Agent-based Modeling Approach Research Articles

COVID-19, Crowdedness, and CMC Dining: An Agent-Based Model Approach to Reducing the Spread of COVID-19

COVID-19, Crowdedness, and CMC Dining: An Agent-Based Model Approach to Reducing the Spread of COVID-19

Multi-level Framework for the Assessment of Additive Manufacturing for Spare Parts Supply

The main objective of spare parts supply is to maximize the service level whilst minimizing costs of tied-up capital, operational activities and administrative processes. Additive Manufacturing (AM) offers the opportunity to decentralize the supply chain and manufacture on-demand without minimum order quantities. While AM could improve sustainability, it also could lead to less eco-efficient localized production or problem shifting within the value chain. To enable a holistic decision support for the changeover to AM, a multi-level framework for the interdisciplinary assessment of AM in spare parts supply is developed and enhanced by an agent-based modelling approach for quantitative assessment.

Sex and Age Group Differences in The Spread of Tuberculosis In Indonesia: An Agent Based Modeling Approach

Tuberculosis (TB) is a serious contagious disease. TB is even categorized as one of the top 10 main causes of death worldwide. According to WHO, in 2019 there were around 10 million cases of the population worldwide affected by TB. It is estimated that there are 1.4 million deaths due to TB worldwide in 2019. Whereas in Indonesia, it is estimated that there are 842,000 TB cases in 2018 and even around 32% of this cases have not been reported. Data and information of the number and distribution of TB cases are very limited in Indonesia, as it requires certain expertise and large costs in the data collection. Numerous research found that incidence of TB vary among different age groups and between men and women. The elderly population are more susceptible to exposure to TB due to multiple comorbidities and deficient immune response, while men are more likely to be exposed to infectious TB by other men. This condition highlights the importance of further studies on the spread of TB by age groups and sex in Indonesia. Therefore, this study used an agent based model to enhance the understanding of the aggregate and the spread of TB in Indonesia. The study results confirmed that at national level, TB incidences varied among different age groups and sexes. The results of this study also revealed the need for further study on the spread of TB in the age group under 15 years. It is expected that this study result can be useful for TB control program in Indonesia.

Potential impacts of policies to reduce purchasing of ultra-processed foods in Mexico at different stages of the social transition: an agent-based modelling approach.

ObjectivesTo develop a simulation framework for assessing how combinations of taxes, nutrition warning labels, and advertising levels could affect purchasing of ultra-processed foods (UPFs) in Latin American countries, and to understand whether policies reinforce or reduce pre-existing social disparities in UPF consumption.DesignWe developed an agent-based simulation model using international evidence regarding the effect of price, nutrition warning labels, and advertising on UPF purchasing.SettingWe estimated policy effects in scenarios representing two stages of the “social transition” in UPF purchasing: 1) a pre-transition scenario, where UPF purchasing is higher among high-income households, similar to patterns in Mexico, 2) a post-transition scenario where UPF purchasing is highest among low-income households, similar to patterns in Chile.ParticipantsA population of 1,000 individual-agents with levels of age, income, educational attainment, and UPF purchasing similar to adult women in Mexico.ResultsA 20% tax would decrease purchasing by 24% relative to baseline in both the pre- and post-transition scenarios, an effect that is similar in magnitude to that of a nutrition warning label policy. A 50% advertising increase or decrease had a comparatively small effect. Nutrition warning labels were most effective among those with higher levels of educational attainment. Labeling reduced inequities in the pre-transition scenario (i.e., highest UPF purchasing among the highest socioeconomic group), but widened inequities in the post-transition scenario.ConclusionsEffective policy levers are available to reduce UPF purchasing, but policymakers should anticipate that equity impacts will differ depending on existing social patterns in UPF purchasing.

