Causal Age vs. Viral Load

Test of SARS-CoV-2 viral-load age-causal-dependence via a non-parametric, hierarchical, Bayesian, and causal model. The following is a Python implementation of the model described in

• Guardiani et al.; Causal, Bayesian, & non-parametric modeling of the SARS-CoV-2 viral load distribution vs. patient’s age, PLoS ONE, https://doi.org/10.1371/journal.pone.0275011 [1].

Please cite [1] when making use of this code.

Installation

The mandatory dependencies can be installed with

sudo apt-get install git python3 python3-pip python3-dev
pip3 install --user git+https://gitlab.mpcdf.mpg.de/ift/nifty.git@NIFTy_8

for a Debian-based distribution. Additionally, to plot results we require Matplotlib, which can be installed as follows

sudo apt-get install python3-matplotlib python3-seaborn

For more information about the NIFTy package we refer to the NIFTy 8 webpage.

Data Visualization

The unprocessed datasets are stored as Cobas_Dataset.csv and LC_480_Dataset.csv. The data can be visualized by running the script data.py which outputs (for the two datasets respectively)

These datasets have been extracted from Jones et al. preprint (Fig. 6).

Run the code

The source code is located in the src directory. The code can be run from the covid_combined_matern.py script. The necessary arguments that need to be parsed are a config file (config.json) which contains the prior settings, the dataset (in .csv format) and the random seed for the reshuffling of the viral load data (reshuffling_seed). If

• reshuffling_seed = 0, the viral-load data will not be reshuffled.
• reshuffling_seed > 0, the viral-load data wil be reshuffled (with random seed being reshuffling_seed).
• reshuffling_seed < 0 and != -3141592, the age and-viral load axes will be switched throughout the analysis (
  X \leftrightarrow Y
  ) and the data will be reshuffled with (with random seed being - reshuffling_seed).
• reshuffling_seed = -3141592, the age and viral-load axes will be switched, but the data will not be reshuffled.

For example, we can start the data analysis for the Cobas dataset without reshuffling via

python3 covid_combined_matern.py --json_file ../config/config.json --csv_file ../Cobas_Dataset.csv --reshuffle_seed 0

Analogously, after minimization, the plots can be generated with

python3 results_matern.py --json_file ../config/config.json --csv_file ../Cobas_Dataset.csv --reshuffle_seed 0

and the evidence calculated and stored in text files with the following

python3 calculate_evidence.py --json_file ../config/config.json --csv_file ../Cobas_Dataset.csv --reshuffle_seed 0

Input

As described in the above, the inputs of the code are (paths to) a prior configuration file (in .json format, e.g. config/config.json), a file containing age and viral-load data (in .csv format, e.g. Cobas_Dataset.csv), and a seed for reshuffling (which can also be used to invert the axes).

Output

The output of the analysis can be divided into three categories.

1. covid_combined_matern.py will output a list of samples from the posterior distribution and some relevant plots produced during minimization to a specified folder (minimization can take up to one day per configuration).
2. results_matern.py will output a series of plots (an example of which is given below) to summarize the results. These plots range from density reconstruction plots to infectivity-prediction plots.
3. calculate_evidence will output evidence results into the specified folders to be used for model comparison.

LICENSE

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.

Copyright(C) 2013-2022 Max-Planck-Society.