Ghent University Academic Bibliography

Advanced

Estimating the prevalence of infections in vector populations using pools of samples

Niko Speybroeck, Christopher J Williams, Kora Brice Lafia, Brecht Devleesschauwer UGent and Dirk Berkvens UGent (2012) MEDICAL AND VETERINARY ENTOMOLOGY. 26(4). p.361-371
abstract
Several statistical methods have been proposed for estimating the infection prevalence based on pooled samples, but these methods generally presume the application of perfect diagnostic tests, which in practice do not exist. To optimize prevalence estimation based on pooled samples, currently available and new statistical models were described and compared. Three groups were tested: (a) Frequentist models, (b) Monte Carlo Markov-Chain (MCMC) Bayesian models, and (c) Exact Bayesian Computation (EBC) models. Simulated data allowed the comparison of the models, including testing the performance under complex situations such as imperfect tests with a sensitivity varying according to the pool weight. In addition, all models were applied to data derived from the literature, to demonstrate the influence of the model on real-prevalence estimates. All models were implemented in the freely available R and OpenBUGS software and are presented in Appendix S1. Bayesian models can flexibly take into account the imperfect sensitivity and specificity of the diagnostic test (as well as the influence of pool-related or external variables) and are therefore the method of choice for calculating population prevalence based on pooled samples. However, when using such complex models, very precise information on test characteristics is needed, which may in general not be available.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (review)
publication status
published
subject
keyword
prevalence, pool of samples, model, arthropod vector, POLYMERASE-CHAIN-REACTION, ONCHOCERCA-VOLVULUS INFECTION, DISEASE PREVALENCE, BAYESIAN-INFERENCE, REGRESSION-MODELS, DIAGNOSTIC-TESTS, JOHNES-DISEASE, BORNE DISEASES, TRANSMISSION, SPECIFICITY
journal title
MEDICAL AND VETERINARY ENTOMOLOGY
Med. Vet. Entomol.
volume
26
issue
4
pages
361 - 371
Web of Science type
Review
Web of Science id
000312081800001
JCR category
VETERINARY SCIENCES
JCR impact factor
2.208 (2012)
JCR rank
9/142 (2012)
JCR quartile
1 (2012)
ISSN
0269-283X
DOI
10.1111/j.1365-2915.2012.01015.x
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
2084274
handle
http://hdl.handle.net/1854/LU-2084274
date created
2012-04-11 19:34:10
date last changed
2013-07-05 14:52:21
@article{2084274,
  abstract     = {Several statistical methods have been proposed for estimating the infection prevalence based on pooled samples, but these methods generally presume the application of perfect diagnostic tests, which in practice do not exist. To optimize prevalence estimation based on pooled samples, currently available and new statistical models were described and compared. Three groups were tested: (a) Frequentist models, (b) Monte Carlo Markov-Chain (MCMC) Bayesian models, and (c) Exact Bayesian Computation (EBC) models. Simulated data allowed the comparison of the models, including testing the performance under complex situations such as imperfect tests with a sensitivity varying according to the pool weight. In addition, all models were applied to data derived from the literature, to demonstrate the influence of the model on real-prevalence estimates. All models were implemented in the freely available R and OpenBUGS software and are presented in Appendix S1. Bayesian models can flexibly take into account the imperfect sensitivity and specificity of the diagnostic test (as well as the influence of pool-related or external variables) and are therefore the method of choice for calculating population prevalence based on pooled samples. However, when using such complex models, very precise information on test characteristics is needed, which may in general not be available.},
  author       = {Speybroeck, Niko and Williams, Christopher J and Lafia, Kora Brice and Devleesschauwer, Brecht and Berkvens, Dirk},
  issn         = {0269-283X},
  journal      = {MEDICAL AND VETERINARY ENTOMOLOGY},
  keyword      = {prevalence,pool of samples,model,arthropod vector,POLYMERASE-CHAIN-REACTION,ONCHOCERCA-VOLVULUS INFECTION,DISEASE PREVALENCE,BAYESIAN-INFERENCE,REGRESSION-MODELS,DIAGNOSTIC-TESTS,JOHNES-DISEASE,BORNE DISEASES,TRANSMISSION,SPECIFICITY},
  language     = {eng},
  number       = {4},
  pages        = {361--371},
  title        = {Estimating the prevalence of infections in vector populations using pools of samples},
  url          = {http://dx.doi.org/10.1111/j.1365-2915.2012.01015.x},
  volume       = {26},
  year         = {2012},
}

Chicago
Speybroeck, Niko, Christopher J Williams, Kora Brice Lafia, Brecht Devleesschauwer, and Dirk Berkvens. 2012. “Estimating the Prevalence of Infections in Vector Populations Using Pools of Samples.” Medical and Veterinary Entomology 26 (4): 361–371.
APA
Speybroeck, Niko, Williams, C. J., Lafia, K. B., Devleesschauwer, B., & Berkvens, D. (2012). Estimating the prevalence of infections in vector populations using pools of samples. MEDICAL AND VETERINARY ENTOMOLOGY, 26(4), 361–371.
Vancouver
1.
Speybroeck N, Williams CJ, Lafia KB, Devleesschauwer B, Berkvens D. Estimating the prevalence of infections in vector populations using pools of samples. MEDICAL AND VETERINARY ENTOMOLOGY. 2012;26(4):361–71.
MLA
Speybroeck, Niko, Christopher J Williams, Kora Brice Lafia, et al. “Estimating the Prevalence of Infections in Vector Populations Using Pools of Samples.” MEDICAL AND VETERINARY ENTOMOLOGY 26.4 (2012): 361–371. Print.