liobalinus-tech/SEIR_Model

A Bayesian SEIR Epidemiological Model for malaria transmission dynamics in Northern Namibia. Built with Python and MCMC sampling to estimate $R_0$ and simulate the impact of public health interventions. # Bayesian SEIR Modeling of Malaria Outbreaks in Northern Namibia This repository contains a modular Python framework for modeling malaria transmission dynamics using Bayesian Inference. The project captures seasonal fluctuations in infection rates and evaluates the statistical impact of health interventions. ### Project Overview The model utilizes a compartmental SEIR (Susceptible-Exposed-Infectious-Recovered) structure to simulate disease spread. By leveraging No-U-Turn Sampling (NUTS), the system calibrates epidemiological parameters against observed clinical data, providing high-fidelity uncertainty quantification. ### Technical Architecture The codebase is decoupled into three primary modules for scalability and testing: * seir_engine.py: The mathematical core, containing the ODE systems and the PyMC Bayesian model configuration. * Visualizer.py: A comprehensive visualization suite that generates clinical figures, including uncertainty intervals and phase portraits. * main.py: The execution controller that orchestrates the inference and generates results. ### Key Results The model generates six critical clinical visuals (located in the /figures directory): * Mass Action Balance: State transitions through the SEIR compartments. * Uncertainty Quantification: Model calibration with 94% Highest Density Intervals (HDI). * Reproduction Number Dynamics: Seasonal oscillations of $R(t)$. * Phase Portrait: Geometric stability of the $S-I$ relationship. * Strategic Sensitivity Analysis: Heatmap identifying peak clinical loads under varying transmission/recovery rates. * Policy Simulation: Quantitative impact of intervention strategies, such as bed-net distribution. ### Getting Started **Prerequisites** Ensure you have the following libraries installed: *Bash*\ pip install numpy pymc arviz matplotlib seaborn scipy pytensor\ **Execution**\ To run the full simulation and generate all figures, execute: *Bash*\ python main.py **Research Documentation**\ For a deep dive i …