Abstract
Peste des Petits Ruminants (PPR) is a highly contagious disease affecting sheep and goats and spreading through livestock movements. The FAO and WOAH have set the goal to eradicate it by 2030, with one of the key steps being the improvement of surveillance networks. In our study, we utilize an SI model to simulate the spread of PPR across three Nigerian States due to animal movements. Our objective is to identify areas that could serve as potential sentinel nodes, which are nodes that may become infected promptly at the onset of epidemics. We reconstructed the mobility network using (the partial) movement data collected through market surveys. Due to the limitations of the study, we implement plausible network modifications and generated eight distinct network configurations. For each of these configurations, we assessed the extent of the epidemics, the presence of recurrent patterns and characterize sentinel nodes. Our findings indicate that minor network modifications do not impact the final sizes of epidemics. However, removing a central market or introducing a significant number of new links can lead to noticeable shifts in epidemic propagation trajectories. Regardless of the specific configurations considered, we identified three clusters of epidemic origins, each exhibiting distinct behaviors. Nevertheless, geographical proximity among these origins does not necessarily imply similar transmission patterns. Furthermore, our study highlights that, in most configurations, attributes such as the in-H index, eigenvector and in-degree centralities emerge as the most influential factors in defining vulnerable nodes. Therefore, we recommend prioritizing data collection efforts in central markets due to their wealth of information resources
M. Ciss, M. Andraud, A. Apolloni and E. Cardinale—contributed equally.
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Mesdour, A. et al. (2024). Towards the Building of a Surveillance Network for PPR-Like Diseases in Nigeria: Identifying Potential Sentinel Node in a Partially-Known Network. In: Cherifi, H., Rocha, L.M., Cherifi, C., Donduran, M. (eds) Complex Networks & Their Applications XII. COMPLEX NETWORKS 2023. Studies in Computational Intelligence, vol 1142. Springer, Cham. https://doi.org/10.1007/978-3-031-53499-7_26
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