Bayesian variable selection in modelling geographical heterogeneity in malaria transmission from sparse data: an application to Nouna Health and Demographic Surveillance System (HDSS) data, Burkina Faso

Bayesian variable selection in modelling geographical heterogeneity in malaria transmission from sparse data: an application to Nouna Health and Demographic Surveillance System (HDSS) data, Burkina Faso

Abstract Background Quantification of malaria heterogeneity is very challenging, partly because of the underlying characteristics of mosquitoes and also because malaria is an environmentally driven disease. Furthermore, in order to assess the spatial and seasonal variability in malaria transmission,...

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Bibliographic Details
Main Authors: Eric Diboulo, Ali Sié, Diallo A Diadier, Dimitrios A Karagiannis Voules, Yazoume Yé, Penelope Vounatsou
Format: Article
Language:English
Published: BMC 2015-02-01
Series:Parasites & Vectors
Subjects:
Bayesian
Zero-inflated
Negative binomial
Stochastic search variable selection
Entomological inoculation rate
Burkina Faso
Online Access:https://doi.org/10.1186/s13071-015-0679-7

Internet

https://doi.org/10.1186/s13071-015-0679-7

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