Network analysis of pig movements in Argentina: identification of key farms in the spread of diseases and relationship with their biosecurity level

Alarcón, Laura; Cipriotti, Pablo Ariel; Monterubbianessi, Mariela; Perfumo, Carlos Juan; Mateu, Enric; Allepuz, Alberto

doi:10.3389/conf.fvets.2019.05.00006

Cited by 2 publications

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“…Here, most farms of interest lacked important biosecurity features, such as cleaning and disinfection stations (Dee et al., 2006). A study in Argentina also highlighted the lack of biosecurity on highly connected farms (Alarcón et al., 2019). On the other hand, in our study, most farms did not utilize rendering, but preferred to compost dead animals.…”

Section: Discussionmentioning

confidence: 99%

Quantifying the dynamics of pig movements improves targeted disease surveillance and control plans

Machado¹,

Galvis²,

Lopes

et al. 2020

Transbound Emerg Dis

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Tracking animal movements over time can fundamentally determine the success of disease control interventions throughout targeting farms that are tightly connected. In commercial pig production, animals are transported between farms based on growth stages, thus it generates time-varying contact networks that will influence the dynamics of disease spread. Still, risk-based surveillance strategies are mostly based on a static network. In this study, we reconstructed the static and temporal pig networks of one Brazilian state from 2017 to 2018, comprising 351,519 movements and 48 million transported pigs. The static networks failed to capture time-respecting movement pathways. Therefore, we propose a time-dependent network susceptible-infected (SI) model to simulate the temporal spread of an epidemic over the pig network globally through the temporal movement of animals among farms, and locally with a stochastic compartmental model in each farm, configured to calculate the minimum number of target farms needed to achieve effective disease control. In addition, we propagated disease on the pig temporal network to calculate the cumulative contacts as a proxy of epidemic sizes and evaluated the impact of network-based disease control strategies. The results show that targeting the first 1,000 farms ranked by degree would be sufficient and feasible to diminish disease spread considerably. Our finding also suggested that assuming a worst-case scenario in which every movement transmit disease, pursuing farms by degree would limit the transmission to up to 29 farms over the two years period, which is lower than the number of infected farms for random surveillance, with epidemic sizes of 2,593 farms. The top 1,000 farms could benefit from enhanced biosecurity plans and improved surveillance, which constitute important next steps in strategizing targeted disease control .

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Section: Discussionmentioning

confidence: 99%

Quantifying the dynamics of pig movements improves targeted disease surveillance and control plans

Machado¹,

Galvis²,

Lopes

et al. 2020

Transbound Emerg Dis

View full text Add to dashboard Cite

show abstract

“…Here, most 465 farms of interest lacked important biosecurity features, such as cleaning and disinfection stations 466 (Dee et al, 2006). A study in Argentina also highlighted the lack of biosecurity on highly 467 connected farms (Alarcón et al, 2019). On the other hand, in our study, most farms did not 468 utilize rendering, but preferred to compost dead animals.…”

mentioning

confidence: 58%

“…Therefore, we argue that 493 the causal fidelity of the temporal network should be presented before considering the use of the 494 static networks in the development of disease surveillance or control strategies (Lentz et al, 495 2016;Payen et al, 2019). Recent studies did not account for this issue (Büttner et al, 2013, 496 2018; Marquetoux et al, 2016;Motta et al, 2017;Alarcón et al, 2019), or considered the 497 temporal network pathway accessibility of each farm. In summary, these studies used static 498 networks to disassemble networks into small islands, which possibly overestimates the size of 499 outbreaks systematically, and therefore, the results should be interpreted with caution.…”

mentioning

confidence: 99%

Quantifying the dynamics of pig movements improves targeted disease surveillance and control plans

Machado¹,

Galvis²,

Lopes³

et al. 2019

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SummaryTracking animal movements over time can fundamentally determine the success of disease control interventions throughout targeting farms that are tightly connected. In commercial pig production, animals are transported between farms based on growth stages, thus it generates time-varying contact networks that will influence the dynamics of disease spread. Still, risk-based surveillance strategies are mostly based on a static network. In this study, we reconstructed the static and temporal pig networks of one Brazilian state from 2017 to 2018, comprising 351,519 movements and 48 million transported pigs. The static networks failed to capture time-respecting movement pathways. Therefore, we propose a time-dependent network susceptible-infected (SI) model to simulate the temporal spread of an epidemic over the pig network globally through the temporal movement of animals among farms, and locally with a stochastic compartmental model in each farm, configured to calculate the minimum number of target farms needed to achieve effective disease control. In addition, we propagated disease on the pig temporal network to calculate the cumulative contacts as a proxy of epidemic sizes and evaluated the impact of network-based disease control strategies. The results show that targeting the first 1,000 farms ranked by degree would be sufficient and feasible to diminish disease spread considerably. Our finding also suggested that assuming a worst-case scenario in which every movement transmit disease, pursuing farms by degree would limit the transmission to up to 29 farms over the two years period, which is lower than the number of infected farms for random surveillance, with epidemic sizes of 2,593 farms. The top 1,000 farms could benefit from enhanced biosecurity plans and improved surveillance, which constitute important next steps in strategizing targeted disease control interventions. Overall, the proposed modeling framework provides a parsimonious solution for targeted disease surveillance when temporal movement data is available.

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Network analysis of pig movements in Argentina: identification of key farms in the spread of diseases and relationship with their biosecurity level

Cited by 2 publications

References 0 publications

Quantifying the dynamics of pig movements improves targeted disease surveillance and control plans

Quantifying the dynamics of pig movements improves targeted disease surveillance and control plans

Quantifying the dynamics of pig movements improves targeted disease surveillance and control plans

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