COVID-19: Agent-Based Simulation-Optimization to Vaccine Center Location Vaccine Allocation Problem

This paper presents an agent-based simulation-optimization modeling and algorithmic framework to determine the optimal vaccine center location and vaccine allocation strategies under budget constraints during an epidemic outbreak. Both simulation and optimization models incorporate population health dynamics, such as susceptible (S), vaccinated (V), infected (I) and recovered (R), while their integrated utilization focuses on the COVID-19 vaccine allocation challenges. We first formulate a dynamic location-allocation mixed-integer programming (MIP) model, which determines the optimal vaccination center locations and vaccines allocated to vaccination centers, pharmacies, and health centers in a multi-period setting in each region over a geographical location. We then extend the agent-based epidemiological simulation model of COVID-19 (Covasim) by adding new vaccination compartments representing people who take the first vaccine shot and the first two shots. The Covasim involves complex disease transmission contact networks, including households, schools, and workplaces, and demographics, such as age-based disease transmission parameters. We combine the extended Covasim with the vaccination center location-allocation MIP model into one single simulation-optimization framework, which works iteratively forward and backward in time to determine the optimal vaccine allocation under varying disease dynamics. The agent-based simulation captures the inherent uncertainty in disease progression and forecasts the refined number of susceptible individuals and infections for the current time period to be used as an input into the optimization. We calibrate, validate, and test our simulation-optimization vaccine allocation model using the COVID-19 data and vaccine distribution case study in New Jersey. The resulting insights support ongoing mass vaccination efforts to mitigate the impact of the pandemic on public health, while the simulation-optimization algorithmic framework could be generalized for other epidemics. [ FROM AUTHOR] Copyright of IISE Transactions is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

EVALUATING THE ETHICAL AND SOCIETAL IMPACTS OF MODELLING PANDEMIC CRISES: EXPERIENCES FROM A WORKSHOP

This paper describes a hybrid (virtual and online) workshop held as part of the EU STAMINA project that aimed to engage project partners to explore ethics and simulation modelling in the context of pandemic preparedness and response. The purpose of the workshop was to consider how the model's design and use in specific pandemic decision-making contexts could have broader implications for issues like transparency, explainability, representativeness, bias, trust, equality, and social injustices. Its outputs will be used as evidence to produce a series of measures that could help mitigate ethical harms and support the greater possible benefit from the use of the models. These include recommendations for policy, data-gathering, training, potential protocols to support end-user engagement, as well as guidelines for designing and using simulation models for pandemic decision-making. This paper presents the methodological approaches taken when designing the workshop, practical concerns raised, initial insights gained, and considers future steps. © SW 2023.All rights reserved

Forecasting of COVID-19 Dynamics by Agent-Based Model

The novel coronavirus pandemic has continued to spread worldwide for more than two years. The development of automated solutions to support decision-making in pandemic control is still an ongoing challenge. This study aims to develop an agent-based model of the COVID-19 epidemic process to predict its dynamics in a specific area. The model shows sufficient accuracy for decision-making by public health authorities. At the same time, the advantage of the model is that it allows taking into account the stochastic nature of the epidemic process and the heterogeneity of the studied population. At the same time, the adequacy of the model can be improved with a more detailed description of the population and external factors that can affect the dynamics of the epidemic process. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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.

Evaluating the Covid-19 Screening Regime for Cross-Border Workers

Global travel and trade have been hit hard by the COVID-19 pandemic. Border closures have impacted both leisure and business travel. The socioeconomic costs of border closure are particularly severe for individuals living and working across state lines, for which previously unhindered passage has been curtailed, and daily commute across borders is now virtually impossible. Here, we examine how the periodic screening of daily cross-border commuters across territories with relatively low COVID-19 incidence will impact the transmission of SARS-CoV-2 across borders using agent-based simulation. We find that periodic testing at practical frequencies of once every 7, 14 or 21 days would reduce the number of infected individuals crossing the border. The unique transmission characteristics of SARS-CoV-2 suggest that periodic testing of populations with low incidence is of limited use in reducing cross-border transmission and is not as cost-effective as other mitigation measures for preventing transmission. © 2022 IEEE.

