From 2fca52352ee63c1b8243de68f3dac915849bda32 Mon Sep 17 00:00:00 2001 From: Anita Applegarth Date: Mon, 20 Jan 2025 15:20:11 +0000 Subject: [PATCH] Updated links and formatted pictuers --- README.md | 25 +++-- walk_through/epigeopop.ipynb | 201 +++++++++++++++++++++++++++++++++++ 2 files changed, 213 insertions(+), 13 deletions(-) create mode 100644 walk_through/epigeopop.ipynb diff --git a/README.md b/README.md index 6003b72..3fcce6f 100644 --- a/README.md +++ b/README.md @@ -1,26 +1,25 @@ +ADD BADGES # rEpiabm rEpiabm enables users familiar with R to use Epiabm (ADD LINK). Epiabm is a simulation tool that models the progress of an epidemic across a specified region of interest within a specific timeframe. It has been developed in python for small-scale implementations and C++ for fast, large-scale simulations. PyEpiabm design is modular, with many options to configure specific requirements. ## Summary of Epiabm functionality ### Basic Architecture -To model an epidemic, contact events are represented by the population spatial structure. The transmission of the disease and its progression within host is represented by a compartment model. These two architectures are highly configurable; this allows us to study a wide range of simulation scenarios. +To model an epidemic, contact events are represented by the population spatial structure (see Figure 1.). The transmission of the disease and its progression within host is represented by a compartment model (see Figure 2.). These two architectures are highly configurable; this allows us to study a wide range of simulation scenarios.
-

Figure 1: Population Spatial Structure

- Population spatial structure -

The environment is modelled using EpiGeoPop, which takes a region of interest, creates layers of sub-regions of different types and populates these with individuals.

+ Population spatial structure +

Figure 1. Population Spatial Structure: The environment is modelled using EpiGeoPop, which takes a region of interest, creates layers of sub-regions of different types and populates these with individuals.

-

Figure 2: Infection Progression

- Infection progression -

The infection progression is represented using a compartment model which tracks the daily progress of the disease within an individual.

+ Infection progression +

Figure 2. Infection Progression: The infection progression is represented using a compartment model which tracks the daily progress of the disease within an individual.

