- Research article
- Open Access
- Open Peer Review
Effect of human movement on airborne disease transmission in an airplane cabin: study using numerical modeling and quantitative risk analysis
© Han et al.; licensee BioMed Central Ltd. 2014
- Received: 11 December 2013
- Accepted: 15 July 2014
- Published: 6 August 2014
Airborne transmission of respiratory infectious disease in indoor environment (e.g. airplane cabin, conference room, hospital, isolated room and inpatient ward) may cause outbreaks of infectious diseases, which may lead to many infection cases and significantly influences on the public health. This issue has received more and more attentions from academics. This work investigates the influence of human movement on the airborne transmission of respiratory infectious diseases in an airplane cabin by using an accurate human model in numerical simulation and comparing the influences of different human movement behaviors on disease transmission.
The Eulerian–Lagrangian approach is adopted to simulate the dispersion and deposition of the expiratory aerosols. The dose–response model is used to assess the infection risks of the occupants. The likelihood analysis is performed as a hypothesis test on the input parameters and different human movement pattern assumptions. An in-flight SARS outbreak case is used for investigation. A moving person with different moving speeds is simulated to represent the movement behaviors. A digital human model was used to represent the detailed profile of the occupants, which was obtained by scanning a real thermal manikin using the 3D laser scanning system.
The analysis results indicate that human movement can strengthen the downward transport of the aerosols, significantly reduce the overall deposition and removal rate of the suspended aerosols and increase the average infection risk in the cabin. The likelihood estimation result shows that the risk assessment results better fit the outcome of the outbreak case when the movements of the seated passengers are considered. The intake fraction of the moving person is significantly higher than most of the seated passengers.
The infection risk distribution in the airplane cabin highly depends on the movement behaviors of the passengers and the index patient. The walking activities of the crew members and the seated passengers can significantly increase their personal infection risks. Taking the influence of the movement of the seated passengers and the index patient into consideration is necessary and important. For future studies, investigations on the behaviors characteristics of the passengers during flight will be useful and helpful for infection control.
- Human movement
- Aerosol dispersion
- Aerodynamic effect
- Infectious disease
- Risk assessment
Nowadays, respiratory infectious diseases are threatening the life of humans around the world . Almost 4 million deaths due to respiratory infections diseases and 1.5 million deaths due to tuberculosis are reported every year . In the past four decades, airborne transmission of respiratory infectious diseases within enclosed environment has been widely reported by many epidemiology reports [3–6]. In an airplane cabin, the airflow from front to back of the cabin (longitudinal) is minimal due to the ventilation system, which means in-flight transmission of disease contaminants should be confined within two rows of an infected passenger [6, 7]. One possible cause of the infection of the passengers seated far away from the index patient might be the movements of the walking crew members or passengers along the aisle in the airplane cabin . The walking persons might also get infected when they move close to the infector .
Previous literatures on the effects of human movement on contaminant transmission
Bjørn and Nielsen 
a life-sized breathing thermal manikin
full-scale test rooms
tracer gas (dinitrogenoxide, N2O)
Exhalation and local effects caused by movement may be worth considering if one wishes to contain contaminants in certain areas
Matsumoto and Ohba 
a movable heated object
a full-scale room model
The moving object mode and speed showed a significant effect on the air temperature distribution and ventilation effectiveness
Shih et al. 
simple object model
an isolated room
tracer gas (carbon dioxide, CO2)
The removal of contaminants was not obviously affected by the moving speed
Choi and Edwards 
a realistic walking human model
a Room–Room and a Room–Hall configuration
The rate of mass transport increases as the walking speed increases, but the total amount of material transported is more influenced by the initial proximity of the human from the doorway.
Mazumdar et al. 
simple object model
a single inpatient ward
tracer gas (sulfur hexafluoride, SF6)
The average concentration change in the breathing levels in the ward was generally small
Poussou et al. 
a moving object
a one-tenth scale water-based model
Human movement inside enclosed environments could significantly influence contaminant transport and personal exposures to contaminants.
