Predictive gravity models of livestock mobility in Mauritania: The effects of supply, demand and cultural factors

Abstract

Animal movements are typically driven by areas of supply and demand for animal products and by the seasonality of production and demand. As animals can potentially spread infectious diseases, disease prevention can benefit from a better understanding of the factors influencing movements patterns in space and time. In Mauritania, an important cultural event, called the Tabaski (Aïd el Kebir) strongly affects timing and structure of movements, and due to the arid and semi-arid climatic conditions, the season can also influence movement patterns. In order to better characterize the animal movements patterns, a survey was carried out in 2014, and those data were analysed here using social network analysis (SNA) metrics and used to train predictive gravity models. More specifically, we aimed to contrast the movements structure by ruminant species, season (Tabaski vs. Non-Tabaski) and mode of transport (truck vs. foot). The networks differed according to the species, and to the season, with a changed proportion of truck vs. foot movements. The gravity models were able to predict the probability of a movement link between two locations with moderate to good accuracy (AUC ranging from 0.76 to 0.97), according to species, seasons, and mode of transport, but we failed to predict the traded volume of those trade links. The significant predictor variables of a movement link were the human and sheep population at the source and origin, and the distance separating the locations. Though some improvements would be needed to predict traded volumes and better account for the barriers to mobility, the results provide useful predictions to inform epidemiological models in space and time, and, upon external validation, could be useful to predict movements at a larger regional scale.

Network analysis of regional livestock trade in West Africa

“…related work [20][21][22][23] having different purpose, geographic coverage and scale, and using different data collection and analysis methods. Because of these differences, it is possible existing research captured transhumance and/or trade-related, formal or informal movements, or different sections of the value chain.…”

“…Motta et al [20], on the other hand, studied the cattle trade network in Cameroon and discussed the implications of the network structure for regional disease spread. More recently, Apolloni et al [21] and Nicolas et al [22] mapped, characterized and attempted to predict livestock movements in Mauritania. All these efforts highlighted the importance of international livestock trade; however, all three used data collected in single countries.…”

Modeling tuberculosis transmission flow in China

“…The gravity model has been widely used to capture the spatial interaction pattern in epidemics and account for distance, population size and other factors [23,51,52]. However, this approach may be insu cient for dyadic OD ows where the origin and destination regions may interact with their neighbors.…”

The structure of livestock trade in West Africa

“…Yet, because network analysis relies on data, few studies have considered the quantitative study of trade networks in West Africa. To date, animal mobility network research has been conducted in Togo (Dean et al, 2013), Cameroon (Motta et al, 2017) and between Senegal and Mauritania (Apolloni et al, 2018;Nicolas et al, 2018, Jahel et al, 2020.…”