Course Description
A variety of approaches for analyzing and interpreting data commonly encountered in infectious disease studies are covered, such as case counts, longitudinal measures of prevalence and incidence, time-series data, and more.
Athena Title
Analysis Infect Disease Data
Prerequisite
BIOS 7010 or BIOS 7010E or permission of department
Grading System
A - F (Traditional)
Course Objectives
Students will be able to: • Appraise different types of analysis approaches commonly used to study infectious disease data • Critically compare and evaluate the strengths and weaknesses of different analysis approaches • Select the appropriate analysis approach for a given dataset • Articulate a research question and outline a data analysis approach suitable to answering this question for a given dataset • Design and implement successful infectious disease data analyses using the R programming language • Explain the importance of workflow management and reproducibility for successful data analysis • Judge the usefulness of different analysis tools described in the primary literature on infectious disease analysis methodology • Evaluate state-of-the art analysis approaches from the research literature • Critically appraise analyses presented in published studies, identify strengths and weaknesses of other people’s analyses • Assess the strengths and weaknesses of different approaches to representing the results of data analyses • Summarize analysis results in a way that is easily understandable for different audiences, such as lay persons, decision makers, and expert colleagues
Topical Outline
1. Introduction to infectious disease data analysis 2. The data analysis workflow 3. Overview of data types and analysis approaches 4. Getting and pre-processing data 5. Preliminary and graphical data analyses 6. Hypothesis testing and model comparison 7. Analysis using regression models 8. Analysis using machine learning approaches 9. Analysis using dynamical, mechanistic models 10. Quantifying uncertainty 11. Model predictions 12. Presentation of analysis results