Bayesian Mapping of Medical Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F15%3A33152467" target="_blank" >RIV/61989592:15310/15:33152467 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-07926-4_37" target="_blank" >http://dx.doi.org/10.1007/978-3-319-07926-4_37</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-07926-4_37" target="_blank" >10.1007/978-3-319-07926-4_37</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Bayesian Mapping of Medical Data

Popis výsledku v původním jazyce

Disease mapping, the visualization of disease rates and the clustering of disease data are still one of the most interesting topics in geosciences. This is because of the nature of the data, which are often purely spatial with a rich descriptive part andwhich are easy to combine with other data (demographic, economic, etc.). This contribution aims to present the usage of empirical Bayesian methods in disease mapping and the subsequent creation of disease maps. Bayesian methods incorporate prior knowledge about the phenomenon (or underlying processes) to provide a more accurate and easily understandable description of the situation. Empirical Bayesian procedures are used for disease rates smoothing in the case of a choropleth map. They also help to identify local clusters of more/less affected areas. The main topic of the case study in this paper is the analysis of the spatial distribution of a disease called campylobacteriosis in the Czech Republic between the years 2008 and 2012 with

Název v anglickém jazyce

Bayesian Mapping of Medical Data

Popis výsledku anglicky

Disease mapping, the visualization of disease rates and the clustering of disease data are still one of the most interesting topics in geosciences. This is because of the nature of the data, which are often purely spatial with a rich descriptive part andwhich are easy to combine with other data (demographic, economic, etc.). This contribution aims to present the usage of empirical Bayesian methods in disease mapping and the subsequent creation of disease maps. Bayesian methods incorporate prior knowledge about the phenomenon (or underlying processes) to provide a more accurate and easily understandable description of the situation. Empirical Bayesian procedures are used for disease rates smoothing in the case of a choropleth map. They also help to identify local clusters of more/less affected areas. The main topic of the case study in this paper is the analysis of the spatial distribution of a disease called campylobacteriosis in the Czech Republic between the years 2008 and 2012 with

Druh

C - Kapitola v odborné knize

CEP obor

DE - Zemský magnetismus, geodesie, geografie

OECD FORD obor

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Projekt

<a href="/cs/project/EE2.3.20.0170" target="_blank" >EE2.3.20.0170: Budování výzkumně-vzdělávacího týmu v oblasti modelování přírodních jevů a využití geoinformačních systémů, s vazbou na zapojení do mezinárodních sítí a programů.</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název knihy nebo sborníku

Modern Trends in Cartography

ISBN

978-3-319-07925-7

Počet stran výsledku

17

Strana od-do

489-505

Počet stran knihy

534

Název nakladatele

Springer International Publishing

Místo vydání

Cham

Kód UT WoS kapitoly

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