(Shapefiles - Validation countries) SEEDNet: A covariate-free multi-country settlement-level database of epidemiological estimates for network analysis

To reduce the sources of measurement error and allow efficient multi-country representation of populations as networks of human settlements here, we present a covariate-free multi-country method to estimate small-area health indicators using standardized georeferenced surveys. The approach utilizes interpolation via local inverse distance weighting. The estimates are compared to those obtained using a Bayesian Geostatistical Model and have been cross-validated.

The estimates are aggregated into population settlements and identified using the Global Human Settlement Layer database. 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. Efficient estimation is achieved by only computing values for human-occupied areas and adopting a logical aggregation of estimates into the complete range of settlement sizes.

An open-access library of standardized georeferenced settlement-level datasets for 15 indicators and 10 countries was validated in this paper, as well as the code used to identify settlements and estimate indicators. The datasets are intended to be used as the basis for population health studies, and the library will continue to be expanded. The novel aspects include using harmonized input sources and estimation procedures across countries and the adoption of real-world units for population data aggregation, creating a specialized library of nodes that serve as a basis for network representations of population health in low- and middle-income countries.