A Bayesian estimate of the risk of tick-borne diseases

Marek Jiruše; Josef Machek; Viktor Beneš; Petr Zeman

Applications of Mathematics (2004)

  • Volume: 49, Issue: 5, page 389-404
  • ISSN: 0862-7940

Abstract

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The paper considers the problem of estimating the risk of a tick-borne disease in a given region. A large set of epidemiological data is evaluated, including the point pattern of collected cases, the population map and covariates, i.e. explanatory variables of geographical nature, obtained from GIS. The methodology covers the choice of those covariates which influence the risk of infection most. Generalized linear models are used and AIC criterion yields the decision. Further, an empirical Bayesian approach is used to estimate the parameters of the risk model. Statistical properties of the estimators are investigated. Finally, a comparison with earlier results is discussed from the point of view of statistical disease mapping.

How to cite

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Jiruše, Marek, et al. "A Bayesian estimate of the risk of tick-borne diseases." Applications of Mathematics 49.5 (2004): 389-404. <http://eudml.org/doc/33191>.

@article{Jiruše2004,
abstract = {The paper considers the problem of estimating the risk of a tick-borne disease in a given region. A large set of epidemiological data is evaluated, including the point pattern of collected cases, the population map and covariates, i.e. explanatory variables of geographical nature, obtained from GIS. The methodology covers the choice of those covariates which influence the risk of infection most. Generalized linear models are used and AIC criterion yields the decision. Further, an empirical Bayesian approach is used to estimate the parameters of the risk model. Statistical properties of the estimators are investigated. Finally, a comparison with earlier results is discussed from the point of view of statistical disease mapping.},
author = {Jiruše, Marek, Machek, Josef, Beneš, Viktor, Zeman, Petr},
journal = {Applications of Mathematics},
keywords = {Bayesian estimation; generalized linear model; epidemiological data; statistical properties; Bayesian estimation; generalized linear model; epidemiological data; statistical properties},
language = {eng},
number = {5},
pages = {389-404},
publisher = {Institute of Mathematics, Academy of Sciences of the Czech Republic},
title = {A Bayesian estimate of the risk of tick-borne diseases},
url = {http://eudml.org/doc/33191},
volume = {49},
year = {2004},
}

TY - JOUR
AU - Jiruše, Marek
AU - Machek, Josef
AU - Beneš, Viktor
AU - Zeman, Petr
TI - A Bayesian estimate of the risk of tick-borne diseases
JO - Applications of Mathematics
PY - 2004
PB - Institute of Mathematics, Academy of Sciences of the Czech Republic
VL - 49
IS - 5
SP - 389
EP - 404
AB - The paper considers the problem of estimating the risk of a tick-borne disease in a given region. A large set of epidemiological data is evaluated, including the point pattern of collected cases, the population map and covariates, i.e. explanatory variables of geographical nature, obtained from GIS. The methodology covers the choice of those covariates which influence the risk of infection most. Generalized linear models are used and AIC criterion yields the decision. Further, an empirical Bayesian approach is used to estimate the parameters of the risk model. Statistical properties of the estimators are investigated. Finally, a comparison with earlier results is discussed from the point of view of statistical disease mapping.
LA - eng
KW - Bayesian estimation; generalized linear model; epidemiological data; statistical properties; Bayesian estimation; generalized linear model; epidemiological data; statistical properties
UR - http://eudml.org/doc/33191
ER -

References

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  1. 10.1080/01621459.2000.10474304, Journal of the American Statistical Association 95 (2000), 1076–1088. (2000) Zbl1004.62090MR1821716DOI10.1080/01621459.2000.10474304
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  4. Assessment of risk of infection by means of a Bayesian method, In: Proceedings S G International Conference on Stereology, Spatial Statistics and Stochastic Geometry, V.  Beneš, J. Janáček, and I. Saxl (eds.), JČMF, Praha, 1999, pp. 197–202. (1999) 
  5. Generalized Linear Models, Chapman & Hall, London, 1992, pp. 26–43, 193–200. (1992) MR0727836
  6. 10.1002/sim.4780100114, Statistics in Medicine 10 (1991), 95–112. (1991) DOI10.1002/sim.4780100114
  7. Inference for extremes in disease mapping, Methods of Disease Mapping and Risk Assessment for Public Health Decision Making, A. Lawson et al. (eds.), Wiley, New York, 1999, pp. 63–84. (1999) 
  8. Modern Applied Statistics with  S-PLUS, Springer, New York, 1997, pp. 242–243. (1997) MR1337030
  9. 10.1093/ije/26.5.1121, International Journal of Epidemiology 26 (1997), 1121–1130. (1997) DOI10.1093/ije/26.5.1121

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