Mazumdar et al. 
simple object model
an airplane cabin
dye/ tracer gas
The movement of a crew member or a passenger could carry contaminants in its wake to as many rows as the person passed
Wang and Chow 
three different moving human models
an isolation room
Human walking disturbed the local velocity field, and the increase of walking speed could effectively reduce the overall number of suspended droplets
Choi and Edwards 
a realistic walking human model
a room compartment
tracer gas (sulfur hexafluoride, SF6)
Faster walking speed resulted in less mass transport from the contaminated room into the clean room
According to the transmission mechanism of respiratory infectious disease, the diffusion and dispersion of the aerosols expelled by respiratory activities are important and necessary for infection risk assessment . The airborne transmission of these aerosols in an airplane cabin is significantly different from the diffusion of gaseous contaminants [8, 18, 19]. The methods used for risk assessment of expiratory aerosols and gaseous contaminants are also different . Until now, studies on the effects of human movement on aerosols transmission and infection risk distribution are still rare.
In this work, the effects of human movement on respiratory infectious disease transmission are investigated. An in-flight outbreak case is used for investigation. A manikin with a detailed human profile is also used in the computational geometry. The Eulerian–Lagrangian approach is adopted to simulate the dispersion and deposition of the expiratory aerosols. The infection risks of the occupants are assessed by using quantitative risk analysis, and the influence of human movement on infectious disease transmission is also analyzed. Likelihood analysis is then performed as a hypothesis test on the input parameters and the different human movement pattern assumptions.
The outbreak case and computational geometry
Gambit (version 2.4.6) was used to build the geometry domain and generate the cells for CFD simulation. The meshes were automatically generated by Gambit according to the mesh type and maximum mesh size. The whole region around the seated passengers and the standing person were meshed by unstructured grids of tetrahedron. The maximum mesh size was 0.03 m and the total number was 9,098,636. Other parts of the cabin (the aisle behind and in front of the standing person) were meshed by structured grids of hexahedron. The maximum mesh size was 0.025 m and the total number was 99,264. The grid system was chosen based on the grid convergence index (GCI) analysis . By comparing the computed velocity magnitudes at 800 selected points in the GCI analysis, finer grid system did not have much improvement in GCI compared to the selected grid system (GCIfiner < 5%) . So a grid system containing 9,197,900 cells was finally adopted in this computational geometry.
In this work, a multiphase numerical model based on the Eulerian–Lagrangian approach was adopted, which has been widely employed in aerosol dynamics simulation in enclosed environments [17, 22–24]. In this approach, the governing equations of the carrier phase were numerically solved in the computational geometry based on the Eulerian framework . Transient species transport model for water vapor was added to the Eulerian carrier phase models to represent the humidity in the air . For the discrete phase, the governing equations were described in the Lagrangian framework. Each aerosol released from the injections point was tracked individually in the Lagrangian frame for its instantaneous position and velocity.
The governing equations for the carrier phase and the discrete phase were solved by using a finite-volume based code, ANSYS (version 12.1.4). In the transient simulation, the interaction between the discrete phase and the continuous phase was also considered as the external body forces and computed during the continuous phase iterations . The particles were tracked using the Lagrangian method along with the flow equations at the end of each time step. The Re-Normalization Group (RNG) k-ϵ model was used for modeling the turbulence in this computational geometry due to its good accuracy, computing efficiency, robustness and affordability [25–27]. The Differential Viscosity Model and the Swirl Dominated Flow in the RNG options were also used. An enhanced two-layer wall treatment was employed for the prediction of aerosol deposition . Wall unit adaptation was applied in wall-adjacent cells when creating the meshes to ensure that the values of the wall unit y+ meet the requirements of the enhanced wall treatment . The turbulent dispersion and the random walking of the aerosols are also considered by using the Thermophoretic Force and the Brownian Motion options in the DPM model. To simulate human movement, the layering meshing scheme of the dynamic mesh method was used [13, 17, 28]. The whole mesh domain was split into two mesh zones: stationary zone and dynamic zone , as shown in Figure 1. The surfaces between the static mesh zone and the dynamic mesh zone are set as grid interfaces. The grid sizes on the two sides of the interfaces are different. The maximum mesh size for the static mesh zones and the dynamic mesh zones is 0.03 m and 0.05 m, respectively. The data exchange in the interface between the static and dynamic mesh domains was realized by the grid interface principles for the sliding mesh theory . The semi-implicit method for pressure-linked equations (SIMPLE) algorithm was employed to solve the pressure–velocity coupling equations in the steady-state. The Pressure-Implicit with Splitting of Operators (PISO) algorithm was employed for transient simulation. The second order upwind scheme was used for the treatment of the convection and diffusion-convection terms in the governing equation. The method used to simulate human movement has been verified by experimental and numerical investigations .
