Spatial and High-Resolution Models

🐼 Pandaesim: Stochastic Simulation for Age-Structured Epidemic Dynamics

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🧭 Overview Pandaesim is an epidemic spreading simulator designed to analyze complex infectious disease dynamics using stochastic or deterministic age-structured compartmental models embedded within a meta-population framework. It was developed to study large-scale epidemics such as COVID-19, where age-specific susceptibility, contact behavior, and spatial movement strongly influence transmission patterns. By combining age stratification with an … Read more

πŸ’§ 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

πŸ›£οΈ 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 pathsβ€”such as pedestrian trajectories, commuting routes, or vehicular flowsβ€”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

🌐 πƒβˆ‡Β² Diffusion Dynamics: Spatiotemporally Continuous Models

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Partial Differential Equations (PDEs) provide a rigorous mathematical framework for modeling infectious disease transmission when epidemic dynamics evolve continuously in both space and time. In contrast to ordinary differential equation models, which assume homogeneous mixing, and metapopulation models, which discretize space into patches, PDE-based approaches describe the smooth spatial propagation of pathogens. These models are … Read more

πŸ—ΊοΈ Modeling Disease Spread: The Geography, Population, Movement (GPM) Framework

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Spatial epidemiological models are deterministic or stochastic frameworks designed to investigate how spatial heterogeneities and host movement dynamics influence local and regional disease patterns. These models are essential for moving beyond homogeneous mixing assumptions and for capturing realistic geographic contexts in which infectious diseases emerge, spread, and respond to localized interventions. ──────────────────────────────────────────── 🧱 Compartmental Structure … Read more

πŸ“ˆ Unlocking Spatial Dynamics: The Kernel-Modulated SIR Model

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The Kernel-Modulated Susceptible–Infectious–Recovered (SIR) model is a mechanistic framework widely used in mathematical epidemiology to simulate contagious disease spread across large geographical scales, ranging from counties to entire continents. The model extends the classical SIR structure by embedding spatial interaction and movement dynamics directly into the transmission process through a modulating kernel. This kernel captures … Read more

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