Parametric analysis of SARS-CoV-2 dose-response models in transportation scenarios.

Transportation systems involve high-density crowds of geographically diverse people with variations in susceptibility; therefore, they play a large role in the spread of infectious diseases like SARS-CoV-2. Dose-response models are widely used to model the relationship between the trigger of a disease and the level of exposure in transmission scenarios. In this study, we quantified and bounded viral exposure-related parameters using empirical data from five transportation-related events of SARS-CoV-2 transmission. Dose-response models were then applied to parametrically analyze the infection spread in generic transportation systems, including a single-aisle airplane, bus, and railway coach, and then examined the mitigating efficiency of masks by performing a sensitivity analysis of the related factors. We found that dose level significantly affected the number of secondary infections. In general, we observed that mask usage reduced infection rates at all dose levels and that high-quality masks equivalent to FFP2/N95 masks are effective for all dose levels. In comparison, we found that lower-quality masks exhibit limited mitigation efficiency, especially in the presence of high dosage. The sensitivity analysis indicated that a reduction in the infection distance threshold is a critical factor in mask usage.

Point-process modeling of secondary crashes.

Secondary crashes or crashes that occur in the wake of a preceding or primary crash are among the most critical incidents occurring on highways, due to the exceptional danger they present to the first responders and victims of the primary crash. In this work, we developed a self-exciting temporal point process to analyze crash events data and classify it into primary and secondary crashes. Our model uses a self-exciting function to describe secondary crashes while primary crashes are modeled using a background rate function. We fit the model to crash incidents data from the Florida Department of Transportation, on Interstate-4 (I-4) highway for the years 2015-2017, to determine the model parameters. These are used to estimate the probability that a given crash is secondary crash and to find queue times. To represent the periodically varying traffic levels and crash incidents, we model the background rate, as a stationary function, a sinusoidal non-stationary function, and a piecewise non-stationary function. We show that the sinusoidal non-stationary background rate fits the traffic data better and replicates the daily and weekly peaks in crash events due to traffic rush hours. Secondary crashes are found to account for up to 15.09% of traffic incidents, depending on the city on the I-4 Highway.

Identifying mitigation strategies for COVID-19 superspreading on flights using models that account for passenger movement.

BACKGROUND: Despite commercial airlines mandating masks, there have been multiple documented events of COVID-19 superspreading on flights. Conventional models do not adequately explain superspreading patterns on flights, with infection spread wider than expected from proximity based on passenger seating. An important reason for this is that models typically do not consider the movement of passengers during the flight, boarding, or deplaning. Understanding the risks for each of these aspects could provide insight into effective mitigation measures. METHODS: We modeled infection risk from seating and fine-grained movement patterns - boarding, deplaning, and inflight movement. We estimated infection model parameters from a prior superspreading event. We validated the model and the impact of interventions using available data from three flights, including cabin layout and seat locations of infected and uninfected passengers, to suggest interventions to mitigate COVID-19 superspreading events during air travel. Specifically, we studied: 1) London to Hanoi with 201 passengers, including 13 secondary infections among passengers; 2) Singapore to Hangzhou with 321 passengers, including 12 to 14 secondary infections; 3) a non-superspreading event on a private jet in Japan with 9 passengers and no secondary infections. RESULTS: Our results show that the inclusion of passenger movement better explains the infection spread patterns than conventional models do. We also found that FFP2/N95 mask usage would have reduced infection by 95-100%, while cloth masks would have reduced it by only 40-80%. Results indicate that leaving the middle seat vacant is effective in reducing infection, and the effectiveness increases when combined with good quality masks. However, with a good mask, the risk is quite low even without the middle seats being empty. CONCLUSIONS: Our results suggest the need for more stringent guidelines to reduce aviation-related superspreading events of COVID-19.

Architecture-Aware Modeling of Pedestrian Dynamics.

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.

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.

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.

Fuel shortages during hurricanes: Epidemiological modeling and optimal control.

Hurricanes are powerful agents of destruction with significant socioeconomic impacts. A persistent problem due to the large-scale evacuations during hurricanes in the southeastern United States is the fuel shortages during the evacuation. Computational models can aid in emergency preparedness and help mitigate the impacts of hurricanes. In this paper, we model the hurricane fuel shortages using the SIR epidemic model. We utilize the crowd-sourced data corresponding to Hurricane Irma and Florence to parametrize the model. An estimation technique based on Unscented Kalman filter (UKF) is employed to evaluate the SIR dynamic parameters. Finally, an optimal control approach for refueling based on a vaccination analogue is presented to effectively reduce the fuel shortages under a resource constraint. We find the basic reproduction number corresponding to fuel shortages in Miami during Hurricane Irma to be 3.98. Using the control model we estimated the level of intervention needed to mitigate the fuel-shortage epidemic. For example, our results indicate that for Naples- Fort Myers affected by Hurricane Irma, a per capita refueling rate of 0.1 for 2.2 days would have reduced the peak fuel shortage from 55% to 48% and a refueling rate of 0.75 for half a day before landfall would have reduced to 37%.

Constrained Linear Movement Model (CALM): Simulation of passenger movement in airplanes.

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.

Synergy effect in hybrid nanocomposites based on carbon nanotubes and graphene nanoplatelets.

Hybrid nanocomposites reinforced with a mixture of graphene nanoplatelets (GNPs) and carbon nanotubes (CNTs) have shown improvement in filler dispersion while providing a cost-effective alternative to CNT monofiller composites. Depending on their composition, hybrid composites can exhibit electrical performance superior to either of the constituent monofiller composites due to synergistic effects. In this work, we develop a three-dimensional tunneling-based continuum percolation model for hybrid nanocomposites filled with hardcore particles of elliptical GNPs and cylindrical CNTs. Using Monte Carlo simulations, parametric studies of the filler content, composition and morphology are carried out to analyze the conditions required for synergy in percolation onset and electrical conductivity. Our results suggest that for hybrid systems with well-dispersed fillers, the electrical performance is linked to the number of tunneling junctions per filler inside the percolated network of the nanocomposites. More importantly, hybrid composites filled with specific morphology of GNP and CNT, exhibit synergy in their electrical performance when the monofiller composites of each of those exact fillers have similar percolation onset values. The simulations results are in agreement with relevant experimental data on hybrid nanocomposites.

Multiscale model for pedestrian and infection dynamics during air travel.

In this paper we develop a 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 the 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 find 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 the 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 the 2014 Ebola epidemic. The methodology developed here is generic and can be readily modified to incorporate the impact from the outbreak of other directly transmitted infectious diseases.