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  1. Abstract Infections produced by non-symptomatic (pre-symptomatic and asymptomatic) individuals have been identified as major drivers of COVID-19 transmission. Non-symptomatic individuals, unaware of the infection risk they pose to others, may perceive themselves—and be perceived by others—as not presenting a risk of infection. Yet, many epidemiological models currently in use do not include a behavioral component, and do not address the potential consequences of risk misperception. To study the impact of behavioral adaptations to the perceived infection risk, we use a mathematical model that incorporates the behavioral decisions of individuals, based on a projection of the system’s future state over amore »finite planning horizon. We found that individuals’ risk misperception in the presence of non-symptomatic individuals may increase or reduce the final epidemic size. Moreover, under behavioral response the impact of non-symptomatic infections is modulated by symptomatic individuals’ behavior. Finally, we found that there is an optimal planning horizon that minimizes the final epidemic size.« less
    Free, publicly-accessible full text available December 1, 2022
  2. Abstract—Evacuation planning methods aim to design routes and schedules to relocate people to safety in the event of natural or man-made disasters. The primary goal is to minimize casualties which often requires the evacuation process to be completed as soon as possible. In this paper, we present QueST, an agent-based discrete event queuing network simulation system, and STEERS, an iterative routing algorithm that uses QueST for designing and evaluating large scale evacuation plans in terms of total egress time and congestion/bottlenecks occurring during evacuation. We use the Houston Metropolitan Area, which consists of nine US counties and spans an areamore »of 9,444 square miles as a case study, and compare the performance of STEERS with two existing route planning methods. We find that STEERS is either better or comparable to these methods in terms of total evacuation time and congestion faced by the evacuees. We also analyze the large volume of data generated by the simulation process to gain insights about the scenarios arising from following the evacuation routes prescribed by these methods.« less
  3. Abstract—It is well known that physical interdependencies exist between networked civil infrastructures such as transportation and power system networks. In order to analyze complex nonlinear correlations between such networks, datasets pertaining to such real infrastructures are required. However, such data are not readily available due to their proprietary nature. This work proposes a methodology to generate realistic synthetic power distribution networks for a given geographical region. A network generated in this manner is not the actual distribution system, but its functionality is very similar to the real distribution network. The synthetic network connects high voltage substations to individual residential consumersmore »through primary and secondary distribution networks. Here, the distribution network is generated by solving an optimization problem which minimizes the overall length of the network subject to structural and power flow constraints. This work also incorporates identification of long high voltage feeders originating from substations and connecting remotely situated customers in rural geographic locations while maintaining voltage regulation within acceptable limits. The proposed methodology is applied to the state of Virginia and creates synthetic distribution networks which are validated by comparing them to actual power distribution networks at the same location. Index Terms—synthetic distribution networks, radial networks, Mixed Integer Linear Programming« less
  4. We develop a methodology for comparing two or more agent-based models that are developed for the same domain, but may differ in the particular data sets (e.g., geographical regions) to which they are applied, and in the structure of the model. Our approach is to learn a response surface in the common parameter space of the models and compare the regions corresponding to qualitatively different behaviors in the models. As an example, we develop an active learning algorithm to learn phase transition boundaries in contagion processes in order to compare two agent-based models of rooftop solar panel adoption.
  5. Closed-loop state estimators that track the movements and behaviors of large-scale populations have significant potential to benefit emergency teams during the critical early stages of disaster response. Such population trackers could enable insight about the population even where few direct measurements are available. In concept, a population tracker might be realized using a Bayesian estimation framework to fuse agent-based models of human movement and behavior with sparse sensing, such as a small set of cameras providing population counts at specific locations. We describe a simple proof-of-concept for such an estimator by applying a particle-filter to synthetic sensor data generated frommore »a small simulated environment. An interesting result is that behavioral models embedded in the particle filter make it possible to distinguish among simulated agents, even when the only available sensor data are aggregate population counts at specific locations.« less
  6. We present a method to apply simulations to the tracking of a live event such as an evacuation. We assume only a limited amount of information is available as the event is ongoing, through population-counting sensors such as surveillance cameras. In this context, agent-based models provide a useful ability to simulate individual behaviors and relationships among members of a population; however, agent-based models also introduce a significant data-association challenge when used with population-counting sensors that do not specifically identify agents. The main contribution of this paper is to develop an efficient method for managing the combinatorial complexity of data association.more »The key to our approach is to map from the state-space to an alternative correspondence-vector domain, where the measurement update can be implemented efficiently. We present a simulation study involving an evacuation over a road network and show that our method allows close tracking of the population over time.« less
  7. Residential energy demand dynamics at household level can be studied through demographic, behavioral and physical characteristics of the household. In this paper, we develop an agent-based model using a bottom-up approach to build disaggregated energy demand estimates at the household level at an hourly interval. A household level analysis is made possible via the use of synthetic populations for the urban and rural areas of Virginia, USA. The energy consumption estimate is based on householders’ demographics, their behaviors and activities, ratings of appliances used in energy-related activities, space conditioning fuels, physical characteristics of the home, and weather conditions. Results frommore »the simulation are then validated with actual demand curves from Rappahannock county in Virginia using dynamic time warping. The simulation results show that the model produces realistic energy demand profiles.« less