Towards the description of livestock mobility in Sahelian Africa: Some results from a survey in Mauritania

Abstract

Understanding spatio-temporal patterns of host mobility is a key factor to prevent and control animal and human diseases. This is utterly important in low-income countries, where animal disease epidemics have strong socio-economic impacts. In this article we analyzed a livestock mobility database, whose data have been collected by the Centre National d'Elevage et de Recherches Vétérinaires (CNERV) Mauritania, to describe its patterns and temporal evolution. Data were collected through phone and face-to-face interviews in almost all the regions in Mauritania over a period of roughly two weeks during June 2015. The analysis has shown the existence of two mobility patterns throughout the year: the first related to routine movements from January to August; the second strictly connected to the religious festivity of Tabaski that in 2014 occurred at the beginning of October. These mobility patterns are different in terms of animals involved (fewer cattle and dromedaries are traded around Tabaski), the means of transportation (the volume of animals moved by truck raises around Tabaski) and destinations (most of the animals are traded nationally around Tabaski). Due to the differences between these two periods, public health officers, researchers and other stakeholders should take account of the time of the year when implementing vaccination campaigns or creating surveillance networks.

Fig 1. Survey design and locations.

Mauritanian and neighboring-country locations are indicated by red and green dots, respectively. Wilaya colors refer to the interview type: (i) face-to-face surveys in Trarza, Brakna, Gorgol, Guidimaka and Tagant (first team, light brown); Hodh Ech Chargui, Hodh El Gharbi and Assaba (second team, yellow); (ii) phone surveys: Adrar, Inchiri, Tiris, Zemmour and Nouadhibou (orange). Map done using R version 3.3.1 (https://www.r-project.org).

Fig 2. Distributions of the degree and average link’s volume.

(A) degree distribution for the aggregated network: the network follows a power law distribution P(k) ≈ k^−γ, γ = −1.71 (B) Average link’s volume distribution: the network follows a power law distribution P(w) ≈ w^−η, η = −1.33.

Fig 3. Distance distribution by type of destination activity.

Histogram corresponds to the number of movements among locations at a certain distance, solid lines to volume exchanged by locations at a certain distance. Color is related to the destination activity type.

Fig 4. Number (top) and volume (bottom) of movements along the year by features (column): Type of movement (A,B), species (C,D).

Fig 5. International and national monthly volume of small ruminants moved by truck or on foot.

The solid lines correspond to international movements, the dashed one to national movements. The points shapes correspond to movement by truck (triangle and cross) or foot (circle and square).

Fig 6. Mobility network.

A) Chord Diagram for the static representation of the network. The size of the sector is proportional to the volume towards the location, color of the band indicates the origin of the flux B) Dominant flows of animals national (black) and international (blue). Map done using R version 3.3.1 (https://www.r-project.org).

Fig 7. Network variation along the year.

A) Temporal variation for the Jaccard indexes of nodes, links and links’ volume B) Identification of mobility periods.

Fig 8. Mobility network along the year according to the species.

Each line corresponds to the network specific to a species. Left column, network during the first period. Right column, network around Tabaski period. Color indicates the volume moved along the connection, normalized to the maximum volume for the period of the year. Maps done using R version 3.3.1 (https://www.r-project.org).

Fig 9. Geographical representation for the four networks.

(A) Backbone, (B) Frequent, (C) Intermediate, (D) Occasional, and (E) Link activity along the year: each line corresponds to a link; a segment is drawn every month a link is active; colors correspond to the frequency they appear during the year, and consequently to the network they belong to. Maps done using R version 3.3.1 (https://www.r-project.org).