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Modeling seasonality in space‐time infectious disease surveillance data
Author(s) -
Held Leonhard,
Paul Michaela
Publication year - 2012
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201200037
Subject(s) - seasonality , disease surveillance , time series , multivariate statistics , variation (astronomy) , infectious disease (medical specialty) , computer science , seasonal influenza , statistics , feature selection , econometrics , geography , data mining , disease , machine learning , mathematics , covid-19 , medicine , physics , pathology , astrophysics
Infectious disease data from surveillance systems are typically available as multivariate times series of disease counts in specific administrative geographical regions. Such databases are useful resources to infer temporal and spatiotemporal transmission parameters to better understand and predict disease spread. However, seasonal variation in disease notification is a common feature of surveillance data and needs to be taken into account appropriately. In this paper, we extend a time series model for spatiotemporal surveillance counts to incorporate seasonal variation in three distinct components. A simulation study confirms that the different types of seasonality are identifiable and that a predictive approach suggested for model selection performs well. Application to surveillance data on influenza in Southern Germany reveals a better model fit and improved one‐step‐ahead predictions if all three components allow for seasonal variation.