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Redesigning the working space for social distancing: Modelling the movement in an open-plan office


Abstract and Figures

The global pandemic has reshaped the use of working space dramatically, mainly due to the implementation of the working from home policy and indoor social distancing requirements. This calls for the rethinking of the current office design approaches and the proposal of specific design strategies to provide a safer and healthier environment for employees to return to their offices. Also, the demand for enhancing office design to accommodate special and rare events like the pandemic is identified. More resilient and flexible office designs are needed for the post- pandemic era. This study aims to provide an insight into the human movement in the open-plan office setting by simulating the movement using agent-based modelling. Normal scenario and special scenarios with social distancing standards and reduced office capacity are simulated. The simulated scenarios contribute to the development of the new adaptive office design approaches for a safe office resume.
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CIBSE Technical Symposium, UK 13-14 July 2021 2020
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Redesigning the working space for social
distancing: Modelling the movement in an open-plan
Department of Architecture, University of Cambridge
Department of Engineering, University of Cambridge
Department of Architecture, University of Cambridge
The global pandemic has reshaped the use of working space dramatically, mainly
due to the implementation of the working from home policy and indoor social
distancing requirements. This calls for the rethinking of the current office design
approaches and the proposal of specific design strategies to provide a safer and
healthier environment for employees to return to their offices. Also, the demand for
enhancing office design to accommodate special and rare events like the pandemic
is identified. More resilient and flexible office designs are needed for the post-
pandemic era. This study aims to provide an insight into the human movement in
the open-plan office setting by simulating the movement using agent-based
modelling. Normal scenario and special scenarios with social distancing standards
and reduced office capacity are simulated. The simulated scenarios contribute to
the development of the new adaptive office design approaches for a safe office
1. Introduction
The COVID-19 pandemic has significantly reshaped the work routine and office
experience. The containment measures including lockdown, travel restrictions and
social-distancing requirements have reduced the mobility of people, while
companies respond to the pandemic challenge with ‘work from home’ policy.
Although ‘work from home’ can lead to some improvements in the productivity (1),
office still has its irreplaceable role on providing face-to-face communication and
building up social connections. The plan for returning to offices is resumed with the
delivery of mass vaccination and the release of lockdown.
It is inevitable for people to gather, contact and interact when they go back to
offices, which may lead to an increase in the risk of transmission. The non-
pharmaceutical interventions like increasing air ventilation and social distancing are
proved to be effective (2), while the design of physical environment can help to
address the interventions. The effectiveness of the potential ventilation strategies in
indoor environment has been discussed in the previous studies (3–6). The questions
of how many people can safely access the office, what is the optimal social
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distance in office, how many people are allowed to use the facilities around the
office and how to alter the furniture layout to avoid overcrowding situation need to
be addressed. The corresponding office design solutions can contribute to reducing
the concentration of people thus reducing the potential of virus spreading. The
understanding of how people use and move around the office space is expected to
contribute to the decision making on the office reopening plans. The simulation
modelling approach can help to model the different scenarios with various
intervention strategies like layout changing, social distancing and population
limitations. An optimum strategy can be found through this process to ensure a safe
This study is going to investigate the application of social distancing strategy in the
office. It models the movement of people in an office environment with agent-based
pedestrian simulation tool and examines the scenarios with various social
distancing requirements and limitations on the populations. This investigation is
essential for figuring out the appropriate strategy for safe reopening. Also, an
enhanced understanding on the movement and potential strategies can contribute
to future design. The effectiveness of different layouts and design strategies can be
tested with the simulation tools. Furthermore, the agent-based modelling in the
normal office setting has the potential to be developed as a comprehensive project,
from the observation and collection of behavioural data to establish a model
applying specifically to the workspace. This study provides an initial insight for the
potential of the investigation.
2. Literature review
The literature review part learns from the existing research on the movement in the
office environment and the simulation of pedestrian movement in the built
environment. The findings provide a background overview and pave the way for the
proposed study.
2.1 Movement in office
A large body of literature has discussed the observation of human activities in the
office environment. The behaviours of sitting, standing and walking are widely
measured and analysed in the domain of physical health research. The physical
activity monitor devices are used to trace the movement and record the sitting and
standing time and walking steps, while the questionnaire is applied to collect the
self-reported data (7–10). The sedentary behaviour, which indicates a prolonged
sitting time of more than 30 minutes (11,12), is considered a threatening to health
(13,14). The researchers test different approaches to reduce the sedentary time,
such as the use of sit-stand work station (15–17), the renovation of office layout
from traditional cells to activity-based type (18) and the seminar, workshop and
training sessions. The collected dataset show that the sitting time usually occupies
over 60% of the working time (16,19,20), while the largest proportion noted is 93%
(21). The recorded standing and walking time is much less than sitting, while the
figure varies in different studies, with a range of 10% to 20% for standing (15,22,23)
and 7% to 12% for stepping (15,23,24). Spinney et al. (25) report the frequency of
trips in an office. The trips from the desk per hour is 1.6, and the average number of
trips to the restroom and kitchen per hour is 0.38 and 0.96 separately.
