Density-equalizing Euclidean minimum spanning trees for the detection of all disease cluster shapes.

Density-equalizing Euclidean minimum spanning trees for the detection of all disease cluster shapes. Research Abstract Details 

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Density-equalizing Euclidean minimum spanning trees for the detection of all disease cluster shapes. Abstract Text:

Shannon C Wieland,John S Brownstein,Bonnie Berger,Kenneth D Mandl,

Existing disease cluster detection methods cannot detect clusters of all shapes and sizes or identify highly irregular sets that overestimate the true extent of the cluster. We introduce a graph-theoretical method for detecting arbitrarily shaped clusters based on the Euclidean minimum spanning tree of cartogram-transformed case locations, which overcomes these shortcomings. The method is illustrated by using several clusters, including historical data sets from West Nile virus and inhalational anthrax outbreaks. Sensitivity and accuracy comparisons with the prevailing cluster detection method show that the method performs similarly on approximately circular historical clusters and greatly improves detection for noncircular clusters.

Density-equalizing Euclidean minimum spanning trees for the detection of all disease cluster shapes. Publishing Authors By Initials

SC Wieland,JS Brownstein,B Berger,KD Mandl,

For similar virus diseases: arbovirus infections: encephalitis, arbovirus: west nile fever research abstracts see: virus diseases: arbovirus infections: encephalitis, arbovirus: west nile fever research

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Density-equalizing Euclidean minimum spanning trees for the detection of all disease cluster shapes. Journal Published:

PUBLICATION TYPE: Research Support, N.I.H., Extr

Journal: Proceedings of the National Academy of Sciences of

VOLUME: 104

Page Numbers: 9404-9

Journal Abbreviation: Proc. Natl. Acad. Sci. U.S.A.

ISSN: 0027-8424

DAY: 22

MONTH: 05

YEAR: 2007

Density-equalizing Euclidean minimum spanning trees for the detection of all disease cluster shapes. Information

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LANGUAGE: eng

NlmUniqueID: 7505876

Density-equalizing Euclidean minimum spanning trees for the detection of all disease cluster shapes. Keywords Mesh Terms:

KEYWORDS: West Nile Fever

MESH TERMS: virology

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Grant and Affiliation Information for Density-equalizing Euclidean minimum spanning trees for the detection of all disease cluster shapes.

AFFILIATION: Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139-4307, USA.

Country: United States

AGENCY: United States NLM

GRANT: LM 007677-03S1

ACRONYM: LM

MEDLINETA: Proc Natl Acad Sci U S A

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