 2.0 times of design value suggested in the standard. The proposals in the text still present practical significance.
Point 12: Please indicate the accuracy of the GPS trackers and what uncertainty is likely with these and if there are any likely data quality issues.
Response 12: The accuracy of the GPS trackers has been explained in Section 3.2. The tracker could receive satellite signals from GPS of the United States and GLONASS (Global Navigation Satellite System) of Russia at the same time. Combined with another three signals including Wi-Fi, base station and gravity induction, GPS trackers could lock the positions of carriers more quickly and accurately. The positioning accuracy could be within a radius of 5m for open sites outdoors. However, in the actual testing process, it was found that the tracker could accurately judge which building occupants are located, but it could not identify their specific locations inside the buildings. In this study, the GPS trackers are mainly used for tracking the occupant movement between campus buildings, and the specific location in the buildings is not need. Therefore, the deviation of the specific locations inside the buildings will not affect the tracing of occupant trajectory of this study. In addition, the trackers could keep in power for one week. However, it is required that 130 volunteers carry trackers for not less than two weeks to obtain a more universal transfer regularity within campus unit. Therefore, in order to avoid the data loss due to insufficient power of the trackers, volunteer are required to charge the trackers every week.
Point 13:Please describe what is meant by buildings with "narrow space"
Response 13: Dormitories are generally muti-person rooms for most of domestic universities. And the rule of ‘entrance guard’ universally exits during night time, when, all the occupants will be required to return their dormitory rooms. The rule of ‘entrance guard’ refers that the dormitory building will be closed around 11 p.m every day and open around 6 a.m next day, anyone will not be allowed to enter or leave the dormitory building and be allowed to activity within the building. The mentioned ‘narrow space’ in the text mainly refers to each dormitory room. The volume of the selected case dormitory room are 35m2, and the per-capita occupied area of each room is no more than 7m2/per. Compared with other four case buildings, the per-capita occupied area in the dormitory room is relatively small and crowded. The original expression ‘narrow’ is not accurate. The word ‘narrow’ has been replaced by the word ‘crowded’. In comparison, the volume of rooms of other four buildings are relatively spacious, and the per-capita occupied area will vary with the occupancy rate. This part of contents have been added and explained in Section 3.1.
Point 14: Please increase the size of a number of figures as they were very hard to interpret. For example Table 3 requires more annotation and clarity on what is being visualised. No indication as to what size or colour or what is on x or y axes is presented.
Response 14: The figures have been enlarged accordingly, so that the words in the figures can be presented more clearly and conveniently. Especially for figures listed in Table 3, the corresponding legend and the scenario assumptions have been explained in the text as ‘One room of four case buildings are used for simulation respectively and the legend of the figures are both 1:3. Three scenarios with 25%, 50% and 75% initial percentage of the infectious occupants are assumed. After designated exposure duration (The longest exposure duration at typical events for different buildings), the distribution of different types of occupants are shown as figures and listed in Table 3. ’Different colors of dots refers to different types of occupants. ‘The red dots - the infected occupants; The gray dots - the susceptible occupants; The blue dots - the non-susceptible occupants.Among, the susceptible occupants and non-susceptible occupants all belong to healthy occupants.’ The figures present the distribution proportion of different types of occupants indoors. They are displayed as the dimension ‘space’, thus x-axis or y-axis is not need.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
i. The authors should do thorough editing of the entire paper.
ii. The authors should support the introduction part highlighted with citations.
iii. The study areas should also be indicated
iv. The number of the universities used should also be stated. If there are no ethical issues, the names of the universities used should be indicated and their choice justified.
v, As part of the methods, the authors should indicate the methods they applied in the data collection. the population and sample size should also be indicated for the buildings and the occupants involved.
vi. Table 3: there is no legend/key to indicate the infected and uninfected occupants in the diagram. This should be done. It makes the interpretation of the diagrams not very clear.
Author Response
Dear Editor and Reviewers,
Thank you for your useful comments and suggestions on our manuscript. We have modified the manuscript accordingly and detailed corrections are listed below.
Comment:
Point 1: The authors should do thorough editing of the entire paper.
Response 1:We have done thorough editing of the entire paper. The errors appeared in the words and the expression of English have been carefully proofread.
Point 2: The authors should support the introduction part highlighted with citations.
Response 2: The introduction section has been rearranged. And related reviews in the introduction section have been highlighted with citations. The introduction section was mainly developed from the three parts: the review of the influence of population controlling on the development of epidemic; the significance of ventilation on the ensure of indoor air clean and indoor occupant safety; the urgency of establishing the prevention and control evaluation system for the resumption of colleges and universities.
