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.

Modelling resource-driven movements of livestock herds to predict the impact of climate change on network dynamics

“…While two distinct seasons, wet and dry, are generally recognised, rainfall patterns are becoming increasingly inconsistent and unpredictable, resulting in long periods without rain even in the wet seasons and extended dry seasons and droughts [9,25]. Ekwem et al [15] showed that the change in rainfall patterns drove unplanned livestock movements, especially during drought. As temperature and precipitation are predicted to increase in the future, and East Africa is more influenced than most other parts of the world [7][8][9]25], we hypothesise that the stresses on resources induced by climate change are likely to result in an overall more connected livestock population, with implications for infectious disease risks and therefore human and animal health.…”

“…To project future patterns we considered the impact of changes in the seasonal pattern of movements caused by changes in precipitation, due to its influence on the availability of pasturelands and water. Previous studies have shown that the frequency of use of resource areas is related to resource availability, which in turn is influenced by season [5, 15]. While two distinct seasons, wet and dry, are generally recognised, rainfall patterns are becoming increasingly inconsistent and unpredictable, resulting in long periods without rain even in the wet seasons and extended dry seasons and droughts [9, 25].…”

“…The observed movement data was based on community participatory mapping, where local farmers identified resource areas on maps and reported movements [15]. This method is subject to recall bias, with the possibility of erroneous reports [27, 28].…”

“…Quantifying this impact requires models for livestock movement patterns that consider the impact of resources constraints. Most methods for quantifying livestock movement patterns rely on intensive data collection [11,12], augmented by statistical modelling approaches [13][14][15]. Recently, network simulation modelling approaches have greatly facilitated the generation and evaluation of contact patterns between animal populations.…”

“…We focused on contacts at communal livestock resource areas (grazing and watering points) in an agropastoralist setting, where movements occur daily. Based on the summary statistics of the network of connections between 95 villages through the use of communal resources in the Serengeti district ( [15]), we used approximate Bayesian computation (ABC) to estimate the parameters of the movement model (Section 4.4). We evaluated the performance of the model by comparing the networks generated from the posterior predictive distribution of the fitted movement model with the observed networks (Sections 2.1 and 2.2).…”