Boundary conditions and case setup
The boundary conditions in the numerical simulation
RH 20% (0.004895)
(9.7 L/s per person)
Nose and mouth of the index patient
10 m/s, t = 0-0.1 s
RH 50% (0.01224)
t = 0-0.4 s, Reflect
6 m/s, t = 0.1-0.2 s
4 m/s, t = 0.2-0.3 s
2 m/s, t = 0.3-0.4 s
t > 0.4 s, Trap
0 m/s, t > 0.4 s
Open area: 0.000968 m2
Back and front surface
Size distribution of the aerosol injections
Original size (μm)
Size distribution 
The cases investigated in the numerical simulation
Movement start time (s)
Moving speed (m/s)
Index person at 9E
t = 0 s
Index person at 9E
t = 0 s
t = 1 s
Index person at 9E
t = 0 s
t = 1 s
t = 3 s
t = 0 s
t = 6 s
t = 0 s
In the numerical simulation, the time step was 0.1 s for t ≤ 10s and 0.2 s for t > 10s when the standing person was not moving. During human movement, the time step was 0.1 s for moving speed of 0.5 m/s and 0.05 s for 1.0 m/s. Each case was computed in a 4-node Linux cluster. Each node of the cluster had eight processors (2.4 GHz Intel 64) and 16 GB of memory. The calculation time of each case was 180–220 hours, depending on the total number of time steps and iterations.
Risk assessment and likelihood analysis
where D(x,t) is the intake fraction of the susceptible passengers for one cough. x is the spatial location c is the pathogen concentration in the expiratory fluid, 106 pfu/ml for the SARS-CoV . p is the pulmonary ventilation rate, 7.5 l/min . f(t) is the viability function of pathogens in the aerosols. Since the SARS-CoV may retain its infectivity for minutes and gradual loss for as long as several days [40, 41], the viability function of the SARS-CoV is taken as 75% after aerosolization and remains the same during the first a few minutes [22, 40, 41]. m is the total number of size bins. N c is the total quantity of pathogens produced in a cough, N c = V c c, where V c is the total volume of the droplets produced in a cough, 6.7 × 10-3 ml[18, 42]h l is the ratio of the number of droplets of the lth size bin in a cough to the number of injected particles in the numerical model, in which the number of droplets of the lth size bin can be calculated according to the original size distribution of the cough and V c . v(x,t) is the volume density of expiratory droplets in the breathing zone of the subject induced by one cough, ml/l of air. The breathing zone of each passenger can be defined as a hemisphere with 0.3 m radius at the nose . For the human model used in this work, the volume of the breathing zone is 0.005721 m 3 for each passenger. Then v(x,t) can be calculated according to the CFD simulation results as the total volume of all the expiratory droplets in the breathing zone divided by the volume of the breathing zone . β l is the respiratory deposition fraction of the aerosols of the lth size bin, %, which can be calculated according to the size and deposition location of the aerosols . According to the infection mechanism of SARS, the human angiotensin 1-converting enzyme 2 (hACE2) has been confirmed to be the receptor of the SARS-CoV. The hACE2 can be detected in ciliated airway epithelial cells of human airway tissues derived from nasal and tracheobronchial regions. And the infectivity of the SARS-CoV on the ciliated airway epithelial cells derived from nasal and tracheobronchial regions shows no difference . So the aerosols that deposit in the head airway and tracheobronchial regions of the respiratory tract can be accounted in the intake dose .