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The correlation between the movement and spatial configuration is also a major
found in the previous research. Carter and Whitehead (26) study the pedestrian
circulation in Rail House and notes down 69 activities that can generate
movements, such as the visit to mailroom and the use of ground floor entrance.
Rassia (27) points out the informal communication for job interaction as a key
reason for movement in the office, while the movement is found along the corridors
and to landing and reception areas. The movement and interaction hotspots are the
individual desks, water coolers and print station (28). The indoor architectural
design can have an influence on the occupants’ movement pattern by considering
the layout carefully (27). The spatial system explains the movement flow in offices
effectively, and the results from two case studies show that the metric analysis
demonstrates the workplace movement best (29). The movement in offices is driven
by the geometric and non-geometric properties and the needs of travelling to
attractors. The geometric property refers to the pure spatial configuration
measurement, while the non-geometric feature takes the functional configuration
(the locations of furniture and functional spaces) into consideration. The travel
concentration measurement constructs paths from each seat to attractors of the
building entrance, the closest canteen, kitchen and restroom and transforms the
paths to the visible zones. This new spatial metric is tested against a large dataset
and proved to have some predictive power to the movement activity (30). The
correlational analysis of spatial configuration features and movement at micro, meso
and macro levels identifies the metrics of visual control, isovist min radial and travel
concentration are the most significant measurements to the movement activities.
The finding supports the argument of movement mainly taking place in the
circulation spaces (31).
2.2 Agent-based movement simulation
The movement simulation has been widely applied in built environment research,
because the understanding of how pedestrians move around urban spaces or
buildings can play a significant role in assisting the planning and design. The
microscopic models like cellular automata, social force and agent-based models are
created for the movement simulation (32,33). The agent-based model has a unique
feature of involving variability and randomness which leads the system to unknown
limits (34,35). Schelhorn et al. (36) propose the agent-based STREETS model to
look at the pedestrian activities in urban districts by integrating the socioeconomic
and behavioural characteristics of agents and the physical environment datasets in
the GIS system. The geometric agent-based pedestrian simulation PEDFLOW for
the microscope environment is addressed in the work of Kerridge, Hine and Wigan
(37). PEDFLOW is a complement work to the STREETS model with a higher level
network and decision structure.
The recent two decades have witnessed the emergence of agent-based crowd
simulations for different cases and scenarios. A classification framework
categorises the type of modelling by environment and behaviour. The type of
environment includes small-scale enclosed spaces, large-scale enclosed spaces,
mixed-mode, open space and hybrid, while the behaviour categories are purposeful
and familiar, purposeful and unfamiliar, purposeless, evacuation, forced-waiting and
temporal constraints (38). Shaaban and Abdelwarith (32) explore the pedestrian
behaviour when crossing a road, while different pedestrian attributes, like gender,
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age, clothing, are considered in the investigation. The human and social behaviour
in emergency evacuations is demonstrated in works (39–41). Rassia and Siettos (42)
introduce the molecular dynamics approach to model the evacuation in office
buildings. The simulations at a larger scale or mix-mode environment, like the
railway station (43), metro station (44) and outdoor recreation spaces (45).
For the post-pandemic world, simulation tools could assist the decision making by
performing possible scenarios of human activities. The importance of simulating the
risks and scenarios is also highlighted by the insight from Arup. The Space Explorer
tool is introduced as a service to understand people’s movement and risk for
returning to workspaces (46). The office behaviours and movements are different
from the normal crowd movements on streets or around public spaces, as the office
is a semi-public environment that involves more complex functions and activities
and a longer staying time. While sitting accounts for the majority of working time,
relatively less time is spent on standing and walking. Many of the movements
around office are generated by the visits to the attractors like printers, restroom and
meeting room. The employees have various levels of interactions like chatting,
visiting and meeting. According to the classification framework of agent-based
pedestrian models by Ronald, Sterling and Kirley (38), the model for the office
environment is classified as the small-scale enclosed spaces modelling with
purposeful and familiar behaviours. This combination remains a gap in the field.
