Social contact data in endemic-epidemic models and probabilistic forecasting with **surveillance**

Play Social contact data in endemic-epidemic models and probabilistic forecasting with **surveillance**
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useR!2017: Social contact data in endemic-epidemic...

Keywords: age-structured contact matrix, areal count time series, infectious disease epidemiology, norovirus, spatio-temporal surveillance data
Webpages: https://CRAN.R-project.org/package=surveillance
Routine surveillance of notifiable infectious diseases gives rise to weekly counts of reported cases stratified by region and age group. A well-established approach to the statistical analysis of such surveillance data are endemic-epidemic time-series models (hhh4) as implemented in the R package surveillance (Meyer, Held, and Höhle 2017). Autoregressive model components reflect the temporal dependence inherent to communicable diseases. Spatial dynamics are largely driven by human travel and can be captured by movement network data or a parametric power law based on the adjacency matrix of the regions. Furthermore, the social phenomenon of "like seeks like" produces characteristic contact patterns between subgroups of a population, in particular with respect to age (Mossong et al. 2008). We thus incorporate an age-structured contact matrix in the hhh4 modelling framework to
  1. assess age-specific disease spread while accounting for its spatial pattern (Meyer and Held 2017)
  2. improve probabilistic forecasts of infectious disease spread (Held, Meyer, and Bracher 2017)
We analyze weekly surveillance counts on norovirus gastroenteritis from the 12 city districts of Berlin, in six age groups, from week 2011/27 to week 2015/26. The following year (2015/27 to 2016/26) is used to assess the quality of the predictions.
References Held, Leonhard, Sebastian Meyer, and Johannes Bracher. 2017. "Probabilistic Forecasting in Infectious Disease Epidemiology: The Thirteenth Armitage Lecture." bioRxiv. doi:10.1101/104000.

Meyer, Sebastian, and Leonhard Held. 2017. "Incorporating Social Contact Data in Spatio-Temporal Models for Infectious Disease Spread." Biostatistics 18 (2): 338–51. doi:10.1093/biostatistics/kxw051.

Meyer, Sebastian, Leonhard Held, and Michael Höhle. 2017. "Spatio-Temporal Analysis of Epidemic Phenomena Using the R Package surveillance." Journal of Statistical Software. http://arxiv.org/abs/1411.0416.

Mossong, Joël, Niel Hens, Mark Jit, Philippe Beutels, Kari Auranen, Rafael Mikolajczyk, Marco Massari, et al. 2008. "Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases." PLoS Medicine 5 (3): e74. doi:10.1371/journal.pmed.0050074.

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