where P I is the infection risk of the susceptible passenger after the flight, which demonstrates the infection possibility of the susceptible passenger; t 0 is the exposure time interval of the flight, hr; f s is the cough frequency, 18/hr ; r l is the infectivity of pathogens in the droplets of the lth size bin; and r is the integrated infectivity factor for all pathogens. For the SARS-CoV, the infectivity of pathogens contained in the droplets of different size classes show no differences, hence the infectivity r l can be expressed as r. According to the experiments on mice for the development of vaccine, efficient replication of the virus is found in the respiratory tract of the mice after they are administered at a very low dose of the SARS-CoV [47, 48], indicating high infectivity of the SARS-CoV. Since no other infectivity data is found, the infectivity factor r is assumed as 1 per pfu of virus in this work because of the high infectivity of the SARS-CoV. In Eq. (2), N c f s t 0 D(x, t 0) can also be regarded as the intake dose of the passenger.
where D(x,t) is the intake fraction of the susceptible passenger, which is defined in Eq. (1) and used in Eq. (2); D q (x,t) is the intake fraction of the susceptible passenger for the qth movement behavior; and a q is the possibility of the qth movement behavior; .
In the risk assessment approach, average or assumed values were used for the input parameters of Eq. (1) and (2), e.g. the pathogen generation rate of the index patient. In the actual outbreak case, the values of these parameters may be different from the average values because of the individual differences of the index patients. These differences may significantly increase the uncertainty of the risk assessment results. So the likelihood analysis was performed as a hypothesis test on the input parameters and assess which human movement pattern assumption was most likely to be the actual case . During the estimation of likelihood, the uncertainties of the unknown parameters were all considered in the quanta generation rate. The quanta generation rate was the generation rate of the infective pathogens, which can be regarded as the multiple of the infectivity factor to the pathogen generation rate (rN c f s , hr − 1), as described in Sze To & Chao . While the risk assessment provided quantitative information on how did human movement affect the infection risks of the passengers, the likelihood analysis served as a better tool to estimate the unknown information in the actual outbreak case.
where is the average relative likelihood; S is the total number of the divided groups; N s is the total number of susceptible people in the sth group; is the relative likelihood of the sth group; is the average infection risk of the sth group; and n s is number of infected people in the sth group.
In this work, the steady-state airflow pattern in the cabin was used as the initial condition for the transient simulation. In steady-state, the cold air comes into the airplane cabin from the supply inlets, flows downward and sideward through the cabin and exits from the outlets located at the bottom of the sidewalls. No symmetrical recirculation zone exists, unlike the airflow pattern in the twin-aisle airplane [18, 19, 22]. Downward airflow can be found on both sides of the aisle, which is induced by the ventilation system. The human thermal plume is not apparent because of the significant downward airflow induced by the ventilation system. The heat effects of the human body can influence the temperature distribution in the local area around the human body. The influence area of these downward airflow is also larger than that of the respiratory exhalation flows of the seated passengers, which indicates that the influence of the personal respiratory exhalation flows of the seated passengers on the airflow field is insignificant comparing with that induced by the ventilation system.
When the standing person moves along the aisle, the airflow pattern in the airplane is affected by human movement, similar to the results given by previous studies [8, 11]. Significant downward airflow exists in the wake behind the torso, which enhances the downward movement of the room air induced by the ventilation system. Hence human movement can disturb the air distribution in a local region and influence the airflow motion in the airplane cabin. More details about the airflow pattern in the airplane cabin, and discussions on the human thermal plume and the effects of human movement on the flow field can be found in the Additional file 1.
