Characterization of Domestic Ruminant Movement Patterns in a Transfrontier Region of North-Eastern KwaZulu-Natal, South Africa

Introduction: Livestock movement patterns play a crucial role in animal and public health management, disease transmission and sustainable livestock farming. Understanding these patterns is vital for disease surveillance and preventing the spread of animal diseases.

Study Area: This study was conducted in the far north-eastern region of KwaZulu-Natal (KZN) province, South Africa, with Eswatini bordering to the west and Mozambique to the north. The study area is located at a wildlife–livestock interface and includes sections classified as a foot-and-mouth disease (FMD) control zone. Animal and animal product movements within, into and out of the area are restricted by state veterinary-issued movement permits.

Aims: The study aimed to quantitatively describe livestock movement characteristics within, into and out of the study area and identify potential hubs for disease transmission.

Study Design and Sampling Strategy: Data sources included official animal movement permit records (2015–2018) from the KZN Department of Agriculture and Rural Development, and the data are obtained via face-to-face interviews with livestock traders (August to November 2020). Traders’ data were used to complement the interpretation of the permit dataset and to understand the livestock movement patterns, especially from the perspective of traders who operate from our study area. The permit data offered a detailed record of official livestock movements over multiple years, enabling us to identify the movement trends. In contrast, the face-to-face interviews provided real-time insights from traders regarding informal movement trends and disruptions not reflected in the permit data. The permit dataset was used to construct stratified animal movement networks by species using social network analysis (SNA), treating dip tanks (origins) and the destination locations (municipalities, districts or provinces) as two disjoint sets before being projected into a one-mode network. Bipartite-specific statistics were computed to compare the constructed networks.

Results: A total of 3598 movements between 2015 and 2018, representing 33,561 animals, were recorded from the permit datasets. Additional 74 movements representing 3296 animals occurred in the traders’ dataset in 2020. Of the total number of animals moved, 64% were directed outside the study area. Overall, the network analysis highlighted distinct movement patterns for cattle and goats, with Ndlondlweni and Phelandaba dip tanks as the key nodes facilitating animal movements. These are both dip tanks with high centrality and highly connected hubs, with the potential for facilitating the transmission of diseases to the entire province and other places.

Conclusion: These findings contribute to a better understanding of livestock trade and animal movement dynamics for effective disease control and management. Two dip tanks emerged as high-frequency hubs for animal movements outside the study area, posing risks for disease transmission to the province and beyond. Intensifying surveillance in these areas is recommended to mitigate the spread of animal diseases. Veterinary authorities should enforce the use of animal movement permits by livestock traders for effective disease prevention and control.