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Analysis Code: Digital contact tracing contributes little to COVID-19 outbreak containment

Angelique; Dirk; Benjamin F

# Digital Contact Tracing

This package provides the simulation, analysis, and figure code for
the manuscript "Digital contact tracing contributes little to COVID-19
outbreak containment" by A. Burdinski, D. Brockmann, and B. F. Maier.

## Prerequisites

The analysis code was used and tested for Python 3.8 on CentOS, Ubuntu, and MacOS.
In order to run code in this collection, first install the requirements:

pip install -r requirements.txt

Models are implemented using [epipack]( To run
large-scale simulations, we use [qsuite](, a CLI
to facilitate simulations on HPC clusters. `qsuite` will be installed when
dependencies are installed from `requirements.txt`.

## Main model

The main model, including an example configuration,
can be found in directory `main_model/`.
To run the simulation, do

cd main_model/
qsuite local

## Analyses 

Almost all simulations and analyses performed in the paper
can be found in `analysis_collection/tracing_sim/`.

All extracted (summarized) data can be found in

Code to produce the figures in the main text from distilled analysis
results and analyses for the locally clustered network with
exponential degree distribution can be found in

Code for plots in the SI can be found in
`analysis_collection/` except for Fig. S7-S8-- those can
be found in the respective directories 
and `analysis_collection/tracing_sim/results_toy_model/`.

In order to replicate the simulations, change to the directory containing the
respective analysis and run `qsuite local`, e.g. 

cd analysis_collection/tracing_sim/results_exponential_DF_NMEAS_100_ONLYSAVETIME_False/
qsuite local

An illustration to justify the choice of `beta = 10^(-6)` as the long range
redistribution parameter beta can be found by running

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