Agent-Based Simulation for Infectious Disease Modelling over a Period of Multiple Days, with Application to an Airport Scenario.

With the COVID-19 pandemic, the role of infectious disease spreading in public places has been brought into focus more than ever. Places that are of particular interest regarding the spread of infectious diseases are international airport terminals, not only for the protection of staff and ground crew members but also to help minimize the risk of the spread of infectious entities such as COVID-19 around the globe. Computational modelling and simulation can help in understanding and predicting the spreading of infectious diseases in any such scenario. In this paper, we propose a model, which combines a simulation of high geometric detail regarding virus spreading with an account of the temporal progress of infection dynamics. We, thus, introduce an agent-based social force model for tracking the spread of infectious diseases by modelling aerosol traces and concentration of virus load in the air. We complement this agent-based model to have consistency over a period of several days. We then apply this model to investigate simulations in a realistic airport setting with multiple virus variants of varying contagiousness. According to our experiments, a virus variant has to be at least twelve times more contagious than the respective control to result in a level of infection of more than 30%. Combinations of agent-based models with temporal components can be valuable tools in an attempt to assess the risk of infection attributable to a particular virus and its variants.

COVID-19 transmission in U.S. transit buses: A scenario-based approach with agent-based simulation modeling (ABSM)

The transit bus environment is considered one of the primary sources of transmission of the COVID-19 (SARS-CoV-2) virus. Modeling disease transmission in public buses remains a challenge, especially with uncertainties in passenger boarding, alighting, and onboard movements. Although there are initial findings on the effectiveness of some of the mitigation policies (such as face-covering and ventilation), evidence is scarce on how these policies could affect the onboard transmission risk under a realistic bus setting considering different headways, boarding and alighting patterns, and seating capacity control. This study examines the specific policy regimes that transit agencies implemented during early phases of the COVID-19 pandemic in USA, in which it brings crucial insights on combating current and future epidemics. We use an agent-based simulation model (ABSM) based on standard design characteristics for urban buses in USA and two different service frequency settings (10-min and 20-min headways). We find that wearing face-coverings (surgical masks) significantly reduces onboard transmission rates, from no mitigation rates of 85% in higher-frequency buses and 75% in lower-frequency buses to 12.5%. The most effective prevention outcome is the combination of KN-95 masks, open window policies, and half-capacity seating control during higher-frequency bus services, with an outcome of nearly 0% onboard infection rate. Our results advance understanding of COVID-19 risks in the urban bus environment and contribute to effective mitigation policy design, which is crucial to ensuring passenger safety. The findings of this study provide important policy implications for operational adjustment and safety protocols as transit agencies seek to plan for future emergencies. © 2023

Agent-based simulation of pedestrian dynamics for exposure time estimation in epidemic risk assessment.

PURPOSE: With the coronavirus disease 2019 (COVID-19) pandemic spreading across the world, protective measures for containing the virus are essential, especially as long as no vaccine or effective treatment is available. One important measure is the so-called physical distancing or social distancing. METHODS: In this paper, we propose an agent-based numerical simulation of pedestrian dynamics in order to assess the behavior of pedestrians in public places in the context of contact transmission of infectious diseases like COVID-19, and to gather insights about exposure times and the overall effectiveness of distancing measures. RESULTS: To abide by the minimum distance of 1.5 m stipulated by the German government at an infection rate of 2%, our simulation results suggest that a density of one person per 16m2 or below is sufficient. CONCLUSIONS: The results of this study give insight into how physical distancing as a protective measure can be carried out more efficiently to help reduce the spread of COVID-19.

