Description of the Cattle and Small Ruminants Trade Network in Senegal and Implication for the Surveillance of Animal Diseases

Abstract

Livestock mobility, particularly that of small and large ruminants, is one of the main pillars of production and trade in West Africa: livestock is moved around in search of better grazing or sold in markets for domestic consumption and for festival-related activities. These movements cover several thousand kilometers and have the capability of connecting the whole West African region, thus facilitating the diffusion of many animal and zoonotic diseases. Several factors shape mobility patterns even in normal years and surveillance systems need to account for such changes. In this paper, we present an approach based on temporal network theory to identify possible sentinel locations, i.e., locations where pathogens circulation can be detected in the early phase of the epidemic (before the peak), using two indicators: vulnerability (i.e., the probability of being reached by the disease) and time of infection (i.e., the time of first arrival of the disease). Using these indicators in our structural analysis of the changing network enabled us to identify a set of nodes that could be used in an early warning system. As a case study, we simulated the introduction of transboundary animal diseases in Senegal and used data taken from 2020 Sanitary certificates (laissez-passer sanitaire (LPS)) issued by the Senegalese Veterinary Services to reconstruct the national mobility network. Our analysis showed that a static approach can significantly overestimate the speed and the extent of disease propagation, whereas temporal analysis revealed that the reachability and vulnerability of the different administrative departments (used as nodes of the mobility network) change over the course of the year. For this reason, several sets of sentinel nodes were identified in different periods of the year, underlining the role of temporality in shaping patterns of disease diffusion.

Figure 1

(a) An example of a directed temporal network and its static counterpart. The dark links are those in the temporal snapshot, while the pale links are those that are possible but are not present in the temporal snapshot. (b) Simple simulation of disease spread in the temporal network (on the left) and the static network (on the right).

Figure 2

Distribution of the volume of animals in the administrative departments of Senegal, according to whether the department is the origin (a) or the destination (b) of livestock movement. The miniature pie charts show the percentage of cattle (yellow) and small ruminants (green) in the total number of animals. Quartiles were chosen for the colors representing the volume of animals traded. Only countries that account for at least 1% of exports (a) or imports (b) are shown.

Figure 3

Number of trade movements (line plot) and volume of livestock traded (bar plot) recorded in 2020 per species and per month. Data concerning cattle are in yellow, and data concerning small ruminants are in green. The dashed line represents the Tabaski festival (July 31), and the dotted line represents the Grand Magal of Touba festival (October 6).

Figure 4

Clustering of livestock network. (a) Spatial representation of nodes colored according to the cluster to which they belong. The size of each dot indicates the volume of animals traded over the course of the year and the division is made in quantiles. (b) Temporal representation of trade by the three clusters over the course of the year, in terms of the volume of animals traded. Imported animals are represented as positive numbers, and exported animals are represented as negative numbers. The dashed line represents the Tabaski festival on July 31, and the dotted line represents the Grand Magal of Touba festival on October 6.

Figure 5

Geographical representation of the livestock network links by the frequency of their activity over the year. Backbone links were active in more than 9 months, frequent links were active between 4 and 9 months, intermediate links were active for 2 or 3 months, and occasional links were active for only 1 month of the study period. We decided to only show the administrative departments of Senegal on these maps. Therefore, concerning national trade, the origin and the destination are both Senegalese departments, while for international trades, they may be a Senegalese department or a central point in a foreign country.

Figure 6

Logarithmic of the epidemic threshold over the course of the year in the three livestock mobility networks. Results for the small ruminants network are in yellow, the cattle network in green, and the combined livestock network in blue.

Figure 7

Geographical representation of infection time in the case of disease propagation from Mali. For each mobility network, the first column on the left represents the spread in the static network, and the other seven columns represent the seven worst scenarios of transmission if that specific week represents the beginning of the disease spread. The colors indicate the infection time of the disease: red for less than 1 month, orange for less than 2 months, yellow for more than 2 months, and green for nodes that have never been touched in 1 year time. For the static networks in the first column on the left, the colors are based on the number of links in the path: up to five in red, and green for nodes that have never been touched in 1 year time. Week 31 identifies the week of the Tabaski festival and week 41 identifies the week of the Grand Magal of Touba festival.