Quantifying aggregated uncertainty in Plasmodim falciparum malaria prevalence and populations at risk via efficient space-time geostatistical joint simulation
Risk maps estimating the spatial distribution of infectious diseases are required to guide public health policy from local to global scales. The advent of model-based geostatistics (MBG) has allowed these maps to be generated in a formal statistical framework, providing robust metrics of map uncertainty that enhances their utility for decision-makers. In many settings, decision-makers require spatially aggregated measures over large regions such as the mean prevalence within a country or admi...Expand abstract
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- Peer reviewed
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- Gething et al.
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- Citation: Gething, P. W., Patil, A. P. & Hay, S. I. (2010). 'Quantifying aggregated uncertainty in Plasmodium falciparum malaria prevalence and populations at risk via efficient space-time geostatistical joint simulation', PLoS Comput Biol 6(4): e1000724 [Available at http://www.ploscompbiol.org]. © 2010 Gething et a. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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