Epidemic Modeling using Compartmental Models (SIR, SEIR, SIRD, SEIRD)

Project Overview

This project demonstrates the application of compartmental models to simulate the spread of infectious diseases within a population. Using epidemiological models such as SIR, SEIR, SIS, SIRD, and SEIRD, this tool allows users to analyze and visualize disease dynamics over time. The project provides insights into factors such as infection rates, recovery rates, and mortality. Users can interactively adjust parameters to simulate different diseases, including COVID-19, Monkeypox, Plague, Avian Flu, Measles, and Ebola.

Key Features

• Interactive Model Selection: Choose between multiple models (SIR, SEIR, SIS, SIRD, SEIRD) to explore various aspects of disease transmission.
• Customizable Parameters: Adjust infection rate (β), recovery rate (γ), incubation rate (σ), and mortality rate (μ) for different diseases.
• Compartmental Representation: Divide the population into Susceptible (S), Infectious (I), Recovered (R), Exposed (E), and Dead (D) compartments based on the model.
• Visualization with Plotly: Interactive plots display the simulation results with color-coded compartments for easy interpretation.

Technologies Used

• Python: Core language for implementing epidemiological models and managing data.
• Streamlit: Provides an interactive web interface for real-time simulation and parameter adjustment.
• Plotly: Used for interactive visualizations of the simulation results.
• SciPy (odeint): Solves the system of differential equations for each compartmental model.
• Docker To ensure portability and ease of deployment .

Scientific and Technical Details

The models are based on a set of differential equations that describe the rates of change between different compartments:

• SIR Model: Describes the transition of individuals from Susceptible (S) to Infectious (I) to Recovered (R).
• SEIR Model: Adds an Exposed (E) compartment for individuals who are infected but not yet infectious.
• SIS Model: Models diseases where recovered individuals can be reinfected (e.g., without immunity).
• SIRD Model: Includes the possibility of death (D) due to the infection.
• SEIRD Model: Combines the SEIR and SIRD models, accounting for both incubation and mortality.

Applications

This project can be applied to:

• Simulate the spread of infectious diseases under different scenarios.
• Explore the impact of public health interventions (e.g., reducing infection rates or increasing recovery rates).
• Educate stakeholders about the importance of epidemiological models in understanding and controlling outbreaks.
• Provide a flexible platform for future expansion, such as incorporating real-world data or more advanced models.

Conclusion

This epidemic modeling project showcases the power of statistical and mathematical models in epidemiology. By offering an interactive, user-friendly interface and customizable simulations, it provides valuable insights into the dynamics of infectious diseases and the potential outcomes of public health strategies.