Therefore, there is a need for an integrated tool to model the movement specifically
for the working environment as an aid to the post-pandemic office design.
3. Method
This section introduces the data and method used in the movement simulation.
Figure 1 illustrates the step-by-step flow chart of this study.
Figure 1 – Steps of the study
3.1 Data collection
The office plans applied in the study are extracted from the collection of plans in
Koutsolampros's work (31). The plan comes from the UK office dataset collected by
SpaceLab, while a set of predictive analysis on the spatial configuration and the
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activities of movement and interaction is performed in the previous works
(30,47,48). The first and second floors of Case 27 are selected for the analysis
considering the availability of relevant information (shown in Figure 2). Except for the
floor plan, the function distribution information is also extracted and illustrated
(shown in Figure 3). There are a total of 180 desks on the first floor and 174 desks
on the second floor with an open office plan. Therefore, the assumptions on the
maximum capacity of the offices are 180 and 174 respectively. Both of the floors
feature a dense seating plan, classified as dense workspace floors (31). The floor
entry and exit are the staircase and three lifts on the corridor. The first floor has four
meeting rooms and one alternative working space. The second floor has five
meeting rooms and two alternative working areas. There is a restroom and a tea
point with seats on each floor and facilities like printers and lockers locating around
the floor. The slight differences in the layouts of the two floors make their simulation
results comparable.
Figure 2 – Floor plan, Case 27 First floor (1F) and Second floor (2F) (adapted
from ref. (31))
Figure 3 – Floor plan with functions, Case 27 First floor (1F) and Second floor
(2F) (adapted from ref. (31))
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3.2 Pedestrian simulation setting
The pedestrian simulation plug-in PedSim Pro working under Grasshopper in Rhino
is applied to simulate the movement in the open-plan office (49). It is an agent-
based simulation tool based on a particle spring system with agents driven by
various forces like repulsion and attraction forces. This tool is selected because it is
easy to access and allows users to run and design their own scenarios. Its
predecessor PedSim is the most popular pedestrian simulation tool in Grasshopper,
while PedSim Pro has more features with greater modelling flexibility. The simulation
is run on a 3.1GHz Intel Core i7 computer with Rhino 6 and PedSim Pro 1.1.1.
The major components in the simulation modelling are stops, interests, obstacles,
person templates and the engine. Stops and interests are the location points on the
plan that agents can visit. Stops are the planned targets on the route, including a
start, a destination and multiple other visits. The movement route of the agent is
defined by the planned stops. Interests are the unplanned stops, as they are visible
and accessible. The interest and stop can be set as a room or a stand. The agents
who visit a room disappear from the map, while those who visit a stand still remain
on the plan and interact with the other agents. Obstacles can be either opaque or
transparent, and the setting of obstacles influences the decision of routes. The
person template defines the type of agents with different combination of stops and
interests. The engine is the essential component that integrates all the stops,
interests, obstacles and person templates.
In the simulation, a full circulation from the entry to the desk to the exit is built for
each agent. The entry and exit are the staircase and lifts. Each staircase or lift can
be either starting point or destination. The planned stop is the desks around the
office, and the desks are grouped into four sets according to their zone. The
restroom, meeting rooms and an alternative working space are set as interest with
the room feature, while the facilities, tea point and an open-plan alternative
workspace are assigned as the interest with the stand feature. Each agent can visit
a maximum of five interests on their designated route.
Type of
Stand or
Number of
(on each floor)
(Unit: s)
(For each
4 groups
180 (1F), 174 (2F)
Meeting room
4 (1F), 5 (2F)
Room and
2 (1F), 3 (2F)
31 (1F), 44 (2F)
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Table 1 – Model setting in PedSim Pro
Table 1 shows the setting of stops in the PedSim Pro model. The default fixed unit
setting is meter (unit: m) for distance and second (unit: s) for time. Due to the
limitation of the computing power, the time frame in simulation is scaled down. In
the model, 1 second corresponds to 1 minute of real-world time. To get a
representative result with the limited computational power, the simulation is run for
90 seconds for each scenario. The profile of the agent, including body radius, mass
and speed, follows the default setting in the plug-in, and the vision is set as
‘panorama’. There are 16 different combinations of ‘entry-desk-exit’ route (two entry
and exit options of lift or staircase and four groups of seats), thus a total of 16 agent
templates are created in modelling. An agent generates from the entry points in
every two seconds, and the maximum capacity of the lifts is 10 for each lift. The
visit time to working desks is about 30 seconds, which indicates a sedentary time of
30 minutes. Each desk can hold only 1 person. The visit time to the meeting room is
set at 20 seconds, and the capacity of the meeting room varies with the size, from 6
people to 12 people. For the tea point, restroom and facilities, the staying time
ranges from 3 seconds to 15 seconds and the capacity differs case by case.
