Local and wide-scale livestock movement networks inform disease control strategies in East Africa

Abstract

Livestock mobility exacerbates infectious disease risks across sub-Saharan Africa, but enables critical access to grazing and water resources, and trade. Identifying locations of high livestock traffic offers opportunities for targeted control. We focus on Tanzanian agropastoral and pastoral communities that account respectively for over 75% and 15% of livestock husbandry in eastern Africa. We construct networks of livestock connectivity based on participatory mapping data on herd movements reported by village livestock keepers as well as data from trading points to understand how seasonal availability of resources, land-use and trade influence the movements of livestock. In communities that practise agropastoralism, inter- and intra-village connectivity through communal livestock resources (e.g. pasture and water) was 1.9 times higher in the dry compared to the wet season suggesting greater livestock traffic and increased contact probability. In contrast, livestock from pastoral communities were 1.6 times more connected at communal locations during the wet season when they also tended to move farther (by 3 km compared to the dry season). Trade-linked movements were twice more likely from rural to urban locations. Urban locations were central to all networks, particularly those with potentially high onward movements, for example to abattoirs, livestock holding grounds, or other markets, including beyond national boundaries. We demonstrate how livestock movement information can be used to devise strategic interventions that target critical livestock aggregation points (i.e. locations of high centrality values) and times (i.e. prior to and after the wet season in pastoral and agropastoral areas, respectively). Such targeted interventions are a cost-effective approach to limit infection without restricting livestock mobility critical to sustainable livelihoods.

Figure 1

Spatial livestock movement networks of village connectivity showing variation in contact patterns across seasons and production systems [(a) agropastoral and (b) pastoral)] investigated in northern Tanzania. The nodes (circles) are villages in their geographical position. The grey edges represent shared resource areas among connected villages (degree). The node size is proportional to the network centrality measure betweenness for the village and colours relate to the centrality measure eigenvector with red being the highest value (see Supplementary Materials, Table S2 for detailed definitions of centrality measures).

Figure 2

Distance and number of connections between villages that share resource areas by month and across livestock management systems. (a) Between-village distance across seasons, including a period of drought. The vertical bars are confidence intervals of the mean. (b) Changes in the number of contacts between villages every month across production systems. In the study sites, the period March—May typically corresponds to the long wet season, while from July to September the long dry season occurs. The months of November and December generally typify the short wet season, which in this study failed in the pastoral setting and was replaced by a drought period that extended into January–February, while the agropastoral setting was typified by the short dry season that typically occurs at this time.

Figure 3

Flow and traffic of traded livestock in the Mara region, northern Tanzania. The five districts where livestock markets were located and traded from are indicated in uppercase. The axis has colours (which is unique for each district or town where cattle were either traded in or moved to), and a scale (which starts from 0 and with each unit representing 800 livestock). The scale on the axis indicates the number of livestock moved. Outgoing livestock flow from each trading point (origin) starts from the higher point on the axis or base of the district where markets were located, while the in-flow into the district or town (destination) is closer to the axis. For example, the axis for Serengeti district shows that 30,400 livestock were transacted, approximately 22,400 were out flow with the majority to Tarime district, and 8000 in flow. Likewise, the axis of Tarime district shows that 46,000 livestock were transacted, approximately 7200 were traded in Tarime and remained in the district, and the rest were inflow from other districts. The black coloured arrows indicate major movements from other districts to Tarime, while the red coloured arrow indicates that almost all of the traded livestock in Tarime were received or remained in the district. The green coloured arrow shows other major towns or cities where livestock were moved within the Mara and neighbouring regions.

Figure 4

Spatial location of villages with the highest centrality measures based on a full directed network of origin and destination of traded livestock in the Mara region of northern Tanzania. All measures were weighted and normalised (see Supplementary Materials, Table S2 for detailed definitions of centrality measures). The shapefiles were based on 2012 Tanzania census, obtained from Tanzania National Bureau of Statistic. The map was developed in Quantum geographical information system (QGIS), version 3.18.