An investigation of hard-disk drive circularity accounting for socio-technical dynamics and data uncertainty

The installed data storage capacity in the U.S. will reach 2.2 Zettabytes by 2025, generating about 50 million units of end-of-life (EOL) hard-disk drives (HDDs) per year. Due to data security concerns, most EOL HDDs are currently shredded (even when still functioning), representing an economic loss. Moreover, raw material extraction linked to the increased demand for storage causes environmental impacts. Besides mitigating the threat posed by sudden restrictions of raw materials, the circular economy (CE) offers to maximize value retention in the economy and reduce the environmental impacts of human activities. Common CE strategies are reusing and recycling products. However, the reuse of hard disk drives is currently burdened by the lack of trust HDD end-users have toward other non-physical means of data removal than shredding. Here, an agent-based modeling (ABM) approach is proposed to explore how techno-economic and social factors affect end-users’ decisions to adopt EOL management practices other than shredding. The proposed method also accounts for data uncertainty by applying a semi-quantitative approach. Results demonstrate how increased green procurement and more robust standards could spur end-users’ trust toward data-wiping technologies. Even when accounting for uncertainty, HDDs’ reuse brings better environmental and economic benefits than HDD shredding followed by material recovery. The semi-quantitative approach proposed in this study could be more universally applied in future ABM, especially given the often-stochastic nature of such models. The developed ABM is also the first to represent several HDD industry stakeholders and demonstrate how the HDD shredding lock-in situation could be resolved.

How to promote residents’ use of green space: An empirically grounded agent-based modeling approach

One focus of those responsible for making urban policies has been the improvement of green space effectiveness, including environmental plans and eco-city initiatives. In the evaluation of policy effectiveness, residents’ needs, values and preferences are critical but often overlooked. This study proposes an agent-based model (ABM) for simulating the effectiveness of policy measures on residents’ decision making with regard to the use of green space. Using a residential questionnaire survey conducted in Shanghai, China, we model individual decision making with artificial neural networks that account for the heterogeneous characteristics and imperfect rationality in the decision-making process, and compare three policy scenarios in local green space provision. The results of the model illustrate the unequal effectiveness of green space policies among different social groups and different types of green space (i.e., urban parks, neighborhood parks, and community gardens), and the sensitivity analysis suggests the key factors in different stages of green space provision. Based on the results, we argue that tailored policies are needed in order to meet residents’ heterogeneous needs; in fact, relatively “soft” policies, particularly those that promote social interaction and participation, play a significant role in the appeal of green space use. Finally, policy suggestions are provided for the optimization of green space provision.

AN OVERVIEW OF AGENT-BASED MODELLING APPROACHES FOR INTEGRATED FLOOD MANAGEMENT

Previously, Integrated Flood Management (IFM) system has been implemented by several hydrological researchers in order to minimize the global flood risk by providing a convincing flood risk assessment and management, as well as sustainable adaptation and disaster alleviation policy. Flood risk is dynamic interaction between natural disasters and human vulnerability. Basically, methods for quantifying flood risk are fully-fledged but tend to treat artificial and economic vulnerabilities as static or subject to changes in external trends. However, interpretive research is rarely conducted to investigate people’s decision-making and acknowledge to flood warnings during flood event. The integration of Agent-Based Model (ABM) in simulating the interactions and dynamic responses of individual or organizations in a spatial environment during the flood events or prior to the events were reviewed. The ABM model is defined as a computational method used to simulate the behaviour and the interaction of autonomous decision-making entities in a network or system it is used to evaluate their impact on the entire system. Therefore, the ABM approach has been chosen to emulate the complexity of the IFM process due to its capability and flexibility to simulate the dynamic of human-environment scenarios in the spatial environment.

A Review of Simulation and Application of Agent-Based Model Approaches

In the past, various traditional methods used experiments and statistical data to examine and solve the occurred problem and social-environmental issue. However, the traditional method is not suitable for expressing or solving the complex dynamics of human environmental crisis (such as the spread of diseases, natural disaster management, social problems, etc.). Therefore, the implementation of computational modelling methods such as Agent-Based Models (ABM) has become an effective technology for solving complex problems arising from the interpretation of human behaviour such as human society, environment, and biological systems. Overall, this article will outline the ABM model properties and its applications in the criminology, flood management, and the COVID-19 pandemic fields. In addition, this article will review the limitations that occurred to be overcome in the further development of the ABM model.

