Spatial statistics for infectious diseases are important because the spatial and temporal scale over which transmission operates determine the dynamics of disease spread. Many methods for quantifying the distribution and clustering of spatial point patterns have been developed (e.g. K function and pair correlation function) and are routinely applied to infectious disease case occurrence data. However, these methods do not explicitly account for overlapping chains of transmission and require knowledge of the underlying population distribution, which can be limiting when analyzing epidemic case occurrence data. Therefore, we developed two novel spatial statistics that account for these effects to estimate: 1) the mean of the spatial transmission kernel, and 2) the τ-statistic, a measure of global clustering based on pathogen subtype. We briefly introduce these statistics and show how to implement them using the IDSpatialStats R package.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2019-043.zip
lgcp, ppmlasso, spdep, ads, spatstat, splancs, IDSpatialStats, DCluster, SGCS, sparr
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For attribution, please cite this work as
Giles, et al., "The IDSpatialStats R Package: Quantifying Spatial Dependence of Infectious Disease Spread", The R Journal, 2019
BibTeX citation
@article{RJ-2019-043,
author = {Giles, John R. and Salje, Henrik and Lessler, Justin},
title = {The IDSpatialStats R Package: Quantifying Spatial Dependence of Infectious Disease Spread},
journal = {The R Journal},
year = {2019},
note = {https://doi.org/10.32614/RJ-2019-043},
doi = {10.32614/RJ-2019-043},
volume = {11},
issue = {2},
issn = {2073-4859},
pages = {308-327}
}