Prospective space–time surveillance with cumulative surfaces for geographical identification of the emerging cluster

Thais Paiva (), Renato Assunção () and Taynãna Simões ()

Computational Statistics, 2015, vol. 30, issue 2, 419-440

Abstract: We developed a space–time prospective surveillance method when the data are point events, monitoring if there is an emerging cluster. Typical application areas are crime or disease surveillance. At each new event, a local Knox score is calculated and spatially spread to form a stochastic surface. The surfaces are accumulated sequentially until they exceed a specified threshold, causing an alarm to go off and identify the region of the probable cluster. The method requires little prior knowledge from the user and provides a way to identify locations and time of possible clusters, through the visualization of the cumulative surface. We present a simulation study for different cluster scenarios, as well as an application to a dataset of meningitis cases in Belo Horizonte, Brazil. Copyright Springer-Verlag Berlin Heidelberg 2015

Keywords: Spatial statistics; Disease mapping; Point pattern; Local Knox score (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1007/s00180-014-0541-y (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:30:y:2015:i:2:p:419-440

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s00180-014-0541-y

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().