• Health Metrics and the Spread of Infectious Diseases Machine Learning Applications and Spatial Modelling Analysis with R Book Club
  • Welcome
    • Book club meetings
    • Pace
  • 1 Introduction
    • Overview
    • Structure of the Book
    • Main Objectives
    • How to use the book
    • Meeting Videos
      • Cohort 1
  • Health Metrics
  • 2 Introduction to Health Metrics
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 3 Methods and Calculations
    • Chapter 2 Recap
    • Chapter 3 overview
    • YLL Calculation
    • Example: YLLs due to Stroke
    • Example: Setup and Excess Deaths
    • Example: Life Expectancy and YLL calculation
    • YLD Calculation
    • Example: YLDs due to Stroke
    • Example: Disability Weights & Severity Levels
    • Example: Calculating YLDs for Stroke
    • Example: Final Calculation of YLDs
    • DALY Calculation
    • Meeting Videos
      • Cohort 1
  • 4 Metrics Components
    • Introduction
    • Cause-specific or Population-wide
    • Life tables and Life expectancy (Motivation)
    • Life tables (Example data)
    • Life expectancy calculation
    • Mortality level and rates
    • Incidence and Prevalence
    • Disability Weights and Severity Levels
    • Summary of DALYs’ Components
    • Meeting Videos
      • Cohort 1
  • 5 Causes and Risks
    • Introduction
    • 🛑 Conditions and Injuries
    • 💥 Risk Factors and Health Metrics
      • 5.0.1 Risk-Specific Exposures
      • 5.0.2 Risk-Specific Outcomes
      • 5.0.3 Risk-Specific Populations
      • 5.0.4 Risk Measures
    • Causal Inference
    • Summarising the Relationship Between Risk and Outcome
    • Meeting Videos
      • Cohort 1
  • Machine Learning
  • 6 Introduction to Machine Learning
    • 6.1 Deterministic and Stochastic Modelling
    • 6.2 Machine Learning Models
    • 6.2.1 Empirically Driven and Mechanistic Models
    • 6.2.1 Empirically Driven Models
    • 6.2.2 Learning Methods
    • 6.2.3 Parameters and Hyper-parameters
    • 6.3 The Steps of Building a Model
    • 6.3.1 Example: Cholera
    • 6.3.2 Example: Epidemic X
      • 6.3.2.1 The SEIR Model
    • 6.3.2.2 Random Forest
    • 6.3.2.3 Optimization with Tidymodels
    • 6.3.3 Example: Epidemic Y
      • 6.3.3.1 INLA: an empirical Bayes approach to GAMs
    • 6.4 Measures of Machine Learning Models
    • 6.4.1 Loss Functions
    • 6.4.2 Evaluation Metrics
    • 6.4.3 Public Health Loss Functions
    • 6.5 Final suggestions for further learning
    • Meeting Videos
      • Cohort 1
  • 7 Techniques for Machine Learning Applications
    • 7.1 Goals of the Analysis and Nature of Data
      • 7.1.1 Output is Continuous
      • 7.1.2 Output is Categorical or Binary
      • 7.1.3 Systemic Modelling / Simulation
      • 7.1.4 Time-Series
    • 7.2 Statistical and Machine Learning Methods
      • 7.2.1 Exploratory Data Analysis
      • 7.2.2 Feature Engineering / Transforming Variables
    • 7.3 Case Study: Predicting Rabies
      • 7.3.1 Goal:
      • 7.3.2 Exploratory Data Analysis (EDA)
      • 7.3.3 Training and Resampling
      • 7.3.4 Preprocessing
      • 7.3.5 Multicollinearity
      • 7.3.6 Model 1: Random forest
      • 7.3.7 Model 2: GLM w lasso penalty
      • 7.3.8 Additional models!
    • 7.4 Summary
    • Meeting Videos
      • Cohort 1
  • 8 Essential R Packages for Machine Learning
    • 8.1 Key R-packages for ML
    • 8.2 How to use mlr3
      • 8.2.1 DALYs due to Dengue
    • 8.3 How to use keras3
      • 8.3.1 General Infection
      • 8.3.2 Neural Network Model
      • 8.3.3 Example Code
    • Meeting Videos
      • Cohort 1
  • 9 Predictive Modelling and Beyond
    • 9.1 Overview of predictive modelling
    • 9.2 Predicting the future
      • 9.2.1 Dengue Test Predictions for 2017-2021
    • 9.3 Time series analysis
      • 9.3.1 SDI Time Series Analysis with Mixed Effect Models
    • Meeting Videos
      • Cohort 1
  • Data Visualisation
  • 10 Introduction to Data Visualisation
    • 10.1 History of Data visualisation
      • Petroglyphs
      • William Playfair
      • Florence Nightingale
      • W.E.B Du Bois
    • 10.2 The Grammar of Graphics
    • 10.3 General Guidelines
      • Common types of plots and their uses
    • 10.4 Example: Visualising Lung Cancer Deaths by Prevalence and Age in Germany
      • Scatter plot
      • Barplot
      • Line plot
    • 10.4.1 Colours and Patterns
    • 10.4.2 Theme, Legends and Guides
    • 10.4.3 Plot Layouts
    • 10.4.4 Saving as an image
    • 10.5 Practising Data Visualisation
      • Considerations for accessibility
      • Contrast
      • Vision impairments
      • Accessible data tables
      • Additional packages for accessibility
    • Meeting Videos
      • Cohort 1
  • 11 Interpreting Model Results Through Visualisation
    • Why Plot Model Fits?
    • Predicted vs Actual Plots
    • Residual Plots
    • Influential Observations
    • Comparing Models
    • Communicating Results
    • ROC Plots
    • Partial Independence Plots
    • Conclusion
    • Meeting Videos
      • Cohort 1
  • 12 Spatial Data Modelling and Visualisation
    • 12.1 Ebola
    • 12.2 Spatial data and models
    • 12.3 Make a Map
      • 12.3.1 Bounding Box
    • 12.4 Grid of points
    • 12.5 Create a Raster of the Temperature
    • 12.6 Dynamics of Disease Transmission
      • 12.6.1 Spatial Proximity with Kriging
      • 12.6.2 Perform Kriging
    • 12.7 Resources
      • 12.7.1 The {sf} package
    • 12.8 Meeting Videos
      • 12.8.1 Cohort 1
  • 13 Advanced Data Visualisation Techniques
    • 13.1 Contour plot
    • 13.2 Pyramid plot
  • Infectious Diseases
  • 14 Introduction to Infectious Diseases
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 15 COVID-19 Outbreaks
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 16 The Case of Malaria
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • 17 Summary: The State of Health
    • SLIDE 1
    • Meeting Videos
      • Cohort 1
  • Published with bookdown

Health Metrics and the Spread of Infectious Diseases Book Club

6.5 Final suggestions for further learning

  • Linear algebra
  • Taylor series and sequences.
  • Probability theory

Highly recommended reading: Spatial and Spatio‐temporal Bayesian Models with R‐INLA. Marta Blangiardo, Michela Cameletti, 2015.