Module 1: Probability and Statistical Inference
Instructors: Jim Hughes and David Yanez
Module description: This module covers the laws of probability and the binomial, multinomial, and normal distributions. It covers descriptive statistics and methods of inference, including maximum likelihood, confidence intervals and simple Bayes methods. Classical hypothesis testing topics, including type I and II errors, two-sample tests, chi-square tests and contingency table analysis, and exact and permutation tests. Resampling methods, such as the bootstrap and jackknife, are covered as well. This module serves as a foundation for almost all of the later modules. Co-taught with the Summer Institute in Statistical Genetics.
Module 2: Mathematical Models of Infectious Diseases
Instructors: Pejman Rohani and John Drake
Module description: This module covers the principles of dynamic models of infectious diseases. The module will focus on the dynamics of compartmental models such as the susceptible-infected-recovered (SIR) model. Topics include incorporating different types of heterogeneities in transmission (resulting from age-structure, behaviour or seasonality), exact stochastic birth-death models, sensitivity analysis and fitting of simple models to data. The module will alternate between lectures and computer labs. Programming will be done in R. Background Reading: Keeling & Rohani (2008) Modeling Infectious Diseases in Humans and Animals, Princeton University Press.
Module 3: Causal Inference
Instructors: Michael Hudgens and Thomas Richardson
Module description: This module provides an introduction to causal inference. Topics including potential outcomes, directed acyclic graphs, confounding, collapsibility, instrumental variables, mediation, and principal stratification will be covered. Applications of causal inference in infectious diseases will include relaxing the no interference assumption, selection bias in analysis of post-infection outcomes, and surrogates of vaccine efficacy. Assumes material in Module 1.
Module 4: Introduction to R
Instructors: Kenneth Rice and Timothy Thornton
Module description: This module introduces the R statistical environment, assuming no prior knowledge. It provides a foundation for the use of R for computation in later modules. In addition to discussing basic data management tasks in R, such as reading in data and producing summaries through R scripts, we will also introduce R’s graphics functions, its powerful package system, and simple methods of looping. Examples and exercises will use data drawn from biological and medical applications, including infectious diseases and genetics. Hands-on use of R is a major component of this module; users require a laptop and will use it in all sessions. Co-taught with the Summer Institute in Statistical Genetics.
Module 5: Stochastic Epidemic Models with Inference
Instructors: Tom Britton and Ira Longini
Module description: The course first studies some basic stochastic models for the spread of an infectious disease, and presents large population results for them including threshold phenomenon (Ro), distribution of the final number infected, and the critical vaccination coverage (the fraction needed to vaccinate to avoid future epidemics). Several extensions towards realism are then discussed: different types of individuals and social structures in the community including households and networks. Then focus is shifted towards statistics and how to obtain estimates of relevant model parameters from epidemic data. The course will give the theoretical background but also numerous examples from empirical situations, including estimation of various vaccine efficacies. During the course there will be class exercises. This course assumes the material in Module 1.
Module 6: Infectious Diseases, Immunology and Within-Host Models
Instructors: Andreas Handel and Paul Thomas
Module description: This module provides an introduction to infectious diseases, the main components of the immune system, and mathematical modeling. Using pathogens such as HIV, TB, malaria, influenza and others, this module will introduce basic immunological concepts and explain how to use mathematical models to study aspects of within-host infection dynamics. The focus will be on simple compartmental deterministic models. The use of those models to analyze the dynamics of pathogens, innate and adaptive immune responses and to design and evaluate intervention strategies, such as vaccines and drug treatments, are covered. Hands-on exercises using the programming language R will show how to construct and implement models. Prior knowledge of R is helpful but not required. Suggested background reading: How the Immune System Works, by Sompayrac, L.M., Wiley-Blackwell, 3rd edition, 2008.
Module 7: MCMC I for Infectious Diseases
Instructors: Kari Auranen, Elizabeth Halloran, and Vladimir Minin
Module description: This module is an introduction to Markov chain Monte Carlo (MCMC) methods. The first half of the course includes a general introduction to Bayesian statistics, Monte Carlo, and MCMC. Some relevant facts from the Markov chain theory are reviewed. Algorithms include Gibbs sampling and Metropolis-Hastings. A practical introduction to convergence diagnostics is included. Motivating practical examples progress from generic toy problems to infectious disease applications, which include chain-binomial and general epidemic models. Programming will be in R. Prior familiarity with R would be helpful. Individuals already familiar with MCMC methods and knowledge of R programming should consider MCMC II. It assumes the material in Module 1.
Module 8: Stochastic Simulation Methods
Instructors: Dennis Chao and Alessandro Vespignani
Module description: This module provides an introduction to the use of stochastic models in studying epidemics. The principles of infectious disease spread in populations will be covered, including population structure, natural history of the infectious agent, and assumed interventions. These topics will be illustrated using the Reed-Frost model and FluTE, an individual-based model of influenza epidemics. The effect of stochasticity in spatial transmission models will be covered including stochastic metapopulation and patch models, reaction-diffusion systems in heterogeneous networks, stochastic effective and mechanistic mobility couplings. Data driven stochastic approaches to the large-scale spreading of infectious diseases integrating mobility and population high-resolution data will be presented. The tutorial will be presented by using the EpiC and the GLEaM computational suites. All software packages used in this module are open source. GIS software will be demonstrated. Familiarity with statistical packages (such as R or similar) would be helpful.
