A full Bayesian methodology has been constructed for assessing the representativeness of surveillance networks, focusing on the INDEPTH network (www.indepth-network.org) in sub-Saharan Africa as an example. We develop a multi-dimensional finite Gaussian mixture model for clustering the existing sites. Using the fitted model we obtain the posterior predictive probability distribution of the cluster membership of each 1 × 1 km grid cell in Africa, the maximum of which is proposed as the criterion for the representativeness of the network for that particular grid cell. The analysis using the INDEPTH network is based on eight gridded socioeconomic and environmental variables/layers for Africa. From the analysis, five clusters of the sites were obtained as shown in Fig. 1. The areas represented by the clusters are displayed in the bottom-centre image, with the dark areas in the bottom right map highlighting low probability values that are poorly represented by the existing network and these constitute where additions to the network will help improve its coverage (see, e.g., the green circles).
Andy Tatem, Edson Utazi
WorldPop, Geography and Environment University of Southampton
(023) 8059 2636
Bill and Melinda Gates Foundation, INDEPTH network, CHAMPS network
Utazi et al (2016) A probabilistic predictive Bayesian approach for determining the representativeness of health and demographic surveillance networks, Spatial Statistics