Build an Agent-Based Model for COVID-19 Effect of Mitigation Policies

Non-Drug Intervention (NDI) is one of the important means to prevent and control the outbreak of coronavirus disease 2019 (COVID-19), and the implementation of this series of measures plays a key role in the development of the epidemic. The purpose of this paper is to study the impact of different mitigation measures on the situation of the COVID 19, and effectively respond to the prevention and control situation in the "post-epidemic era". The present work is based on the Susceptible-Exposed-Infectious-Remove-Susceptible (SEIRS) Model, and adapted the agent-based model (ABM) to construct the epidemic prevention and control model framework to simulate the COVID-19 epidemic from three aspects: social distance, personal protection, and bed resources. The experiment results show that the above NDI are effective mitigation measures for epidemic prevention and control, and can play a positive role in the recurrence of COVID-19, but a single measure cannot prevent the recurrence of infection peaks and curb the spread of the epidemic;When social distance and personal protection rules are out of control, bed resources will become an important guarantee for epidemic prevention and control. Although the spread of the epidemic cannot be curbed, it can slow down the recurrence of the peak of the epidemic;When people abide by social distance and personal protection rules, the pressure on bed resources will be eased. At the same time, under the interaction of the three measures, not only the death toll can be reduced, but the spread of the epidemic can also be effectively curbed. © 2022 ACM.

Platform Behavior under Market Shocks: A Simulation Framework and Reinforcement-Learning Based Study

We study the behavior of an economic platform (e.g., Amazon, Uber Eats, Instacart) under shocks, such as COVID-19 lockdowns, and the effect of different regulation considerations. To this end, we develop a multi-agent simulation environment of a platform economy in a multi-period setting where shocks may occur and disrupt the economy. Buyers and sellers are heterogeneous and modeled as economically-motivated agents, choosing whether or not to pay fees to access the platform. We use deep reinforcement learning to model the fee-setting and matching behavior of the platform, and consider two major types of regulation frameworks: (1) taxation policies and (2) platform fee restrictions. We offer a number of simulated experiments that cover different market settings and shed light on regulatory tradeoffs. Our results show that while many interventions are ineffective with a sophisticated platform actor, we identify a particular kind of regulation - fixing fees to the optimal, no-shock fees while still allowing a platform to choose how to match buyers and sellers - as holding promise for promoting the efficiency and resilience of the economic system. © 2023 ACM.

AN AGENT-BASED MODELLING APPROACH FOR ASSESSING ENTREPRENEURIAL INTENTIONS AND DEVELOPMENT STRATEGIES IN THE RURAL HOSPITALITY INDUSTRY

Rural hospitality and tourism (RHT) play a key role in rural revitalization, especially due to the impact of COVID-19, with more citizens choosing to travel to the countryside for a staycation. Local SMEs, especially family-owned enterprises, make up the majority of the RHT sector, not only providing services and products to satisfy tourists, but also helping with local employment. However, entrepreneurs operating in rural areas face many challenges in terms of capital, skills and education. Hence, it is important to explore the entrepreneurial intention (EI) of local people and how policies can support or change their behaviours. Current research on the RHT industry, rarely study the EI of local people, and the literature on rural entrepreneurship concentrates on developed countries. This study therefore uses agent-based modelling to explore how locals' EI in Chongming island (China) respond to the current impact of COVID-19, and whether policies will bring about changes on the supply side of RHT sector. © SW 2023.All rights reserved

Coupling Machine Learning and Agent-Based Modeling to Characterize Contamination Sources in Water Distribution Systems for Changing Demand Regimes

