Background: Millions of Somali have been deprived of basic health services due to the unstable political situation
of their country. Attempts are being made to reconstruct the health sector, in particular to estimate the extent of
infectious disease burden. However, any approach that requires the use of modelled disease rates requires
reasonable information on population distribution. In a low-income country such as Somalia, population data are
lacking, are of poor quality, or become outdated rapidly. Modelling methods are therefore needed for the
production of contemporary and spatially detailed population data.
Results: Here land cover information derived from satellite imagery and existing settlement point datasets were
used for the spatial reallocation of populations within census units. We used simple and semi-automated methods
that can be implemented with free image processing software to produce an easily updatable gridded population
dataset at 100 × 100 meters spatial resolution. The 2010 population dataset was matched to administrative
population totals projected by the UN. Comparison tests between the new dataset and existing population
datasets revealed important differences in population size distributions, and in population at risk of malaria
estimates. These differences are particularly important in more densely populated areas and strongly depend on
the settlement data used in the modelling approach.
Conclusions: The results show that it is possible to produce detailed, contemporary and easily updatable
settlement and population distribution datasets of Somalia using existing data. The 2010 population dataset
produced is freely available as a product of the AfriPop Project and can be downloaded from: http://www.afripop.
org.