Causal Covid

Installation

Clone with

git clone [email protected]:Priesemann-Group/causal_covid.git

Notes:

You need python>3.8.

Before you can use the code and rerun the analyses you have to:

• init the submodules:

   #Init
   git submodule init
   # Update package manual (inside covid19_inference folder)
   cd covid19_inference
   git pull origin master

• install the requirements with

   pip install -r requirments.txt

Installation as package

Install

pip install git+ssh://[email protected]/Priesemann-Group/causal_covid.git

To run a scenario, open a interactive window and run:

from causal_covid import run_scenario
run_scenario.single_dimensional("data/2022-02-09_16-39-19_young_to_old_cap/vaccination_policy/U_2.npy", "data/2022-02-09_16-39-19_young_to_old_cap/vaccination_policy/u_3.npy")

Getting started

Scenario calculation

You can use one of the inferred models (saved in ./data/traces/) to investigate what would happen in a counterfactual scenario with an alternative vaccination policy. Go into ./scripts/ and run calculate_scenario.py (./scripts has to be the current working directory). In calculate_scenario.py the path for the U2 and u3 matrices of the alternative scenario have to be set.

Inference

To infer the base reproduction number that is necessary for the scenario calculations afterwards, go into ./scripts/ and run infer_single_age_group.py -i 1 (for the single dimensional models). Parameters are set inside the script.

Filepath setting

The case data, population data, waning immunity and vaccination used by the inference is centrally set in the .\scripts\params.py file.