3.3 Proposed scenarios
Total population
allowed in the office
Scenario 1
Full capacity
Scenario 2
3/4 (75%) of full capacity
Scenario 3
1/2 (50%) of full capacity
Table 2 – Scenario settings
Three basic scenarios with different virus containment strategies are proposed for
simulation (summarised in Table 2). Scenario 1 is the normal scenario with the full
capacity. The population presented in the office is 180 on the first floor and 174 on
the second floor. Scenario 2 simulates the situation with a reduced number of
people and a social distance of 2 meters. Only 75 per cent of the full population can
access the office, while the number of people allowed in the meeting rooms, lifts
and alternative working spaces is reduced to half of the original capacity. Only one
agent can use one facility every time. In scenario 3, half of the population is allowed
in the office. The social distancing requirement and the capacity limitations on the
facilities and functional rooms are the same as the scenario 2 settings. Additionally,
the gap between seats is placed. Only half of the desks are accessible (shown in
Figure 4). The snapshots for each scenario are taken, showing the position of
agents. All scenarios are run for the first floor, while only the normal scenario is
conducted for the second floor. Figure 5 presents the series of snapshots at 30, 60
and 90 seconds for scenario 1 on the first floor, while Figure 6 illustrates the
snapshots for all simulated scenarios at the time of 60 seconds.
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Figure 4 – Illustration of the seat gap
Figure 5 – Scenario snapshots for Case 27, First floor, Scenario 1 (t=30s, 60s,
Figure 6 – Scenario snapshots for Case 27, First floor, Scenario 2, Scenario 3;
Second floor Scenario 1 (t=60)
The data generated from the model is visualised in the format of heat maps, which
shows the movement density around the space. Following the previous analysis
(31), the lattice grid with a cell size of 45 by 45 centimetres is used. The number of
visits to each cell is counted and presented in the heatmap. The number of visits to
the interests is counted to represent their attractiveness.
4. Result and Discussion
4.1 Simulation results
Figure 7 presents the groups of movement density heat maps, and Figure 8 shows
the visit count to each interest. Generally, the highest density occurs around the
corridor. The visiting density decreases as the distance from the lift and staircases
increases. The facilities and desks located far from the corridor have the least visit
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counts. The tea point is less visited, which could be explained by its poorly visible
location. The meeting room and restroom along the corridor are the most visited
hotspots, while the visit count to the meeting rooms in the distant area is much less.
The facilities around the circulation area also cause some density concentration.
Figure 7 – Heat map
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Figure 8 – Visit count of interests
The reduction on the number of occupants in the office has effectively reduced the
movement density and the concentration around the interests. The movement area
contracts as the number of agents reduces, as the agents do not travel further to
reach the desks at the distant corners. In scenario 3, the overcrowding situation still
occurs at the entry and exit points and the restroom, while the movement density
along the corridor decreases significantly. The impact of social distancing and the
seat gap strategies is not apparent on the heatmap. The effectiveness of social
distancing measurement is demonstrated by the difference in the use of corridor. In
scenario 1 of the first floor, the flow concentrates in the middle of the corridor, while
the highest density occurs at the two sides of the corridor near the wall in scenario
2 and 3. The movement flows are separated to maintain the social distance. A
noticeable increasing in the distance between agents are shown on the snapshots
(Figure 6).
The normal scenario is run for both the first and second floor, while there are some
differences in the layout. The main distinctions in the layout occur on the right side
of the plan. The upper right quadrant on the second floor has a denser seating
arrangement comparing to the first floor. However, there is no significant variation
found in the movement around the area. The set of facilities locating along the
circulation area opposite to the tea point are visited 16 times, but those interest
points do not lead to extra concentration comparing to the same location on the
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first floor. As the entry and exit points, the restroom, the corridor and the circulation
remain in the same location, the general movement flow in both floors looks similar.
The results echo some of the findings from spatial configuration analysis (shown in
Figure 9). The three most important metrics are visual control and isovist min radial
based on the accessible areas and the in-floor travel concentration (31). Visual
control compares the space visible from a cell to the other directly visible cells.
