ABMs

📈 Beyond Mean-Field: The Pair-Approximation Epidemic Model 🕸️

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🧭 Conceptual Overview In spatial and network epidemiology, the Pair-approximation epidemic model represents a major methodological advance beyond classical well-mixed assumptions. Instead of assuming that every infectious individual can contact every susceptible individual, this framework explicitly incorporates local spatial correlations. Transmission is constrained to occur only between neighboring individuals connected by a social or spatial … Read more

📈 Frequency-Dependent Incidence: Modeling Transmission in Saturated Networks 🦠

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🧬 Overview and Conceptual Motivation In infectious disease modeling, the choice of the incidence function fundamentally shapes how transmission risk is represented. The Frequency-Dependent Incidence SIR model, also known as the Standard Incidence model, is designed for settings in which the number of contacts an individual makes per unit time is independent of total population … Read more

🔬 Synergistic Spatial Modeling: Coupling PDEs and ABMs for Viral Dynamics

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🧭 Overview Hybrid epidemiological models represent a major methodological advancement by explicitly coupling Agent-Based Models (ABMs) with Partial Differential Equation (PDE) frameworks. This approach addresses a central limitation in large-scale epidemic modeling: ABMs provide realistic, individual-level resolution but become computationally prohibitive at scale, while PDEs efficiently describe continuous spatial spread but lack behavioral granularity. Hybrid … Read more

🔬 Stochastic Efficiency: The Compartment–Agent Mixed Model (CAMM)

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🧭 Overview The Compartment–Agent Mixed Model (CAMM) is a stochastic epidemiological modeling framework designed to overcome the computational limitations of large-scale Agent-Based Models while preserving essential stochastic behavior. CAMM provides a hybrid paradigm that combines the mathematical clarity and efficiency of classical compartmental models with the flexibility of agent-based simulation. Instead of modeling every individual … Read more

💧 Hydrology Meets Epidemiology: The HYDREMATS Vector Model

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🧭 Overview The HYDREMATS (Hydrology, Entomology, and Malaria Transmission Simulation) model is a spatially explicit Agent-Based Model designed to simulate malaria vector dynamics, particularly for Anopheles mosquito species, at the village scale. Its defining characteristic is the tight yet modular coupling of a distributed hydrology model with a mosquito agent-based model, allowing environmental physics to … Read more

🦟 MOMA: A Spatially Explicit Agent-Based Model for Aedes aegypti Population Dynamics

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🧭 Overview The MOMA (Model Of Mosquito Aedes) is a spatially explicit Agent-Based Simulation Model designed to investigate the population dynamics of the female Aedes aegypti mosquito, the principal vector of Dengue virus. The model represents mosquitoes as individual agents interacting with a heterogeneous environment, allowing localized biological processes and spatial constraints to collectively generate … Read more

🌐 Mob-Cov: Hierarchical Mobility Meets Epidemic Dynamics 📈

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Mob-Cov is a stochastic, spatially explicit Agent-Based Model (ABM) developed to analyze COVID-19 transmission under hierarchical geographical mobility patterns. The model represents human movement through nested spatial containers—ranging from rooms and buildings to cities and countries—capturing how multiscale mobility structures shape epidemic diffusion. By embedding stochastic infection processes within realistic mobility hierarchies, Mob-Cov provides a … Read more

🛣️ Trajectory Networks in Epidemiology: Tracking Epidemic Spread through Human Mobility

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Trajectory Network modeling is an advanced approach at the intersection of spatial epidemiology, network science, and human mobility analysis. It focuses on epidemic spreading driven by explicit individual movement paths—such as pedestrian trajectories, commuting routes, or vehicular flows—rather than assuming static contacts or homogeneous mixing. By transforming raw trajectory data into dynamic networks, this framework … Read more

🌐 Networked Dynamics: Spatial Metapopulation Models for Epidemic Forecasting

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Spatial Network Models, most commonly implemented through the Metapopulation framework, are core tools in mathematical epidemiology for forecasting infectious disease spread across geographically distinct populations. These models explicitly link local disease dynamics within each population unit to mobility-driven interactions between units, enabling rigorous analysis of how human movement shapes the large-scale diffusion, synchronization, and timing … Read more

🔎 Tracing the Origin: Dynamic Network Models for Epidemic Source Detection

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Dynamic Network Models are a central class of tools in mathematical epidemiology for studying infectious disease spread in populations characterized by heterogeneous and time-varying contact patterns. Unlike classical compartmental models that assume homogeneous mixing, these models explicitly represent individuals and their evolving interactions, making them particularly effective for identifying the source of an epidemic and … Read more