Point 3: The study areas should also be indicated
Response 3: The study areas have been indicated and added in the Section 3.1 as ‘This study was conducted in a campus in Tianjin, China. The areas of case campus were shown in Figure 2. The campus was constructed in the form of teaching group, that is, some basic facilities such as teaching zone, office zone, living zone, meal zone, library zone, and other activity center, etc., were built within a certain range. Occupants in the campus generally transferred between five types of buildings as dormitory building, lecture building, office building, canteen and library. The five types of buildings will be determined as the targeted objects and assigned as code ‘B1’, ‘B2’, ‘B3’, ‘B4’ and ‘B5’. And the areas outsides were assigned as code ‘0’. The basic building information have been listed in Table 2.
Point 4: The number of the universities used should also be stated. If there are no ethical issues, the names of the universities used should be indicated and their choice justified.
Response 4: The data about occupant movement was obtained from June 15 to July 15, 2019 and served as the basic scenario to evaluate the risk level of infection without any prevention measurement. Afterwards, with the outbreak of COVID-19 at the end of 2019, it becomes more difficult to obtain similar data about occupant movement in other universities due to the strict control of the region and campus, especially for the occupants outside. In addition, since the epidemic, some prevention or disinfection measures such as occupant flow control, staggered peak for dining and other measures have been implemented, thus the data after the epidemic will not be used as the base scenario without any prevention measure for the comparison of scenarios with building ventilation intervention or occupant distribution constraint. I consider that although only one case university is selected to evaluate the effect of prevention measures on the decrease of infection risk, it still has certain practical significance and representativeness. The reason and the rationality of the choice have been explained in the Section 3.3 as ‘The selected case campus could represent the distribution of multiple functional buildings of general university nationally and internationally. Occupants transferred between campus buildings usually for living, studying, meal or other basic activities. The main differences between universities nationally and internationally lie in the occurring time point of typical events. The earliest starting time points, the latest ending time points and the course duration of different universities may vary due to the diversity of sunrise and sunset time in different regions. In addition, influenced by the regional culture, the dining time varies especially for the dinner time. In the actual process of risk prediction and prevention management, the occupancy schedule corresponding to above typical events can be moved forward or backward in time in accordance with the region.’. In addition, I promise that this study dose not exit any ethical issues, however, as it involves privacy issues, it it better not to indicate the name of universities.
Point 5: As part of the methods, the authors should indicate the methods they applied in the data collection. the population and sample size should also be indicated for the buildings and the occupants involved.
Response 5: The methods applied in the data collection have been indicated in Section 2.1 as ‘ In terms of data collection, GPS trackers (Detailed introduction in section 3.2) will be issued to the testers to record their tracks for a period of time and obtain their transferring proportion between the time points of adjacent typical events, which will be applied as a training set to predict the occupancy rate based on the stationary distribution. Taking the prediction results obtained by stationary distribution, the effect of 25%, 50% and 75% occupancy restriction on the reduction of virus infection were evaluated respectively. ’ The occupants in Colleges and universities are mainly as students and teachers. The campus students could be further divided into two stages as undergraduates and postgraduates. Due to the variety of course arrangement, accommodation conditions and nature of learning, the transfer regularity of students may be extremely different. When exploring the transfer regularity of students, it is necessary to distinguish student group as undergraduates and postgraduates. The movement behavior of student are random, their transfer regularity will be recorded and explored by using GPS trackers, the detailed data collection method has been presented in section 3.2. Compared with campus students, the activities of teachers within campus were relatively fixed and uniform. Their transfer regularity could be obtained through the questionnaires targeted at the hourly positioning in a day. The sample size of students and teachers involved for the movement data collection have been also indicated in section 3.2. In addition, the detailed information about the buildings including the volume, the maximum capacity for building level and room level have been supplemented in the section 3.1.
Point 6: Table 3: there is no legend/key to indicate the infected and uninfected occupants in the diagram. This should be done. It makes the interpretation of the diagrams not very clear.
Response 6: The corresponding legend and the scenario assumptions have been explained in the text as ‘One room of four case buildings are used for simulation respectively and the legend of the figures are both 1:3. Three scenarios with 25%, 50% and 75% initial percentage of the infectious occupants are assumed. After designated exposure duration (The longest exposure duration at typical events for different buildings), the distribution of different types of occupants are shown as figures and listed in Table 3. ’Different colors of dots refers to different types of occupants. ‘The red dots - the infected occupants; The gray dots - the susceptible occupants; The blue dots - the non-susceptible occupants. Among, the susceptible occupants and non-susceptible occupants all belong to healthy occupants.’
Author Response File: Author Response.pdf
Reviewer 3 Report
The study focuses on the prevention measures for virus trasmission analyzed on a case study of a University Campus, with the help of machanical and natural ventilation.
The text is well structured and I appreciated the organization and the research goals.
The research is original and the are many relevant references.
Author Response
Dear Editor and Reviewers,
Thanks for your recognition of our paper. I aware there are still many deficiencies, and I have made further improvement and revision.
Author Response File: Author Response.pdf
