# Project

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## Multivariate analysis of dependent count data

 Applicant Held Leonhard 124429 Project funding (Div. I-III) Institut für Epidemiologie, Biostatistik und Prävention Universität Zürich University of Zurich - ZH Mathematics 01.04.2009 - 31.07.2010 106'600.00
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### All Disciplines (3)

Discipline
 Mathematics
 Methods of Epidemiology and Preventive Medicine
 Medical Statistics

### Keywords (7)

Bayesian statistics; Infectious disease epidemiology; Markov chain Monte Carlo; Spatial epidemiology; Statistical modelling; Structured Poisson regression; large nested project Swiss National Cohort

### Lay Summary (English)

Lay summary
The goal of this project is to develop novel statistical methods for the analysis of dependent multivariate count data from epidemiology. There are two main areas of research. First, new methodology for structured Poisson regression models will be developed. This framework includes important special cases such as age-period-cohort models for count data stratified by age group and calendar time or spatial and spatio-temporal models from geographical epidemiology. Modern inference techniques, in particular efficient Markov chain Monte Carlo(MCMC) algorithms based on auxiliary variables and numerical methods based on integrated nested Laplace approximations will be considered.The methodology will be applied to multivariate mortality data obtained from the Swiss National Cohort.Secondly, statistical methods for the analysis of multivariate infectious disease surveillance data will be advanced further. Random effects will be incorporated in the multivariate models developed so far. This will enable the analysis of high-dimensional multivariate time series of counts with heterogeneity in the model coefficients.The predictive properties of the models proposed will be validated based on proper scoring rules and related techniques. Finally, a regression-based approach to incorporate possible dependence of infectiveness parameters on covariates will be considered.
 Direct link to Lay Summary Last update: 21.02.2013

### Responsible applicant and co-applicants

Name Institute
 Held Leonhard Center for Reproducible Science (CRS) EBPI University of Zurich

### Employees

Name Institute
 Riebler Andrea Dept. of Mathematical Sciences NTNU Trondheim

### Associated projects

Number Title Start Funding scheme
 130002 Multivariate analysis of dependent count data 01.08.2010 Project funding (Div. I-III)
 196247 SUSPend: Impact of Social distancing policies and Underreporting on the SPatio-temporal spread of COVID-19 01.06.2020 Special Call on Coronaviruses
 116776 Multivariate analysis of dependent count and survival data 01.04.2007 Project funding (Div. I-III)
 130002 Multivariate analysis of dependent count data 01.08.2010 Project funding (Div. I-III)
 137919 Statistical methods for spatio-temporal modelling and prediction of infectious diseases 01.03.2012 Project funding (Div. I-III)

### Abstract

The goal of this project is to develop novel statistical methods for the analysis of dependent multivariate count data from epidemiology. It builds upon the project “Multivariate analysis of dependent count and survival data”, which is funded by SNF since April 2007. There are two main areas of research. First, new methodology for structured Poisson regressionmodels will be developed. This framework includes important special cases such as age-period-cohort models for count data stratified by age group and calendar time or spatial and spatio-temporal models from geographical epidemiology. Modern inference techniques, in particular efficient Markov chain Monte Carlo (MCMC) algorithms based on auxiliary variables and numerical methods based on integrated nested Laplace approximations will be considered. The methodology will be applied to multivariate mortality data obtained from the Swiss National Cohort. Secondly, statistical methods for the analysis of multivariate infectious disease surveillance data will be advanced further. Random effects will be incorporated in the multivariate models developed so far. This will enable the analysis of high-dimensional multivariate time series of countswith heterogeneity in the model coefficients. The predictive properties of the models proposed will be validated based on proper scoring rules and related techniques. Finally, a regression-based approach to incorporate possible dependence of infectiveness parameters on covariates will be considered.
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