Aerosol dispersion and deposition
From Figure 2(a), similar trends can be found for all cases. The average lateral positions first increase to 3.61 m in 0.8 s after the injections and then keep decreasing. Higher moving speeds lead to a slower decreasing rate. In the computational geometry, the index patient was seated at X = 3.585 m and the position of the centerline was X = 2.29 m. So Figure 2(a) suggest that the airflow induced by human movement will slightly prevent the aerosols from moving across the aisle of the airplane cabin. Figure 2(b) shows that the average longitudinal position keeps increasing after the injections for case 1 ~ 3. Since the airflow from front to back of the cabin (longitudinal direction) is minimal [6, 7], the longitudinal movement of the aerosols of case 1 is mainly due to the initial momentum given by the cough. Figure 2(b) also indicates that human movement may enhance the mixing of room air in the airplane cabin, which may result in the transport of the aerosols along the moving path in both the positive and negative longitudinal direction, not only following the coughing direction or the moving direction. This is also proven by the results of Exposure assessment shown in Section 3.3, in which the aerosol concentrations in the breathing zones of the passengers who are seated ahead and behind the index patient will increase after the movement of the standing person. As shown in Figure 2(c), the average vertical position of case 1 remains at about 1.20 m and starts to fluctuate after 25 s. But for case 2 and 3, it keeps decreasing and is lower than 0.5 m after 40 s. So human movement may cause the suspended aerosols to fall down to the ground and result in a downward transport effect. During human walking, apparent downwash flow and downward contaminant transport can be found in the wake behind the torso of the human body . Thus the downward transport of the aerosols may be mainly due to flow characteristics of the wake behind the human body. In an airplane cabin, not only the ventilation system but also the wake of a moving human can strengthen the downward transport of the aerosols. Higher moving speed may have a stronger downward transport effect.
Risk assessment and likelihood analysis
With the moving person and the index patient excluded, the average infection risk of all the seated passengers is 0.2015, 0.2051 and 0.2096 for case 1 ~ 3, respectively. This result also proves that human movement may increase the infection risk in the airplane cabin. Especially, in case 2 and 3, the infection risk of the moving person is 0.55 for moving speed of 0.5 m/s and 0.51 for 1.0 m/s, significantly higher than most of the seated passengers (62 passengers for case 2 and 58 for case 3). This result indicates that the infection possibilities of the crew members (who need to walk in the airplane cabin frequently during the flight) may be even higher than 50%. For the seated passengers, walking along the aisle (e.g. go to the washroom) and passing through the high-dose region (around the index patient) may also significantly increase their intake doses and result in much higher infection risks.
Grouping of the passengers in the likelihood analysis
Case 1 (no movement)
Case 2 (0.5 m/s)
Case 3 (1.0 m/s)
Susceptible people N s
Infected people n s
where D 1(x,t), D 2(x,t) and D 3(x,t) are the intake fractions of the susceptible passengers for case 1 ~ 3, respectively, which are calculated by Eq. (2) and shown in Figure 6; a 1 and a 2 are the rates that demonstrate how often the moving person walks in the airplane at moving speeds of 0.5 m/s and 1.0 m/s during the coughs of the index patient, a 1 + a 2 ≤1.
where D 2(MP,t) and D 3(MP,t) are the intake fractions of the moving person calculated in case 2 and 3, respectively; b 1 and b 2 demonstrate how often the seated passengers walk in the airplane at moving speeds of 0.5 m/s and 1.0 m/s during the coughs of the index patient. So b 1 and b 2 are the average possibilities for all the 71 passengers. Due to the limitation of the space in the aisle and the short influence duration of a cough, it can be assumed that only one person may walk through the aisle during each cough. So the average possibility that each passenger walks in the airplane during a cough of the index patient is 1/71 = 0.014, which means b1 + b2 ≤0.014.
By using likelihood analysis, the likelihood values under a range of quanta generation rate, b 1 and b 2 can be obtained (data not shown). The maximum likelihood is 0.763, when the quanta generation rate is 1.36 million, b 1 is 0.014 and b 2 is zero. This likelihood value is much higher than that of the previous cases. So, the risk assessment results will better fit the real case when the movement of the seated passengers is considered. An average walking possibility of 1.4% can be used to represent and simulate the movement behaviors of all the seated passengers.
According to the risk assessment results, the average infection risk of all the seventy-one seated passengers increases by 1.7% ~ 2.2% due to human movement. So the frequent walking of the crew members or the passengers will raise the infection risk level in the airplane cabin. However, a 1.7% ~ 2.2% increase is not so significant. By estimating the likelihoods of a series of human movement assumptions, the no movement case has the highest likelihood and is most likely to be the real case. This conclusion fits with the estimated results given by a previous study which stated that no human movement is the most probable case . This result does not imply that there is no human movement in the real outbreak case, which is impossible. The crew members or the passengers may still walk frequently in the airplane when the index patient is not coughing. It needs to be emphasized that although the no movement case is the most likely case in this outbreak case, the conclusion may not be generalized to all outbreak cases because the numerical simulation and the risk assessment are case-dependent.
