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

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

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.

Study on population behavior under home quarantine policies of COVID-19 in China based on double-layer network evolutionary games

In the practice of COVID-19 prevention and control in China, the home quarantine policy directly connects and manages the residents, which plays a significant role in preventing the spread of the epi-demic in the community. We evaluate the effectiveness of current home quarantine policy in the actual execution process based on the evolutionary game relationship between the community and res-idents. This paper establishes a double-layer coupled complex network game model, and uses the multi-agent modeling method to study the game relationship between the community and residents in the context of home quarantine policies. The results show that initial strategy of the community with strict supervision and reasonable government reward allocation will increase the proportion of the residents complying with the quarantine rule. When 80% of the communities chose to supervise strictly at the beginning, people are more likely to follow the rules. While when the residents can only get 20% of the government's reward, the proportion of choosing to violate the quarantine rules is much higher than that when they can get 80% of the reward. Besides, the structure of small-world network and environmental noise will also affect the residents' strategy. As the probability of reconnection of the small-world network rises from 0.2 to 0.8, the proportion of residents who choose to comply with the strategy becomes much higher. When the environmental noise reaches 0.5, the ratio of residents who choose to violate the strategy is higher than the ratio of complianc. The study is helpful to provide the basis for the government to formulate the quarantine policy and propose an optimization for making effective quarantine measures. In this way, the government can adjust the parameters to make residents achieve the possible level of compliance with quarantine policies as high as possible to contain the spread of the epidemic. [ FROM AUTHOR] Copyright of Journal of Intelligent & Fuzzy Systems is the property of IOS Press 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.)

Compliance to "unpleasant" actions of crisis management: Some remarks from a management control perspective

In managing the Covid-16 pandemic, policy makers took actions which require the cooperation of individual citizens to succeed while the actions partially come at remarkable costs for individuals. The brief paper employs a thought experiment to identify factors which affect individuals' propensity to cooperate in the public goods game. These factors reasonably comprise, for example, risk perception and attitude towards risk, embeddedness in a social network or the desire for social approval and may differ remarkably among the individuals of a collective. The paper adopts a management control perspective which appears to be particularly helpful to identify how to implement policy makers' actions with respect to the diverse individuals in a collective. In order to predict the overall outcome of "unpleasant" actions, an approach is required which allows to capture the heterogeneity of individuals within a collective which makes agent-based modelling a promising candidate. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Modelling the impact of non-pharmaceutical interventions on COVID-19 exposure in closed-environments using agent-based modelling

Businesses can play a key role in reducing exposure to COVID-19 in closed environments. This is possible by assessing the impact of Non-Pharmaceutical Interventions (NPIs) in mitigating disease exposure. This study aims to assess the impact of NPIs on COVID-19 exposure in closed environments. This is achieved by proposing an innovative COVID-19 exposure prediction framework. The developed framework consists of three modules: Agent-Based Modelling (ABM) approach, Clustering Module (CM), and Decision Tree (DT) technique. The framework also integrates these modules considering the exposure time factor to identify the level of exposure to COVID-19 in closed environments. A supermarket based in Jordan is considered a case study to test the applicability of the proposed framework in predicting exposure levels and numbers. The impact of Individual and combined NPIs application in closed environment facilities is assessed based on the exposure level and other OIs such as opening time, body temperature measurement, and the number of people inside the supermarket. Key results show that wearing Mask, Face Shield and leaving Social Distance guarantees no exposure to COVID-19 and increases the safety level to 61.9% in a closed environment such as supermarkets with a potential exposure rate of up to 28.5% if otherwise. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

How will mass-vaccination change COVID-19 lockdown requirements in Australia?

Background To prevent future outbreaks of COVID-19, Australia is pursuing a mass-vaccination approach in which a targeted group of the population comprising healthcare workers, aged-care residents and other individuals at increased risk of exposure will receive a highly effective priority vaccine. The rest of the population will instead have access to a less effective vaccine. Methods We apply a large-scale agent-based model of COVID-19 in Australia to investigate the possible implications of this hybrid approach to mass-vaccination. The model is calibrated to recent epidemiological and demographic data available in Australia, and accounts for several components of vaccine efficacy. Findings Within a feasible range of vaccine efficacy values, our model supports the assertion that complete herd immunity due to vaccination is not likely in the Australian context. For realistic scenarios in which herd immunity is not achieved, we simulate the effects of mass-vaccination on epidemic growth rate, and investigate the requirements of lockdown measures applied to curb subsequent outbreaks. In our simulations, Australia's vaccination strategy can feasibly reduce required lockdown intensity and initial epidemic growth rate by 43% and 52%, respectively. The severity of epidemics, as measured by the peak number of daily new cases, decreases by up to two orders of magnitude under plausible mass-vaccination and lockdown strategies. Interpretation The study presents a strong argument for a large-scale vaccination campaign in Australia, which would substantially reduce both the intensity of future outbreaks and the stringency of non-pharmaceutical interventions required for their suppression. Funding Australian Research Council; National Health and Medical Research Council.

