User Settings
Article

Spatial Pattern of Tuberculosis Prevalence in Nigeria: A Comparative Analysis of Spatial Autocorrelation Indices

Saad Eddin Ibrahim,Isah Hamisu,Usman Lawal Gulma-2015-01-01
6

TL;DRAbstract

This paper presents comparative analytical procedures of spatial autocorrelation indices for identifying cluster and modelling spatial pattern of tuberculosis (TB) prevalence. It compares global and Local Indicators of Spatial Association for finding cluster and hotspot locations. It also assessed three interpolation methods for disaggregating TB prevalence to enable analysis of local cluster and hotspot locations due to data limitation. Inverse Distance Weighting (IDW) was selected for its lowest variance (3.4) and Root Mean Square Error (RMSE) in comparison to other models. The global Moran’ I test was -0.09 and a p-value of 0.70 using aggregated TB prevalence for the year 2008 as the best case scenario considering all the years. Hence, global Moran’ I do not identify any significant cluster. In the case of general G, the situation is different for the clustering pattern, though quite low in some years but even unique to year 2010 with significant high z value (1.14) and very low p-v

Chat with Paper

AI Agents for this Paper

This paper presents comparative analytical procedures of spatial autocorrelation indices for identifying cluster and modelling spatial pattern of tuberculosis (TB) prevalence. It compares global and Local Indicators of Spatial Association for finding cluster and hotspot locations. It also assessed three interpolation methods for disaggregating TB prevalence to enable analysis of local cluster and hotspot locations due to data limitation. Inverse Distance Weighting (IDW) was selected for its lowest variance (3.4) and Root Mean Square Error (RMSE) in comparison to other models. The global Moran’ I test was -0.09 and a p-value of 0.70 using aggregated TB prevalence for the year 2008 as the best case scenario considering all the years. Hence, global Moran’ I do not identify any significant cluster. In the case of general G, the situation is different for the clustering pattern, though quite low in some years but even unique to year 2010 with significant high z value (1.14) and very low p-v

Keywords

Inverse distance weightingSpatial analysisStatisticsGeographyWeightingSpatial epidemiologyCluster (spacecraft)Mathematics

Chat

Click to start Chat