Effect of Vaccination on Risk of Exposure to Airborne Infectious Disease During the Boarding Process in a Commercial Aircraft Using Agent-Based Simulation

As increasing proportions of the world's population have received at least one dose of the vaccine against COVID-19, everyday activities start to be resumed, including travels. The present study investigates the impact of immunization on the risk of exposure to an infectious disease such as COVID-19, during the boarding process in a commercial airplane. An agent-based simulation model considers different vaccine types and vaccination rates among passengers. The results show significant decrease in the median exposure risk, when the vaccination rate increases from 0% to 100%, but also that people in seats adjacent to an infectious passenger are in much higher risk, for a similar vaccination coverage. Such results provide quantitative evidence of the importance of mass immunization, and also that, when full vaccination is not guaranteed for 100% of passengers, it may be recommendable to avoid full occupancy of the aircraft, by implementing physical distancing when assigning seats. © 2022 IEEE.

Identifying Correlates of Emergent Behaviors in Agent-Based Simulation Models Using Inverse Reinforcement Learning

In large agent-based models, it is difficult to identify the correlate system-level dynamics with individual-level attributes. In this paper, we use inverse reinforcement learning to estimate compact representations of behaviors in large-scale pandemic simulations in the form of reward functions. We illustrate the capacity and performance of these representations identifying agent-level attributes that correlate with the emerging dynamics of large-scale multi-agent systems. Our experiments use BESSIE, an ABM for COVID-like epidemic processes, where agents make sequential decisions (e.g., use PPE/refrain from activities) based on observations (e.g., number of mask wearing people) collected when visiting locations to conduct their activities. The IRL-based reformulations of simulation outputs perform significantly better in classification of agent-level attributes than direct classification of decision trajectories and are thus more capable of determining agent-level attributes with definitive role in the collective behavior of the system. We anticipate that this IRL-based approach is broadly applicable to general ABMs. © 2022 IEEE.

From Research to Crisis Management: Multiagent Simulation for Local Governments

During the COVID-19 pandemic, a rise of (agent-based) simulation models for predicting future developments and assessing intervention scenarios has been observed. At the same time, dashboarding has become a popular way to aggregate and visualise large quantities of data. The AScore Pandemic Management Cockpit brings together multiagent-based simulation (MABS) and analysis functionalities for crisis managers. It combines the presentation of data and forecasting on the effects of containment measures in a modular, reusable architecture that streamlines the process of use for these non-researcher users. In this paper, the most successful features and concepts for the simplification of simulation usage are presented: definition of scenarios, limitation of parameters, and integrated result visualisation, all bundled in a web-based service to offer a low-barrier entry to the usage of MABS in decision-making processes. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The Effects of COVID-19 On The Food Supply Chain As A Result Of Living In Close Quarters

An efficient and functional supply chain is essential for economies to prosper. Pandemics, however, have proven to be a global challenge that has disrupted the supply chain's routine operations. Inspired by the impact of the COVID-19 pandemic, this paper is studies the effects of COVID-19 on the food supply chain as a result of living in close quarters. To determine how much the food supply chain has been impacted by the COVID-19 pandemic, we employ an agent-based simulation model, combined with an SEISR (Susceptible, Exposed, Infectious, Symptomatic, and Recovered) disease model, to quantify the impact on the food supply chain in terms of productivity, disruption time, and the number of sick workers. In relation to how many contacts workers have in a day, five social distance metrics were varied taking into account infection probabilities. A key finding is that social distance practices and the level of contacts that occur at a time along with the level of infection probability define the level of impact the pandemic has on the food supply chain. Essentially it is seen that the pandemic indeed has a disruptive effect on the food supply chain and workers living in close quarters. © 2022 IISE Annual Conference and Expo 2022. All rights reserved.

Investigation of airborne exposure risk to infectious diseases during aircraft boarding process using agent-based modeling

The aircraft boarding process is characterized by great movement and close contact between passengers in a confined space, which is a situation of particular concern considering the risk of exposure to airborne infectious diseases such as the COVID-19. In order to evaluate the airborne exposure risk during a commercial aircraft boarding process, an agent-based simulation model approach is adopted in the present work. Since the elderly population is one of the most at risk groups, special features are included in the simulation model in order to evaluate how this group is affected in the process. Three aspects are considered: priority boarding (elders boarding order);boarding strategy;and social distancing measures. The main findings are that care must be taken when interpreting average exposure risks, since although the overall risk of exposure is low, there may be cases in which significant risk is presented. © 2022, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

High Performance Agent-Based Modeling to Study Realistic Contact Tracing Protocols

Contact tracing (CT) is an important and effective intervention strategy for controlling an epidemic. Its role becomes critical when pharmaceutical interventions are unavailable. CT is resource intensive, and multiple protocols are possible, therefore the ability to evaluate strategies is important. We describe a high-performance, agent-based simulation model for studying CT during an ongoing pandemic. This work was motivated by the COVID-19 pandemic, however framework and design are generic and can be applied in other settings. This work extends our HPC-oriented ABM framework EpiHiper to efficiently represent contact tracing. The main contributions are: (i) Extension of EpiHiper to represent realistic CT processes. (ii) Realistic case study using the VA network motivated by our collaboration with the Virginia Department of Health. © 2021 IEEE.

Measuring Proximity of Individuals during Aircraft Boarding Process with Elderly Passengers Through Agent-Based Simulation

The COVID-19 pandemic imposed severe restrictions to the mobility of people worldwide, bringing as a consequent great losses to the air transportation industry. The evaluation of biosafety risk has never been more critical for the recovery of transport and economic activity. Elderly passengers constitute a specially vulnerable population to infectious diseases. The main objective of the present study was to investigate the factors that lead to increased proximity of individuals during the boarding process when elderly passengers are present. In order to do so, an agent-based simulation model was built to represent the boarding process in a Boeing 737 aircraft. The simulated results indicate that elderly passengers are less exposed to contact with other individuals during boarding process when they are the last passengers to come onboard and social distancing actions are taken, although this strategy may increase total boarding time. © 2021 IEEE.