at the Department of Statistics, Ludwig-Maximilians-Universität
8-11 October 2012. Each day from 08:30am to 04:00pm
Seminar Room (Room 144) & CIP-Pool, Department of Statistics,
Ludwigstr. 33, 80539 Munich, Germany
The lecture aims at students of the Master's programme in
Biostatistics, but is also open for Master's students such as the
Master in Statistics or the Master in Epidemiology.
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
Mathematical models for communicable diseases
Chain binomial model
Stochastic continuous time Susceptible-Infectious-Recovered
Deterministic continuous time SIR model
Catalytic models for endemic diseases
Estimating the force of infection
Estimation of vaccine efficacy
The screening method by Farrington
Self-controlled case series method
Clustering of infectious diseases
Temporal detection of outbreaks
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.
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.
An understanding of statistical methodology including generalized
linear models, survival analysis and stochastic processes.
R is a must.
Deutsch. In case of non-German speakers the lecture can optionally
be held in English. Slides will be in English.
Homework (in groups up to 2 persons). An exercise sheet to be handed
in as homework will be given following the course. Students have
approx. 4 weeks to complete the project work an write a report
including R code.