This project investigated the use of probabilistic programming techniques to model and detect outliers in Covid-19 wastewater data.
This project uses the Julia programming language and the Gen probabilistic modeling and inferencing framework to model the data and to identify outliers among the sample data points.
For ease of understanding, this project is broken up into a set of steps:
In this step, we import our data from a file using Julia utilities such as dataframes.
Next, we use a linear probabilistic model to fit a line through the data points along with identifying outliers.
Rather than trying to approximate our data with a single line, we break the data into groups and fit a set of linear segments through the sequence of groups.
Since our data points encompass a large range of values and because epidemic trends frequently follow exponential trends, we transform the values to a log scale and fit linear segments to the data in the logarithmic space.
In order to overcome the limitations of using a linear model to fit varying data, we use a quadratic model instead, fitting the data to a series of quadratic spline segments.
In order to smooth the segmented quadratic spline from the previous step, we match the derivatives at the endpoints of the quadratic spline segments.
- Julia - The Julia Programming Language
- Gen - PPL Framework for Generative Modeling and Probabilistic Inference
- Jupyter - Notebooks for Interactive Computing
To get a local copy up and running follow these simple steps.
- Julia
To install Julia, download and run the appropriate installer: Julia Downloads
- Jupyter
To run Jupyter notebooks, follow the instructions provided: Jupyter Installation
- Clone the repo
git clone https://github.com/AFIDSI/PPL_OutlierPrediction.git
The project examples are provided in two different forms (1) as Julia script files that may be executed from the command line and (2) as Jupyter notebook files that may be viewed in a Jupyter editor.
- Go to the desired project step
cd src/step01-importing-data
- Run the Julia code from the command line OR open the notebook file in a Jupyter editor.
julia step01.jl
jupyter notebook step01.ipynb
We are not accepting contributions to this project at this time.
Distributed under the MIT License. See LICENSE
for more information.
Steve Goldstein - (mailto:[email protected]) - email
Project Link: https://github.com/AFIDSI/PPL_OutlierPrediction
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This project looks to analyze Covid-19 outliers using probabilistic programing models in Gen. https://drive.google.com/drive/folders/1m5wJr5PzOk0qeckA_VT_dturEy3F8h4B?usp=sharing