Water distribution systems (WDSs) deliver clean, safe drinking water to consumers, providing an essential service to constituents. WDSs are increasingly at risk of contamination due to aging infrastructure and intentional acts that are possible through cyber-physical vulnerabilities. Identifying the source of a contamination event is challenging due to limited system-wide water quality monitoring and non-uniqueness present in solving inverse problems to identify source characteristics. In addition, changes in the expected demand patterns that are caused by, for example, social distancing during a pandemic, adoption of water conservation behaviors, or use of decentralized water sources can change the anticipated propagation of contaminant plumes in a network. This research develops a computational framework to characterize contamination sources using machine learning (ML) techniques and simulate water demands and human exposure to a contaminant using agent-based modeling (ABM). An ABM framework is developed to simulate demand changes during the COVID-19 pandemic. The ABM simulates population movement dynamics, transmission of COVID-19 within a community, decisions to social distance, and changes in demands that occur due to social distancing decisions. The ABM is coupled with a hydraulic simulation model, which calculates flows in the network to simulate the movement of a contaminant plume in the network for several contamination event scenarios. ML algorithms are applied to determine the location of source nodes. Research results demonstrate that ML using random forests can identify source nodes based on inline and mobile sensor data. Sensitivity analysis is conducted to explore the number of mobile sensors that are needed to accurately identify the source node. Rapidly identifying contamination source nodes can increase the speed of response to a contamination event, reducing the impact to the community and increasing the resiliency of WDSs during periods of changing demands. © World Environmental and Water Resources Congress 2023.All rights reserved

Design Experience of Laboratory Practices in COVID Times

The development of laboratory practices is necessary for training mechatronics engineering students because they must learn in scenarios that allow checking the theories reviewed in class and implementing their solutions to real-world challenges posed in a course. Unexpectedly, the COVID pandemic caused a rethinking of how to develop the laboratory as a form of teaching, looking for online alternatives using simulation platforms, portable instruments, and 3D printing to design prototypes. This work presents the experience of two online laboratory practice activities in two mechatronics engineering subjects, which allowed students to complement their training without the risk of contagion, develop the planned competencies, and acquire skills in this form of teaching. © 2023 IEEE.

A European Drug-Discovery Platform: From In Silico to Experimental Validation

The COVID-19 pandemic highlighted an urgent need for streamlined drug development processes. Enhanced virtual screening methods could expedite drug discovery via rapid screening of large virtual compound libraries to identify high-priority drug candidates. The EXSCALATE4CoV (EXaSCale smArt pLatform Against paThogEns for CoronaVirus) consortium (E4C) research team developed EXSCALATE (EXaSCale smArt pLatform Against paThogEns), the most complex screening simulation to date, containing a virtual library of >500 billion compounds and a high-throughput docking software, LiGen (Ligand Generator). Additionally, E4C developed a smaller virtual screen of a "safe-in-man” drug library to identify optimal candidates for drug repurposing. To identify compounds targeting SARS-CoV-2, EXSCALATE performed >1 trillion docking simulations to optimize the probability of identifying successful drug candidates. Ligands identified in simulations underwent subsequent in vitro experimentation to determine drug candidates that have anti-SARS-CoV-2 agency and have probable in-human efficacy. While many compound candidates were validated to have anti-SARS-CoV-2 properties, raloxifene had the best outcome and subsequently demonstrated efficacy in a phase 2 clinical trial in patients with early mild-to-moderate COVID-19, providing proof of concept that the in silico approaches used here are a valuable resource during emergencies. After its emergence in 2019, the SARS-CoV-2 coronavirus spread internationally at a rapid pace, leading to the designation of COVID-19 as a pandemic in March 2020. In addition to a devastating impact on public health, COVID-19 has resulted in extensive negative social and economic effects in every corner of the globe. When the pandemic arrived, the medical and scientific communities identified an urgent need to establish more rapid therapeutic and vaccine development processes for COVID-19. However, it was clear that any new measures needed to be implemented in a way that also supported rapid mobilization to fight potential future pandemics. Therapeutic discovery is a complicated and prolonged process, often taking 10–15 years to complete all stages, and typically involves a linear workflow starting with in silico investigations, followed by increasingly complex and correspondingly expensive in vitro, in vivo, and clinical studies. In the context of the pandemic, the importance of the in silico stage increased because of the capacity of exascale computational methods to identify and prioritize small molecule (and biological) agents with the greatest therapeutic potential. Better in silico-generated starting points for drug-discovery efforts increase the likelihood of success in downstream laboratory-based experimental stages and can contribute to vitally needed reductions in costs and time to market for new therapies. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

