twinstim: An endemic-epidemic modeling framework for spatio-temporal point patterns

The availability of geocoded health data and the inherent temporal structure of communicable diseases have led to an increased interest in statistical models and software for spatio-temporal data with epidemic features. The R package surveillance can handle various levels of aggregation at which infective events have been recorded. This vignette illustrates the analysis of point-referenced surveillance data using the endemic-epidemic point process model “twinstim” proposed by Meyer, Elias, and Höhle (2012) and extended in Meyer and Held (2014). We first describe the general modeling approach and then exemplify data handling, model fitting, visualization, and simulation methods for time-stamped geo-referenced case reports of invasive meningococcal disease (IMD) caused by the two most common bacterial finetypes of meningococci in Germany, 2002–2008.