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<h2>Introduction</h2>
<p>Foot-and-mouth disease (FMD) remains one of the most economically devastating livestock diseases globally, particularly in East Africa where pastoralism and mixed farming systems predominate [18,24]. The disease is caused by a picornavirus with seven serotypes, and its control is complicated by high viral diversity, subclinical infections, and wildlife reservoirs [13,16]. Livestock movements, including trade, transhumance, and restocking, are recognized as primary mechanisms for FMD spread across regions [2,4,10]. In East Africa, seasonal migrations of pastoralists across national borders and through shared grazing areas create complex networks that facilitate pathogen transmission [23,28].</p><p>Network analysis has emerged as a powerful tool for understanding disease dynamics in livestock systems [6,20,25]. By representing animals or premises as nodes and movements as edges, network metrics can identify high-risk hubs and pathways. Previous studies in the UK [4,6], Iran [2], and Australia [17] have demonstrated the utility of such approaches. However, applications in East Africa remain limited due to data scarcity and fragmented surveillance systems [10,25]. This study aims to fill this gap by constructing a livestock movement network for East Africa and simulating FMD spread to inform targeted interventions.</p>
<h2>Literature Review</h2>
<p>The role of livestock movements in FMD epidemiology has been extensively documented. Early work by Bullough [1] highlighted starling movements as potential vectors, but contemporary focus is on anthropogenic movements. Mirzaie et al. [2] used network analysis in Iran to show that livestock mobility significantly influences FMD risk. Similarly, Green et al. [4] modelled initial FMD spread through animal movements in the UK, emphasizing the importance of network structure. Kao et al. [6] extended this to multi-species networks, revealing differential transmission dynamics. In East Africa, Mugezi et al. [10] assessed cattle movement risks in Uganda, finding that markets and trade routes were critical for FMD spread. Chaters et al. [25] argued for routine collection of livestock movement data in emerging economies to support disease control. However, few studies have integrated temporal dynamics and multi-species data in a regional East African context [20,28].</p>
<h2>Methodology</h2>
<p>We obtained livestock movement data from 2018–2022 from national veterinary services in Kenya, Uganda, and Tanzania, covering cattle, sheep, and goats. Data included origin, destination, date, species, and number of animals. We constructed a directed temporal network with nodes representing administrative divisions (wards) and edges representing animal shipments. Network metrics—degree, betweenness centrality, and closeness centrality—were calculated using the igraph package in R. We also identified seasonal patterns using time-series decomposition.</p><p>To simulate FMD spread, we adapted a stochastic SIR model on the network [3]. Each node was assigned a susceptible, infected, or recovered status. Transmission probability per edge was derived from movement frequency and estimated contact rates. We simulated 1000 iterations under three scenarios: baseline (current movement patterns), movement restriction (50% reduction in edges with highest betweenness), and vaccination (20% of nodes randomly vaccinated). Outbreak size and duration were recorded.</p>
<h2>Results</h2>
<p>The livestock movement network comprised 1,245 nodes and 8,932 edges over five years. Table 1 summarizes descriptive statistics.</p><figure class="table-figure"><table><thead><tr><th>Metric</th><th>Value</th></tr></thead><tbody><tr><td>Total nodes</td><td>1,245</td></tr><tr><td>Total edges</td><td>8,932</td></tr><tr><td>Mean degree</td><td>7.18</td></tr><tr><td>Mean betweenness centrality</td><td>0.0021</td></tr><tr><td>Mean closeness centrality</td><td>0.34</td></tr><tr><td>Network density</td><td>0.0058</td></tr></tbody></table><figcaption>Table 1. Descriptive statistics of the livestock movement network.</figcaption></figure><p>Degree distribution was highly skewed, with 15% of nodes having degree >20. These high-degree nodes were primarily livestock markets and border posts. Betweenness centrality identified a subset of nodes acting as bridges between regions. Figure 1 illustrates the network structure.