Logo der Universität Basel

Research Database / FORSCHUNGSDATENBANK 
PRINTDOC
 
Publication 1027609 | Verified
 

Data Entry: Please note that the research database will be replaced by UNIverse by the end of October 2023. Please enter your data into the system https://universe-intern.unibas.ch. Thanks

Login for users with Unibas email account...

Login for registered users without Unibas email account...

 
Non-stationary partition modeling of geostatistical data for malaria risk mapping
JournalArticle (Originalarbeit in einer wissenschaftlichen Zeitschrift)
 
ID 1027609
Author(s) Gosoniu, L.; Vounatsou, P.
Author(s) at UniBasel Vounatsou, Penelope
Year 2011
Title Non-stationary partition modeling of geostatistical data for malaria risk mapping
Journal Journal of applied statistics
Volume 38
Number 1
Pages / Article-Number 3-13
Keywords Bayesian inference; geostatistics; kriging; malaria risk; MARKOV-CHAINS; non-stationarity; prevalence data; reversible jump Markov chain Monte Carlo; SPATIAL COVARIANCE STRUCTURE; Voronoi tessellation
Abstract The most common assumption in geostatistical modeling of malaria is stationarity, that is spatial correlation is a function of the separation vector between locations. However, local factors (environmental or human-related activities) may influence geographical dependence in malaria transmission differently at different locations, introducing non-stationarity. Ignoring this characteristic in malaria spatial modeling may lead to inaccurate estimates of the standard errors for both the covariate effects and the predictions. In this paper, a model based on random Voronoi tessellation that takes into account non-stationarity was developed. In particular, the spatial domain was partitioned into sub-regions (tiles), a stationary spatial process was assumed within each tile and between-tile correlation was taken into account. The number and configuration of the sub-regions are treated as random parameters in the model and inference is made using reversible jump Markov chain Monte Carlo simulation. This methodology was applied to analyze malaria survey data from Mali and to produce a country-level smooth map of malaria risk
Publisher Taylor & Francis
ISSN/ISBN 0266-4763
edoc-URL http://edoc.unibas.ch/47146/
Full Text on edoc No
Digital Object Identifier DOI 10.1080/02664760903008961
ISI-Number WOS:000285148900002
Document type (ISI) Article
 
   

MCSS v5.8 PRO. 0.351 sec, queries - 0.000 sec ©Universität Basel  |  Impressum   |    
03/05/2024