Annotation of Potential Vaccine Targets and Design of a Multi-Epitope Subunit Vaccine against Yersinia pestis through Reverse Vaccinology and Validation through an Agent-Based Modeling Approach.

Yersinia pestis is responsible for plague and major pandemics in Asia and Europe. This bacterium has shown resistance to an array of drugs commonly used for the treatment of plague. Therefore, effective therapeutics measurements, such as designing a vaccine that can effectively and safely prevent Y. pestis infection, are of high interest. To fast-track vaccine development against Yersinia pestis, herein, proteome-wide vaccine target annotation was performed, and structural vaccinology-assisted epitopes were predicted. Among the total 3909 proteins, only 5 (rstB, YPO2385, hmuR, flaA1a, and psaB) were shortlisted as essential vaccine targets. These targets were then subjected to multi-epitope vaccine design using different linkers. EAAK, AAY, and GPGPG as linkers were used to link CTL, HTL, and B-cell epitopes, and an adjuvant (beta defensin) was also added at the N-terminal of the MEVC. Physiochemical characterization, such as determination of the instability index, theoretical pI, half-life, aliphatic index, stability profiling, antigenicity, allergenicity, and hydropathy of the ensemble, showed that the vaccine is highly stable, antigenic, and non-allergenic and produces multiple interactions with immune receptors upon docking. In addition, molecular dynamics simulation confirmed the stable binding and good dynamic properties of the vaccine–TLR complex. Furthermore, in silico and immune simulation of the developed MEVC for Y. pestis showed that the vaccine triggered strong immune response after several doses at different intervals. Neutralization of the antigen was observed at the third day of injection. Conclusively, the vaccine designed here for Y. pestis produces an immune response; however, further immunological testing is needed to unveil its real efficacy.

Simulating thermal energy community formation: Institutional enablers outplaying technological choice

Energy communities are key elements for local energy transitions, collectively generating, distributing and consuming energy, using renewable energy technologies. Thermal energy communities, as one type of energy community, are focused on thermal energy applications, such as heating, cooling, bathing, showering and providing hot tap water. As thermal energy applications and systems receive increasing academic and policy attention, there is a need to better understand the formation processes they undergo. In this study, various technical and institutional conditions are explored that influence thermal energy community formation processes by using an agent-based modelling approach. The results show that technology selection is not the most crucial and determining factor for the success of thermal energy communities, yet the surrounding institutional conditions are. Key factors that influence these formation processes pertain to providing training, so that the thermal energy community leaders become more skilled, and allocating subsidies based on the projects’ degree of environmental friendliness. For all stakeholders, finding the balance between all of the decision-making criteria is key to success. The results are useful for practitioners - and especially for policy makers - to develop more impactful policies and strategies to support the expansion of local thermal energy communities.

Assessing the efficacy of interventions to control indoor SARS-Cov-2 transmission: An agent-based modeling approach

Nonpharmaceutical interventions for minimizing indoor SARS-CoV-2 transmission continue to be critical tools for protecting susceptible individuals from infection, even as effective vaccines are produced and distributed globally. We developed a spatially-explicit agent-based model for simulating indoor respiratory pathogen transmission during discrete events taking place in a single room within a sub-day time frame, and used it to compare effects of four interventions on reducing secondary SARS-CoV-2 attack rates during a superspreading event by simulating a well-known case study. We found that preventing people from moving within the simulated room and efficacious mask usage appear to have the greatest effects on reducing infection risk, but multiple concurrent interventions are required to minimize the proportion of susceptible individuals infected. Social distancing had little effect on reducing transmission if individuals were randomly relocated within the room to simulate activity-related movements during the gathering. Furthermore, our results suggest that there is potential for ventilation airflow to expose susceptible people to aerosolized pathogens even if they are relatively far from infectious individuals. Maximizing the vertical aerosol removal rate is paramount to successful transmission-risk reduction when using ventilation systems as intervention tools.

