Comparing Livestock Mobility-Informed Strategies for Peste des Petits Ruminants Control in Nigeria: The Central Role of the Network Backbone

  1. Asma Mesdour
  2. Sandra Ijoma
  3. Muhammad-Bashir Bolajoko
  4. Mamadou Ciss
  5. Stephen Eubank
  6. Eric Cardinale
  7. Mathieu Andraud
  8. Andrea Apolloni

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Abstract

Animal mobility is central to pastoral livelihoods and regional trade in West Africa, but it also facilitates the spread of transboundary animal diseases such as Peste des petits ruminants (PPR). In Nigeria, PPR outbreaks recur regularly, yet surveillance and control remain limited in the absence of routine animal-movement tracking. Here, we assess and compare movement-informed control options for PPR using a reconstructed livestock mobility network from a one-time market survey conducted in three northern Nigerian states. We simulate transmission on this network and evaluate three intervention strategies: (i) targeting vulnerable villages, (ii) targeting the links that connect movement communities, and (iii) targeting villages belonging to the network backbone. Across scenarios, backbone-based targeting consistently produced the largest reductions in network connectivity and epidemic outcomes, outperforming strategies focused on vulnerable nodes or inter-community links. These results suggest that backbone-informed control could provide a practical, resource-efficient pathway to strengthen PPR control in settings where routine movement data are scarce.

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  1. Peer Community in Animal Science

    Peste des Petits Ruminants (PPR) remains a major transboundary animal disease affecting small-ruminant production systems, particularly in regions where livestock mobility is central to trade and pastoral livelihoods. In such settings, routine movement traceability is often limited, making it difficult to identify priority targets for surveillance and control. Network-based approaches are therefore especially valuable because they can help reveal the locations and connections that contribute disproportionately to disease spread (Gates & Woolhouse, 2015).

    In this study, Mesdour et al. (2026) investigate whether movement-informed network structures can be used to improve the efficiency of PPR control in Nigeria. Using a reconstructed weighted and directed small-ruminant movement network based on market survey data from three northern Nigerian states (Ijoma et al., 2025), the authors compare six intervention scenarios: four based on the removal of vulnerable village sets defined by disease severity, one based on cutting links between contagion clusters, and one based on targeting villages belonging to the network backbone. Contagion clusters were identified using the COCLEA algorithm (Nath et al., 2019), and the backbone was extracted using the disparity filter method (Serrano et al., 2009; Neal, 2022).

    A major strength of the work is its comparative design. Rather than evaluating a single candidate strategy in isolation, the authors systematically assess alternative intervention scenarios using both network metrics and epidemic simulations. They examine the effects of each scenario on the largest connected component, global efficiency, and total flow, and then compare their consequences for final epidemic size under an SIR framework. This integrated approach allows a clear and operationally meaningful comparison of control options.

    The results are consistent and compelling. Interventions targeting vulnerable-node groups or inter-cluster bridge links produced only limited changes in network robustness and epidemic size, whereas targeting backbone villages caused a marked collapse in network connectivity and functionality. In particular, removal of backbone nodes reduced the largest connected component from 229 to 25 nodes and reduced the number of links from 355 to 28, alongside a sharp decrease in both global efficiency and total flow. Epidemic simulations further showed that backbone-based intervention produced by far the strongest reduction in final epidemic size.

    The manuscript is clearly structured, well written, and methodologically coherent. The research question is relevant, the analytical framework is transparent, and the conclusions are well supported by the reported results. The authors also acknowledge important limitations, including the use of a static network representation and the constraints associated with reconstructed movement data, while outlining sensible directions for future work. In addition, the availability of data and scripts strengthens the transparency and reproducibility of the study.

    From an applied animal health perspective, this study provides useful evidence that control efforts focused on backbone villages may offer a more efficient and realistic way to prioritize surveillance, vaccination, and movement management in resource-constrained settings. The work therefore makes a meaningful contribution to the epidemiology and control of transboundary livestock diseases. For these reasons, I recommend this preprint for PCI Animal Science.

     

    References

    Mesdour A, Ijoma SI, Bolajoko M-B, Ciss M, Eubank S, Cardinale E, Andraud M, Apolloni A. 2026. Comparing Livestock Mobility-Informed Strategies for Peste des Petits Ruminants Control in Nigeria: The Central Role of the Network Backbone. bioRxiv, 2025.12.16.693877, ver. 4 peer-reviewed and recommended by Peer Community in Animal Science.  https://doi.org/10.64898/2025.12.16.693877. 

    Gates MC, Woolhouse MEJ. 2015. Controlling infectious disease through the targeted manipulation of contact network structure. Epidemics 12: 11–19. https://doi.org/10.1016/j.epidem.2015.02.008. 

    Ijoma SI, Mesdour A, Bolajoko M-B, et al. 2025. Combining market surveys and participative approaches to map small ruminant mobility in three selected states in northern Nigeria. PLOS ONE 20(9): e0311030. https://doi.org/10.1371/journal.pone.0311030. 

    Nath M, Venkatramanan S, Kaperick B, et al. 2019. Using Network Reliability to Understand International Food Trade Dynamics. 812: 524–535. 

    Neal ZP. 2022. Backbone: An R Package to Extract Network Backbones. PLOS ONE 17(5): e0269137. https://doi.org/10.1371/journal.pone.0269137. 

    Serrano MA, Boguñá M, Vespignani A. 2009. Extracting the multiscale backbone of complex weighted networks. Proceedings of the National Academy of Sciences 106(16): 6483–6488. https://doi.org/10.1073/pnas.0808904106.

     

  2. Version published to 10.64898/2025.12.16.693877 on bioRxiv