• I assume you watched the 2 lectures, here I’m giving a brief summary and touching on some of the highlights.

• I allocated most of the time for discussion/Q&A.

• Are widely used in all areas of science.
• Are implementations of specific processes/mechanisms as a computer model.

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

• Phenomenological/non-mechanistic/(statistical) models
  • Look for patterns in data
  • Do not describe mechanisms leading to the observed outcomes (data)

\[ y = b_0 + b_1 x_1 + b_2 x_2 + ... \]

• Mechanistic/process/simulation models
  • Try to represent simplified versions of mechanisms
  • Can be used without and with data (and then also become statistical)

• Exploring the behavior of a system.
• Making predictions about the behavior of a system.
• (With data) Performing inference, testing hypotheses.

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

• Compartmental models are the simplest and most widely used.

• Agent-based or network models are more detailed/complex.

  

  Acute virus infection. (Handel et al 2009 J Roy Soc Interface)

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