TTI Explorer - Sensitivity Analysis and Optimisation (COVID-19)

This is a repository for tti-explorer simulation analysis. This repository is built to analyse and explore the impact of various test-trace-isolate strategies and social distancing measures on the spread of COVID-19 in the UK. It also aims to employ unconstrained and constrained optimisation techniques to discover optimal strategy for reducing the disease' effective reproduction number.

Note:

• the tti-explorer library that contains the simulation code can be found on tti-explorer.
• Accompanying papers include Kucharski et al. (2020), Klepac et al. (2018), He et al. (2020), The Delve Initiative (2020).

Requirements:

tti_explorer

• Python 3.6+
• numpy
• scipy
• pandas
• matplotlib
• dataclasses (for Python 3.6)

scripts, tests and notebooks

• jupyter
• tqdm
• pytest

Folder Structure:

• data: contains datasets used for the simulation.
• tti_explorer: contains related simulations codes.
• notebooks: contains analysis codes (sensitivity analysis, causal analysis, policy optimisation).
• results: contains experiments results (in csv and pickle).
• paper.pdf: our paper documenting methods and results.

Setup:

git clone https://github.com/rs-delve/tti-explorer
cd tti-explorer
pip install -r requirements.txt
pip install .

Authors:

• Maleakhi Wijaya: [email protected]
• Chuan Tan: [email protected]
• Jakub Mach: [email protected]