Computational Model for Pedestrian Movement and Infectious Diseases Spread During Air Travel: A Molecular Dynamics-Like Numerical Approach

There is direct evidence of the transmission of fatal infectious pathogens in large human gatherings. Air transportation is no exception. The mixing of susceptible and infectious individuals in this high-density man-made environment involves pedestrian movement which is generally not taken into account in modeling studies of disease dynamics. This thesis addresses this problem through a multiscale model that combines pedestrian dynamics with stochastic infection spread models. This generic model is applicable to several directly transmitted diseases. Through this multiscale framework, the effectiveness of certain layout and strategies in suppressing the disease spread in highly crowded locations such as airplanes, airports and waiting queues is quantified. Inherent variability in human behavior leads to a larger parameter space. This large parameter space is addressed by using novel parallel algorithms for parameter sweep based on low discrepancy parameter sweep, compared to a default lattice-based sweep. This dissertation shows that certain pedestrian movement strategies may be adopted during an outbreak to reduce pedestrian-to-pedestrian contacts. For instance, two-section boarding leads to lower infections whereas all deplaning strategies have a similar effect. Winding queues configurations at security checkpoints or theme parks have a major effect on pedestrians’ interaction. A queue of two-zones with two inlets and outlets and vertically portioned short aisles is superior over the other assessed configurations in terms of reduced infection. In terms of parameter sweep of the large domain, a low discrepancy Halton sequence is used for uncertainty quantification. This method has proven to be efficient and less time consuming when applied to at least one model of the entire multidisciplinary model compared to the default lattice-based model

Random Actuation Pattern Optimization by Genetic Algorithm for Ultrasonic Structural Health Monitoring of Plates

The objective of this research is to investigate an optimized two-dimensional random pattern of uniformly excited points using the Genetic Algorithm (GA) technique for structural health monitoring. The point excitations generate ultrasonic waves in both isotropic and anisotropic materials that can be effective in diagnosing structural defects. The formed ultrasonic waves can constructively interfere and send out an intense wave beam to a predetermined target. The constructed wave beams can be steered to different directions with variable target distances. In the GA, the cost function is constructed to reduce main lobe beamwidth, eliminate grating lobes and suppress sidelobes’ levels. Mathematical modelling, finite element simulations, and optimizations are successively performed to achieve the objectives. Secondly Firstly, a mathematical beamforming model is developed to describe the excitation pattern of which each point is excited at the same time delay with a uniform weighting factor. The derived methodology accounts for enclosing all excitations within a certain aperture. The centroid of the emitting sources is also kept at the origin of the Cartesian coordinate within a slight tolerance range. For the near field, in isotropic materials, the excitation points lay on equally spaced circular arcs centered at the target point. In anisotropic materials, such as composites, the wave amplitude and phase velocity are highly dependent on fiber directions. Because of anisotropic nature, the excitation geometry becomes quite complicated. Secondly, finite element models for aluminum and composite plates are simulated to extract wave characteristics, such as displacement amplitudes, phase velocity profiles and slowness curves. These data are implemented later in the optimization algorithm. A quarter plate of radius 150mm and 1.125mm thickness is modelled as a three-dimensional solid part. A concentrated force with a 2.5 cycle-Hanning window sinusoidal signal is applied at the center of the plate and the boundaries are chosen to be symmetrical. Radial sensors at 5 degrees increments are positioned at 50mm from the excitation source to measure wave properties. The simulation results show that the amplitude and velocity are uniform for isotropic materials whereas the waves propagate rapidly with higher amplitudes along the fibers in anisotropic materials. Thirdly, after collecting all the required information, a GA optimization technique is applied to generate the excitation population of x- and y-coordinates. The pre-determined population is permutated, cross-overed and mutated so that additional possibilities are produced. The same process is repeated for many generations until the local optimum result is obtained. Finally, the near field beamforming is plotted in MATLAB at different actuation point numbers for the isotropic and anisotropic materials. The results are then compared to other linear, circular and planar patterns found in literature

Multiscale Pedestrian Dynamics and Infection Spread Model for Policy Analysis

In this paper, we present a formulation for a multiscale model combining a social force based pedestrian movement including collision avoidance and a stochastic infection dynamics framework to evaluate the spread of multiple infectious diseases during air travel. We apply the multiscale model to evaluate pedestrian movement strategies that can reduce infection spread during air travel. The results are presented for airport lounge and airplane boarding and deplaning. Use of parallel computing to evaluate the vast parameter space created due to stochasticity and discretionary pedestrian behavior is addressed

