ODE

👥 The Age-Structured SIR Model: Demographics in Disease Dynamics

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📉 Conceptual Overview Infectious disease transmission rarely occurs in a homogeneous population. Real epidemics are shaped by age-specific biological susceptibility, social behavior, and contact patterns. The Age-Structured SIR Model explicitly incorporates these heterogeneities by partitioning the population into discrete age cohorts and coupling them through age-dependent contact rates. This framework is fundamental for designing targeted … Read more

📊 The Age-of-Infection Model: Precision in Epidemic Modeling

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📈 Overview and Conceptual Motivation In classical epidemiology, it is often assumed that all infected individuals are equally infectious throughout their infectious period. The Age-of-Infection (also called Infection-Age) Model relaxes this assumption by explicitly recognizing that both infectiousness and removal depend on the time elapsed since infection. This framework is essential for diseases in which … Read more

🌡️ Climate-Sensitive Mechanistic Models: The Core of Vector-Borne Disease Forecasting

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Mechanistic (process-based) epidemiological models derived from the Ross–MacDonald framework form the backbone of vector-borne disease forecasting. These models explicitly encode biological and ecological processes and allow climatic drivers—particularly temperature (T) and precipitation (P)—to directly modulate transmission dynamics. By embedding climate-dependent functions into transmission, survival, and incubation processes, these models provide a principled framework for projecting … 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

🐼 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

🦟 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

🛣️ 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

🗺️ 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

🦟 Modeling Dengue Persistence: The Host–Vector–Eggs (HME) Dynamic Framework

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The Host–Vector–Eggs (HME) model is a structured extension of the classical Susceptible–Infectious–Susceptible (SIS) and SIR-type formulations, specifically tailored for vector-borne diseases such as Dengue Fever. Its defining feature is the explicit incorporation of the mosquito life cycle, including the egg and immature stages, which play a decisive role in determining adult vector density and, consequently, … Read more