Global estimation of unobserved COVID-19 infection
Project description
NIDDK SICR Model for estimating SARS-CoV-2 infection in a population
Code and data used for Chow et al, "Global prediction of unreported SARS-CoV2 infection from observed COVID-19 cases", to model the progression of the COVID-19 epidemic and estimate the unobserved SARS-CoV-2 infected population from reported cases, case recoveries, and case deaths globablly. Models are implemented in Stan and fit using PyStan.
The core model is a variation of the SIR model with a latent variable I
for the number of unobserved infected which is distinguished from C
the observed cases. This model follows:
Several variants of this model are discussed in the Supplemental Material of the preprint. The code required to fit these models to data is provided in the models
directory. Data sources include The Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) and The COVID Tracking Project.
Requirements to run code here:
- python 3.5+
- Install the Python package:
- From pypi:
pip install covid-sicr
- Or from source:
git clone http://github.com/nih-niddk-mbs/covid-sicr
cd covid-sicr
pip install -e . # This installs the covid-sicr package from source
- Or place this directory in your
PYTHONPATH
and install the contents ofrequirements.txt
.
- From pypi:
pystan
may not be easily pip installable on some systems, so consider conda:conda install -c conda-forge pystan
Important scripts:
-
New data can be downloaded from data sources with
scripts/get-data.py
:- For all data sources:
python scripts/get-data.py
. - This will use Johns Hopkins and COVID Tracking by default.
- Other options can be seen with the
--help
flag. - Data sources follow a functional pattern and are extensible.
- For all data sources:
-
Stan models can be run with Python file
scripts/run.py
:- Run a single region with:
python scripts/run.py MODEL_NAME --roi=REGION_NAME
- e.g.
python scripts/run.py SICRLMQ --roi=US_MI
- Other optional arguments for specifying paths and some fitting parameters can be examined with
python scripts/run.py --help
. - A pickle file containing the resultant fit will be produced in your
fits-path
(see help). - A
scripts/run-many.py
file is provided for reference but much better performance will be obtained by runningscripts/run.py
on a cluster.
- Run a single region with:
-
Analyze finished fits for all regions with
scripts/visualize.py
:- For all regions (with fits) with
python scripts/visualize.py MODEL_NAME
- e.g.
python visualize-master.py --nonlinearmodel
- As above, help is available with the
--help
flag. - Jupyter notebooks containining all analyzed regions will be created in your
--fits-path
.
- For all regions (with fits) with
-
Tables summarizing fit parameters can be generated with
scripts/make-tables.py
:python scripts/make-tables.py
- e.g.
python scripts/make-tables.py --model-names nonlinearmodel fulllinearmodel
- As above, help is available with the
--help
flag. .csv
files of the resulting dataframes will be created in the--fit-path
directory in thetables
subdirectory.
Reproducbility of a particular version of the manuscript will require using a particular tag, e.g. "medrXiv-v1" (see tags and releases here). Using the most recent version of any other branch (including master) may lead to different results as our research is on-going.
This code is open source under the MIT License. Correspondence on modeling should be directed to carsonc at nih dot gov or vattikutis at mail dot nih dot gov. Correspondence on the python code should be directed to rgerkin at asu dot edu.
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