SEIRSPlus: Advanced Computation Models for Predicting the Dynamics of Outbreaks and the Impact of Interventions
SEIRSPlus is a pivotal GitHub project designed to predict the dynamics of outbreaks with social distancing measures and other health-based interventions. This project is vital in the era of unpredictable infectious diseases, where understanding and planning for outbreak management is more critical than ever before.
Project Overview:
The core objective of SEIRSPlus project is to provide in-depth, realistic models of outbreak trajectories. Rooted in the well-known SEIRS (Susceptible-Exposed-Infectious-Recovered-Susceptible) Epidemiological models, SEIRSPlus goes a step further by incorporating factors like testing, quarantine, and contact tracing. The project does not merely model the dynamics of an outbreak; it also simulates the impact of possible interventions. Its target audience is quite broad as it caters to researchers, data scientists, health experts, policy makers and anyone interested in understanding the intricacies of outbreak dynamics.
Project Features:
One predominant feature of the SEIRSPlus project is its ability to integrate traditional SEIRS modeling with modern interventions like social distancing and network-structured populations. Besides, it offers the ability to simulate stochastic demographic events and importation of new cases, as well as other ad-hoc interventions. The lucidity of these models is visible in their application; for instance, the project has been used to predict the resurgence of SARS-CoV-2 due to testing delays and effectively illustrate the concept of "flattening the curve."
Technology Stack:
The SEIRSPlus project is predominantly written in Python, reflecting the language's versatility in handling complex data models. This project leverages the NetworkX library for creating and visualizing complex network structures and utilizes NumPy for numerical computations. The advantage of these tools lies in their capacity to handle and compute large-scale data, essential for this project's prediction and modeling activities.
Project Structure and Architecture:
The structure of this GitHub project is intuitively organized, ensuring straightforward navigation for interested users. The principal components of the project include the 'notebooks' directory with Jupyter notebooks for visualizations and the 'model' directory containing the Python scripts for the models. Also, any scripts for testing the models can be found in the 'tests' directory.