## Running a simulation -The overview below describes the user-input needed to run a basic simulation, using default values for parameters for other options. There is a comprehensive jupyter notebook showing a detailed, more complex example here (ADD LINK). +The overview below describes how the simulation works and the user-input required to run a basic simulation. A more detailed, complex example is illustrated in this jupyter notebook (ADD LINK). Also, [the Wiki](https://github.com/SABS-R3-Epidemiology/epiabm/wiki/Overview-of-the-Ferguson-Model) details optional parameters available to the user as well as those whose values are mentioned, but changing them is not recommended. ### Step 1: Use EpiGeoPop to generate the population spatial structure As shown in Figure 1, the region of interest is broken into a spatial structure: @@ -29,9 +28,9 @@ As shown in Figure 1, the region of interest is broken into a spatial structure: * *Households* - quantity per microcell is based on a probabilistic distribution. All individuals are assigned to one household and do not move households during the simulation. * *Places* - quantity per microcell is based on a probabilistic distribution. These are spaces where individuals might meet other individuals from different households, a workplace or a public park for example. -This structure is created using [EpiGeoPop](https://github.com/SABS-R3-Epidemiology/EpiGeoPop). The user states a region of interest, Oxford or UK for example, and the tool creates a csv file as an output. This file contains one line per microcell for each cell, with the number of households, places and individuals to be used in the simulation. The quantity of individuals are extracted for the region using Census data. +This structure is created using [EpiGeoPop](https://github.com/SABS-R3-Epidemiology/EpiGeoPop). The user states a region of interest, Oxford or UK for example, and the tool extracts information from [Natural Earth](https://www.naturalearthdata.com/) and [JRC](https://data.jrc.ec.europa.eu/csv), providing a csv file as output. This file contains one line per microcell for each cell, with the number of households, places and individuals to be used in the simulation (the quantity of individuals are extracted from Census data). -In summary, at the end of this step, we have a spatial structure with details on the number of cells and microcells. Within each microcell, we know the number of households, places, and the number of individuals. This is exported as a csv file. +In summary, the spatial structure for a region is generated using EpiGeoPop. This tool exports into a csv file the number of households, places, and individuals for each microcell. ### Step 2: Configure the simulation The following parameters are essential and need to be stated by the user to run a simulation: @@ -42,7 +41,7 @@ The following parameters are essential and need to be stated by the user to run * Time for the simulation to run (in days) * Select any output options required -There are many further optional parameters which are described in detail here (ADD link to wiki) +There are many further optional parameters which are described in [the Wiki](https://github.com/SABS-R3-Epidemiology/epiabm/wiki/Overview-of-the-Ferguson-Model) **Common adjustments:** * At the start, infected individuals are distributed across all cells by default, you may want to put them in one cell. @@ -58,7 +57,7 @@ Once configured, the simulation takes the generated population and performs the * InitialisePlaceSweep - Assign individuals to places * InitialInfectedSweep - Assign which individuals are initially infected -There are optional modules such as recording demographics, which are described in detail here (ADD link to wiki). +There are optional modules such as recording demographics, which are described in [the Wiki](https://github.com/SABS-R3-Epidemiology/epiabm/wiki/Overview-of-the-Ferguson-Model) **Simulation sweeps:** Individual’s location and infection status is updated each day: @@ -75,5 +74,5 @@ A simulation produces one output file by default: Infection status (S, E, Imild, etc) for each day by cell -Further optional files are available, details described here (ADD link to wiki) or see jupyter notebook with a detailed illustration here (ADD LINK). These data files can be used to produce plots for further analysis. +Further optional files are available, details described in [the Wiki](https://github.com/SABS-R3-Epidemiology/epiabm/wiki/Overview-of-the-Ferguson-Model) or see jupyter notebook with a detailed illustration here . These data files can be used to produce plots for further analysis. diff --git a/walk_through/epigeopop.ipynb b/walk_through/epigeopop.ipynb new file mode 100644 index 0000000..7754bbc --- /dev/null +++ b/walk_through/epigeopop.ipynb @@ -0,0 +1,201 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Clone Github repository EpiGeoPop - I did it using VSCode" + ] + }, + { + "attachments": { + "image.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you move the directory, the structure should be as below:\n", + "\n", + "![image.png](attachment:image.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a python 3.11 environment using the command below (it will not run on python 3.12):\n", + "On a mac:\n", + "/usr/local/opt/python@3.11/bin/python3.11 -m venv .venv" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Activate the environment:\n", + "source .venv/bin/activate" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Install the dependencies:\n", + "pip install -r requirements.txt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It may crash with an error ending:\n", + " :78: DeprecationWarning: The 'warn' function is deprecated, use 'warning' instead\n", + " WARNING:root:Failed to get options via gdal-config: [Errno 2] No such file or directory: 'gdal-config'\n", + " CRITICAL:root:A GDAL API version must be specified. Provide a path to gdal-config using a GDAL_CONFIG environment variable or use a GDAL_VERSION environment variable.\n", + " [end of output]\n", + "\n", + " note: This error originates from a subprocess, and is likely not a problem with pip.\n", + "error: subprocess-exited-with-error\n", + "× Getting requirements to build wheel did not run successfully.\n", + "│ exit code: 1\n", + "╰─> See above for output.\n", + "note: This error originates from a subprocess, and is likely not a problem with pip.\n", + "\n", + "\n", + "This is because Fiona, which is a Python package for reading and writing spatial data, requires GDAL (Geospatial Data Abstraction Library) to be installed on your system first.\n", + "\n", + "On a mac, you can install as follows:\n", + "brew install gdal\n", + "export GDAL_CONFIG=/usr/local/bin/gdal-config\n", + "export GDAL_VERSION=$(gdal-config --version)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To select a region of interest, go to configs/countries\n", + "Copy one of the parameter json files\n", + "Change line 16 to the country of your choice, for example:\n", + " \"country\": \"Andorra\",\n", + "\n", + "Save file" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, the file \"prep.sh\" downloads data from two different websites. However, the websites have been updated so the following changes need to be made:\n", + "comment out the first 'curl' line\n", + "\n", + "curl -O https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/GHSL/GHS_POP_MT_GLOBE_R2019A/GHS_POP_E2015_GLOBE_R2019A_4326_30ss/V1-0/GHS_POP_E2015_GLOBE_R2019A_4326_30ss_V1_0.zip\n", + "and replace with \n", + "\n", + "curl -O https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/GHSL/GHS_POP_GLOBE_R2023A/GHS_POP_E2025_GLOBE_R2023A_4326_30ss/V1-0/GHS_POP_E2025_GLOBE_R2023A_4326_30ss_V1_0.zip\n", + "\n", + "Then copy the filename from the end of the path, GHS_POP_E2025_GLOBE_R2023A_4326_30ss_V1_0.zip, \n", + "and replace the filename after 'unzip' command:\n", + "\n", + "unzip GHS_POP_E2025_GLOBE_R2023A_4326_30ss_V1_0.zip\n", + "\n", + "Finally, comment out the remaining lines of code. We will need to download the files from the website directly.\n", + "\n", + "Save the file and run the file:\n", + "bash prep.sh" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Go here https://www.naturalearthdata.com/downloads/10m-cultural-vectors/\n", + "and click on the link:\n", + "Download without boundary lakes\n", + "https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_admin_0_countries_lakes.zip\n", + "and save in the folder (which was created by prep.sh):\n", + "(data/raw)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These datafiles will be used by Snakefile to create the csv file. However, the Snakefile needs to be amended as follows:\n", + "Open the Snakefile, amend row 8 to be the country of your choice (replace Luxembourg)\n", + "\n", + " \"data/processed/countries/Luxembourg_microcells.csv\",\n", + "\n", + " comment out row 9:\n", + " \"data/processed/countries/Luxembourg_pop_dist.json\",\n", + " \n", + " comment out row 19:\n", + " \"outputs/dag.pdf\"\n", + "\n", + " comment out the first rule:\n", + " rule render_dag:\n", + " input:\n", + " \"Snakefile\"\n", + " output:\n", + " \"outputs/dag.pdf\"\n", + " shell:\n", + " \"snakemake --dag | dot -Tpdf > outputs/dag.pdf\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Replace the 'tif' file references in row 31, 40 and 49 with the following:\n", + "\n", + "GHS_POP_E2025_GLOBE_R2023A_4326_30ss_V1_0.tif\n", + "\n", + "as this is the new file downloaded using prep.sh" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, scroll to the bottom of Snakefile and comment out the following:\n", + "\n", + "rule make_pop_dist:\n", + " input:\n", + " \"data/raw/WPP2022_PopulationByAge5GroupSex_Medium.csv\",\n", + " \"configs/{region}/{place}_parameters.json\"\n", + " output:\n", + " \"data/processed/{region}/{place}_pop_dist.json\"\n", + " script:\n", + " \"scripts/get_pop_dist.py\"\n", + "\n", + "as this is not needed." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the tool using:\n", + "snakemake --cores 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The programm will show an error when it tries to make output.dag - please ignore.\n", + "\n", + "However, it should produce an output file in data/processed folder:\n" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}