TRACE-Omicron: Policy Counterfactuals to Inform Mitigation of COVID-19 Spread in the United States

The Omicron wave is the largest wave of COVID-19 pandemic to date, more than doubling any other in terms of cases and hospitalizations in the United States. In this paper, a large-scale agent-based model of policy interventions that could have been implemented to mitigate the Omicron wave is presented. The model takes into account the behaviors of individuals and their interactions with one another within a nationally representative population, as well as the efficacy of various interventions such as social distancing, mask wearing, testing, tracing, and vaccination. We use the model to simulate the impact of different policy scenarios and evaluate their potential effectiveness in controlling the spread of the virus. The results suggest the Omicron wave could have been substantially curtailed via a combination of interventions comparable in effectiveness to extreme and unpopular singular measures such as widespread closure of schools and workplaces, and highlight the importance of early and decisive action. © 2023 Wiley-VCH GmbH.

Predicting COVID-19 Mortalities for Patients with Special Health Conditions Using an Agent-Based Model

The spread of COVID-19 has thrown the world into a panic. We are constantly learning more about the virus every day, from how it spreads to who is more susceptible to becoming infected by different variants. Those with underlying respiratory conditions and other immunocompromised individuals need to be extra cautious regarding the virus. Many researchers have created COVID-19 trackers to detect the spread of COVID-19 around the world and show hot spots where COVID-19 cases are more prevalent. Previous work lacks the consideration of comorbidity as a factor of death rate. This work aims to create an agent-based model to predict comorbidity death rate caused by a health condition in addition to COVID-19. The model is evaluated using the symmetric mean absolute percentage error metric and proved to be very efficient. © 2023 IEEE.

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.

PCTran-Cloud: an Accident Simulator for Nuclear Power Plants Based on Cloud Technology

In the current environment where COVID-19 is serious, the space, place and resources required for teaching nuclear power plants are restricted to a great extent. To solve such problems and improve the utilization of education resources, this study improved an accident simulator for nuclear power plants based on the concept of cloud technology. We build the Browser / Server architecture so that the platform has successfully implemented multiterminal, multiplatform and multiuser simultaneous applications. Through the simulation results of the Small Break Loss of Coolant Accident (SBLOCA) and the test results of platform performance by PCTran-Cloud, the correctness of PCTran-Cloud in the accident simulation function and results were verified. In general, PCTran-Cloud has the characteristics of high scalability, high concurrency and high security. The platform can provide an environment for the training and education of nuclear power professionals. © 2022 IEEE.

Scale matters: Variations in spatial and temporal patterns of epidemic outbreaks in agent-based models

Agent-based modellers frequently make use of techniques to render simulated populations more computationally tractable on actionable timescales. Many generate a relatively small number of "representative” agents, each of which is "scaled up” to represent some larger number of individuals involved in the system being studied. The degree to which this "scaling” has implications for model forecasts is an underdeveloped field of study;in particular, there has been little known research on the spatial implications of such techniques. This work presents a case study of the impact of the simulated population size, using a model of the spread of COVID-19 among districts in Zimbabwe for the underlying system being studied. The impact of the relative scale of the population is explored in conjunction with the spatial setup, and crucial model parameters are varied to highlight where scaled down populations can be safely used and where modellers should be cautious. The results imply that in particular, different geographical dynamics of the spread of disease are associated with varying population sizes, with implications for researchers seeking to use scaled populations in their research. This article is an extension on work previously presented as part of the International Conference on Computational Science 2022 (Wise et al., 2022)[1]. © 2023 The Authors

