Estimation of the instantaneous reproduction number is a well-researched problem in infectious disease epidemiology. We introduce a simple framework using sequential Monte Carlo (SMC) methods. Our approach:
👍 Is conceptually simple
👍 Flexible
👍 Produces valid credible intervals
👍 Can accommodate underreporting, missing/incomplete data, imported cases, uncertain serial intervals, temporally aggregated data, multiple data sources, etc - all at the same time!
This repository contains all data, scripts, and notebooks required to follow along with the primer.
This tutorial is designed for researchers with a basic understanding of statistical inference. We walk through the steps of constructing an SMC model to build a gold-standard
We also highlight the accompanying paper, An introduction to sequential Monte Carlo methods for reproduction number estimation.
We avoid using any external dependencies, so you can run all code in this repository with a standard Julia installation. To get started, all you need to do is:
- Install Julia from julialang.org
- Clone this repository
- Run Julia in your terminal and run:
using Pkg
Pkg.add("IJulia")
using IJulia
notebook()This will open a Jupyter notebook in your browser. Navigate to the github repository and open main.ipynb to get started.
Alternatively, you can open the cloned repository in VS Code (or your preferred IDE) and run the notebooks this way.
The files and folders you should care about:
gettingstarted.ipynb: A jupyter notebook to help with setup and navigation/notebooks/: Contains the main tutorial notebooks/data/: Contains all example data used in the tutorial/src/: Contains crticial source code
You can ignore (but feel free to explore):
/docs/: Contains the rendered tutorial/site/: Contains the quarto website files (these are rendered to/docs/)/assets/: Contains images and other assets used in the tutorials and readme files- Files like
.gitignoreand.nojekyllwhich are for repo management