The IDSpatialStats R package: Quantifying spatial dependence of infectious disease spread

John R. Giles, Henrik Salje, Justin Lessler

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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. Kfunction 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 t-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.

Original languageEnglish (US)
Pages (from-to)308-327
Number of pages20
JournalR Journal
Issue number2
StatePublished - Dec 1 2019

ASJC Scopus subject areas

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty


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