Agent-Based Social Simulation for Policy Making

In agent-based social simulations (ABSS), an artificial population of intelligent agents that imitate human behavior is used to investigate complex phenomena within social systems. This is particularly useful for decision makers, where ABSS can provide a sandpit for investigating the effects of policies prior to their implementation. During the Covid-19 pandemic, for instance, sophisticated models of human behavior enable the investigation of the effects different interventions can have and even allow for analyzing why a certain situation occurred or why a specific behavior can be observed. In contrast to other applications of simulation, the use for policy making significantly alters the process of model building and assessment, and requires the modelers to follow different paradigms. In this chapter, we report on a tutorial that was organized as part of the ACAI 2021 summer school on AI in Berlin, with the goal of introducing agent-based social simulation as a method for facilitating policy making. The tutorial pursued six Intended Learning Outcomes (ILOs), which are accomplished by three sessions, each of which consists of both a conceptual and a practical part. We observed that the PhD students participating in this tutorial came from a variety of different disciplines, where ABSS is mostly applied as a research method. Thus, they do often not have the possibility to discuss their approaches with ABSS experts. Tutorials like this one provide them with a valuable platform to discuss their approaches, to get feedback on their models and architectures, and to get impulses for further research. © 2023, Springer Nature Switzerland AG.

An Agent-based Modeling and Simulation Tool for the Exploration of the Effects of Masks and Vaccines on COVID-19 Spread

The pandemic's complexity made it difficult to understand the epidemiological impacts of health interventions, primarily masks and vaccines. Compartmental models alone, which are frequently employed, fall short in evaluating complex systems and heterogeneity of individuals, thus limiting research on these control measures. This study aims to explore the effects of health interventions on Corona Virus Disease 2019 (COVID-19) spread using agent-based modeling and simulation. The SEIR framework of compartmental models is employed along with the specific interventions implemented with NetLogo. Exploring the different scenarios demonstrated that respirators and medical masks, for the types of masks, and Pfizer-BioNTech and Moderna, for the brands of vaccines, are the most effective in reducing infection curve peaks, total infection, and death, when used uniformly. The model can be further extended to comprehend other scenarios and combinations of different control measures for effective planning and policymaking in mitigating the effects of COVID-19. © 2022 IEEE.

The Design and Utilisation of PanSim, a Portable Pandemic Simulator

The COVID-19 pandemic has presented a clear and present need for urgent decision making. Set in an environment of uncertain and unreliable data, and a diverse range of possible interventions, there is an obvious need for integrating HPC into workflows that include model calibration, and the exploration of the decision space. In this paper, we present the design of PanSim, a portable, performant, and productive agent-based simulator, which has been extensively used to model and forecast the pandemic in Hungary. We show its performance and scalability on CPUs and GPUs, then we discuss the workflows PanSim integrates into. We describe the heterogeneous, resource-constrained HPC environment available to us, and formulate a scheduling optimisation problem, as well as heuristics to solve them, to either minimise the execution time of a given number of simulations or to maximise the number of simulations executed in a given time frame. © 2022 IEEE.

Interactive Teaching Model Reform Based on Network Platform and Virtual Simulation Technology

Information technology changes people's way of working, learning and thinking at an amazing speed, which will inevitably lead to comprehensive reform and development in the education field. The epidemic situation of COVID-19 makes online teaching the main teaching mode of 'teaching without stopping and learning without stopping'. The application of modern education technology based on 'Chaoxing Platform+Tencent Meeting' in teaching process is explored. Based on teaching environment, interactive teaching mode construction, teaching management and evaluation, the teaching design is carried out in combination with the actual characteristics of each teaching link. The application of virtual simulation modern educational technology in the course Circuit Analysis is studied, and the convenience and the intuitiveness brought by virtual simulation technical resources and interactive platform are given full play, and the virtual simulation technology can effectively solve the obscure circuit analysis problem in theoretical teaching. The aim of teaching model reform has been achieved. © 2022 IEEE.