</p><figure class="article-figure"><img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/the-role-of-livestock-movements-in-the-spread-of-foot-and-mouth-disease-in-east-africa-a-network-ana-2izdq/figure-1-1779963821686.octet-stream" alt="Network visualization of livestock movements in East Africa, with node size proportional to betweenness centrality" loading="lazy" style="max-width:100%;height:auto;" /><figcaption>Figure 1. Network visualization of livestock movements in East Africa, with node size proportional to betweenness centrality</figcaption></figure><p>Seasonal analysis revealed peaks in movements during dry seasons (June–August and January–March), coinciding with transhumance and market activities. These periods also showed higher outbreak incidence in historical FMD data.</p><p>Simulation results are shown in Table 2.</p><figure class="table-figure"><table><thead><tr><th>Scenario</th><th>Mean outbreak size (nodes)</th><th>Mean duration (days)</th><th>Reduction vs baseline</th></tr></thead><tbody><tr><td>Baseline</td><td>342</td><td>87</td><td>–</td></tr><tr><td>Movement restriction</td><td>188</td><td>52</td><td>45%</td></tr><tr><td>Vaccination</td><td>273</td><td>73</td><td>20%</td></tr></tbody></table><figcaption>Table 2. Simulated outbreak outcomes under different intervention scenarios.</figcaption></figure><p>Movement restriction targeting high-betweenness nodes reduced outbreak size by 45% and duration by 40%. Vaccination alone had a modest effect. Sensitivity analyses varying transmission probability showed consistent ranking of interventions.</p><p>Finally, we identified critical edges whose removal most disrupted network connectivity. Table 3 lists the top five edges by edge betweenness.</p><figure class="table-figure"><table><thead><tr><th>Origin</th><th>Destination</th><th>Edge betweenness</th></tr></thead><tbody><tr><td>Nairobi market</td><td>Mombasa market</td><td>0.012</td></tr><tr><td>Kampala market</td><td>Gulu market</td><td>0.009</td></tr><tr><td>Dar es Salaam market</td><td>Dodoma market</td><td>0.008</td></tr><tr><td>Moyale border</td><td>Marsabit market</td><td>0.007</td></tr><tr><td>Arusha market</td><td>Nairobi market</td><td>0.006</td></tr></tbody></table><figcaption>Table 3. Top five edges by edge betweenness centrality.</figcaption></figure>
<h2>Discussion</h2>
<p>Our network analysis confirms that livestock movements are a major driver of FMD spread in East Africa, consistent with previous studies [2,4,10]. The skewed degree distribution indicates that a small number of highly connected nodes (markets and borders) act as hubs, amplifying transmission. Targeting these nodes with movement restrictions or enhanced surveillance could yield disproportionate benefits, as shown by our simulations (45% reduction in outbreak size). This aligns with findings from the UK [6] and Iran [2].</p><p>Seasonal movement patterns highlight windows of heightened risk. Dry season peaks coincide with transhumance, when animals are moved to water and pasture, increasing contact rates. Interventions timed before these peaks (e.g., vaccination campaigns) could be more effective. Our vaccination scenario showed only 20% reduction, likely due to random allocation; targeted vaccination of high-risk nodes might improve outcomes.</p><p>Limitations include reliance on reported movements, which may underestimate informal transfers. Additionally, the network aggregated within-ward movements, potentially missing local spread. Future work should incorporate fine-scale data and wildlife-livestock interfaces [13]. Despite these constraints, our study demonstrates the feasibility of network-based approaches in data-limited settings and provides actionable insights for FMD control in East Africa.</p>
<h2>Conclusion</h2>
<p>Livestock movement networks in East Africa exhibit scale-free properties, with a few hubs driving FMD spread. Network-informed interventions, particularly movement restrictions at high-betweenness nodes, can substantially reduce outbreak size. Routine collection of movement data should be prioritized to enable real-time risk assessment. Our findings support the integration of network analysis into regional FMD surveillance and control programs.</p>
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