# 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

## Access Full Article

top## Abstract

top## How to cite

topJiruše, 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

top- 10.1080/01621459.2000.10474304, Journal of the American Statistical Association 95 (2000), 1076–1088. (2000) Zbl1004.62090MR1821716DOI10.1080/01621459.2000.10474304
- An application of density estimation to geographical epidemiology, Statistics in Medicine 9 (1980), 691–701. (1980)
- Overview of statistical methods for disease mapping and its relationship to cluster detection, In: Spatial Epidemiology: Methods and Applications, P. Elliott et al. (eds.), Oxford University Press, Oxford, 2000, pp. 87–103. (2000)
- 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)
- Generalized Linear Models, Chapman & Hall, London, 1992, pp. 26–43, 193–200. (1992) MR0727836
- 10.1002/sim.4780100114, Statistics in Medicine 10 (1991), 95–112. (1991) DOI10.1002/sim.4780100114
- 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)
- Modern Applied Statistics with S-PLUS, Springer, New York, 1997, pp. 242–243. (1997) MR1337030
- 10.1093/ije/26.5.1121, International Journal of Epidemiology 26 (1997), 1121–1130. (1997) DOI10.1093/ije/26.5.1121

## NotesEmbed ?

topTo embed these notes on your page include the following JavaScript code on your page where you want the notes to appear.