Self-Isolation and Testing Behaviour During the COVID-19 Pandemic: An Agent-Based Model.

Since the beginning of the COVID-19 pandemic, various models of virus spread have been proposed. While most of these models focused on the replication of the interaction processes through which the virus is passed on from infected agents to susceptible ones, less effort has been devoted to the process through which agents modify their behaviour as they adapt to the risks posed by the pandemic. Understanding the way agents respond to COVID-19 spread is important, as this behavioural response affects the dynamics of virus spread by modifying interaction patterns. In this article, we present an agent-based model that includes a behavioural module determining agent testing and isolation propensity in order to understand the role of various behavioural parameters in the spread of COVID-19.

Study on population behavior under home quarantine policies of COVID-19 in China based on double-layer network evolutionary games

In the practice of COVID-19 prevention and control in China, the home quarantine policy directly connects and manages the residents, which plays a significant role in preventing the spread of the epi-demic in the community. We evaluate the effectiveness of current home quarantine policy in the actual execution process based on the evolutionary game relationship between the community and res-idents. This paper establishes a double-layer coupled complex network game model, and uses the multi-agent modeling method to study the game relationship between the community and residents in the context of home quarantine policies. The results show that initial strategy of the community with strict supervision and reasonable government reward allocation will increase the proportion of the residents complying with the quarantine rule. When 80% of the communities chose to supervise strictly at the beginning, people are more likely to follow the rules. While when the residents can only get 20% of the government's reward, the proportion of choosing to violate the quarantine rules is much higher than that when they can get 80% of the reward. Besides, the structure of small-world network and environmental noise will also affect the residents' strategy. As the probability of reconnection of the small-world network rises from 0.2 to 0.8, the proportion of residents who choose to comply with the strategy becomes much higher. When the environmental noise reaches 0.5, the ratio of residents who choose to violate the strategy is higher than the ratio of complianc. The study is helpful to provide the basis for the government to formulate the quarantine policy and propose an optimization for making effective quarantine measures. In this way, the government can adjust the parameters to make residents achieve the possible level of compliance with quarantine policies as high as possible to contain the spread of the epidemic. [ FROM AUTHOR]

Risk Assessment of Large-Scale Sports Events in the Context of COVID-19

This paper assesses the potential risks of epidemic situation and public opinion during the Beijing Winter Olympic Games by analyzing the epidemic situation and public opinion of the Tokyo Olympic Games. The results show that there is a strong time-lag correlation between the COVID-19 epidemic and the public opinion of the Tokyo Olympics. For the epidemic situation, the multi-agent modeling method is used at the city level to simulate the possible spread of diseases in the city where the event was held. At the Olympic village level, the modified the SEIR transmission model is modified to simulate the virus transmission in the Olympic Village during the Beijing Winter Olympic Games. At the end, the risk analysis of the Beijing Winter Olympic Games is carried out based on the time series prediction model. © 2022, Editorial Board of Journal of the University of Electronic Science and Technology of China. All right reserved.

A Methodology for DSML-Assisted Participatory Agent-Based Enterprise Modelling

Participatory agent-based modelling (ABM) can help bring the benefits of simulation to domain users by actively involving stakeholders in the development process. Collaboration in enterprise modelling can improve the model developer's understanding of the domain and therefore improve the effectiveness of domain analysis. Where many agent-oriented methodologies focus on the development of one-off models, domain-specific modelling languages (DSML) can improve the re-use of concepts identified in domain analysis across multiple case studies and expose modelling concepts in domain-appropriate terms, increasing model accessibility. To realise the benefits of DSMLs we need to understand how DSML development can be incorporated into typical agent-based modelling. In this paper we discuss existing methodologies for ABM development and DSML development, and we discuss the benefits merging the two can bring. We present a methodology for DSML-assisted participatory agent-based modelling, and support the methodology with a case study—a modelling exercise conducted in collaboration with a hospital emergency department on the topic of infection control for COVID-19 and Influenza. © 2022, IFIP International Federation for Information Processing.

