From Kalk Bay to Cape Point, adventures anew,

Week two has arrived, with projects to pursue.

Modeling disease, our knowledge takes flight,

In groups, we practice, from dawn until night. -- ChatGPT

2

• Thank you
• The workshop is evolving…
• We really are taking your feedback into account�– for future workshops if not now

Response to feedback

3

• Participatory coding: two more to come this week
• Encourage you to try use the projects as an opportunity to practice coding – you can even do a ‘participatory coding’ session with your advisor
• Past years’ participatory coding scripts (and all tutorials): https://github.com/ici3d/rtutorials
• For the package, https://github.com/ici3d/ici3d-pkg�(R folder)
• One-on-one time

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R coding/labs

4

• Role of faculty/mentors in labs
• We have all developed more familiarity with the materials over time
• The role of any of us when asked a question is to provide an adequate answer if able to in the moment, OR to connect you to someone who can provide one
• Mentors play a key role in the clinic to form ‘connections’
• Please do ask questions

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R coding/labs

5

• First year experimenting with knowledge checks
• Not used for grading purposes
• They help us understand what the immediate learning for you has been

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Knowledge checks

6

• Support beyond the main advisors -- Jonathan will be available for consultations to all groups
• We will be taking advisor styles/contribution into account
• Detailed/timely guidance on the requirements for ‘assignments’

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Projects

7

• General communication
• Renovations, water, internet

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Other challenges

8

One-on-one sessions

Public Health, Epidemiology, & Models (Day 1)

Simple Models (Day 1)

Foundations of Dynamic Modeling (Day 1)

(Hidden) Assumptions of Simple ODE’s (Day 2)

Breaking Assumptions!

Consequences of Heterogeneity (Day 6)

Introduction stochastic simulation models (Day 3)

Heterogeneity tutorial�(Day 6)

Introduction to Infectious Disease Data (Day 1)

Thinking about Data�(Day 2)

Data management and cleaning (Day 9)

Creating a Model World�(Day 4)

Study design and analysis in epidemiology (Day 3)

Introduction to Statistical Philosophy (Day 4)

Variability, Sampling Distributions, & Simulation (Day 10)

HIV in Harare tutorial�(Day 3)

Integration!

Introduction to Likelihood (Day 4)

Fitting Dynamic Models I – III (Day 5, 8, & 9)

Modeling for Policy (Day 11)

Model Assessment (Day 10)

MCMC Lab (Day 9)

MLE Fitting SIR model to prevalence data (Day 5)

Likelihood Lab (Day 4)

000

Public Health, Epidemiology, & Models (Day 1)

Simple Models (Day 1)

Foundations of Dynamic Modeling (Day 1)

(Hidden) Assumptions of Simple ODE’s (Day 2)

Breaking Assumptions!

Consequences of Heterogeneity (Day 6)

Introduction stochastic simulation models (Day 3)

Heterogeneity tutorial�(Day 6)

Introduction to Infectious Disease Data (Day 1)

Thinking about Data�(Day 2)

Data management and cleaning (Day 9)

Creating a Model World�(Day 4)

Study design and analysis in epidemiology (Day 3)

Introduction to Statistical Philosophy (Day 4)

Variability, Sampling Distributions, & Simulation (Day 10)

HIV in Harare tutorial�(Day 3)

Integration!

Introduction to Likelihood (Day 4)

Fitting Dynamic Models I – III (Day 5, 8, & 9)

Modeling for Policy (Day 11)

Model Assessment (Day 10)

MCMC Lab (Day 9)

MLE Fitting SIR model to prevalence data (Day 5)

Likelihood Lab (Day 4)

000

Public Health, Epidemiology, & Models (Day 1)

Simple Models (Day 1)

Foundations of Dynamic Modeling (Day 1)

(Hidden) Assumptions of Simple ODE’s (Day 2)

Breaking Assumptions!

Consequences of Heterogeneity (Day 6)

Introduction stochastic simulation models (Day 3)

Heterogeneity tutorial�(Day 6)

Introduction to Infectious Disease Data (Day 1)

Thinking about Data�(Day 2)

Data management and cleaning (Day 9)

Creating a Model World�(Day 4)

Study design and analysis in epidemiology (Day 3)

Introduction to Statistical Philosophy (Day 4)

Variability, Sampling Distributions, & Simulation (Day 10)

HIV in Harare tutorial�(Day 3)

Integration!

Introduction to Likelihood (Day 4)

Fitting Dynamic Models I – III (Day 5, 8, & 9)

Modeling for Policy (Day 11)

Model Assessment (Day 10)

MCMC Lab (Day 9)

MLE Fitting SIR model to prevalence data (Day 5)

Likelihood Lab (Day 4)

000

Public Health, Epidemiology, & Models (Day 1)

Simple Models (Day 1)

Foundations of Dynamic Modeling (Day 1)

(Hidden) Assumptions of Simple ODE’s (Day 2)

Breaking Assumptions!

Consequences of Heterogeneity (Day 6)

Introduction stochastic simulation models (Day 3)

Heterogeneity tutorial�(Day 6)

Introduction to Infectious Disease Data (Day 1)

Thinking about Data�(Day 2)

Data management and cleaning (Day 9)

Creating a Model World�(Day 4)

Study design and analysis in epidemiology (Day 3)

Introduction to Statistical Philosophy (Day 4)

Variability, Sampling Distributions, & Simulation (Day 10)

HIV in Harare tutorial�(Day 3)

Integration!

Introduction to Likelihood (Day 4)

Fitting Dynamic Models I – III (Day 5, 8, & 9)

Modeling for Policy (Day 11)

Model Assessment (Day 10)

MCMC Lab (Day 9)

MLE Fitting SIR model to prevalence data (Day 5)

Likelihood Lab (Day 4)

000

Public Health, Epidemiology, & Models (Day 1)

Simple Models (Day 1)

Foundations of Dynamic Modeling (Day 1)

(Hidden) Assumptions of Simple ODE’s (Day 2)

Breaking Assumptions!

Consequences of Heterogeneity (Day 6)

Introduction stochastic simulation models (Day 3)

Heterogeneity tutorial�(Day 6)

Introduction to Infectious Disease Data (Day 1)

Thinking about Data�(Day 2)

Data management and cleaning (Day 9)

Creating a Model World�(Day 4)

Study design and analysis in epidemiology (Day 3)

Introduction to Statistical Philosophy (Day 4)

Variability, Sampling Distributions, & Simulation (Day 10)

HIV in Harare tutorial�(Day 3)

Integration!

Introduction to Likelihood (Day 4)

Fitting Dynamic Models I – III (Day 5, 8, & 9)

Modeling for Policy (Day 11)

Model Assessment (Day 10)

MCMC Lab (Day 9)

MLE Fitting SIR model to prevalence data (Day 5)

Likelihood Lab (Day 4)

000