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Open Access Highly Accessed Research article

Risk mapping of Rinderpest sero-prevalence in Central and Southern Somalia based on spatial and network risk factors

Angel Ortiz-Pelaez1*, Dirk U Pfeiffer1, Stefano Tempia2, F Tom Otieno3, Hussein H Aden2 and Riccardo Costagli2

Author Affiliations

1 Veterinary Epidemiology & Public Health Group, Department of Veterinary Clinical Sciences, The Royal Veterinary College, University of London, Hawkshead Lane, North Mymms, Hatfield, Herts, AL9 7TA, UK

2 Terra Nuova East Africa, Raphta Road n.87 (Westlands), Maisonette 14/15, PO Box 74916, 00200 Nairobi, Kenya

3 International Livestock Research Institute (ILRI), PO Box 30709, 00100 Nairobi, Kenya

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BMC Veterinary Research 2010, 6:22  doi:10.1186/1746-6148-6-22

Published: 28 April 2010

Abstract

Background

In contrast to most pastoral systems, the Somali livestock production system is oriented towards domestic trade and export with seasonal movement patterns of herds/flocks in search of water and pasture and towards export points. Data from a rinderpest survey and other data sources have been integrated to explore the topology of a contact network of cattle herds based on a spatial proximity criterion and other attributes related to cattle herd dynamics. The objective of the study is to integrate spatial mobility and other attributes with GIS and network approaches in order to develop a predictive spatial model of presence of rinderpest.

Results

A spatial logistic regression model was fitted using data for 562 point locations. It includes three statistically significant continuous-scale variables that increase the risk of rinderpest: home range radius, herd density and clustering coefficient of the node of the network whose link was established if the sum of the home ranges of every pair of nodes was equal or greater than the shortest distance between the points. The sensitivity of the model is 85.1% and the specificity 84.6%, correctly classifying 84.7% of the observations. The spatial autocorrelation not accounted for by the model is negligible and visual assessment of a semivariogram of the residuals indicated that there was no undue amount of spatial autocorrelation. The predictive model was applied to a set of 6176 point locations covering the study area. Areas at high risk of having serological evidence of rinderpest are located mainly in the coastal districts of Lower and Middle Juba, the coastal area of Lower Shabele and in the regions of Middle Shabele and Bay. There are also isolated spots of high risk along the border with Kenya and the southern area of the border with Ethiopia.

Conclusions

The identification of point locations and areas with high risk of presence of rinderpest and their spatial visualization as a risk map will be useful for informing the prioritization of disease surveillance and control activities for rinderpest in Somalia. The methodology applied here, involving spatial and network parameters, could also be applied to other diseases and/or species as part of a standardized approach for the design of risk-based surveillance activities in nomadic pastoral settings.