ABMs

βœˆοΈπŸ™οΈ Bidirectional Mobility Agent-Based Models: Modeling Spread Through Dynamic Location Exchange

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The Bidirectional Mobility Model, when implemented as an Agent-Based Model (ABM), is designed to capture the dynamic spatial spread of infectious diseases driven by the movement of individuals between discrete, geographically distinct locations, such as neighborhoods, cities, or regions. In this framework, the population is represented as a collection of agents, each possessing both a … Read more

πŸ“ΆπŸ”— Multilayer Network Agent-Based Models: Modeling Multiple Contact Structures Simultaneously

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Multilayer Network Agent-Based Models (ABMs) represent a major advance in epidemiological modeling by explicitly recognizing that individuals simultaneously participate in multiple, distinct contact environments, such as households, workplaces, schools, and the broader community. In this framework, the population is modeled as a single set of agents, while interactions are represented through several superimposed network layers, … Read more

πŸ“Š Configuration Model Agent-Based Models: Prescribing Epidemic Dynamics via Degree Distribution

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The Configuration Model (CM) occupies a central position among network-based Agent-Based Models (ABMs) by explicitly encoding observed social heterogeneity into the model structure. Unlike purely random network constructions, the Configuration Model allows the modeler to prescribe a fixed degree distribution P(k), representing the exact number of contacts held by each individual agent, while connections between … Read more

πŸŒπŸ”¬ Small-World Network Agent-Based Models: Modeling Local Clustering and Global Reach

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Small-world network models provide a powerful Agent-Based Modeling (ABM) framework for infectious disease dynamics because they simultaneously capture two dominant features of real human contact systems: strong local clustering and short global separation. In this representation, individuals are modeled as nodes connected by links that reflect social or physical contacts. Most connections occur within tightly … Read more

πŸ”—πŸ“ˆ Scale-Free Network Agent-Based Models: Modeling Epidemics in Heterogeneous Populations

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Scale-free network models represent a fundamental Agent-Based Modeling (ABM) framework for studying infectious disease dynamics in highly heterogeneous populations. In these models, individuals are represented as nodes connected by links that encode social or contact interactions. The defining feature of a scale-free network is its power-law degree distribution, in which a small number of nodes … Read more

πŸ”—πŸŽ² Random Graph Agent-Based Models (ErdΕ‘s–RΓ©nyi): The Foundation of Stochastic Network Modeling

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The Random Graph model, specifically of the ErdΕ‘s–RΓ©nyi (ER) type, represents the foundational network architecture for studying infectious disease spread in stochastic settings. Within this Agent-Based Model (ABM) framework, the population is represented as a collection of nodes (agents), while contacts between individuals are represented as edges formed independently and uniformly at random with a … Read more

πŸ•ΈοΈπŸ“Š Network Agent-Based Models: Unlocking Transmission Dynamics Through Topology

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Network Agent-Based Models (ABMs), also referred to as Individual-Based Models (IBMs), are central to modern mathematical epidemiology because they move beyond the simplifying mass-action assumption used in classical compartmental models. In these models, the population is represented as a graph in which individuals are modeled as nodes and their interactions as edges. Disease transmission emerges … Read more

πŸ•ΈοΈπŸ“² Network Agent-Based Models (ABMs): Modeling Heterogeneity and Social Contact

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While Reaction–Diffusion partial differential equations address spatial components in a continuous manner, Agent-Based Models (ABMs) are the discrete and stochastic counterparts required to capture heterogeneity, individual behavior, and complex contact structures. Among spatially and socially explicit ABM frameworks, network-based simulations are the most widely used and analytically informative. ──────────────────────────────────────────── 🧬 1. Compartmental Structure and Flow … Read more

πŸ—ΊοΈπŸ¦  POI Agent-Based Models (ABMs): Precision Epidemiology in Dynamic Environments

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Agent-Based Models (ABMs) parameterized by Points of Interest (POIs) represent a sophisticated class of stochastic epidemiological models that move beyond traditional homogeneous mixing assumptions. By integrating dynamic location data and individual behavioral characteristics, these models provide high-resolution insights into disease spread influenced by spatial and social heterogeneity. As a result, they are invaluable tools for … Read more

πŸ”¬ Microscale Dynamics: Cellular Automata & Lattice Epidemiology 🌐

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Cellular Automata (CA) and Lattice Models represent a vital class of microscale epidemiological modeling frameworks, allowing disease spread to be examined at high spatial and individual resolution. These models discretize space into cells arranged on a lattice and time into sequential updates. Instead of relying on macroscopic averages found in classical Ordinary Differential Equation models, … Read more