Bayesian statistics; Infectious disease epidemiology; Markov chain Monte Carlo; Spatial epidemiology; Statistical modelling; Structured additive regression; Gaussian Markov random fields
Held Leonhard, Paul Michaela (2013), Statistical modeling of infectious disease surveillance data, in M’ikanatha N. M. (ed.), Wiley-Blackwell, Hoboken, NJ, 535-544.
Held Leonhard, Riebler Andrea (2012), A conditional approach for inference in multivariate age-period-cohort models, in Statistical Methods in Medical Research
, 21(4), 311-329.
Riebler Andrea, Held Leonhard, Rue Håvard (2012), Estimation and extrapolation of time trends in registry data - borrowing strength from related populations, in Annals of Applied Statistics
, 6(1), 304-333.
Riebler Andrea, Held Leonhard, Rue Håvard, Bopp Matthias (2012), Gender-specific differences and the impact of family integration on time trends in age-stratified Swiss suicide rates, in Journal of the Royal Statistical Society-Series A
, 175(2), 473-490.
Held Leonhard, Paul Michaela (2012), Modeling seasonality in space-time infectious disease surveillance data, in Biometrical Journal
, 54(6), 824-843.
Herzog Sereina, Paul Michaela, Held Leonhard (2011), Heterogeneity in vaccination coverage explains the size and occurrence of measles epidemics in German surveillance data, in Epidemiology and Infection
, 139(4), 505-515.
Riebler Andrea, Held Leonhard, Rue Håvard (2011), Modelling seasonal patterns in longitudinal profiles with correlated random walks
, Proceedings of the 26th International Workshop on Statistical Modelling, Valencia, Spain.
Paul Michaela, Held Leonhard (2011), Predictive assessment of a non-linear random effects model for multivariate time series of infectious disease counts, in Statistics in Medicine
, 30(10), 1118-1136.
Riebler Andrea, Held Leonhard (2010), The analysis of heterogeneous time trends in multivariate age-period-cohort models, in Biostatistics
, 11(1), 57-69.
There is a trend in biomedical and public health research towardsoutcome measures of increasing complexity. This project aims todevelop novel statistical methodology for epidemiological countdata. Firstly, correlated Gaussian Markov random field models will bedeveloped. This class of models will provide great flexibility forstructured additive regression and will be particularly suitable forlongitudinal and space-time data. Algorithmic routines based on bothMarkov chain Monte Carlo (MCMC) and integrated nested Laplaceapproximations (INLA) will be developed and compared. Secondly,statistical methodology for the analysis of space-time data oninfectious diseases will be developed. Such data need specificallytailored models to capture the typical epidemic behaviour. We willextend a recently proposed model framework to allow the infectiousnessparameters to depend on observed covariates and to allow forcorrelated random effects. Weights which represent the influence ofpast counts in neighboring regions will be treated as unknown to obtain data-driven information on the spatio-temporal spread ofinfectious diseases. This will be of particular public healthimportance to provide realistic descriptions and predictions of futureinfectious disease epidemics.