Simulating Personal Carbon Trading (PCT) with an Agent-Based Model (ABM): Investigating Adaptive Reduction Rates and Path Dependence

A decade after the publication of seminal papers on personal carbon trading (PCT), few empirical studies on its implementation exist. Investigating how to design, set up and implement a PCT scheme for a community or country raises several difficulties. For instance, it is unclear how to introduce a reduction rate of CO2 allowances to ensure a steady decrease in CO2 emissions from households. Computational approaches have been introduced to address these challenges of PCT by providing an opportunity to test counterfactual scenarios. Among the benefits of an agent-based modeling approach (ABM) is the potential to directly address dynamic developments and introduce counterfactual situations. In this paper, we review existing modeling approaches and present an ABM for PCT. With simulations of an artificial population of 1000 and 30,000 agents, we address questions on the price and reduction rate of allowances. A key contribution of our model is the inclusion of an adaptive reduction rate, which reduces the yearly allocated amount of allowances depending on a set CO2 abatement target. The results confirm that increased emissions targets are related to higher allowance prices and a higher proportion of buying households. Our analysis also suggests a significant path dependence in the dynamics of allowance prices and availability, but that adaptive reduction rates have little impact on outcomes other than the price. We discuss data availability and computational challenges to modeling a PCT scheme with an ABM. Ideal data to populate an ABM on PCT are not available due to the lack of real-world implementations of a PCT. Nonetheless, meaningful insights about the dynamics and the focal variables in a PCT scheme can be generated by the exploratory use of an ABM.

Simulating the spatial distribution of pollutant loads from pig farming using an agent-based modeling approach.

This research developed an agent-based model (ABM) for simulating pollutant loads from pig farming. The behavior of farmer agents was captured using concepts from the theory of planned behavior. The ABM has three basic components: the household or farmer agent, the land patches, and global parameters that capture the environmental context. The model was evaluated using a sensitivity analysis and then validated using data from a household survey, which showed that the predictive ability of the model was good. The ABM was then used in three scenarios: a baseline scenario, a positive scenario in which the number of pigs was assumed to remain stable but supporting policies for environmental management were increased, and a negative scenario, which assumed the number of pigs increases but management measures did not improve relative to the baseline. The positive scenario showed reductions in the discharged loads for many sub-basins of the study area while the negative scenario indicated that increased loads will be discharged to the environment. The scenario results suggest that to maintain the development of pig production while ensuring environmental protection for the district, financial, and technical support must be provided to the pig producers. The experience and education level of the farmers were significant factors influencing behaviors related to manure reuse and treatment, so awareness raising through environmental communication is needed in addition to technical measures.

An agent-based modeling approach combined with deep learning method in simulating household energy consumption

Household has been one of main energy consumption sectors in China, which brings about more challenges for the control of air pollutants and greenhouse gases. Due to the heterogeneities among household individuals, it is much more difficult to regulate the household energy consumption. So, it becomes necessary to understand and guide household energy consumption behaviors. In this paper, a multi-level agent-based model was constructed to aggregate the macro-level energy consumption (such as regions) from the micro-level entities (such as devices), and deep learning models were conducted and embedded in to predict the household energy consumption behaviors with factors as inputs and behavior parameters as outputs, meanwhile an energy substitution mechanism was designed. A series of scenarios were simulated based on actual survey samples, and the results indicated the great potential of our proposed method in the household energy simulation. The simulation fit the actual statistics well in the validation step. Under scenario analysis, the whole coal-to-electricity conversion was deeply determined by low-income individuals, and the effect pattern of certain factor was distinct and could be enhanced by others, while the marginal utility changed non-linearly with peak point effected by income growth rate. It is inspired that the local economic and socio-demographic situations should be comprehensively considered to achieve a trade-off between costs and expected results of regional energy strategies. Our research proposes a novelty study framework to simulate the household energy consumption from the micro individual perspective, and it is a valuable attempt to understand and quantify complex household energy consumption behaviors.

