Bayesian Disease Mapping in The Presence of Under-reporting

Show simple item record Oti-Boateng, Emmanuel 2018-02-15T06:51:09Z 2018-02-15T06:51:09Z 2018-02-15
dc.identifier.citation Oti-Boateng, 2017. en_US
dc.description MSc. Mathematical Statistics en_US
dc.description.abstract In real life situations, the values of the response variable, which is the count data is mostly under-reported. In this work, we develop a model to cater for under-reporting in count data. In particular, we allow under-reporting to vary spatially by regions and it is captured by a binomial probability. Poisson distribution is used in modeling the count response under the assumption that over-dispersion does not exist. In the case of under-reporting, it was made to also vary spatially from one unit to the other through a probability captured by a binomial distribution. The spatial variations of the disease were divided into correlated and uncorrelated parts. When a Poisson Regression analysis was used, both the correlated and uncorrelated parts were all found to share a significant relationship with the relative risk for each region with more contribution coming from the uncorrelated part. The model obtained was applied to diabetes data in Ghana. Disease maps for the diseases are also developed for Ghana at administrative (district) level. These maps are critical and informative to policy makers. These maps allow them to target policies and use the already meagre resources well. en_US
dc.description.sponsorship Dr. Ngesa Owino Oscar. Taita Taveta University Taita-Taveta-Kenya. Dr. Osei Badu Frank. 6University of Energy and Natural Resources. Sunyani-Ghana. en_US
dc.language.iso en en_US
dc.publisher JKUAT-PAUSTI en_US
dc.subject Bayesian Disease en_US
dc.subject Mapping en_US
dc.subject Under-Reporting en_US
dc.title Bayesian Disease Mapping in The Presence of Under-reporting en_US
dc.type Thesis en_US

Files in this item

This item appears in the following Collection(s)

Show simple item record

Search DSpace


My Account