Network modelling analysis of the current and future role of livestock movements in the spread of infectious disease in northern Tanzania
Creators: name: Sulaimon, Tijani Ahmad
Publisher: name: The University of Edinburgh
Year: 2024
Subjects: network analysis
disease dynamics
livestock movements
Climate change
networks
zoonoses
network modelling
Tanzania
interventions
targeted control intervention

description: Livestock movements contribute to the geographic spread of many diseases, including Rift Valley fever (RVF) — a vaccine-preventable arboviral fever. In East Africa, most livestock movements are made for trade and to access essential resources for survival. Where dense comprehensive data on livestock movements are available, these can be harnessed to guide policy on targeted interventions for controlling infectious livestock diseases. However, livestock movement data in Tanzania are not routinely collected or centrally recorded, limiting their use to guide disease control programmes. The available movement data are imperfect, and the impact of this uncertainty in the utility of movement data on informing the targeting of intervention is not fully understood. Moreover, climate change in East Africa, including Tanzania, is likely to have a profound impact on livestock management and, consequently, on the potential spread of infectious diseases within and between animal and human populations. Climate change in the coming decades could affect the availability and distribution of these key resources, potentially impacting the direction and extent of resource-driven livestock movements and, therefore, altering patterns of infectious disease spread. As livestock are a vital part of the local economy, any substantial change in the risk of infectious diseases will have a considerable impact on human and animal health. Thus, understanding the spatial pattern of contacts between populations and how they might change in the future is essential for effective disease control. This study developed three models to understand the impact of livestock movements on RVF transmission. In Chapter 2, a network simulation model of RVF transmission between administrative areas in northern Tanzania was used to investigate how uncertainty in market movement data affects the targeting of vaccination. In Chapter 4, I developed a network model to describe livestock movements to grazing and watering sites, fitted it to data from the Serengeti district of Tanzania, and used it to explore how projected changes in resource availability due to climate change could impact future network structures, using 2050 and 2080 as exemplar scenarios. Lastly, in Chapter 5, I developed a climate-driven vector-host model to assess the potential impacts of changing climate and livestock movement patterns on future RVF transmission. In Chapter 2, I showed that pre-emptive vaccination guided by market movement permit data alone could prevent large outbreaks. Targeting control (either at locations of high risk of RVF introduction or onward transmission) at any level of imperfect movement information is preferred over random vaccination, and any improvement in information reliability is advantageous to their effectiveness. Using the resource-driven network model in Chapter 4, I showed that climate change could increase connections between villages in both grazing and watering networks in all future scenarios, potentially increasing the propagation of infectious diseases among livestock populations. A larger increase in modelled connectivity is observed further in the future, corresponding to progressively larger changes in vegetation availabil- ity. Furthermore, the results suggest that villages currently identifiable by network centrality measures for disease control may be less valuable targets for intervention in the future as overall connectivity increases and thus their central nature diminishes. Finally, the climate-driven vector-host model presented in Chapter 5 predicts that the mean cumulative incidence of new RVF infections among cattle will increase from 2.7% (mean = 12 751, sd = 285) in 2016 to 7.8% (mean = 36 620, sd = 272) in 2050 and further to 14.6% (mean = 68 778, sd = 90) in 2080. I tested the hypothesis that targeted interventions may become less effective due to increased village connectivity due to climate-induced resource scarcity. The findings show that vaccination, especially when targeted using betweenness centrality, was the most effective strategy. The effectiveness of movement bans, while generally inferior to vaccination, also varied significantly depending on the network structure, the targeting strategy, and the proportion of targeted villages, suggesting that movement restrictions could still play a role under specific conditions. Increased connectivity diminishes distinctions between the effectiveness of network-based strategies, resulting in outcomes more similar to random interventions. The findings will be useful for policymakers in northern Tanzania, who have limited resources to mitigate the burden of livestock and zoonotic pathogens. Moreover, the generic nature of the approaches developed in this study holds promise for application in other developing countries.