Dr. Ashok Srinivasan

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.

Airlines have introduced a back-to-front boarding process in response to the COVID-19 pandemic. It is motivated by the desire to reduce passengers’ likelihood of passing close to seated passengers when they take their seats. However, our prior work on the risk of Ebola spread in aeroplanes suggested that the driving force for increased exposure to infection transmission risk is the clustering of passengers while waiting for others to stow their luggage and take their seats. In this work, we examine whether the new boarding processes lead to increased or decreased risk of infection spread. We also study the reasons behind the risk differences associated with different boarding processes. We accomplish this by simulating the new boarding processes using pedestrian dynamics and compare them against alternatives. Our results show that back-to-front boarding roughly doubles the infection exposure compared with random boarding. It also increases exposure by around 50% compared to a typical boarding process prior to the outbreak of COVID-19. While keeping middle seats empty yields a substantial reduction in exposure, our results show that the different boarding processes have similar relative strengths in this case as with middle seats occupied. We show that the increased exposure arises from the proximity between passengers moving in the aisle and while seated. Such exposure can be reduced significantly by prohibiting the use of overhead bins to stow luggage. Our results suggest that the new boarding procedures increase the risk of exposure to COVID-19 compared with prior ones and are substantially worse than a random boarding process.

An overview of high-fidelity modeling of pathogen propagation, transmission and mitigation in the built environment is given. In order to derive the required physical and numerical models, the current understanding of pathogen, and in particular virus transmission and mitigation is summarized. The ordinary and partial differential equations that describe the flow, the particles and possibly the UV radiation loads in rooms or HVAC ducts are presented, as well as proper numerical methods to solve them in an expedient way. Thereafter, the motion of pedestrians, as well as proper ways to couple computational fluid dynamics and computational crowd dynamics to enable high-fidelity pathogen transmission and infection simulations is treated. The present review shows that high-fidelity simulations of pathogen propagation, transmission and mitigation in the built environment have reached a high degree of sophistication, offering a quantum leap in accuracy from simpler probabilistic models. This is particularly the case when considering the propagation of pathogens via aerosols in the presence
of moving pedestrians.

The spread of infectious diseases arises from complex interactions between disease dynamics and human behavior. Predicting the outcome of this complex system is difficult. Consequently, there has been a recent emphasis on comparing the relative risks of different policy
options rather than precise predictions. Here, one performs a parameter sweep to generate a large number of possible scenarios for human behavior under different policy options and identifies the relative risks of different decisions regarding policy or design choices. In particular, this approach has been used to identify effective approaches to social distancing in crowded locations, with pedestrian dynamics used to simulate the movement of individuals. This incurs a large computational load, though. The traditional approach of optimizing the implementation of existing mathematical models on parallel systems leads to a moderate improvement in computational performance. In contrast, we show that when dealing with human behavior, we can create a model from scratch that takes computer architectural features into account, yielding much higher performance without requiring complicated parallelization efforts. Our solution is based on two key observations. (i) Models do not capture human behavior as precisely as models for scientific phenomena describe natural processes. Consequently, there is some leeway in designing a model to suit the computational architecture. (ii) The result of a parameter sweep, rather than a single simulation, is the semantically meaningful result. Our model leverages these features to perform efficiently on CPUs and GPUs. We obtain a speedup factor of around 60 using this new model on two Xeon Platinum 8280 CPUs and a factor 125 speedup on 4 NVIDIA Quadro RTX 5000 GPUs over a parallel implementation of the existing model. The careful design of a GPU implementation makes it fast enough for real-time decision-making. We illustrate it on an application to COVID-19.

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.

Pedestrian dynamics models the walking movement of individuals in a crowd. It has recently been used in the analysis of procedures to reduce the risk of disease spread in airplanes, relying on the SPED model. This is a social force model inspired by molecular dynamics; pedestrians are treated as point particles, and their trajectories are determined in a simulation. A parameter sweep is performed to address uncertainties in human behavior, which requires a large number of simulations. The SPED model’s slow speed is a bottleneck to performing a large parameter sweep. This is a severe impediment to delivering real-time results, which are often required in the course of decision meetings, especially during emergencies. We propose a new model, called CALM, to remove this limitation. It is designed to simulate a crowd’s movement in constrained linear passageways, such as inside an aircraft. We show that CALM yields realistic results while improving performance by two orders of magnitude over the SPED model.

Public health advisories recommend against the use of the N95 respirator by the general public in the current COVID-19 pandemic. These advisories are primarily motivated by the collective goal of reducing the reproduction number to below one. However, cultural factors may dissuade the public from adopting recommendations from models optimized for the collective good. This article presents a discussion of mathematical issues that ought to guide an advisory from an individualistic perspective. In particular, we argue that the public health advisory does not appear justified if one considers non-linearity in the dose-response relationship and heterogeneity in infection load in the context of the COVID-19 pandemic. The N95 respirator promises far greater effectiveness than homemade or surgical masks. However, due to a considerable variation in masks' brands and efficiencies, the public should look into the specific details of each available mask option.

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.

Reducing the number of contacts between passengers on an airplane can potentially curb the spread of infectious diseases. In this paper, a social force based pedestrian movement model is formulated and applied to evaluate the movement and contacts among passengers during boarding and deplaning of an airplane. Within the social force modeling framework, we introduce location dependence on the self-propelling momentum of pedestrian particles. The model parameters are varied over a large design space and the results are compared with experimental observations to validate the model. This model is then used to assess the different approaches to minimize passenger contacts during boarding and deplaning of airplanes. We find that smaller aircrafts are effective in reducing the contacts between passengers. Column wise deplaning and random boarding are found to be two strategies that reduced the number of contacts during passenger movement, and can potentially lower the likelihood of infection spread.

Compute-intensive tasks in high-end high performance computing (HPC) systems often generate large amounts of data, especially floating point data that need to be transmitted over the network. Although computation speeds are very high, the overall performance of these applications is affected by the data transfer overhead. Moreover, as data sets are growing in size rapidly, bandwidth limitations pose a serious bottleneck in several scientific applications. Fast floating point compression can ameliorate the bandwidth limitations. If data is compressed well, then the amount of data transfer is reduced. This reduction in data transfer time comes at the expense of the increased computation required by compression and decompression. It is important for compression and decompression rates to be greater than the network bandwidth; otherwise, it will be faster to transmit uncompressed data directly [1]. Accelerators such as Graphics Processing Units (GPU) provide much computational power. In this paper, we show that the computational power of GPUs and CellBE processor can be harnessed to provide sufficiently fast compression and decompression for this approach to be effective for data produced by many practical applications. In particularly, we use Holt`s Exponential smoothing algorithm from time series analysis, and encode the difference between its predictions and the actual data. This yields a lossless compression scheme. We show that it can be implemented efficiently on GPUs and CellBE to provide an effective compression scheme for the purpose of saving on data transfer overheads.