Spatio-temporal network analysis of pig trade to inform the design of risk-based disease surveillance

Cárdenas, Nicolás Céspedes; VanderWaal, Kimberly; Veloso, Flávio Pereira; Galvis, Jason A.; Amaku, Marcos; Grisi-Filho, José Henrique Hildebrand

doi:10.1016/j.prevetmed.2021.105314

Cited by 13 publications

(22 citation statements)

References 43 publications

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“…An unexpected result was that for small ruminants, random removal of nodes had a better impact in reducing transmission than using PageRank network metric, partly because this network is more disconnected compared to the other host-networks which leads to PageRank failing to recognize important farms for the spread of disease. Previous studies that also utilized large datasets and similar node-removal approaches but in single host networks, reported similar results as ours, especially for bovine networks [ 33 , 35 , 44 , 45 ] and swine networks [ 21 , 37 , 41 ]. Studies that considered more than one host species and network-based target control actions reported a larger reduction in the expected number of cases directly generated by one case in a population, size of the connected components, and the number of infected farms using degree-based interventions [ 10 , 14 ].…”

Section: Discussionsupporting

confidence: 85%

“…When evaluating the cluster coefficients, bovine and swine movements remain steady while small ruminants have large fluctuations. The swine network centralization was much higher than bovine or small ruminants, which could be related to how the industry is organized with the formation of groups of producers with commercial contracts with an integrator [ 21 , 37 ]. For the GSCC and GWCC, the bovine monthly-snapshot network followed the same temporal patterns with the nodes indicating an important dependency in the number of active farms, while small ruminants showed a seasonality with a higher percentage of GSCC and GWCC at the end of each year.…”

Section: Resultsmentioning

confidence: 99%

“…This is likely because of the pyramidal structure commercial pig production utilizes to improve performance and productivity. Because of such continuous flow, nurseries and wean to finishers tend to receive and send a great number of pigs, thus have a higher degree in comparison with other production types [ 21 , 37 , 39 ]. In contrast, the network connectivity in bovine farms was low despite having more than 20 000 edges and high node activity in a monthly view.…”

Section: Discussionmentioning

confidence: 99%

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Multiple species animal movements: network properties, disease dynamics and the impact of targeted control actions

Cárdenas

Sykes

Lopes

et al. 2022

Vet Res

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Infectious diseases in livestock are well-known to infect multiple hosts and persist through a combination of within- and between-host transmission pathways. Uncertainty remains about the epidemic dynamics of diseases being introduced on farms with more than one susceptible host species. Here, we describe multi-host contact networks and elucidate the potential of disease spread through farms with multiple hosts. Four years of between-farm animal movement among all farms of a Brazilian state were described through a static and monthly snapshot of network representations. We developed a stochastic multilevel model to simulate scenarios in which infection was seeded into single host and multi-host farms to quantify disease spread potential, and simulate network-based control actions used to evaluate the reduction of secondarily infected farms. We showed that the swine network was more connected than cattle and small ruminants in both the static and monthly snapshots. The small ruminant network was highly fragmented, however, contributed to interconnecting farms, with other hosts acting as intermediaries throughout the networks. When a single host was initially infected, secondary infections were observed across farms with all other species. Our stochastic multi-host model demonstrated that targeting the top 3.25% of the farms ranked by degree reduced the number of secondarily infected farms. The results of the simulation highlight the importance of considering multi-host dynamics and contact networks while designing surveillance and preparedness control strategies against pathogens known to infect multiple species.

