2022-07-18 15:39:42

Introduction

  • This lecture is a very short introduction to within-host simulation modeling.
  • More information and details are in the previously recorded lectures and the readings on the SMI website.
  • We’ll have a Q&A/Discussion at the end. Also use Slack for any questions/thoughts/feedback.

Phenomenological/non-mechanistic/(statistical) models

  • Examples: t-test, linear/logistic regression model, deep neural network
  • Always applied to data
  • Are sometimes causal
  • Do not describe mechanisms underlying the system of study
Source: xkcd.com

Source: xkcd.com

Descriptive Analysis

Source: Cooksey 2020 "Illustrating Statistical Procedures: Finding Meaning in Quantitative Data"

Source: Cooksey 2020 “Illustrating Statistical Procedures: Finding Meaning in Quantitative Data”

Inference

Not real data. See [here](http://ds.gregvi.al/2016/11/19/smoking-is-good/) for details.

Not real data. See here for details.

Prediction

Mechanistic/simulation models

  • Include mechanisms
  • Are always causal
  • Usually have a time/dynamic component
  • Can be used with or without data

Inference

\[ \begin{aligned} \dot U &= - b(1-e_1)UV\\ \dot I &= bUV - d_I I \\ \dot V &= p(1-e_2)I - d_V V\\ & - g b(1-e_1) UV \end{aligned} \]

Source: _Simulation modelling for immunologists_

Source: Simulation modelling for immunologists

Prediction

Causal exploration

Also called what-if explorations
Exploring/predicting cytokine-based interventions for TB (Wigginton and Kirschner, 2001 J Imm)

Exploring/predicting cytokine-based interventions for TB (Wigginton and Kirschner, 2001 J Imm)

Our models

  • Compartmental models (tracking total numbers of different types/compartments)
  • Ordinary differential (or difference) equations

\[ \begin{aligned} \textrm{Bacteria} \qquad \dot{B} & = g B(1-\frac{B}{B_{max}}) - d_B B - kBI\\ \textrm{Immune Response} \qquad \dot{I} & = r BI - d_I I \end{aligned} \]

Our topics

  • We will explore/play with a few simple models.
  • We will do some activities that do not involve writing code, we’ll also at the end of the course look at and modify some code.
  • We unfortunately can’t cover fitting models to data, but see DSAIRM and ask questions.

Discussion, Q&A

  • Type in Slack or Zoom Chat or just unmute yourself and ask.