Multi-Compartment Models for Interventions and Dynamics

🐼 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

🏥 Analyzing Healthcare Constraints: SIR Models with Capacity-Limited Treatment Functions

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The effectiveness of disease control hinges not only on intrinsic biological rates but also on the external operational constraints of the healthcare system. Models incorporating non-linear removal terms are essential for accurately simulating disease outcomes under resource limitations, such as finite hospital capacity or constrained medical staff availability. ⚙ Compartmental Structure and Flow Explanation We … Read more

📈 Beyond Bilinear Incidence: Nonlinear Transmission in Epidemic Models

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Introduction Classical compartmental epidemic models such as the SIR model assume a bilinear incidence term of the form β S I, meaning new infections occur in direct proportion to the product of susceptible (S) and infectious (I) individuals. This simple incidence function assumes homogeneous mixing and unlimited contacts. Real disease transmission often deviates from this … Read more

🧠 Modeling Long-Term Disease Dynamics: The SIR Model with Vital Dynamics

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The Susceptible–Infectious–Recovered (SIR) model augmented with Vital Dynamics is a foundational epidemiological framework specifically designed to analyze disease spread over temporal scales sufficiently long that demographic events—namely births and natural deaths—cannot be ignored. This inclusion transforms the analysis from acute outbreak prediction (epidemic) to steady-state prevalence assessment (endemic). 🧩 Compartmental Structure and Flow Explanation The … Read more