Module 9: Contact Network Epidemiology
Instructors: Thomas Hladish and Joel Miller
Module description: Interpreting population interactions as contact networks provides powerful mathematical and computational frameworks for modeling infectious diseases. This course introduces network concepts (e.g., nodes, degree, clustering, and modularity), and analytical and simulation-based approaches to contact network epidemiology. We will discuss both idealized and empirical networks and how modeling assumptions affect the tractability of different methods. Students will use simple analytical models to predict disease properties such as threshold conditions, final sizes, epidemic probability, and epidemic dynamics. They will use the Python programming language and NetworkX software library to represent and analyze networks, construct epidemic simulations, and model various intervention strategies. Previous programming experience is helpful but not required.
Module 10: Simulation-based Inference for Epidemiological Dynamics
Instructors: Edward Ionides and Aaron King
Module description: This module introduces statistical inference techniques and computational methods for dynamic models of epidemiological systems. The course will explore deterministic and stochastic formulations of epidemiological dynamics and develop inference methods appropriate for a range of models. Special emphasis will be on exact and approximate likelihood as the key elements in parameter estimation, hypothesis testing, and model selection. Specifically, the course will cover sequential Monte Carlo and synthetic likelihood techniques. Students will learn to implement these in R to carry out maximum likelihood and Bayesian inference. Knowledge of the material in Module 1 is assumed. Students new to R should complete a tutorial before the module.
Module 11: Introduction to Metagenomic Data Analysis
Instructors: Alexander V. Alekseyenko and Paul J. McMurdie II
Module description: This course is concerned with multivariate statistical analysis of microbiome data. We will briefly cover foundational concepts in microbial ecology, molecular biology, bioinformatics, and DNA sequencing. The main focus of the course will be on developing an understanding of multivariate analysis of microbiome data. Practical skills to be developed in this course include managing high-dimensional and structured data in metagenomics, visualization and representation of high-dimensional data, normalization, filtering, and mixture-model noise modeling of count data, as well as clustering and predictive model building. Programming will be done in R and fluency at the level of ‘SISMID/SISG Module 4: Introduction to R’ will be expected. Pre-requisites: knowledge of Module 1, Probability and Statistical Inference. Co-listed with the Summer Institute in Statistical Genetics.
Module 12: Evolutionary Dynamics and Molecular Epidemiology of Viruses
Instructors: Philippe Lemey and Marc A. Suchard
Module description: This module covers the use of phylogenetic and bioinformatic tools to analyze pathogen genetic variation and to gain insight in the processes that shape their diversity. The module focuses on phylogenies and how these relate to population genetic processes in infectious diseases. In particular, the module will cover Bayesian Evolutionary Analysis by Sampling Trees (BEAST). This software will be used in class exercises that are mainly focused on estimating epidemic time scales, reconstruction changes in viral population sizes through time and inference of spatial diffusion of viruses. Evolutionary processes including recombination and selection will also be considered. Assumes material in Module 1. Co-listed with the Summer Institute in Statistical Genetics.
Module 13: Spatial Statistics
Instructors: Jon Wakefield and Lance Waller
Module description: Spatial methods are now used in many disciplines and play an important role in epidemiology and public health. This module gives an introduction to spatial methods. In particular, we will present methods for assessment of clustering, cluster detection, spatial regression, and disease mapping. Methods will be described for both point data, in which cases and non-cases (or a sample thereof) have an associated point location, and count data, in which the numbers of cases and non-cases in a set of geographical areas are available. An introduction to Geographic Information Systems (GIS) will be provided. The important extension to space-time analysis will be described, which is crucial for the analysis of infectious disease data with a spatial component. Many examples will be presented, with analysis carried out in the R programming environment. Reference: Waller, L. and Gotway, C. (2004). Applied Spatial Statistics for Public Health Data. New York, John Wiley and Sons. Assumes the material in Module 1. Some prior knowledge of R would be helpful.
Module 14: MCMC II for Infectious Diseases
Instructors: Theo Kypraios and Philip O’Neill
Module description: This module continues on from Module 4 by looking in detail at practical implementation issues for MCMC methods when applied to data from infectious disease outbreaks. The main focus will be towards inference for the SIR (susceptible-infected-removed) model. Topics include parameterisation, methods for improving convergence, assessing MCMC output, and data augmentation methods. Programming will be carried out in R. The course assumes all the material in Module 7. The material from Module 2 or 5 would be helpful, but not required.
Module 15: Pathogen Evolution, Selection, and Immunity
Instructors: Trevor Bedford and Sarah Cobey
Module description: This module provides an introduction to modeling antigenically diverse pathogen populations. Complementary epidemiological and evolutionary approaches will be covered. The first part of the course will introduce multistrain compartmental models and potential mechanisms of competition. These simple models will be contrasted with models with more complex assumptions (e.g., multiple forms of immunity and spatial structure). We will review how to statistically fit multistrain models to longitudinal data from individuals and time series data from populations. The second part of the course will show how, using the coalescent as a neutral expectation, evolutionary pressures can be quantified using sequence data. We will detail bioinformatic methods to build phylogenies, quantify selective pressures and estimate pathogen population structure. Methods to measure pathogen phenotypic similarity and antigenic evolution, such as antigenic cartography, will be introduced. Assumes material from Module 2. Material from Module 12 would be helpful, but not required.