koneboi/sir-seir-models

Infectious disease compartmental models (SIR/SEIR/SEIRD) for epidemic modelling (Mali/West Africa context) # Infectious Disease Compartmental Models (SIR/SEIR) **Real-world mathematical modelling of infectious disease transmission (Mali/West Africa context)** This repository provides a complete, professional toolkit for modelling the transmission dynamics of infectious diseases using classical compartmental models (SIR, SEIR, SEIRD). The models are implemented in Python with `scipy.integrate.solve_ivp`, and the repository includes model fitting to case data, parameter sensitivity analysis, and vaccination impact scenario modelling. Realistic West African context is used throughout — measles is treated as a worked example because of its well-characterised basic reproduction number and the relevance of high birth rates and vaccination coverage in the region. --- ## Overview of Contents ``` sir-seir-models/ ├── README.md # This document ├── requirements.txt # Python dependencies ├── models/ │ ├── sir_model.py # SIR compartmental model (core) │ ├── seir_model.py # SEIR model (adds exposed/latent compartment) │ └── seird_model.py # SEIRD model (adds death compartment & CFR) ├── scripts/ │ ├── 01_basic_sir.py # Baseline measles-like epidemic simulation │ ├── 02_parameter_sensitivity.py # Sweep R0, build heatmaps & final size curves │ ├── 03_vaccination_impact.py # Vaccination coverage, Re, herd immunity │ └── 04_fit_to_case_data.py # Fit model parameters to (synthetic) case data └── output/ # Generated publication-quality figures (PNG) ``` ## Theory ### Basic reproduction number, R₀ The **basic reproduction number** R₀ is the expected number of secondary infections produced by a single infectious individual in a fully susceptible population. It is the single most important summary parameter in infectious disease epidemiology: - R₀ > 1 : an outbreak can occur; the infection spreads - R₀ e accounts for the proportion of the population that is no longer susceptible. If a …