Isovist min radial measures the distance from the isovist origin to the nearest
obstacle. Travel concentration evaluates the effect of attractors. It is not surprising
to see the corridor become the movement hotspot in the plan considering the
relatively high indoor travel concentration and isovist min radial value shown in the
previous analysis. The high values of the spatial structure metrics are also found
around the circulation area outside the tea point, while the area features a relatively
dense movement as well.
Figure 9 –The three most important spatial configuration metrics to movement,
Case 27 First Floor (extracted from ref. (31))
4.2 Potential strategies
In addition to the containment strategies of introducing social distancing, limiting
capacity and reducing crowding, the design strategies can be applied to the office
space including changing the layout, setting up the one-way system and adding
barriers between seats. Also, the ‘Working safely during coronavirus’ guideline
provides a series of suggestions, such as carrying out risk assessments, arranging
the workspace to keep people apart, using back-to-back or side-to-side seating,
increasing the frequency and restricting the access to different areas (50). The
simulation results support the application of social distancing and capacity
limitation, while the findings also lead to further reflections on the design of the
In the floor plans examined in this study, a high concentration of movement is found
in the corridor area, mainly due to the location of staircase and lifts. At the same
time, employees need to pass the corridor to get access to the meeting rooms and
restroom, which adds to the crowding density. To reduce the high traffic around the
corridor, diverting the movement flow in the corridor is essential. The entry and exit
points could be separated, which indicates a greater distance between the staircase
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and lifts. The meeting rooms can be re-layout from their current location to the less
accessible areas. This measure could alleviate the travel concentration around the
corridor and discourage employees to visit the meeting rooms, as meeting rooms
are small and enclosed areas with a higher risk of spreading. Some of the facilities
can be relocated from the locations around the circulation area to less accessible
5. Conclusion and limitations
In conclusion, this study provides an initial insight into the movement simulation in
the workspace. The literature review indicates that there is a need for more targeted
simulation tools for different scales, scenarios and environments. The study
demonstrates how the movement simulation in the office environment could
potentially support the decision making in the office reopening in the post-
pandemic era. While the government guideline sets the general approaches, the
movement simulation tool helps to provide more detailed analysis and solutions by
looking into a specific case and modelling case-specific scenarios. Comparing to
the existing agent-based modelling research which focuses more on the design of
models, this study explores the potential of applying the simulation results to real
cases. In this case, the results highlight that the corridor is the high movement
density area. There is a need for some specific considerations on reducing the
movement concentration around the corridor.
There are several limitations of this study, which influence the accuracy of the
simulation. Firstly, the model inputs are designed based on the secondary sources
and assumptions with a lack of primary data involved. The generalised and
simplified movement pattern used for simulation is relatively simple, and it could not
accurately reflect the complex situation in the real office environment. Secondly, the
tool used in this simulation is designed for the understanding of general movement
instead of for office-specific scenarios. This plug-in is unable to simulate the vertical
movement between different floors, though the two plans used in this study are
from the same case. Only the movement within floor is modelled. The personal
templates allow some degree of diversity and flexibility in the agent setting, the
agents still tend to be homogenised in the model. Thirdly, the agent-based
simulation results involve randomness, which indicates that the results for every
single simulation could have slight differences. The future investigation can be
coupled with environment modelling, especially the simulation of air ventilation in
the office space, to understand the behaviour and assess the risk of returning to the
The authors would like to thank Dr Petros Koutsolampros for his valuable
discussion in office space interactions and the office case study from his thesis.
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... Aviv et al. ( 2022) used Grasshopper to develop a raytracing-based radiant heat transfer model to resolve the radiant asymmetries between occupants and the built environment. PedSim Pro, a pedestrian movement simulation tool developed for Grasshopper, was used by Pan et al. ( 2021) to generate time-and space-varying building occupancy profiles. Yi ( 2020) achieved a similar outcome by using Grasshopper to couple a BPS model with a hybrid agent-based model of occupant movement and behavior. ...
... The entrance visible without any obstruction by the user from the external area, and the internal space is easily accessed from the standing point Additionally, the micro-pedestrian simulation in buildings can reproduce the walking behavior characteristics of people in real scenes based on pedestrian dynamics, thereby testing the effectiveness of different physical layouts. Pan et al. [81] investigated the impact of spatial design on social distancing by simulating movement in an open office and compared some results with spatial configuration (Figure 6). Corridors are found to be high-density moving areas with relatively high indoor travel concentration and Isovist min radial value, which are mainly determined by the location of elevators, stairs, entrances and exits. ...
