Statistical Methods in Infectious Disease Epidemiology
Course information
Class STA427 in the Biostatistics master program, Spring semester 2012
Instructors:
PD Dr. Michael Höhle (Robert Koch Institute, Department for Infectious Disease Epidemiology, Berlin and Department of Statistics, Ludwig-Maximilians-University, Munich) and Dr. Michaela Paul (University of Zurich, Institute for Social and Preventive Medicine, Division of Biostatistics)
Time and venue:
- February 9, 2012: 9-12 and 14-15:45 lecture
- February 10, 13 and 14, 2012: 9-12 lecture, 14-15:45 exercises
Institute for Social and Preventive Medicine, Division of Biostatistics, Hirschengraben 84, Room F05.
Exam:
More details to follow.
Contents:
Infectious diseases remain a continuous threat to human and animal health. Understanding and controlling infectious diseases is thus a key element in public health. Here, the role of statistics is to bring stochastic models and observational data into sync when trying to characterize the biological and social processes governing disease spread. This course gives an overview on how such statistical methods look and how they can be applied in practice.
Contents of the course are as follows:
- Introduction to infectious disease epidemiology
- Departmental look on disease dynamics
- Estimating the incubation time
- Case fatality
- Catalytic models for endemic diseases
- Estimating the force of infection
- Back calculation method
- Transmission models and their parameter estimation
- Chain binomial model
- Stochastic continuous time Susceptible-Infectious-Recovered (SIR) model
- Deterministic continuous time SIR model
- Vaccination
- Estimation of vaccine efficacy
- The screening method by Farrington
- Self-controlled case series method
- Clustering of infectious diseases
- Temporal detection of outbreaks
- Spatial clustering of health events
- Outlook
The course content will in the lectures be illuminated both from a theoretical and an applied data oriented perspective. Additional computer exercises with R aim at enhancing the practical understanding of the methods.
Requirements:
Extensive knowledge of likelihood based inference, generalized linear models and survival analysis as well as a basic understanding of stochastic processes.
Literature:
- Becker, N. (1989), Analysis of infectious disease data, Chapman & Hall/CRC.
- Farrington, P. (2005). Chapter: Communicable diseases. In: Armitage, Peter and Coulton, Theodore eds. Encyclopedia of Biostatistics, 2nd Edition. John Wiley and Sons.