Modules
| Module 1: Infectious Diseases and Immunology Instructors: Andreas Handel and Paul Thomas This module provides an introduction to infectious diseases, the main components of the immune system, and within-host infectious disease dynamics. Simple deterministic models of within-host dynamics are introduced. Basic concepts of infectious disease epidemiology, such as the basic reproductive number, serial interval, latent, incubation and infectious period, and their applications to within-host infection dynamics are discussed. The use of models to study within-host dynamics of intervention strategies, such as vaccines and drug treatment, are covered. Background reading: How the Immune System Works, by Sompayrac, L.M., Wiley-Blackwell, 3rd edition, 2008. Standard reference book: Janeway’s Immunobiology, by Murphy, K.M., Travers, P. and Walport, M. Garland Science Publishing, 7th edition, New York and London, 2008. |
| Module 2: Mathematical Models of Infectious Disease Instructors: Pejman Rohani and John Drake This module covers the principles of deterministic mathematical models of infectious diseases. The focus is on dynamic models. The module will focus on the dynamics of susceptible-infected-recovered (SIR) models, and variants such as SI, SIRS, and SEIR models. Topics include different types of mixing patterns, theoretical results, optimization, and fitting of deterministic models to data. The module will cover modeling longitudinal data and temporal patterns of infectious diseases. Programming will done in Matlab. Background Reading: Matt J. Keeling and Pejmani Rohani. Modeling Infectious Diseases in Humans and Animals. 2007. Princeton University Press. |
| Module 3: Probability and Statistical Inference Instructors: James Hughes and N. David Yanez This course covers the laws of probability 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, and resampling methods, such as bootstrap and jackknife are covered. Examples will be drawn from important infectious disease applications. BACK TO TOP |
| Module 4: MCMC Methods for Infectious Disease Studies Instructors: Kari Auranen, Elizabeth Halloran and Vladimir Minin This module covers the use of Bayesian statistics and Markov chain Monte Carlo methods with applications in infectious disease studies. Familiarity with the R statistical package or other computing language would be helpful. The first day includes an introduction to Bayesian statistics, Monte Carlo, and MCMC. Algorithms include Gibbs sampling and Metropolis-Hastings. Practical issues arising in applications are emphasized. It assumes the material in either Module 1 or Module 2, and Module 3. |
| Module 5: Design and Analysis of Vaccine Clinical Trials Instructors: Devan Mehrotra and Stephanie Klopfer Topics including Phase I through Phase IV clinical trials will be covered. Topics include safety and immunogenicity, sample size calculation, target populations, different methods of analysis depending on the design of the study and type of data collected. This will cover statistical issues in all phases of vaccine development, including those pertaining to evaluation of immunogenicity, efficacy, and safety, as well as “bridging” trials and lot consistency trials. Assumes the material in either Module 1 or Module 2, and Module 3. |
| Module 6: Evaluating Indirect Effects of Vaccination Instructors: Elizabeth Halloran and Ira M. Longini An overview of different effects of vaccines in populations will be covered. How different levels of information that can be collected influence the estimation of different vaccine effects will be presented. Topics include study designs for evaluating direct, indirect, total, and overall effects of vaccination and other interventions. Methods for evaluating indirect effects and population level effects of vaccination from observational studies and surveillance data will be considered. Group-randomized studies for evaluating population level effects will be presented. Topics include household-based studies or studies in other transmission units such as schools. Assumes the material in Module 1, Module 3 and Module 4. BACK TO TOP |
| Module 7: Stochastic Simulation Methods for Infectious Diseases Instructors: Dave Goldsman, Dennis Chao, Kwok Tsui The principles of infectious disease spread in populations will be covered, including population structure, natural history of the infectious agent, and assumed interventions. The course will discuss the basic elements of stochastic simulation, including the simulation process-interaction modeling world view, random variate generators, input modeling, analysis of simulation output, variance reduction techniques, and simulation optimization methods. These topics will be illustrated using the Reed-Frost and Greenwood models, as well as simulations involving influenza and cholera. Brief tutorials will be presented (i) showing how R can be used to analyze simulation data, and (ii) demonstrating Arena, a popular discrete-event simulation language, which allows for animations of disease propagation under various intervention strategies. Assumes the material in either Module 1 or Module 2. Familiarity with the R statistical package would be helpful, but not required. |
| Module 8: Evaluating Surrogates of Protection Instructors: Peter Gilbert and Ivan Chan Topics include methods for determining immunological correlates of risk and surrogates of protection in vaccine studies. Approaches include regression models and methods of causal inference. Immunologic surrogates include serologic as well as cell mediated surrogates. Assumes the material in Module 1, Module 3, and Module 5. |
| Module 9: Inference for Graphs and Network Theory in Infectious Diseases Instructors: Lauren Ancel Meyers and Tom Hladish Topics include introduction to concepts of graph theory, including nodes, edges, shortest path, giant component, among others. Methods to estimate characteristics from a complete graph will be covered. Methods on how to model the spread of infectious disease on a contact network will be taught. The types of data necessary to infer graphs and the analysis of such data to understanding contact structures in populations will be covered. The software statnet will be demonstrated. Knowledge of R is assumed. Assumes material in Module 3. Module 5 is recommended. BACK TO TOP |
| Module 10: Causal Inference in Infectious Disease Epidemiology Instructors: Michael Hudgens and Thomas Richardson This module provides an introduction to the potential outcomes approach to causal inference. Topics such as potential outcomes, underlying assumptions about the assignment mechanism, no interference between units will be covered. Approaches for observational studies, such as marginal structural models will be covered. Applications of causal inference in infectious diseases will include the relaxation of the no interference assumption, selection bias in postinfection outcomes, and surrogates of protection. Assumes material in Module 3, and preferably Module 5. |
| Module 11: Evolutionary Inference and Infectious Disease Phylodynamics Instructors: Eddie Holmes and Philippe Lemey 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. Approaches include sequence alignment, maximum likelihood and Bayesian phylogenetics and evolutionary hypothesis testing. Statistical inference that combines these models to reconstruct viral epidemic histories is also considered. The module will provide both theoretical lectures and practical courses. Assumes material in Module 1, Module 3 and Module 4. BACK TO TOP |