Multi-paradigm Methodology for Enterprise Modelling using Agent-based Modelling and System Dynamics

The Covid-19 pandemic has significantly altered business operating models. Enterprise decision makers responsible for devising actionable business operational strategies are confronted with making informed decisions in the state of continuously evolving pandemic landscape. As pandemic concerns subside, their objective is to formulate a workplace opening strategy that mitigates employee infections and the subsequent impact on project delivery. It is therefore critical to appropriately model the underlying aspects of the enterprise system and enable strategy evaluation. Enterprise in this context represents a complex, dynamic system composed of multiple sub-systems with varying characteristics, levels of uncertainty, granularity, data availability and scale. Owing to these distinctions, different modelling paradigms are better suited to individually model these sub-systems, and their integration results in a comprehensive model that is a close approximation of the real system. This paper presents a hybrid/multiparadigm approach for modelling the enterprise ecosystem, by building on the established concepts of Agent Based Modelling (ABM) and System Dynamics (SD) that enables evaluating the impact of operational strategies on employee infections. The model is formulated as integration of multiple subsystems and their interactions - infection module, employee and dependent, office infrastructure and society modules. These four dimensions, comprising the enterprise ecosystem, significantly influence the employee infection dynamics. While the SD model quantifies the aggregated infection dynamics of society at the population scale, ABM models fine-grained specifics of employees, dependents, infrastructure, and the resulting infection dynamics. © 2022 Copyright for this paper by its authors.

Simulating Common Indoor Crowd Behaviours in 3D Environments Using ABMU

Being able to model common crowd behaviours is vital in architecture and similar built environment fields. This is becoming even more pressing, especially in recent years with COVID-19 having a significant impact in how we interact in indoor spaces. As such, common crowd simulation approaches such as Agent-Based Modelling (ABM) need to evolve rapidly to be able to accommodate new requirements in crowd interaction;for indoor crowds, this usually requires a model to accommodate three-dimensional spaces, as the existing 2D modelling approaches are often inadequate in capturing the nuances of crowd interaction in indoor spaces, which are inherently three-dimensional. In this paper we present an application of such a 3D ABM modelling framework through a model of queuing behaviour, a common behaviour in indoor crowds. The model is developed in three-dimensional space on the Unity3D development platform, using the Agent-Based Modelling Framework for Unity3D (ABMU). © 2022 IEEE.

Spatial heterogeneity affects predictions from early-curve fitting of pandemic outbreaks: a case study using population data from Denmark.

The modelling of pandemics has become a critical aspect in modern society. Even though artificial intelligence can help the forecast, the implementation of ordinary differential equations which estimate the time development in the number of susceptible, (exposed), infected and recovered (SIR/SEIR) individuals is still important in order to understand the stage of the pandemic. These models are based on simplified assumptions which constitute approximations, but to what extent this are erroneous is not understood since many factors can affect the development. In this paper, we introduce an agent-based model including spatial clustering and heterogeneities in connectivity and infection strength. Based on Danish population data, we estimate how this impacts the early prediction of a pandemic and compare this to the long-term development. Our results show that early phase SEIR model predictions overestimate the peak number of infected and the equilibrium level by at least a factor of two. These results are robust to variations of parameters influencing connection distances and independent of the distribution of infection rates.

Statistical and agent-based modelling of the transmissibility of different SARS-CoV-2 variants in England and impact of different interventions.

The English SARS-CoV-2 epidemic has been affected by the emergence of new viral variants such as B.1.177, Alpha and Delta, and changing restrictions. We used statistical models and the agent-based model Covasim, in June 2021, to estimate B.1.177 to be 20% more transmissible than the wild type, Alpha to be 50-80% more transmissible than B.1.177 and Delta to be 65-90% more transmissible than Alpha. Using these estimates in Covasim (calibrated 1 September 2020 to 20 June 2021), in June 2021, we found that due to the high transmissibility of Delta, resurgence in infections driven by the Delta variant would not be prevented, but would be strongly reduced by delaying the relaxation of restrictions by one month and with continued vaccination. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Modelling herd immunity requirements in Queensland: impact of vaccination effectiveness, hesitancy and variants of SARS-CoV-2.

