.. _ibm_course: =========================================== 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 `_ 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 `_ 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 `_] * Part 2: Python dictionaries, accessing and updating dictionaries [`Start `_] * Part 3: Time step loop, population loop, combining loops and using dictionaries [`Start `_] * Part 4: Generating random numbers/ages, round function, "if" statements, updating variable values, the first time step, all other time steps [`Start `_] * Part 5: Random choice method, pandas and using tables, using probabilities, "else" statements [`Start `_] * Part 6: Adding body mass index (BMI), "elif" statements, creating and calling a function, docstrings [`Start `_] * Part 7: Refactoring updating rules into functions [`Start `_] * Part 8: Visualizing changes in variable distribution over time, matplotlib and pyplot, values method [`Start `_] * Part 9: Adding blood pressure, ordering and data selection, append method [`Start `_] * Part 10: Adding mortality, age-specific death rate, booleans, integrating deaths into the model [`Start `_] * Part 11: CVD death risk, the math.exp method, adding cause of death, "None" keyword [`Start `_] * Part 12: Life years (person-years at risk), CVD death rate, simulating health policy intervention [`Start `_] * Part 13: Adding time of death, running model with intervention, comparing results between groups [`Start `_] Building a transmission model ==================================== * Part 14: Introduction and breakdown of new model structure [`Start `_] * Part 15: Exercise: Adding vaccination variable to the new model [`Start `_] * Part 16: Adding interactions to the model, the random sample method [`Start `_] * Part 17: Adding infection variables to the model (Section 1) [`Start `_] * Part 18: Adding infection variables to the model (Section 2) [`Start `_] * Part 19: Finalizing our basic transmission model [`Start `_] Acknowledgments ==================================== This content was created by George Phillips, `Loveleen Bansi-Matharu `_ and `Andrew Phillips `_. Suggested Citation ==================================== Phillips G, Bansi-Matharu, Phillips AN. (2024) Building and Understanding Individual Based Models to Inform Health Policy. Available on www.tlomodel.org