title: 'scanstatistics: space-time anomaly detection using scan statistics' authors: - affiliation: 1 name: Benjamin Allévius orcid: 0000-0002-0927-7183 date: "2 May 2018" bibliography: paper.bib tags: - scan statistic - cluster detection - anomaly detection - spatiotemporal affiliations: - index: 1 name: Department of Mathematics, Stockholm University
The R package scanstatistics
enables the detection of anomalous space-time
clusters using the scan statistics methodology. Scan statistics are commonly
applied in disease surveillance, where they are used to detect disease outbreaks
as they emerge locally. In this setting, cases of a given disease are recorded
continuously across a country, and are then aggregated spatially to (say)
district level, and temporally to (say) weekly counts. Scan statistics
accomplish the detection task by searching the recent records of clusters of
neighboring districts for patterns that seem anomalous given either past counts
or the counts outside the cluster currently searched.
The scanstatistics
package implements several scan statistics, making it a
partially overlapping complement to existing scan statistic software such as
SaTScan. For example, the conditional Poisson
[@Kulldorff2001] and space-time permutation [@Kulldorff2005] scan statistics
are available in both SaTScan and scanstatistics
,
while only the latter implements scan statistics for zero-inflated data
[@Allevius2018], count data with overdispersion [@Tango2011], an unconditional
(expectation-based) Poisson scan statistic [@Neill2005], and a Bayesian scan
statistic [@Neill2006].
The R package scanstatistics
is available on
CRAN and its source code
is available on GitHub.
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