Uncertainties and errors still exist in this work. In the risk assessment and the likelihood estimation, an average walking possibility is used for all the seated passengers. This is not accurate because each individual may have a unique behavioral characteristic. So investigations on behavioral characteristics of the occupants in the airplane are strongly needed. And also, only the coughing droplets are numerical simulated in this study. Droplets expelled by breathing and sneezing can also influence the infection risk of each individual. An aerosol source model that takes the droplets expelled by multiple respiratory activities into account may also be helpful for better evaluating the aerosol disseminate process in the airplane cabin. The pathogen concentration in the expiratory fluid was considered as constant in this work. Since the volume concentration of the virus varies with the size of the cough droplets, changes of the pathogen concentration in the droplets of different size may also influence the risk assessment results . Besides, the re-suspension of the aerosols is not considered in this work. During human walking, the movement may lead to the re-suspension of the aerosols from the floor. When taking the re-suspension of the exhaled droplets into consideration, the risk assessment results will be more accurate. However, since the airflow induced by the ventilation system is downward to the outlet (located on the side wall and close to the floor), the re-suspended aerosols can hardly rise to the breathing level of the seated passengers. So the effects of the re-suspended aerosols on the risk assessment results can be considered as insignificant in this case.
In this work, the influence of human movement on airborne disease transmission in an airplane cabin is investigated. An on-flight outbreak case of SARS is chosen to demonstrate the risk assessment process. The Eulerian–Lagrangian approach is used to simulate the dispersion and deposition of infectious droplets expelled by the index patient. The simulation result shows that human movement improves the mixing of the air in the airplane cabin, strengthens the downward transport of the aerosols, and decreases the deposition and removal rate of the aerosols.
The infection risks of the occupants are assessed by using the dose–response model in infection risk assessment. The average infection risk of the seated passengers is 0.2015, 0.2051 and 0.2096 for no human movement, moving speeds of 0.5 m/s and 1.0 m/s, respectively. The assessment result shows that human movement may increase the average infection risk in the cabin, especially for the passengers seated three rows ahead and one row behind the index patient. The likelihood of each case is estimated and the highest likelihood can be found in the no movement case with a quanta generation rate of 0.141 million. So in this SARS outbreak case, the effect of human movement on airborne disease transmission, as it changes the dispersion and mixing pattern of infectious expiratory aerosol, is found to be very insignificant. Since the numerical simulation and the risk assessment are case-dependent, this result may not be generalized to all cases. Moreover, if the seated passengers walk in the airplane cabin, the intake doses of that passengers may also significantly increase and lead to a high infection risks. So the movements of the seated passengers also have a strong influence on the airborne disease transmission in the airplane cabin and may result in significantly higher infection risks for the moving persons.
The results of this work imply that the infection risk distribution in the airplane cabin highly depends on the movement behaviors of the passengers and the index patient. Taking the influence of the movement of the seated passengers into consideration is necessary. A better understanding and estimation of the behavior characteristics of the index patient is also important for infection risk assessment and control in airplane cabin. To reduce the pathogen concentration in the high-dose region close to the index patient, a personal ventilation system may be a feasible solution which is still needed to be carefully designed and verified in airplane cabin operation. Using N95 respirator masks of the index patient or the passengers can also be a simple and viable method to prevent exposure from inhalation the expiratory aerosols. In future studies, investigations on the behaviors characteristics of the passengers during flight will be useful and helpful for infection control.
This paper was supported by HK S.A.R. government general research fund (Grant no. 611310), National Natural Science Foundation of China (Grant No. 91024018), China National Key Basic Research Special Funds Project (Grant No. 2012CB719705), and Tsinghua University Initiative Scientific Research Program (Grant No. 2012THZ02160).
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