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The COVID-19 pandemic has lasted from 2019 to 2022, severely disrupting human health and daily life. The combined effects of spatial, environmental, and behavioral factors on indoor COVID-19 spread and their interactions are usually ignored. Especially, there is a lack of discussion on the role of spatial factors in reducing the risk of virus transmission in complex and diverse indoor environments. This paper endeavours to summarize the spatial factors and their effects involved in indoor virus transmission. The process of release, transport, and intake of SARS-CoV-2 was reviewed, and six transmission routes according to spatial distance and exposure way were classified. The triangular relationship between spatial, environmental and occupant behavioral parameters during virus transmission was discussed. The detailed effects of spatial parameters on droplet-based, surface-based and air-based transmission processes and virus viability were summarized. We found that spatial layout, public-facility design and openings have a significant indirect impact on the indoor virus distribution and transmission by affecting occupant behavior, indoor airflow field and virus stability. We proposed a space-based indoor multi-route infection risk assessment framework, in which the 3D building model containing detailed spatial information, occupant behavior model, virus-spread model and infection-risk calculation model are linked together. It is also applicable to other, similar, respiratory infectious diseases such as SARS, influenza, etc. This study contributes to developing building-level, infection-risk assessment models, which could help building practitioners make better decisions to improve the building’s epidemic-resistance performance.
... Regarding indoor environments, studies involving ABMs have predominantly analyzed university buildings and campuses, supermarkets, or public spaces such as museums, yet mainly apply exposure-time and contact-distance-based or traditional compartmental SEIR (Susceptible, Exposed, Infected, Recovered) [10,25] virus transmission models to calculate the number of infected people after a given time [26][27][28][29][30]. IAQ and, specifically, possible airborne transmission via aerosols is often not taken into account. Some other studies apply (pedestrian) movement models to simulate indoor movement patterns without taking into account virus-related parameters [31,32]. When simulating indoor environments, only a few recent studies explicitly address airborne transmission via aerosols in their ABM [33][34][35][36]. ...
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Recent evidence suggests that SARS-CoV-2, which is the virus causing a global pandemic in 2020, is predominantly transmitted via airborne aerosols in indoor environments. This calls for novel strategies when assessing and controlling a building’s indoor air quality (IAQ). IAQ can generally be controlled by ventilation and/or policies to regulate human-building-interaction. However, in a building, occupants use rooms in different ways, and it may not be obvious which measure or combination of measures leads to a cost- and energy-effective solution ensuring good IAQ across the entire building. Therefore, in this article, we introduce a novel agent-based simulator, ArchABM, designed to assist in creating new or adapt existing buildings by estimating adequate room sizes, ventilation parameters and testing the effect of policies while taking into account IAQ as a result of complex human-building interaction patterns. A recently published aerosol model was adapted to calculate time-dependent carbon dioxide (CO2) and virus quanta concentrations in each room and inhaled CO2 and virus quanta for each occupant over a day as a measure of physiological response. ArchABM is flexible regarding the aerosol model and the building layout due to its modular architecture, which allows implementing further models, any number and size of rooms, agents, and actions reflecting human-building interaction patterns. We present a use case based on a real floor plan and working schedules adopted in our research center. This study demonstrates how advanced simulation tools can contribute to improving IAQ across a building, thereby ensuring a healthy indoor environment.
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Sedentary behaviour within buildings has become a major public health issue particularly now that a large number of people are engaged in one form of office work or the other. In the midst of this, there is a paucity of research on how the design of office spaces can help discourage sedentary behaviour and reduce the health risks associated with it among office workers, especially in the developing countries. This study investigated sedentary behaviour among office workers in Enugu, Nigeria, with a view to improving understanding of the architectural design strategies for checking it. The data were sourced from a survey of 106 office workers in the study area and analysed using descriptive statistics, ANOVA and Duncan multiple comparison test. The findings show that the predominant office layouts identified were personalized and co-working layouts with workers spending around 93% of the total time at work sitting. Variations were observed in the level of physical activity of workers in the different office layouts with those in personal office layouts manifesting sedentary behaviour more than those in co-working and open-plan office layouts. The findings are incisive in noting that to discourage sedentary behaviour in offices and reduce the exposure of workers to sedentary related health risks, architects and engineers should pay adequate attention to co-working and open plan office layouts, centralised and shared office resources and the use of lobbies and corridors in linking main office spaces to ancillary facilities.
