Methodological Perspectives for Surveying Rare and Clustered Population: Towards a Sequentially Adaptive Approach

  • Federico Andreis
  • Emanuela Furfaro
  • Fulvia Mecatti
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 227)


Sampling a rare and clustered trait in a finite population is challenging: traditional sampling designs usually require a large sample size in order to obtain reasonably accurate estimates, resulting in a considerable investment of resources in front of the detection of a small number of cases. A notable example is the case of WHO’s tuberculosis (TB) prevalence surveys, crucial for countries that bear a high TB burden, the prevalence of cases being still less than 1%. In the latest WHO guidelines, spatial patterns are not explicitly accounted for, with the risk of missing a large number of cases; moreover, cost and logistic constraints can pose further problems. After reviewing the methodology in use by WHO, the use of adaptive and sequential approaches is discussed as natural alternatives to improve over the limits of the current practice. A simulation study is presented to highlight possible advantages and limitations of these alternatives, and an integrated approach, combining both adaptive and sequential features in a single sampling strategy is advocated as a promising methodological perspective.


Spatial pattern Prevalence surveys logistic constraints Poisson sampling Horvitz-Thompson estimation 


  1. 1.
    Baddeley, A., Turner, R.: spatstat: an R package for analyzing spatial point patterns. J. Stat. Softw. 12(6), 1–42 (2005).
  2. 2.
    Bondesson, L., Thorburn, D.: A list sequential sampling method suitable for real-time sampling. Scand. J. Stat. 35, 466–483 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Glaziou, P., van der Werf, M.J., Onozaki, I., Dye, C.: Tuberculosis prevalence surveys: rationale and cost. Int. Tuberc. Lung Dis. 12(9), 1003–1008 (2008)Google Scholar
  4. 4.
    Grafström, A.: On a generalization of Poisson sampling. J. Stat. Plann. Infer. 140(4), 982–991 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Grafström, A., Lundström, N.L.P., Schellin, L.: Spatially balanced sampling through the pivotal method. Biometrics 68(2), 514–520 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Grafström, A.: Spatially correlated Poisson sampling. J. Stat. Plann. Infer. 142(1), 139–147 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Grafström, A., Lisic, J.: BalancedSampling: balanced and spatially balanced sampling. R package version 1.5.1. (2016)
  8. 8.
    R Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (2015)
  9. 9.
    Thompson, S.K.: Adaptive cluster sampling. J. Am. Stat. Assoc. 85, 1050–1059 (1990)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Thompson, S.K., Seber, G.A.F.: Adaptive Sampling. Wiley (1996)Google Scholar
  11. 11.
    Thompson, W.L.: Sampling Rare or Elusive Species. Island Press, New York (2004)Google Scholar
  12. 12.
    WHO: Tubercoulosis Prevalence Surveys: A Handbook. WHO Press, Geneva (2011)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Federico Andreis
    • 1
  • Emanuela Furfaro
    • 2
  • Fulvia Mecatti
    • 2
  1. 1.Carlo F. Dondena Centre for Research on Social Dynamics and Public PolicyUniversità Commerciale Luigi BocconiMilanItaly
  2. 2.Università degli Studi di Milano-BicoccaMilanItaly

Personalised recommendations