Towards an agent-based model to simulate osseointegration in powder-bed 3D printed implant-like structures

The orthopedic industry is still searching for an efficient way to replace bone loss due to surgical procedures such as arthroplasty and limb-sparing surgery. Additive manufacturing (AM) presents an opportunity to manufacture affordable patient-specific implants. Optimization of the implant–bone interface to maximize osseointegration (bone ingrowth) has not been appropriately addressed. Mechanobiological models, suited to predict mechanical adaptation of bone, cannot be used to predict osseointegration inside implants as the implant is not exposed to any mechanical loading until it is fully accepted by the host body. Biological models relying on partial differential equations based on continuum approximation are not well-suited to predict the discrete phenomenon of osseointegration. This study proposes an agent-based modeling (ABM) approach for representing the osseointegration process for orthopedic implants produced by powder-bed additive manufacturing processes. Agent-Based Modeling (ABM) is a cellular automata based discrete computing technique that uses rule-based mathematics derived from experimental studies to simulate evolutionary phenomena. In this paper, osseointegration inside a hexagonal closed packing of AM powder particles is modeled using ABM. Cellular agents such as pre-osteoblasts and osteoblasts are realistically modeled as cubic cells. The proposed model underpredicts osseointegration at early stages but predicts osseointegration at around 21 days with sufficient accuracy when compared to the in vitro test conducted by Xue et al. in 2007.

Operational response simulation tool for epidemics within refugee and IDP settlements: A scenario-based case study of the Cox's Bazar settlement.

The spread of infectious diseases such as COVID-19 presents many challenges to healthcare systems and infrastructures across the world, exacerbating inequalities and leaving the world’s most vulnerable populations most affected. Given their density and available infrastructure, refugee and internally displaced person (IDP) settlements can be particularly susceptible to disease spread. In this paper we present an agent-based modeling approach to simulating the spread of disease in refugee and IDP settlements under various non-pharmaceutical intervention strategies. The model, based on the June open-source framework, is informed by data on geography, demographics, comorbidities, physical infrastructure and other parameters obtained from real-world observations and previous literature. The development and testing of this approach focuses on the Cox’s Bazar refugee settlement in Bangladesh, although our model is designed to be generalizable to other informal settings. Our findings suggest the encouraging self-isolation at home of mild to severe symptomatic patients, as opposed to the isolation of all positive cases in purpose-built isolation and treatment centers, does not increase the risk of secondary infection meaning the centers can be used to provide hospital support to the most intense cases of COVID-19. Secondly we find that mask wearing in all indoor communal areas can be effective at dampening viral spread, even with low mask efficacy and compliance rates. Finally, we model the effects of reopening learning centers in the settlement under various mitigation strategies. For example, a combination of mask wearing in the classroom, halving attendance regularity to enable physical distancing, and better ventilation can almost completely mitigate the increased risk of infection which keeping the learning centers open may cause. These modeling efforts are being incorporated into decision making processes to inform future planning, and further exercises should be carried out in similar geographies to help protect those most vulnerable.

Immunoinformatics and Immunogenetics-Based Design of Immunogenic Peptides Vaccine against the Emerging Tick-Borne Encephalitis Virus (TBEV) and Its Validation through In Silico Cloning and Immune Simulation.

Pegivirus, HPgV, earlier known as Gb virus and hepatitis G virus, is an enveloped, positive-stranded RNA and lymphotropic virus classified into the Flaviviridae family. The transmission routes primarily involve blood products, with infections worldwide, leading up to 25% of persistent infections. To date, no effective therapeutic means are available to resolve Pegivirus infections. Effective vaccine therapeutics are the best alternative to manage this disease and any associated potential pandemic. Thus, whole proteome-based mining of immunogenic peptides, i.e., CTL (cytotoxic T lymphocytes), HTL (helper T lymphocytes) and B cell epitopes were mapped to design a vaccine ensemble. Our investigation revealed that 29 different epitopes impart a critical role in immune response induction, which was also validated by exploring its physiochemical properties and experimental feasibility. In silico expression and host immune simulation using an agent-based modeling approach confirmed the induction of both primary and secondary immune factors such as IL, cytokines and antibodies. The current study warrants further lab experiments to demonstrate its efficacy and safety.