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This feasibility study evaluated the effects of an individual-level intervention to target office workers total and prolonged sedentary behaviour during working hours, using an e-health smartphone application. A three-arm (Prompt-30 or 60 min Intervention arm and a No-Prompt Comparison arm), quasi-randomised intervention was conducted over 12 weeks. Behavioural outcomes (worktime sitting, standing, stepping, prolonged sitting, and physical activity) were monitored using accelerometers and anthropometrics measured at baseline, 6 weeks and 12 weeks. Cardiometabolic measures were taken at baseline and 12 weeks. Fifty-six office workers (64% female) completed baseline assessments. The Prompt-60 arm was associated with a reduction in occupational sitting time at 6 (−46.8 min/8 h workday [95% confidence interval = −86.4, −6.6], p < 0.05) and 12 weeks (−69.6 min/8 h workday [−111.0, −28.2], p < 0.05) relative to the No-Prompt Comparison arm. Sitting was primarily replaced with standing in both arms (p > 0.05). Both Intervention arms reduced time in prolonged sitting bouts at 12 weeks (Prompt-30: −27.0 [−99.0, 45.0]; Prompt-60: −25.8 [−98.4, 47.4] min/8 h workday; both p > 0.05). There were no changes in steps or cardiometabolic risk. Findings highlight the potential of a smartphone e-health application, suggesting 60 min prompts may present an optimal frequency to reduce total occupational sedentary behaviour.
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Coronavirus disease 2019, otherwise referred to as COVID-19, started in China and quickly became a worldwide pandemic. Beginning in March 2020, nonessential businesses in the United States were closed, and many communities were under shelter-in-place orders. As of May 2020, some business sectors started reopening, even amidst concerns of worker health as the pandemic continued. In addition to physical distancing, cleaning and disinfection routines, and using face coverings, building ventilation can also be an important risk mitigation measure for controlling exposure to SARS-CoV-2 indoors. A number of studies to date, however, have focused on ventilation in medical facilities (e.g. hospitals) as the risk of transmission of SARS-CoV-2 is higher there (because of the close proximity of workers to patients who have the disease and their treatment procedures). Few studies have focused on ventilation use in nonmedical settings (e.g. office buildings and school classrooms), despite the large population of workers and community members in these facilities. In this article, we review the role that building ventilation can play in minimizing the risk of SARS-CoV-2 transmission in nonmedical environments and some recommended protocols to follow for its proper use, including cleaning and maintaining mechanical ventilation systems for businesses, schools, and homes.
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Although the relative importance of airborne transmission of the SARS-CoV-2 virus is controversial, increasing evidence suggests that understanding airflows is important for estimation of the risk of contracting COVID-19. The data available so far indicate that indoor transmission of the virus far outstrips outdoor transmission, possibly due to longer exposure times and the decreased turbulence levels (and therefore dispersion) found indoors. In this paper we discuss the role of building ventilation on the possible pathways of airborne particles and examine the fluid mechanics of the processes involved.
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Crossing a road outside of a crosswalk is a major cause of pedestrian fatalities. The aim of this study was to investigate this type of behavior for different pedestrian attributes in terms of risk and gap acceptance using agent-based modeling techniques. An agent-based model was developed and tested to represent pedestrian behavior in different situations. Different pedestrian attributes were analyzed, including gender, age, type of clothing, carrying bags, using mobile phones, and crossing in a group. The results showed that pedestrians add a positive risk factor to the speed of approaching vehicles before evaluating a gap, then proceed with the crossing decision. The factor for the female pedestrians was smaller in comparison to their male counterparts, which may infer that they are more prone to taking risks during crossing compared to male pedestrians. Another interpretation can be that they have a better ability to discern vehicle speeds and thus a better assessment of the critical gap. Compared to pedestrians crossing individually, the factor was smaller for pedestrians crossing in a group, which can be an indication that pedestrians have a higher sense of safety when crossing as a group. Moreover, the analysis suggested that there is no difference in perception between old and middle-age pedestrians, pedestrians carrying bags or not, and pedestrians using a mobile phone while crossing or not. These results can be useful in evaluating pedestrian safety at midblock crossings and providing a framework for modeling this type of behavior in simulation models.
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Background: Prolonged sedentary behaviour (SB) is associated with risk of chronic diseases. Digital interventions in SB require mixed method evaluations to understand potential for impact in real-world settings. In this study, the RE-AIM QuEST evaluation framework will be used to understand the potential of a digital health promotion application which targets reducing and breaking up SB across multiple workplace settings. Methods: Four companies and 80 employees were recruited to use a digital application. Questionnaires were used to measure SB, and additional health and work-related outcomes at baseline, one month, three month and six month follow-up. Qualitative data was collected through focus groups with employees and interviews with stakeholders. Questionnaire data was analysed using Wilcoxon Sign Rank tests and qualitative data was thematically analysed. Results: The digital application significantly increased standing time at one month for the total group and transitions per hour in one of the companies. Facilitators and barriers were identified across RE-AIM. Conclusions: Addressing the barriers which have been identified, while maintaining the positive attributes will be critical to producing an effective digital application which also has the potential for impact in the real world.