Multi-scale Models for Transportation Systems Under Emergency Conditions

The purpose of this study is to investigate human behavior in emergencies. More specifically, agent-based simulation and social force models were developed to examine the impact of various human and environmental factors on the efficiency of the evacuation process, through a series of case studies. The independent variables of the case studies include the number of exits, the number of passengers, the evacuation policies, and instructions, as well as the queue configuration and wall separators. The results revealed the location of the exits, number of exits, evacuation strategies, and group behaviors all significantly impact the total time of the evacuation. For the queue configuration, short aisles lower infection spread when rope separators were used. The findings provide new insights in designing layout, planning, practice, and training strategies for improving the effectiveness of the pedestrian evacuation process under emergency

Parameter space exploration in pedestrian queue design to mitigate infectious disease spread

Reducing the interactions between pedestrians in crowded environments can potentially curb the spread of infectious diseases including COVID-19. The mixing of susceptible and infectious individuals in many high-density man-made environments such as waiting queues involves pedestrian movement, which is generally not taken into account in modeling studies of disease dynamics. In this paper, a social force-based pedestrian-dynamics approach is used to evaluate the contacts among proximate pedestrians which are then integrated with a stochastic epidemiological model to estimate the infectious disease spread in a localized outbreak. Practical application of such multiscale models to real-life scenarios can be limited by the uncertainty in human behavior, lack of data during early stage epidemics, and inherent stochasticity in the problem. We parametrize the sources of uncertainty and explore the associated parameter space using a novel high-efficiency parameter sweep algorithm. We show the effectiveness of a low-discrepancy sequence (LDS) parameter sweep in reducing the number of simulations required for effective parameter space exploration in this multiscale problem. The algorithms are applied to a model problem of infectious disease spread in a pedestrian queue similar to that at an airport security check point. We find that utilizing the low-discrepancy sequence-based parameter sweep, even for one component of the multiscale model, reduces the computational requirement by an order of magnitude.Journal ArticleFinal article published online ahead of prin

Multiscale model for the optimal design of pedestrian queues to mitigate infectious disease spread

There is direct evidence for the spread of infectious diseases such as influenza, SARS, measles, and norovirus in locations where large groups of people gather at high densities e.g. theme parks, airports, etc. The mixing of susceptible and infectious individuals in these high people density man-made environments involves pedestrian movement which is generally not taken into account in modeling studies of disease dynamics. We address this problem through a multiscale model that combines pedestrian dynamics with stochastic infection spread models. The pedestrian dynamics model is utilized to generate the trajectories of motion and contacts between infected and susceptible individuals. We incorporate this information into a stochastic infection dynamics model with infection probability and contact radius as primary inputs. This generic model is applicable for several directly transmitted diseases by varying the input parameters related to infectivity and transmission mechanisms. Through this multiscale framework, we estimate the aggregate numbers and probabilities of newly infected people for different winding queue configurations. We find that the queue configuration has a significant impact on disease spread for a range of infection radii and transmission probabilities. We quantify the effectiveness of wall separators in suppressing the disease spread compared to rope separators. Further, we find that configurations with short aisles lower the infection spread when rope separators are used.Journal ArticleFinal article publishe

Model based policy analysis for infection spread during air transportation

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Multiscale model for pedestrian and infection dynamics during air travel

In this paper, we developed a novel multiscale model combining social-force based pedestrian movement with a population level stochastic infection transmission dynamics framework. The model is then applied to study the infection transmission within airplanes and the transmission of Ebola virus through casual contacts. Drastic limitations on air-travel during epidemics, such as during the 2014 Ebola outbreak in West Africa, carry considerable economic and human costs. We use the computational model to evaluate the effects of passenger movement within airplanes and air-travel policies on the geospatial spread of infectious diseases. We found that boarding policy by an airline is more critical for infection propagation compared to deplaning policy. Enplaning in two sections resulted in fewer infections than the currently followed strategy with multiple zones. In addition, we found that small commercial airplanes are better than larger ones at reducing number of new infections in a flight. Aggregated results indicate that passenger movement strategies and airplane size predicted through these network models can have significant impact on an event like 2014 Ebola epidemic. The methodology developed here is generic and can be readily modified to incorporate impact from outbreak of other directly transmitted infectious diseases.Journal ArticlePublishe