%0 Journal Article %1 meyer_spacetime_2011 %A Meyer, Sebastian %A Elias, Johannes %A Höhle, Michael %D 2011 %J Biometrics %K Conditional_intensity_function Infectious_disease_surveillance_data Spatiotemporal_point_process Stochastic_epidemic_modeling je molecular_epidemiology %R 10.1111/j.1541-0420.2011.01684.x %T A Space-Time Conditional Intensity Model for Invasive Meningococcal Disease Occurrence %U http://onlinelibrary.wiley.com/doi/10.1111/j.1541-0420.2011.01684.x/abstract %X Summary A novel point process model continuous in space–time is proposed for quantifying the transmission dynamics of the two most common meningococcal antigenic sequence types observed in Germany 2002–2008. Modeling is based on the conditional intensity function (CIF), which is described by a superposition of additive and multiplicative components. As an epidemiological interesting finding, spread behavior was shown to depend on type in addition to age: basic reproduction numbers were 0.25 (95\% CI 0.19–0.34) and 0.11 (95\% CI 0.07–0.17) for types B:P1.7–2,4:F1–5 and C:P1.5,2:F3–3, respectively. Altogether, the proposed methodology represents a comprehensive and universal regression framework for the modeling, simulation, and inference of self-exciting spatiotemporal point processes based on the CIF. Usability of the modeling in biometric practice is promoted by an implementation in the R package surveillance.

@article{meyer_spacetime_2011, abstract = {Summary A novel point process model continuous in space–time is proposed for quantifying the transmission dynamics of the two most common meningococcal antigenic sequence types observed in Germany 2002–2008. Modeling is based on the conditional intensity function {(CIF)}, which is described by a superposition of additive and multiplicative components. As an epidemiological interesting finding, spread behavior was shown to depend on type in addition to age: basic reproduction numbers were 0.25 (95\% {CI} 0.19–0.34) and 0.11 (95\% {CI} 0.07–0.17) for types {B:P1.7–2,4:F1–5} and {C:P1.5,2:F3–3}, respectively. Altogether, the proposed methodology represents a comprehensive and universal regression framework for the modeling, simulation, and inference of self-exciting spatiotemporal point processes based on the {CIF.} Usability of the modeling in biometric practice is promoted by an implementation in the R package surveillance.}, added-at = {2011-10-20T16:39:31.000+0200}, author = {Meyer, Sebastian and Elias, Johannes and Höhle, Michael}, biburl = {https://www.bibsonomy.org/bibtex/219336391a8af6e4ecf0f3b1dc9f7332a/ag_vogel}, doi = {10.1111/j.1541-0420.2011.01684.x}, interhash = {922380aaddd29a9b5c89f3c96a1f47af}, intrahash = {19336391a8af6e4ecf0f3b1dc9f7332a}, issn = {1541-0420}, journal = {Biometrics}, keywords = {Conditional_intensity_function Infectious_disease_surveillance_data Spatiotemporal_point_process Stochastic_epidemic_modeling je molecular_epidemiology}, timestamp = {2014-02-06T18:28:59.000+0100}, title = {A Space-Time Conditional Intensity Model for Invasive Meningococcal Disease Occurrence}, url = {http://onlinelibrary.wiley.com/doi/10.1111/j.1541-0420.2011.01684.x/abstract}, year = 2011 }