The Coronavirus disease (Covid-19), caused by the SARS-CoV-2 virus, has produced significant social and economic disruptions in different countries. Current evidence suggests a strong correlation between the infection and the cohabitation of indoor spaces. International organizations and experts consider that the airborne transmission through aerosols can occur in specific conditions and that inadequate ventilation increases the risk of infection. As a result, the increase in ventilation rates and air filtration efficiencies are recommended for public buildings in the context of Covid-19, with significant impacts on energy consumption, and a paradigm shift in the design of ventilation systems is necessary for this new context. Therefore, this study has assessed the comparative performance of the displacement ventilation and the mixed ventilation mode on reducing the risk of long-range airborne infection for the Covid-19 in a small office application. A coupled multizone-CFD (Computational Fluid Dynamics) software developed by the National Institute of Standards and Technology was used in this study to assess the relative performance of several design solutions related to different ventilation modes, filter efficiencies, and outdoor air flow rates. The results demonstrate that the displacement ventilation technique produces a better overall performance in reducing the SARS-CoV-2 airborne infection risk than the conventional mixed ventilation for all the studied cases.
The study of human behaviour in office spaces has a long and varied history from the 1970s to a recent resurgence of interest today, examining elements of collaboration and activity and how those elements are affected by the design and configuration of space. These studies however produced scattered and some times contradictory results, due to the lack of larger datasets and common sets of methodologies. This thesis examines one such large dataset using a newly developed unified framework that includes a common structure for all data, a spatial model to represent configuration in multiple scales and a set of statistical methods to extract meaningful information. The dataset contains around 40 companies in the UK, most of which are based around London and are of different scales, from single-floor workplaces to large multiple-campus offices. A complete workflow for working with this dataset is described, including existing metrics from Visibility Graph Analysis in extensive detail, but also newly developed ones such as 'Travel Concentration', a metric meant to capture attractor-driven effects. The analysis focuses on examining spatial configuration against movement and interaction in three scales, at the floor level (macro), the room level (meso) and the location level (micro), allowing for insights to emerge for the various parts of the design process. A variety of statistical models is presented with different levels of predictive strength, and which can be used for different purposes, but which may also depend on the size of the dataset and how biased an approach is to be taken. The results show that, in general, movement was more predictable than interaction, but also that the latter was a much more complex activity that became more predictable when broken down to other types, such as visiting and chatting interactions. More specifically, it was found that in the larger scales, both activities were mainly affected by the seat density of the workplaces, while in smaller scales the attractor-driven nature of movement became more apparent. Interaction on the other hand was found to relate very much to the availability of space and thus potential people to interact with as it happened mainly in workspaces. The thesis provides these results in the form of characteristics of spaces that tend to attract each activity (as predicted by each statistical model), but also as actionable insights that a designer might use in the design process.
Social distancing and ventilation were emphasized broadly to control the ongoing pandemic COVID-19 in confined spaces. Rationales behind these two strategies, however, were debated, especially regarding quantitative recommendations. The answers to “what is the safe distance” and “what is sufficient ventilation” are crucial to the upcoming reopening of businesses and schools, but rely on many medical, biological, and engineering factors. This study introduced two new indices into the popular while perfect-mixing-based Wells-Riley model for predicting airborne virus related infection probability – the underlying reasons for keeping adequate social distance and space ventilation. The distance index Pd can be obtained by theoretical analysis on droplet distribution and transmission from human respiration activities, and the ventilation index Ez represents the system-dependent air distribution efficiency in a space. The study indicated that 1.6-3.0 m (5.2-9.8 ft) is the safe social distance when considering aerosol transmission of exhaled large droplets from talking, while the distance can be up to 8.2 m (26 ft) if taking into account of all droplets under calm air environment. Because of unknown dose response to COVID-19, the model used one actual pandemic case to calibrate the infectious dose (quantum of infection), which was then verified by a number of other existing cases with short exposure time (hours). Projections using the validated model for a variety of scenarios including transportation vehicles and building spaces illustrated that (1) increasing social distance (e.g., halving occupancy density) can significantly reduce the infection rate (20-40%) during the first 30 minutes even under current ventilation practices; (2) minimum ventilation or fresh air requirement should vary with distancing condition, exposure time, and effectiveness of air distribution systems.