A statistical framework for the analysis of multivariate infectious disease surveillance counts

DOI10.1191/1471082X05st098oazbMath1111.62105OpenAlexW2110435679MaRDI QIDQ3413102

Mathias Hofmann, Leonhard Held, Michael Höhle

Publication date: 3 January 2007

Published in: Statistical Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1191/1471082x05st098oa

zbMATH Keywords

maximum likelihoodspace-time modelsbranching process with immigrationinfectious disease surveillancemultivariate time series of countsobservation-drivenparameter-driven

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Epidemiology (92D30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Applications of branching processes (60J85)

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Uses Software

• R
• Surveillance
• GMRFLib

Cites Work

• Unnamed Item
• Markov Regression Models for Time Series: A Quasi-Likelihood Approach
• A regression model for time series of counts
• A state space model for multivariate longitudinal count data
• A Statistical Algorithm for the Early Detection of Outbreaks of Infectious Disease
• A stochastic model for extinction and recurrence of epidemics: estimation and inference for measles outbreaks

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