Agent-based modeling approach for evaluating underground logistics system benefits and long-term development in megacities

Mobility, pollution, and other barriers against sustainable goods movement are pushing local administrators to seek innovations in urban transportation infrastructure. The urban underground logistics system (ULS) has been recognized as a prospective alternative to realize large-scale automated freight distribution within and around megacities. This paper proposes an integrated approach combing system dynamics and agent-based modeling to evaluate the long-term development and operating status of a city-wide ULS project. The project boundaries regarding underground network expansion, stakeholders’ attributes, and social-environmental benefit metrics were structured as eight highly-interacted agent modules. Critical decision variables of agents in terms of supply-demand equilibrium, investment plan, pricing-to-market and willingness-to-pay were incorporated into three formulized subsystem models. From empirical perspective, the urban territory of Beijing, China, was taken as a case to simulate the development footprints of ULS project under different funding options and market acceptance degrees. Results show that ULS has significant competence with respect to service capacity and profitability, while enabling billions of dollars of external cost-saving annually. Moreover, the comprehensive performance of ULS project regarding economic incomes, benefits, market demand, and construction schedule can reach satisfactory trade-offs through adaptively adjusting the funding policies, incentives and pricing portfolios during project development.

Spatio-temporal modelling of solar photovoltaic adoption: An integrated neural networks and agent-based modelling approach

This paper investigates the spatio-temporal patterns of solar photovoltaic (PV) adoption, solving the ongoing need to inform the management of the distribution networks with spatially explicit estimations of PV adoption rates. This work addresses a key limitation of agent-based models (ABMs) that use rule or equation-based decision-making. It achieves this by adopting an aggregated definition of the agents using artificial neural networks (ANN) as the criteria for decision-making. This novel approachdraws from both ABM and Spatial Regression methods. It incorporates spatial and temporal dependencies as well as social dynamics that drive the adoption of PVs. Consequently, the model yields a more realistic characterisation of decision-making whilst reflecting individual behaviours for each location following the real-world layout. The model utilises the ANN’s approximation capabilities to generate knowledge from historical PV data, as well as adapt to changes in data trends. First, an autoregressive model is developed. This is then extended to capture the population heterogeneity by introducing socioeconomic variables into the agent’s decision-making. Both models are empirically validated and benchmarked against the Bass Model.Results suggest that the model can account for the spatio-temporal and social dynamics that drive the adoption process and that the ABM and ANN integrated model has superior adaptive capabilities to the Bass model. The proposed model can estimate spatio-temporally explicit forecasts for up to five months with an accuracy of 80%. In line with the literature, results suggest that income, electricity consumption and the average household size variables yield the best results.

Emergence of Consumer Impulse Buying Behavior with Agent-Based Modeling Approach

Because of the dissemination of impulse buying behavior in consumers its academic studies have increased over the last decade. Because in large stores, sales have to be increased, the behavior of consumers in impulse buying to be taken into account by the researchers and managers of the stores. The purpose of this paper is to model agent-based the impulse buying behavior of consumers (customers), with regards to the factors of discount and swarm in the purchase. In terms of executive purpose and with agent-based modeling approach, the present paper examines the existing reality of consumer impulse buying behavior. This paper develops consumption models, examines and analyzes consumer behavior under the NetLogo software environment. In comparing the optimal points of discounts and sales volume in both discount and swarm-discount functions that lead the stores to maximize profits and sales volume simultaneously, it can be debated that with running this model (swarm-discount) stores would be gaining more sales by less discounts. Results could describe customer behavior by implementing discount and swarm factors. Understanding the Customer behavior prepared the comparing possibility of customer behavior in store in each introduced mathematical model. The contributions could be considered in two points of view. On the applicable view, this research can provide the managers and decision makers with significant information, includes possibility of forecasting sales volume and incomes of any policies in stores, so the comparing of policies and strategies analysis would be possible. This method is rather less expensive, because of virtual environment nature. Users of this model can study other sections by changing the research assumptions.