Skip to main content
SpringerLink
Log in

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

  • Original Paper
  • Published:
Computational Statistics Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  • Abrams AM, Kleinman K, Kulldorff M (2010) Gumbel based p-value approximations for spatial scan statistics. Int J Health Geogr 9(1):61

    Article  Google Scholar 

  • Assunção R, Correa T (2009) Surveillance to detect emerging space–time clusters. Comput Stat Data Anal 53(8):2817–2830

    Article  MATH  Google Scholar 

  • Diggle P, Rowlingson B, Su T (2005) Point process methodology for on-line spatio-temporal disease surveillance. Environmetrics 16(5):423–434

    Article  MathSciNet  Google Scholar 

  • Fricker RD, Chang JT (2008) A spatio-temporal methodology for real-time biosurveillance. Quality Eng 20(4):465–477. doi:10.1080/08982110802334096

  • Höhle M (2007) “surveillance:” An R package for the monitoring of infectious diseases. Comput Stat 22(4):571–582

    Article  MATH  Google Scholar 

  • Jacquez GM (1996) A k nearest neighbour test for space–time interaction. Stat Med 15(18):1935–1949

    Article  Google Scholar 

  • Knox EG (1964) The detection of space–time interactions. J R Stat Soc Ser C (Applied Statistics) 13(1):25–30

    Google Scholar 

  • Kulldorff M (1999) The Knox method and other tests for space–time interaction. Biometrics 55(2):544–552

    Article  MATH  Google Scholar 

  • Kulldorff M (2001) Prospective time periodic geographical disease surveillance using a scan statistic. J R Stat Soc A 164(1):61–72

    Article  MATH  MathSciNet  Google Scholar 

  • Kulldorff M, Heffernan R, Hartman J, Assunção R, Mostashari F (2005) A space–time permutation scan statistic for disease outbreak detection. PLoS Med 2(3):e59

    Article  Google Scholar 

  • Lewis PAW, Shedler GS (1979) Simulation of nonhomogeneous poisson processes by thinning. Nav Res Logist Q 26(3):403–413

    Article  MATH  MathSciNet  Google Scholar 

  • Mantel N (1967) The detection of disease clustering and a generalized regression approach. Cancer Res 27(2):209–220

    Google Scholar 

  • Marshall JB, Spitzner DJ, Woodall WH (2007) Use of the local Knox statistic for the prospective monitoring of disease occurrences in space and time. Stat Med 26(7):1579–1593

    Article  MathSciNet  Google Scholar 

  • Neill DB, Moore AW, Sabhnani M, Daniel K (2005) Detection of emerging space–time clusters. In: KDD ’05: Proceedings of the eleventh ACM SIGKDD international conference on knowledge discovery in data mining, pp 218–227.

  • Piroutek A, Assunção R, Paiva T (2014) Space–time prospective surveillance based on knox local statistics. Stat Med 33(16):2758–2773. doi:10.1002/sim.6118

    Article  MathSciNet  Google Scholar 

  • Piterbarg VI (1996) Asymptotic methods in the theory of Gaussian processes and fields. AMS translations of mathematical monographs, vol 148. Providence, RI

  • Robertson C, Nelson TA, MacNab YC, Lawson AB (2010) Review of methods for space–time disease surveillance. Spat Spatio-temporal Epidemiol 1(2):105–116

    Article  Google Scholar 

  • Rodeiro C, Lawson A (2006) Monitoring changes in spatio-temporal maps of disease. Biomet J 48(3):463–480

    Article  Google Scholar 

  • Rogerson PA (2001) Monitoring point patterns for the development of space–time clusters. J R Stat Soc A 164(1):87–96

    Article  MATH  MathSciNet  Google Scholar 

  • Rue H, Held L (2005) Gaussian Markov random fields: theory and applications. Monographs on statistics and applied probability, vol 104. CRC Press

  • Simões TC, Assunção RM (2005) Sistema de vigilância para detecção de interações espaço-tempo de eventos pontuais. In: GEOINFO 2005 - VII Simpósio Brasileiro de Geoinformática, pp 281–291

  • Sonesson C, Bock D (2003) A review and discussion of prospective statistical surveillance in public health. J R Stat Soc A 166(1):5–21

    Article  MathSciNet  Google Scholar 

  • Unkel S, Farrington CP, Garthwaite PH, Robertson C, Andrews N (2012) Statistical methods for the prospective detection of infectious disease outbreaks: a review. J R Stat Soc Ser A (Statistics in Society) 175(1):49–82

    Article  MathSciNet  Google Scholar 

  • Woodall WH, Marshall JB, Joner MD Jr, Fraker SE, Abdel-Salam AG (2008) On the use and evaluation of prospective scan methods in health-related surveillance. J R Stat Soc Ser A 171(1):223–237

    MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Statistical Science, Duke University, Durham, NC, USA

    Thais Paiva

  2. Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil

    Renato Assunção

  3. Departamento de Epidemiologia e Métodos Quantitativos em Saúde, Fiocruz, Rio de Janeiro, RJ, Brazil

    Taynãna Simões

Authors
  1. Thais Paiva
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Renato Assunção
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Taynãna Simões
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Thais Paiva.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Paiva, T., Assunção, R. & Simões, T. Prospective space–time surveillance with cumulative surfaces for geographical identification of the emerging cluster. Comput Stat 30, 419–440 (2015). https://doi.org/10.1007/s00180-014-0541-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00180-014-0541-y

Keywords

Search

Navigation