Add IBM course from Andrew

Author: tbhallett

docs/ibm_course.rst

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+===========================================
+Building and Understanding Individual Based Models to Inform Health Policy
+===========================================
+
+Welcome to this tutorial to help you self-learn how to build individual-based models to inform health policy.  This is intended to be an accessible introduction which requires no existing knowledge.
+
+What is an Individual-based Model?
+====================================
+
+Each time the model program is run it generates values of variables that represent the characteristics of individuals and simulates how these change over time. By building a model that accurately reflects real changes in people over time, we can evaluate the potential effects of a policy of introducing a health intervention.
+
+`Here <https://mediacentral.ucl.ac.uk/Player/5G0ag7CC>`_ is a video describing individual-based models to inform health care policy:
+
+(You don’t need to have watched this video to get started on the tutorial!)
+
+Get started
+====================================
+We will use a programming language called Python.  We will use Google Colaboratory (“Colab”) which is a website which makes it easy to get started.
+
+The links to click for all the content are below.
+
+You may want to watch `this very brief video <https://youtu.be/VPl2emXVJ1M>`_ that shows you what to expect when you click on the first link.
+
+
+Building an individual-based model
+====================================
+
+* Part 1: Introduction to Colab, time steps, variables, print function, range function, "for" loops. [`Start <https://colab.research.google.com/drive/1QF_aUgIkM-H1JXJRBb9DSONohC2WEvbH?usp=sharing>`_]
+
+* Part 2: Python dictionaries, accessing and updating dictionaries [`Start <https://colab.research.google.com/drive/1VVSDFXGtIgJEqEgp1rqltUfluhuhdYW9?usp=sharing>`_]
+
+* Part 3: Time step loop, population loop, combining loops and using dictionaries [`Start <https://colab.research.google.com/drive/1TSGba5w_nuRXd7QAYWWPT2tMVccaqc8I?usp=sharing>`_]
+
+* Part 4: Generating random numbers/ages, round function, "if" statements, updating variable values, the first time step, all other time steps [`Start <https://colab.research.google.com/drive/1wq0BeCgVVWPj_SIFzsTyjA_KKzAUtDZb?usp=sharing>`_]
+
+* Part 5: Random choice method, pandas and using tables, using probabilities, "else" statements [`Start <https://colab.research.google.com/drive/1G-GHM-A6Z1dk0YBGI8cqX6EekJ5i-hrR?usp=sharing>`_]
+
+* Part 6: Adding body mass index (BMI), "elif" statements, creating and calling a function, docstrings [`Start <https://colab.research.google.com/drive/1gBYGIQK42XN2ovG-EFA7LQW_2dO_GC7f?usp=sharing>`_]
+
+* Part 7: Refactoring updating rules into functions [`Start <https://colab.research.google.com/drive/1FVKyP-51IjwW4A6CAmufuZGXx7e0sG0_?usp=sharing>`_]
+
+* Part 8: Visualizing changes in variable distribution over time, matplotlib and pyplot, values method [`Start <https://colab.research.google.com/drive/1_ZmPJPcsy6soCnDJSU7SeTElAJb7-rna?usp=sharing>`_]
+
+* Part 9: Adding blood pressure, ordering and data selection, append method [`Start <https://colab.research.google.com/drive/1PjHptwX-SkJepsFeqEhrDr738V2euRiO?usp=sharing>`_]
+
+* Part 10: Adding mortality, age-specific death rate, booleans, integrating deaths into the model [`Start <https://colab.research.google.com/drive/1fb8XkWEsCi-BVkzUtQKwYrrUej-aqCCg?usp=sharing>`_]
+
+* Part 11: CVD death risk, the math.exp method, adding cause of death, "None" keyword [`Start <https://colab.research.google.com/drive/1T7QWmeDBux6Y2N-OR3Pt80qJoAlPBcfB?usp=sharing>`_]
+
+* Part 12: Life years (person-years at risk), CVD death rate, simulating health policy intervention [`Start <https://colab.research.google.com/drive/1lsSSGO07vefkhjp5mF2BodRB2OhgtyAv?usp=sharing>`_]
+
+* Part 13: Adding time of death, running model with intervention, comparing results between groups [`Start <https://colab.research.google.com/drive/1_T14-Dv3G_oTMpRRIdu1VpXrPG0mHaci?usp=sharing>`_]
+
+
+Building a transmission model
+====================================
+* Part 14: Introduction and breakdown of new model structure [`Start <https://colab.research.google.com/drive/1Yhf-aL4Qe8A3FsftrcSsoErFWRNvjwLf?usp=sharing>`_]
+
+* Part 15: Exercise: Adding vaccination variable to the new model [`Start <https://colab.research.google.com/drive/1-wHtdpFJu_wiDQvFz2Q4aAbtS-UsZcU4?usp=sharing>`_]
+
+* Part 16: Adding interactions to the model, the random sample method [`Start <https://colab.research.google.com/drive/1HmukuID6MLxrEhc5xQRV74-cwdhrFnND?usp=sharing>`_]
+
+* Part 17: Adding infection variables to the model (Section 1) [`Start <https://colab.research.google.com/drive/16I36MtjT9oho7IzlNqaT9AwHtIoDD2vw?usp=sharing>`_]
+
+* Part 18: Adding infection variables to the model (Section 2) [`Start <https://colab.research.google.com/drive/1lo06dzQ-z6D13E64MfMLdAHcA_YCUDD_?usp=sharing>`_]
+
+* Part 19: Finalizing our basic transmission model [`Start <https://colab.research.google.com/drive/1K4ZpgSkG3TAPft3ek_OTwZgVrMFSXwTq?usp=sharing>`_]
+
+
+
+Acknowledgments
+====================================
+This content was created by `Prof Andrew Phillips <https://profiles.ucl.ac.uk/5430>`_ and `Dr Loveleen Bansi-Mantharu <https://profiles.ucl.ac.uk/1985-loveleen-bansi-matharu>`_ (both at University College London).
+
+
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