A local space–time kriging approach applied to a national outpatient malaria data set
Gething, P.W., Atkinson, P.M., Noor, A.M., Gikandi, P.W., Hay, S.I. and Nixon, M.S. (2007) A local space–time kriging approach applied to a national outpatient malaria data set. Computers & Geosciences, 33, 1337-1350. (doi:10.1016/j.cageo.2007.05.006).
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Increases in the availability of reliable health data are widely recognised as essential for efforts to strengthen health-care
systems in resource-poor settings worldwide. Effective health-system planning requires comprehensive and up-to-date
information on a range of health metrics and this requirement is generally addressed by a Health Management
Information System (HMIS) that coordinates the routine collection of data at individual health facilities and their
compilation into national databases. In many resource-poor settings, these systems are inadequate and national databases
often contain only a small proportion of the expected records. In this paper, we take an important health metric in Kenya
(the proportion of outpatient treatments for malaria (MP)) from the national HMIS database and predict the values of MP
at facilities where monthly records are missing. The available MP data were densely distributed across a spatiotemporal
domain and displayed second-order heterogeneity. We used three different kriging methodologies to make cross-validation
predictions of MP in order to test the effect on prediction accuracy of (a) the extension of a spatial-only to a space–time
prediction approach, and (b) the replacement of a globally stationary with a locally varying random function model.
Space–time kriging was found to produce predictions with 98.4% less mean bias and 14.8% smaller mean imprecision than
conventional spatial-only kriging. A modification of space–time kriging that allowed space–time variograms to be
recalculated for every prediction location within a spatially local neighbourhood resulted in a larger decrease in mean
imprecision over ordinary kriging (18.3%) although the mean bias was reduced less (87.5%).
|Keywords:||space–time geostatistics, local kriging, malaria, public health, Kenya|
|Subjects:||G Geography. Anthropology. Recreation > G Geography (General)
G Geography. Anthropology. Recreation > GE Environmental Sciences
G Geography. Anthropology. Recreation > GB Physical geography
|Divisions:||University Structure - Pre August 2011 > School of Geography > Remote Sensing and Spatial Analysis
|Date Deposited:||01 Aug 2008|
|Last Modified:||06 Aug 2015 02:44|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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