The Impact of E-commerce Delivery Alternatives on Urban Freight Movements and Cost: A Carrier Perspective

The potential impact of e-commerce on freight movements in cities is an important consideration for freight operators and policy makers, however little research focuses on the impact of e-commerce freight movements in urban areas, especially in the South African context. With the growth of e-commerce and the ongoing COVID-19 pandemic, it is necessary to investigate methods to improve last-mile delivery planning for e-commerce deliveries in urban areas. The paper therefore focuses on evaluating the potential impacts of different e-commerce delivery methods (home delivery, collection points, and click-and-collect) on ecommerce freight movements and carrier cost. Results provide a good starting point to understand the potential impacts of delivery decisions and omni-channel design on delivery cost. Results from this analysis can be used by planners, decision-makers, and delivery service providers to glean some useful insights for improved planning of ecommerce operations and offerings. © 2022 IEEE.

Impact of Using Soft Exposure Thresholds in Automatic Contact Tracing

Current automatic exposure notification apps primarily operate based on hard distance/time threshold guidelines (e.g., 2 m/15 min in the United States) to determine exposures due to close contacts. However, the possibility of virus transmission through inhalation for distances over the specified distance threshold might necessitate consideration of soft distance/time thresholds to accommodate all transmission scenarios. In this paper, using a simplifying approximation on the instantaneous rate of the viral exposure versus distance, we extend the definition of "contact"by proposing a soft distance/time threshold which includes the possibility of getting exposed at any distance (within certain limits) around an infected person. We then analyze the performance of automatic exposure notification with Bluetooth-based proximity detection by comparing the exposure results when soft or hard thresholds are used. This study is done through an agent-based simulation platform that allows for a comprehensive analysis using several system parameters. By tuning the parameters of the proposed soft thresholds, a more accurate determination of possible exposures at any distance would be possible. This would enhance the effectiveness of an automatic contact tracing system. Our results indicate the noticeable impact of using the soft distance/time threshold on the exposure detection accuracy. © 2022 IEEE.

Developing Modern Agent Technologies in Combating Covid-19 Exposure: An Application in a Healthcare Facility

This study aims to identify the impact of adherence to Non-Pharmaceutical Interventions (NPI) such as facemask type Cotton Fabric Mask and social distancing on the rate of COVID-19 exposure in waiting areas inside an emergency department. As a methodology, a Multi-Agent Simulation approach was used to model and capture the flow of patients inside the emergency department in this research. Each agent represents a physical entity, including its attributes defined. These agents will collaborate based on the defined rules to achieve the best mimic of the system being modeled. This methodology aims to quantitatively evaluate the performance of preventive measures based on the agent's proximity and exposure time. The number of infections was affected by the application of the facemask. Infections were reduced when facemask adherence and social distancing were applied. The study showed that the application of social distancing has a similar effect to a 20% adherence of agents wearing a facemask. The model also reveals that more agents adhere to the facemask, and the time required to get an agent to the state exposed increases. Waiting areas are a potentially significant contributor to transmission. © 2022 IEEE.

High Resolution Spatio-Temporal Model for Room-Level Airborne Pandemic Spread

Airborne pandemics have caused millions of deaths worldwide, large-scale economic losses, and catastrophic sociological shifts in human history. Researchers have developed multiple mathematical models and computational frameworks to investigate and predict pandemic spread on various levels and scales such as countries, cities, large social events, and even buildings. However, attempts of modeling airborne pandemic dynamics on the smallest scale, a single room, have been mostly neglected. As time indoors increases due to global urbanization processes, more infections occur in shared rooms. In this study, a high-resolution spatio-temporal epidemiological model with airflow dynamics to evaluate airborne pandemic spread is proposed. The model is implemented, using Python, with high-resolution 3D data obtained from a light detection and ranging (LiDAR) device and computing model based on the Computational Fluid Dynamics (CFD) model for the airflow and the Susceptible–Exposed–Infected (SEI) model for the epidemiological dynamics. The pandemic spread is evaluated in four types of rooms, showing significant differences even for a short exposure duration. We show that the room's topology and individual distribution in the room define the ability of air ventilation to reduce pandemic spread throughout breathing zone infection.