Human Mobility-based Individual-level Epidemic Simulation Platform

COVID-19 has spread worldwide, and over 140 million people have been confirmed infected, over 3 million people have died, and the numbers are still increasing dramatically. The consensus has been reached by scientists that COVID-19 can be transmitted in an airborne way, and human-to-human transmission is the primary cause of the fast spread of COVID-19. Thus, mobility should be restricted to control the epidemic, and many governments worldwide have succeeded in curbing the spread by means of control policies like city lockdowns. Against this background, we propose a novel fine-grained transmission model based on real-world human mobility data and develop a platform that helps the researcher or governors to explore the possibility of future development of the epidemic spreading and simulate the outcomes of human mobility and the epidemic state under different epidemic control policies. The proposed platform can also support users to determine potential contacts, discover regions with high infectious risks, and assess the individual infectious risk. The multi-functional platform aims at helping the users to evaluate the effectiveness of a regional lockdown policy and facilitate the process of screening and more accurately targeting the potential virus carriers. © 2022 held by the owner/author(s). Publication rights licensed to ACM.

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.

Using a hybrid simulation model to assess the impacts of combined COVID-19 containment measures in a high-speed train station

In this study, we present a hybrid agent-based model (ABM) and discrete event simulation (DES) framework where ABM captures the spread dynamics of COVID-19 via asymptomatic passengers and DES captures the impacts of environmental variables, such as service process capacity, on the results of different containment measures in a typical high-speed train station in China. The containment and control measures simulated include as-is (nothing changed) passenger flow control, enforcing social distancing, adherence level in face mask-wearing, and adding capacity to current service stations. These measures are evaluated individually and then jointly under a different initial number of asymptomatic passengers. The results show how some measures can consolidate the outcomes for each other, while combinations of certain measures could compromise the outcomes for one or the other due to unbalanced service process configurations. The hybrid ABM and DES models offer a useful multi-function simulation tool to help inform decision/policy makers of intervention designs and implementations for addressing issues like public health emergencies and emergency evacuations. Challenges still exist for the hybrid model due to the limited availability of simulation platforms, extensive consumption of computing resources, and difficulties in validation and optimisation. © 2023 The Operational Research Society.

A fast face recognition system based on annealing algorithm to optimize operator parameters

In order to improve the recognition rate and operational efficiency of the system, a method in which the image features compensation coefficients are optimized by using an improved simulated annealing algorithm is proposed. Firstly, eight computational factors with low computational complexity are given, which can be used to compensate image features. Secondly, the design flow of face recognition algorithm is presented. Thirdly, an improved simulated annealing algorithm is designed to solve the optimal combination of feature compensation coefficients in the face recognition system. Fourthly, the results of the feature compensation coefficients recommended by the improved simulated annealing algorithm are applied to the Efficient Face Recognition Algorithm (EFRA) in this paper, and verified on the simulation platform. Experiments show that the recognition rate can reach 100% when the training images are 6 in ORL. The proposed algorithm also performs well in MU_PIE dataset. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

School's Out? Simulating Schooling Strategies During COVID-19

Multi-agent based systems offer the possibility to examine the effects of policies down to specific target groups while also considering the effects on a population-level scale. To examine the impact of different schooling strategies, an agent-based model is used in the context of the COVID-19 pandemic using a German city as an example. The simulation experiments show that reducing the class size by rotating weekly between in-person classes and online schooling is effective at preventing infections while driving up the detection rate among children through testing during weeks of in-person attendance. While open schools lead to higher infection rates, a surprising result of this study is that school rotation is almost as effective at lowering infections among both the student population and the general population as closing schools. Due to the continued testing of attending students, the overall infections in the general population are even lower in a school rotation scenario, showcasing the potential for emergent behaviors in agent-based models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.