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Section: Discussionsupporting

confidence: 85%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Multiple species animal movements: network properties, disease dynamics and the impact of targeted control actions

Cárdenas

Sykes

Lopes

et al. 2022

Vet Res

Self Cite

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“…In formula ( 11 ), f ( x ) represents the current particle; f ( x +1) represents the particle after random disturbance; and g ( x ) represents the way to represent random disturbances. In the process of random disturbance, the state of particles before and after disturbance obeys Markov state distribution, so when the temperature is the same, the number of particle search processes can be set as Markov chain length L [ 39 ]. It can be seen from Table 1 that the BP neural network and simulated annealing algorithm have commonness and similar characteristics, so they can be combined to build the SA-BP neural network.…”

Section: Construction Of the Obesity Monitoring Model Based On Sa Algorithmmentioning

confidence: 99%

[Retracted] Obesity Mass Monitoring in Medical Big Data Based on High‐Order Simulated Annealing Neural Network Algorithm

Ren

Zhao

2021

Computational Intelligence and Neuroscience

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With the rapid development of information technology, hospital informatization has become the general trend. In this context, disease monitoring based on medical big data has been proposed and has aroused widespread concern. In order to overcome the shortcomings of the BP neural network, such as slow convergence speed and easy to fall into local extremum, simulated annealing algorithm is used to optimize the BP neural network and high-order simulated annealing neural network algorithm is constructed. After screening the potential target indicators using the random forest algorithm, based on medical big data, the experiment uses high-order simulated annealing neural network algorithm to establish the obesity monitoring model to realize obesity monitoring and prevention. The results show that the training times of the SA-BP neural network are 1480 times lower than those of the BP neural network, and the mean square error of the SA-BP neural network is 3.43 times lower than that of the BP neural network. The MAE of the SA-BP neural network is 1.81 times lower than that of the BP neural network, and the average output error of the obesity monitoring model is about 2.35 at each temperature. After training, the average accuracy of the obesity monitoring model was 98.7%. The above results show that the obesity monitoring model based on medical big data can effectively complete the monitoring of obesity and has a certain contribution to the diagnosis, treatment, and early warning of obesity.

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“…This is especially true for national efforts to move from a country-wide disease-free to vaccine-free FMD status, which is expected to be achieved by 2023, as infected animals can move from one region to another with disease-free and/or geographically distant status. In this context, contact network analysis is a powerful tool for describing, predicting and evaluating the role of trade in the spread of disease, producing important data for understanding, preventing and mitigating possible outbreaks in the region [7].…”

Section: Introductionmentioning

confidence: 99%

Distribution of Ascariosis and Asophagostomosis of Pigs in Central Yakutia

Reshetnikov

Kozlova

Barashkova

2021

Fundamental and Applied Scientific Research in the Development of Agriculture in the Far East (AFE-2021)

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Purpose of research: To study the spread of ascariosis and esophagostomosis of pigs in industrial pig breeding and in farms of various types in Central Yakutia. Materials and methods: The study of the species composition of pathogens and the spread of intestinal nematodes was carried out in 2001-2004. In industrial pig farm and subsidiary plots pigs were tested for infection helminthological diseases. For this, a saturated solution of sodium chloride was prepared (400-420 g of sodium chloride per 1 L of boiling water. 3 g of faeces were weighed according to a sample, which were placed in a glass with a capacity of 50 ml, were poured with a small amount of a saturated solution of sodium chloride and stirred. 3 drops were removed from the film surface with a metal loop, transferred to a glass slide and examined under a microscope with low magnification. A total of 1236 samples of pig feces were examined, including 262 young animals from 2 to 4 months and 974 adult pigs. Results and discussion: The pigs of Yakutia are parasitized by 2 types of nematodes: Ascaris suum, Oesophagostomum dentatum.

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Spatio-temporal network analysis of pig trade to inform the design of risk-based disease surveillance

Cited by 13 publications

References 43 publications

Multiple species animal movements: network properties, disease dynamics and the impact of targeted control actions

Multiple species animal movements: network properties, disease dynamics and the impact of targeted control actions

[Retracted] Obesity Mass Monitoring in Medical Big Data Based on High‐Order Simulated Annealing Neural Network Algorithm

Distribution of Ascariosis and Asophagostomosis of Pigs in Central Yakutia

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