Agent-Based Simulation of Social Determinants of Health for Equitable COVID-19 Intervention

In the COVID-19 pandemic, one-size-flts-all interventions have been implemented based on COVID-19 disease models which simulate disease spread on a more generalized scale, lacking specificity for communities in different settings. This approach, not considering the important local health indicators Social Determinants of Health (SDOH), renders inequities and disparity in intervention effectiveness at the local level. This research answers the following questions: how specific SDOH risk profiles impact COVID-19 outbreak severity and how should interventions be implemented to achieve net positive health impact? A novel agent-based disease model was developed using NetLogo to simulate COVID-19 transmission and intervention using relevant SDOH in specific localities. The model is fitted with COVID-19 variant-specific constants such as susceptibility, mortality rate, recovery time, incubation period, mask efficacy, vaccine efficacy, and reinfection rate. Those constants are further calibrated with SDOH such as healthcare access (vaccination and booster rates) and social context (population size, population density, racial profile, and age demographics). Model inputs also include intervention used (mask mandate, testing and isolation, lockdown) and compliance rate to such interventions. The model was validated in Westchester County, NY for two different time periods with Alpha and Omicron yielding 84.2% and 68.5% accuracy respectively. Sensitivity analysis demonstrated: 1) a higher elderly population, lower young population, lower vaccination rate, and higher Hispanic and Black population were all factors that increased outbreak severity;2) all variants had similar death rate after reaching ~25% of population vaccinated;and 3) boosters affected Omicron more than other variants, especially in reducing breakthroughs. Scenario analyses were conducted for four U.S. counties: Hunterdon, NJ;Levy, FL;Monterey, CA;and Coles, IL. These analyses showed that 1) informed interventions based on localities' SDOH would dramatically reduce inequity, 2) interventions have higher impact in localities with higher risk SDOH, and 3) weighing other health and social economic consequences against predicted COVID-19 mortality can achieve holistic equity. The model enables local officials to assess the type, intensity, and timing of interventions to achieve maximum health outcomes. They can weigh the benefits of interventions against the socioeconomic or other risks of inequity to local populations. This research empowers local officials in diverse settings with an accessible modeling tool to remain nimble, stay conscious of health disparities, and better focus limited resources in health related decisions for their communities. © 2022 IEEE.

COVID-19 transmission in U.S. transit buses: A scenario-based approach with agent-based simulation modeling (ABSM)

The transit bus environment is considered one of the primary sources of transmission of the COVID-19 (SARS-CoV-2) virus. Modeling disease transmission in public buses remains a challenge, especially with uncertainties in passenger boarding, alighting, and onboard movements. Although there are initial findings on the effectiveness of some of the mitigation policies (such as face-covering and ventilation), evidence is scarce on how these policies could affect the onboard transmission risk under a realistic bus setting considering different headways, boarding and alighting patterns, and seating capacity control. This study examines the specific policy regimes that transit agencies implemented during early phases of the COVID-19 pandemic inUSA, in which it brings crucial insights on combating current and future epidemics. We use an agent-based simulation model (ABSM) based on standard design characteristics for urban buses in USA and two different service frequency settings (10-min and 20-min headways). We find that wearing face-coverings (surgical masks) significantly reduces onboard transmission rates, from no mitigation rates of 85% in higher-frequency buses and 75% in lower-frequency buses to 12.5%. The most effective prevention outcome is the combination of KN-95 masks, open window policies, and half-capacity seating control during higher-frequency bus services, with an outcome of nearly 0% onboard infection rate. Our results advance understanding of COVID-19 risks in the urban bus environment and contribute to effective mitigation policy design, which is crucial to ensuring passenger safety. The findings of this study provide important policy implications for operational adjustment and safety protocols as transit agencies seek to plan for future emergencies.

