Network analysis of pig movement data as an epidemiological tool: an Austrian case study

Puspitarani, Gavrila A.; Fuchs, Reinhard; Fuchs, Klemens; Ladinig, Andrea; Desvars-Larrive, Amélie

doi:10.1038/s41598-023-36596-1

Cited by 2 publications

(1 citation statement)

References 89 publications

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“…Further, when stochastic network transmission models are used, information can be retrieved for early warning systems and to determine the spatial extent of the epidemic (5). Epidemic patterns depend on the interplay between the structure of the networks and the characteristics of the epidemic (6,7). The models’ assumptions, structure, complexity, and reliability depend on the data’s quality and degree of detail used to reconstruct the network and the information concerning the disease under study (4).…”

Section: Introductionmentioning

confidence: 99%

Assessing the impact of structural modifications in the construction of surveillance network for transboundary animal diseases: the role of backbone and sentinel nodes

Mesdour,

Ijoma,

Bolajoko

et al. 2024

Preprint

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Peste des Petits Ruminants (PPR) is a highly contagious disease that mainly affects sheep and goats and is transmitted through livestock movements. Because of its socio-economic impact, the Food and Agriculture Organisation (FAO) and the World Organization for Animal Health (WOAH) have set the goal to eradicate it by 2030, one of the key steps being the improvement of surveillance networks. The present study aimed to provide tools to identify areas that could serve as sentinel nodes, i.e. areas that may be rapidly infected at the onset of epidemics. Using data from a market survey conducted in the Northern Region of Nigeria, we analyzed which nodes, under which conditions, could serve as sentinel nodes. We considered several modified networks to get around the problem of data only being available for part of the overall network structure and to account for potential errors made during the field study. For each configuration, we simulated the spread of PPR using a stochastic Susceptible-Infectious (SI) model based on animal movements to assess the epidemics' extent and the presence of recurrent patterns to identify potential sentinel nodes. We extracted the backbone of the reference network and checked for the presence of sentinel nodes within it. We then explored the possibility of using the backbone nodes as sentinel nodes. We investigated how the origin (seed) of the epidemics could affect the propagation pattern by comparing and grouping seeds based on their respective transmission paths. Results showed that the isolated backbone contains 45% sentinel nodes that remain stable or undergo only minor changes in 9 out of 11 configurations. On top of that, the characteristics of sentinel nodes identified in the backbone are not influenced by the severity of the disease. The H index, in-degree, and eigenvector are the most essential variables. This study provides an overview of the major axes of animal movements in Nigeria and the most vulnerable locations that should be prioritized for monitoring livestock diseases like PPR.

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Section: Introductionmentioning

confidence: 99%

Assessing the impact of structural modifications in the construction of surveillance network for transboundary animal diseases: the role of backbone and sentinel nodes

Mesdour,

Ijoma,

Bolajoko

et al. 2024

Preprint

View full text Add to dashboard Cite

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Investigating the impact of edge weight selection on the pig trade network topology

Puspitarani,

Liao,

Fuchs

et al. 2024

Preprint

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Traceability of animal movements and robust surveillance are crucial for identifying and controlling animal diseases. Risk-based surveillance, i.e. network-based approaches, can identify higher-risk holdings or trades. However, node ranking, useful to identify "influential" nodes (holdings) in the network, varies with the considered metrics. We use a dataset of pig movements in Upper Austria from 2021 to study the robustness of node ranking through three centrality metrics and compare them with epidemic model ranking. Incorporating edge weights may influence the network analysis, therefore, we simulate two representations using edge weights based on: i) the frequency of exchanges between holdings ("frequency-based") and ii) the number of pigs exchanged ("volume-based"). We compare the impact of the edge weight on the network topology, community structure, and node ranking in a network with 5,766 nodes and 92,914 edges. Results revealed distinct edge weight distributions: frequency-based network exhibited a bimodal pattern, while volume-based was more uniform. Strength centrality exhibited the highest correlation with simulation-based rankings, particularly for the top 5% highest-ranked nodes (tau b = 0.51 for frequency-based and tau b = 0.5 for volume-based). These findings highlight that using strength centrality to identify critical nodes can significantly enhance surveillance strategies, making them more efficient and field-deployable, enhancing traditional methods without requiring extensive simulations.

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Network analysis of pig movement data as an epidemiological tool: an Austrian case study

Cited by 2 publications

References 89 publications

Assessing the impact of structural modifications in the construction of surveillance network for transboundary animal diseases: the role of backbone and sentinel nodes

Assessing the impact of structural modifications in the construction of surveillance network for transboundary animal diseases: the role of backbone and sentinel nodes

Investigating the impact of edge weight selection on the pig trade network topology

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