Related papers: Infection in a Confined Space using an Agent-Based…
Agent based models (ABMs) are a useful tool for modeling spatio-temporal population dynamics, where many details can be included in the model description. Their computational cost though is very high and for stochastic ABMs a lot of…
Cell migration is frequently modelled using on-lattice agent-based models (ABMs) that employ the excluded volume interaction. However, cells are also capable of exhibiting more complex cell-cell interactions, such as adhesion, repulsion,…
Agent-based modelling (ABM) is a facet of wider Multi-Agent Systems (MAS) research that explores the collective behaviour of individual `agents', and the implications that their behaviour and interactions have for wider systemic behaviour.…
Corona Virus Disease 2019 (COVID-19), due to its extremely high infectivity, has been spreading rapidly around the world and bringing huge influence to socioeconomic development as well as people's daily life. Taking for example the virus…
This paper develops an agent-based disease spread model on a contact network in an effort to guide efforts at surveillance testing in small to moderate facilities such as nursing homes and meat-packing plants. The model employs Monte Carlo…
The potential power provided and possibilities presented by computation graphs has steered most of the available modeling techniques to re-implementing, utilization and including the complex nature of System Biology (SB). To model the…
We build an agent-based model of incarceration based on the SIS model of infectious disease propagation. Our central hypothesis is that the observed racial disparities in incarceration rates between Black and White Americans can be…
The COVID-19 pandemic prompted a surge in computational models to simulate disease dynamics and guide interventions. Agent-based models (ABMs) are well-suited to capture population and environmental heterogeneity, but their rapid deployment…
Cellular Agent-Based Models are commonly employed to describe a variety biological systems. Over the course of the past years, many modeling tools have emerged which solve particular research questions. In this short opinion piece, we argue…
Understanding infectious disease transmission in institutional settings requires models that capture how contacts arise from structured routines, roles, and spatial constraints. In aged care facilities, interactions are driven by care…
The understanding of human central nervous system depends on knowledge of its wiring. However, there are still gaps in our understanding of its wiring due to technical difficulties. While some information is coming out from human…
This study offers a new paradigm of individual-level modeling to address the grand challenge of incorporating human behavior in epidemic models. Using generative artificial intelligence in an agent-based epidemic model, each agent is…
Agent-based modeling and simulation has evolved as a powerful tool for modeling complex systems, offering insights into emergent behaviors and interactions among diverse agents. Integrating large language models into agent-based modeling…
The risk of indoor airborne transmission among co-located individuals is generally non-uniform, which remains a critical challenge for public health modelling. Thus, we present CompARE, an integrated risk assessment framework for indoor…
This Overview, Design Concepts, and Details Protocol (ODD) provides a detailed description of an agent-based model (ABM) that was developed to simulate hospitalizations during the COVID-19 pandemic. Using the descriptions of submodels,…
Agent-Based Models (ABM) are computational scenario-generators, which can be used to predict the possible future outcomes of the complex system they represent. To better understand the robustness of these predictions, it is necessary to…
Pandemics, notably the recent COVID-19 outbreak, have impacted both public health and the global economy. A profound understanding of disease progression and efficient response strategies is thus needed to prepare for potential future…
Agent-based modeling (ABM) is a well-established paradigm for simulating complex systems via interactions between constituent entities. Machine learning (ML) refers to approaches whereby statistical algorithms 'learn' from data on their…
Agent-Based Models are a powerful class of computational models widely used to simulate complex phenomena in many different application areas. However, one of the most critical aspects, poorly investigated in the literature, regards an…
The design of agent-based models (ABMs) is often ad-hoc when it comes to defining their scope. In order for the inclusion of features such as network structure, location, or dynamic change to be justified, their role in a model should be…