SEEDNet: Covariate-free multi-country settlement-level epidemiological estimates datasets for network analysis

The study of population health through network science holds high promise, but data sources that allow complete representation of populations are limited in low-and middle-income countries. Large national health surveys designed to gather nationally representative health and development data are promising data sources but are not designed to produce small-area estimates of health indicators. Methods for producing these from national surveys tend to rely on varied covariate data sources and are computationally demanding, limiting their use for network representations of populations. To reduce the sources of measurement error and allow efficient multi-country representation of populations as networks of human settlements here, we present SEEDNet (Settlement-level Epidemiological Estimates Datasets for Network Analysis) ¹ , a data library of multi-country representations of population health across human settlements. Our covariate-free method uses georeferenced national surveys to produce SAEs of health indicators through local inverse-distance weighted interpolation and includes an algorithm for the comprehensive identification of population settlements of all sizes across the globe. Our estimates are cross-validated against those obtained using a Bayesian Geostatistical Model. The method is fully automated, requiring a single standard georeferenced survey data source for mapping populations, eliminating the need for indicator or country-specific covariate selection by investigators. Computational efficiency is achieved by restricting computation to human-occupied areas and by adopting a logical aggregation of estimates into the complete range of settlement sizes. Standardized georeferenced settlement-level datasets for 15 indicators and 10 countries were validated in this paper, as well as the novel method to identify settlements. SEEDNet ¹ is a specialized library of nodes that can serve as a basis for network representations of population health in low-and middle-income countries.