A distributed digital twin implementation of a hemodialysis unit aimed at helping prevent the spread of the Omicron COVID-19 variant

In order to monitor and assess the spread of the Omicron variant of COVID-19, we propose a Distributed Digital Twin that virtually mirrors a hemodialysis unit in a hospital in Toronto, Canada. Since the solution involves heterogeneous components, we rely on the IEEE HLA distributed simulation standard. Based on the standard, we use an agent-based/discrete event simulator together with a virtual reality environment in order to provide to the medical staff an immersive experience that incorporates a platform showing predictive analytics during a simulation run. This can help professionals monitor the number of exposed, symptomatic, asymptomatic, recovered, and deceased agents. Agents are modeled using a redesigned version of the susceptible-exposed-infected-recovered (SEIR) model. A contact matrix is generated to help identify those agents that increase the risk of the virus transmission within the unit. © 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.

Household Structure Projection: A Monte-Carlo based Approach

The kernel of an agent based simulation system for spreading of infectious disease needs a so called household structure (HSD) of the area being simulated which contains a list of households with the age of each member in the household being recorded. Such a household structure is available in a Census that is usually released every 10 years. Previous researches have shown the changing of the household structure has a great impact on disease spreading patterns. It is observed that the changing of the household structure e.g., the average citizen ages and household size, is at a faster speed. However, serious infectious diseases, such as SARS (year 2002), H1N1 (year 2009) and COVID-19 (year 2019), occur with a higher frequency now than previous eras. For example, it would be bad to use HSD2010 built using Census 2010 to simulate COVID-19. In view of this situation, we need a better way to obtain a good household structure in between the Census years in order for an agent-based simulation system to be effective. Note that though a detailed Census is not available every year, aggregated information such as the number of households with a particular size, and the number of people of a particular age are usually available almost monthly. Given HSDx, the household structure for year x, and the aggregated information from year y where y > x, we propose a Monte-Carlo based approach "patching" HSDx to get an approximated HSDy. To validate our algorithm, we pick x and y - x + 10 which both Censuses are available and find out the root-mean-square error (RMSE) between Census's HSDy and generated HSDy is fairly small for x = 1990 and 2000. The spreading patterns obtained by our simulation system have good matches. We hence obtain HSD2020 to be used in your system for studying the spreading of COVID-19.

Simulating inter-individual contact in the inter-station passenger transfer system connecting multiple metro stations based on space–time path data

Determining passengers’ inter-individual contact in the metro station area (MSA) is an important issue to simulate and mitigate the spread of the Coronavirus disease 2019 (COVID-19) pandemic. Taking the inter-station passenger transfer system (IPTS) as an example, this study aimed to verify the passenger flows’ influence on the inter-individual contact around the MSA. Based on actual observed data, the passengers’ space–time paths (STP) in the network were obtained through an agent-based simulation. In this study, the direct contact model and the mediate contact model were used to describe the inter-individual contact in view of the passengers’ STP. The contact count and the exposure duration were defined as indicators to measure the contact degree of individual and the system. The results show that the time-varying trip distribution of the metro passengers significantly affected the inter-individual contact degree and the spatial distribution of contact risk region in the MSA. The intersection of passenger flow in different directions and the concentrated movement of passenger flow in the same direction increased the inter-individual contact and prolonged exposure in the morning. Through simulation experiments, the study verified the effects of controlling the flow direction and equalizing passenger flow generation measures aiming to reduce inter-individual contact and cumulative exposure duration.

The Impact of Effectiveness of Luggage Arrangement on the Airplane Passengers' Boarding Process

The boarding process is the role activity to maintain the airline's efficiency in the turnaround process on the ground. One of the scenarios to optimize the boarding process is the arrangement of passengers who enter the plane based on the amount of carry-on luggage, adjusted to the selected boarding strategy. This research aims to develop an agent-based simulation model to increase the effectiveness of passengers' boarding process by applying the luggage arrangement method for an airplane with a 180-seat configuration. The simulation results showed that applying the Ascending luggage arrangement method reduced the overall boarding process performance by 6.12%, while the Descending method increased boarding performance by 2.50%, compared to the standard Random method.