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OBJECTIVE: To determine the sub-national seasonal prevalence and trends in wasting from 2007 to 2010 among children aged 6-59 months in Somalia using remote sensing and household survey data from nutritional surveys. METHODS: Bayesian hierarchical space-time model was implemented using a stochastic partial differential equation (SPDE) approach in integrated nested Laplace approximations (INLA) to produce risk maps of wasting at 1 × 1 km(2) spatial resolution and predict to seasons in each year of study from 2007 to 2010. RESULTS: The prevalence of wasting was generally at critical levels throughout the country, with most of the areas remaining in the upper classes of critical and very critical levels. There was minimal variation in wasting from year-to-year, but a well-defined seasonal variation was observed. The mean difference of the prevalence of wasting between the dry and wet season ranges from 0% to 5%. The risks of wasting in the South Central zone were highest in the Gedo (37%) and Bay (32%) regions. In North East zone the risk was highest in Nugaal (25%) and in the North West zone the risk was high in Awdal and Woqooyi Galbeed regions with 23%. CONCLUSION: There was a clear seasonal variation in wasting with minimal year-to-year variability from 2007 to 2010 in Somalia. The prevalence was high during the long dry season, which affects the prevalence in the preceding long rainy season. Understanding the seasonal fluctuations of wasting in different locations and at different times is important to inform timely interventions.

Original publication




Journal article


Spat Spatiotemporal Epidemiol

Publication Date





77 - 87


Bayesian, Malnutrition, Seasonality, Space–time model, Wasting, Bayes Theorem, Child, Preschool, Female, Humans, Infant, Male, Prevalence, Rain, Risk Factors, Seasons, Somalia, Spatio-Temporal Analysis, Wasting Syndrome