Long-term control of SARS-CoV-2 outbreaks depends on the widespread coverage of effective vaccines. In Australia, two-dose vaccination coverage of above 90% of the adult population was achieved. However, between August 2020 and August 2021, hesitancy fluctuated dramatically. This raised the question of whether settings with low naturally derived immunity, such as Queensland where less than [Formula: see text] of the population is known to have been infected in 2020, could have achieved herd immunity against 2021's variants of concern. To address this question, we used the agent-based model Covasim. We simulated outbreak scenarios (with the Alpha, Delta and Omicron variants) and assumed ongoing interventions (testing, tracing, isolation and quarantine). We modelled vaccination using two approaches with different levels of realism. Hesitancy was modelled using Australian survey data. We found that with a vaccine effectiveness against infection of 80%, it was possible to control outbreaks of Alpha, but not Delta or Omicron. With 90% effectiveness, Delta outbreaks may have been preventable, but not Omicron outbreaks. We also estimated that a decrease in hesitancy from 20% to 14% reduced the number of infections, hospitalizations and deaths by over 30%. Overall, we demonstrate that while herd immunity may not be attainable, modest reductions in hesitancy and increases in vaccine uptake may greatly improve health outcomes. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Enhancing resilience in construction against infectious diseases using stochastic multi-agent approach.

To recover from the adverse impacts of COVID-19 on construction and to avoid further losses to the industry in future pandemics, the resilience of construction industry needs to be enhanced against infectious diseases. Currently, there is a gap for modelling frameworks to simulate the spread of infectious diseases in construction projects at micro-level and to test interventions' effectiveness for data-informed decision-making. Here, this gap is addressed by developing a simulation framework using stochastic agent-based modelling, which enables construction researchers and practitioners to simulate and limit the spread of infectious diseases in construction projects. This is specifically important, since the results of a building project case-study reveals that, in comparison to the general population, infectious diseases may spread faster among construction workers and fatalities can be significantly higher. The proposed framework motivates future research on micro-level modelling of infectious diseases and efforts for intervening the spread of diseases in construction projects.

How Culture Influences Individual Behavior During a Pandemic: A Social Simulation of the COVID-19 Crisis

Since its first appearance in Wuhan (China), countries have been employing, to varying degrees of success, a series of non-pharmaceutical interventions aimed at limiting the spread of SARS-CoV-2 within their populations. In this article, we build on scientific work that demonstrates that culture is part of the explanation for the observed variability between countries in their ability to effectively control the transmission of SARS-CoV-2. We present a theoretical framework of how culture influences decision-making at the level of the individual. This conceptualization is formalized in an agent-based model that simulates how cultural factors can combine to produce differences across populations in terms of the behavioral responses of individuals to the COVID-19 crisis. We illustrate that, within our simulated environment, the culturally-dependent willingness of people to comply with public health related measures might constitute an important determinant of differences in infection dynamics across populations. Our model generates the highest rates of non-compliance within cultures marked as individualist, progressive and egalitarian. Our model illustrates the potential role of culture as a population-level predictor of infections associated with COVID-19. In doing so, the model, and theoretical framework on which it is based, may inform future studies aimed at incorporating the effect of culture on individual decision-making processes during a pandemic within social simulation models. © 2022, University of Surrey. All rights reserved.

Development of the approach to the analysis of aviation industry's adaptation to seasonal disruptions

The main purpose of this study is to develop a model that will allow predicting the stability of the aviation industry to various failures. The main focus is on the Covid-19 period, as it had a serious impact on the work of airlines and caused huge economic crises. The study proposes a predictive model for the integrated measurement of the stability of the aviation industry. Data from the United States Bureau of Statistics were used, as they contain information about the main US air carriers, and Covid statistics provided by Johns Hopkins University were also used. The proposed model was tested, and it showed a noticeable increase in performance on both training and test data. From the analysis using a predictive machine learning model, it can be seen that this is a reliable approach when it comes to choosing an operator to manage failures, and the model can potentially help air carriers identify likely risk factors and optimize their business strategy. This study will contribute to the development of the aviation industry and provide air carriers and airline managers with recommendations that can help improve their organizational stability and productivity. © 2022 Elsevier B.V.. All rights reserved.

The Importance of Scaling for an Agent Based Model: An Illustrative Case Study with COVID-19 in Zimbabwe

Agent-based models frequently make use of scaling techniques to render the simulated samples of population more tractable. The degree to which this scaling has implications for model forecasts, however, has yet to be explored;in particular, no research on the spatial implications of this has been done. This work presents a simulation of the spread of Covid-19 among districts in Zimbabwe and assesses the extent to which results vary relative to the samples upon which they are based. It is determined that in particular, different geographical dynamics of the spread of disease are associated with varying population sizes, with implications for others seeking to use scaled populations in their research. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.