diff --git a/README.md b/README.md
index 3430012..7ac5ee8 100644
--- a/README.md
+++ b/README.md
@@ -1,35 +1,41 @@
 # PyGOM - Python Generic ODE Model
 
 [![pypi version](https://img.shields.io/pypi/v/pygom.svg)](https://pypi.python.org/pypi/pygom)
-[![Documentation Status](https://readthedocs.org/projects/pygom/badge/?version=master)](https://pygom.readthedocs.io/en/master/?badge=master)
-[![licence](https://img.shields.io/pypi/l/pygom?color=green)](https://raw.githubusercontent.com/PublicHealthEngland/pygom/master/LICENSE.txt)
-[![Github actions](https://github.com/PublicHealthEngland/pygom/workflows/pygom/badge.svg)](https://github.com/PublicHealthEngland/pygom/actions/)
-[![Jupyter Book Badge](https://jupyterbook.org/badge.svg)](https://hwilliams-phe.github.io/pygom/intro.html)
+[![licence](https://img.shields.io/pypi/l/pygom?color=green)](https://raw.githubusercontent.com/ukhsa-collaboration/pygom/master/LICENSE.txt)
+[![Github actions](https://github.com/ukhsa-collaboration/pygom/workflows/pygom/badge.svg)](https://github.com/ukhsa-collaboration/pygom/actions/)
+[![Jupyter Book Badge](https://jupyterbook.org/badge.svg)](http://ukhsa-collaboration.github.io/pygom/md/intro.html)
 
 A generic framework for Ordinary Differential Equation (ODE) models, especially compartmental type systems.
 This package provides a simple interface for users to construct ODE models backed by a comprehensive and easy to use tool–box implementing functions to easily perform common operations such as parameter estimation and solving for deterministic or stochastic time evolution.
 With both the algebraic and numeric calculations performed automatically (but still accessible),
 the end user is free to focus on model development.
+Full documentation for this package is avalible on the [documentation](http://ukhsa-collaboration.github.io/pygom/md/intro.html) page.
 
 ## Installation
+The easiest way to install a copy of PyGOM is via PyPI and pip
+    
+    pip install pygom
 
-You can download a local copy of the PyGOM source files from this GitHub repository:
+Alternatively, you can download a local copy of the PyGOM source files from this GitHub repository:
 
     git clone https://github.com/ukhsa-collaboration/pygom.git
 
 Please be aware that there may be redundant files within the package as it is under active development.
 
 > [!NOTE]
-> The latest fully reviewed version of PyGOM will be on the `master` branch and we generally recommend that users install this version. However, the current version being prepared for release (featuring up-to-date documentation) is hosted on the `feature/prep-doc` branch.
+> The latest fully reviewed version of PyGOM will be on the `master` branch and we generally recommend 
+> that users install this version. However, the latest version being prepared for release is hosted on 
+> the `dev` branch.
 
-When running the following command line commands, ensure that your current working directory is the one where the PyGOM source files were downloaded to. This should be found from your home directory:
+When running the following command line commands, ensure that your current working directory is the one 
+where the PyGOM source files were downloaded to. This should be found from your home directory:
 
     cd pygom
 
-Activate the relevant branch for installation via Git Bash, if you have been recruited to test code for the
-new release then this is the `feature/prep-doc` branch:
+Activate the relevant branch for installation via Git Bash. for example if you want
+new release then this is the `dev` branch:
 
-    git checkout feature/prep-doc
+    git checkout dev
 
 Package dependencies can be found in the file, `requirements.txt`.
 An easy way to install these to create a new [conda](https://conda.io/docs) environment in Anaconda Prompt via:
@@ -48,17 +54,17 @@ Alternatively, you may add dependencies to your own environment through conda:
 
     pip install -r requirements.txt
 
-The final prerequisite, if you are working on a Windows machine, is that you will also need to install:
+The final prerequisites, if you are working on a Windows machine, is that you will also need to install:
 - [Graphviz](https://graphviz.org/)
 - Microsoft Visual C++ 14.0 or greater, which you can get with [Microsoft C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/)
 
 You should now be able to install the PyGOM package via command line:
 
-    python setup.py install
+    pip install .
 
 and test that installation has completed successfully
 
-    python setup.py test
+    python -m unittest discover --verbose --start-directory tests
 
 This will run a few test cases and can take some minutes to complete.
 
@@ -88,7 +94,7 @@ Please be aware that if the module tests fails, then the documentation for the p
 
 ## Contributors
 
-Thomas Finnie (Thomas.Finnie@phe.gov.uk)
+Thomas Finnie (Thomas.Finnie@ukhsa.gov.uk)
 
 Edwin Tye
 
@@ -101,6 +107,7 @@ Martin Grunnill
 Joseph Gibson
 
 ## Version
+0.1.8 Updated and much better documentation.
 
 0.1.7 Add Approximate Bayesian Computation (ABC) as a method of fitting to data