Prediction of the spread of highly pathogenic avian influenza using a multifactor network: Part 2 – Comprehensive network analysis with direct/indirect infection route

Hyung-jin, Lee; Suh, Kyo; Jung, Nam-Su; Lee, In-Bok; Seo, Il Won; Moon, Oun-Kyung; Lee, Jeong-jae

doi:10.1016/j.biosystemseng.2013.11.009

Cited by 15 publications

(17 citation statements)

References 39 publications

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“…International trade of live birds day-old chicks [28,29] hatching eggs [28] chickens [30,31] ducks, turkeys [31] Illegal trade of live birds spent hens, ducklings, day-old chicks [27] National movements day old duckling, hatching eggs duck [72,73] ostrich chicks [72] Movements to poultry show chicken, duck [72] Fomite movements Slaughterhouses vehicles ducks [41] poultry [42,50,51,71] broiler chickens [47,70,74] layers [47,74] Catching team or bird pick up poultry [42,48,71] broiler chickens [46,47,55,56,74] layers [46,47,55,56,74] turkeys and broiler ducks [46] ready to lay parents and parents and grandparents stock [46] Feed deliveries poultry [50,52,71] broiler chickens [43,49,54,65,68,70,73,…”

Section: International Movementsmentioning

confidence: 99%

Knowledge and remaining gaps on the role of animal and human movements in the poultry production and trade networks in the global spread of avian influenza viruses – A scoping review

2020

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Poultry production has significantly increased worldwide, along with the number of avian influenza (AI) outbreaks and the potential threat for human pandemic emergence. The role of wild bird movements in this global spread has been extensively studied while the role of animal, human and fomite movement within commercial poultry production and trade networks remains poorly understood. The aim of this work is to better understand these roles in relation to the different routes of AI spread. A scoping literature review was conducted according to the PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) using a search algorithm combining twelve domains linked to AI spread and animal/human movements within poultry production and trade networks. Only 28 out of 3,978 articles retrieved dealt especially with the role of animal, human and fomite movements in AI spread within the international trade network (4 articles), the national trade network (8 articles) and the production network (16 articles). While the role of animal movements in AI spread within national trade networks has been largely identified, human and fomite movements have been considered more at risk for AI spread within national production networks. However, the role of these movements has never been demonstrated with field data, and production networks have only been partially studied and never at international level. The complexity of poultry production networks and the limited access to production and trade data are important barriers to this knowledge. There is a need to study the role of animal and human movements within poultry production and trade networks in the global spread of AI in partnership with both public and private actors to fill this gap.

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Section: International Movementsmentioning

confidence: 99%

Knowledge and remaining gaps on the role of animal and human movements in the poultry production and trade networks in the global spread of avian influenza viruses – A scoping review

2020

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“…The contact patterns among farms form a social network. Social network analysis (SNA) has been studied using both animal movement or trade networks of poultry (Van et al ., 2009; Martin et al ., 2011 a ; Hosseini et al ., 2013; Lee et al ., 2014; Moyen et al ., 2018) and other animals (Nöremark et al ., 2011; Lebl et al ., 2016). Networks can be presented by graphs, adjacency matrices, or a set of pairs.…”

Section: Small-data Modelingmentioning

confidence: 99%

“…Several metrics, such as centrality measures (Lee et al ., 2014; Moyen et al ., 2018) are calculated to highlight AI introduction or spread risks in a defined network among chickens or flocks. In SNA model used in AI, a node usually represents a flock, market, or trader while an edge demonstrates a connection, usually movement, between those nodes.…”

Section: Small-data Modelingmentioning

confidence: 99%

“…Therefore, transmission models have been used in AI modeling to generate hypotheses on the impact of control measures and find optimal prevention solutions (Mannelli et al ., 2007; Mulatti et al ., 2010; Lee et al ., 2014; Backer et al ., 2015). Between-flock transmission models in AI have used probability-based (Dorigatti et al ., 2010; Ssematimba et al ., 2012; Backer et al ., 2015), agent-based (Patyk et al ., 2013; Lewis et al ., 2017), and network-based (Van et al ., 2009; Lee et al ., 2014) approaches. Probability-based methods follow a top-down approach to estimate a kernel function that usually combines disease dynamics and distance between farms.…”

Section: Small-data Modelingmentioning

confidence: 99%

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A review of knowledge discovery process in control and mitigation of avian influenza

Naghani

Dara

Poljak

et al. 2019

Anim. Health. Res. Rev.

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In the last several decades, avian influenza virus has caused numerous outbreaks around the world. These outbreaks pose a significant threat to the poultry industry and also to public health. When an avian influenza (AI) outbreak occurs, it is critical to make informed decisions about the potential risks, impact, and control measures. To this end, many modeling approaches have been proposed to acquire knowledge from different sources of data and perspectives to enhance decision making. Although some of these approaches have shown to be effective, they do not follow the process of knowledge discovery in databases (KDD). KDD is an iterative process, consisting of five steps, that aims at extracting unknown and useful information from the data. The present review attempts to survey AI modeling methods in the context of KDD process. We first divide the modeling techniques used in AI into two main categories: data-intensive modeling and small-data modeling. We then investigate the existing gaps in the literature and suggest several potential directions and techniques for future studies. Overall, this review provides insights into the control of AI in terms of the risk of introduction and spread of the virus.

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“…Seo et al used computational fluid dynamics to estimate the dispersion of the virus attached to aerosols produced by livestock [2] . Lee et al constructed a direct HPAI spread network based on the relationships between farms using poultry-related business data and an indirect HPAI spread network using the aerial spread from each farm during the HPAI outbreak in 2008 [3]. Tuncer and Le studied the effects of air travel on the spread of avian influenza from Asian and Australian cities to the United States.…”

Section: Introductionmentioning

confidence: 99%

Propagation Routes Analysis of HPAI Outbreaks using Sequential Pattern Mining

Xu¹

2016

Proceedings of the Fourth International Conference on Information Science and Cloud Computing — PoS(ISCC2015)

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Highly pathogenic avian influenza (HPAI) virus can spread rapidly, resulting in high mortality and severe economic damage. To minimize the damage incurred from such diseases, it is necessary to develop technology for analysing livestock disease and predicting livestock disease propagation. In this study, we propose a novel big data analytics model using extensive volumes of livestock disease occurrence data accumulated over an extended period. In particular, we describe a sample process based on a specific scenario that elicits information that generates sequential dissemination routes of HPAI outbreaks by applying sequential pattern mining in this paper. 2015 18-19, December, ISCC

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Prediction of the spread of highly pathogenic avian influenza using a multifactor network: Part 2 – Comprehensive network analysis with direct/indirect infection route

Cited by 15 publications

References 39 publications

Knowledge and remaining gaps on the role of animal and human movements in the poultry production and trade networks in the global spread of avian influenza viruses – A scoping review

Knowledge and remaining gaps on the role of animal and human movements in the poultry production and trade networks in the global spread of avian influenza viruses – A scoping review

A review of knowledge discovery process in control and mitigation of avian influenza

Propagation Routes Analysis of HPAI Outbreaks using Sequential Pattern Mining

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