Development of a network based model to simulate the between-farm transmission of the porcine reproductive and respiratory syndrome virus

Thakur, Krishna K.; Sánchez, Javier; Hurnik, Daniel; Poljak, Zvonimir; Opps, Sheldon B.; Revie, Crawford W.

doi:10.1016/j.vetmic.2015.09.010

Cited by 25 publications

(33 citation statements)

References 38 publications

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“…Although network studies have often been motivated by the outcome of animal movements on pathogen epidemiology (Keeling, 2005;Rautureau et al, 2012;Buttner et al, 2015;Thakur et al, 2015), the specific role of animal shipments in pathogen transmission and/or exposure has rarely been quantified, especially in the swine sector. The primary advantage of our study lies in combining epidemiology and network analysis to quantify both the impact of animal movements on pathogen prevalence within farms and the risk of areas being exposed to diseases due to between-area movements.…”

Section: Discussionmentioning

confidence: 99%

Combining network analysis with epidemiological data to inform risk-based surveillance: Application to hepatitis E virus (HEV) in pigs

Salines

Andraud

Rose

2018

Preventive Veterinary Medicine

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Animal movements between farms are a major route of pathogen spread in the pig production sector. This study aimed to pair network analysis and epidemiological data in order to evaluate the impact of animal movements on pathogen prevalence in farms and assess the risk of local areas being exposed to diseases due to incoming movements. Our methodology was applied to hepatitis E virus (HEV), an emerging foodborne zoonotic agent of concern that is highly prevalent in pig farms. Firstly, the pig movement network in France (data recorded in 2013) and the results of a nation-wide seroprevalence study (data collected in 178 farms in 2009) were modelled and analysed. The link between network centrality measures of farms and HEV seroprevalence levels was explored using a generalised linear model. The in-degree and ingoing closeness of farms were found to be statistically associated with high HEV within-farm seroprevalence (p<0.05). Secondly, the risk of a French département (i.e. French local administrative areas) being exposed to HEV was calculated by combining the distribution of farm-level HEV prevalence in source départements with the number of movements coming from those same départements. By doing so, the risk of exposure for départements was mapped, highlighting differences between geographical patterns of HEV prevalence and the risk of exposure to HEV. These results suggest that not only highly prevalent areas but also those having at-risk movements from infected areas should be monitored. Pathogen management and surveillance options in the pig production sector should therefore take animal movements into consideration, paving the way for the development of targeted and risk-based disease surveillance strategies.

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Section: Discussionmentioning

confidence: 99%

Combining network analysis with epidemiological data to inform risk-based surveillance: Application to hepatitis E virus (HEV) in pigs

Salines

Andraud

Rose

2018

Preventive Veterinary Medicine

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“…In addition to direct contact among farms via animal movements, indirect contact may occur between farms due to windborne propagation of aerosols and dissemination of fomites by personnel, contaminated vehicles, and feed (Alonso et al, 2014;Alvarez et al, 2016;Beam et al, 2015;Dee et al, 2014;Kim et al, 2017;Lowe et al, 2014;O'Dea et al, 2015;Pasick et al, 2014). Although the potential importance of such mechanisms in creating transmission opportunities between swine premises has been shown in experimental studies and outbreak investigations (Alonso et al, 2014;Bowman et al, 2015;Lowe et al, 2014;Pasick et al, 2014), indirect contact is less often accounted for in epidemiological models (Arruda et al, 2016;Martinez-Lopez et al, 2011;Thakur et al, 2015;Yadav et al, 2016). Models of pathogen spread in livestock populations focus primarily on animal movement and, in some cases, local spatial spread based on proximity between premises (Brooks-Pollock et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Role of animal movement and indirect contact among farms in transmission of porcine epidemic diarrhea virus

et al. 2018

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Epidemiological models of the spread of pathogens in livestock populations primarily focus on direct contact between farms based on animal movement data, and in some cases, local spatial spread based on proximity between premises. The roles of other types of indirect contact among farms is rarely accounted for. In addition, data on animal movements is seldom available in the United States. However, the spread of porcine epidemic diarrhea virus (PEDv) in U.S. swine represents one of the best documented emergences of a highly infectious pathogen in the U.S. livestock industry, providing an opportunity to parameterize models of pathogen spread via direct and indirect transmission mechanisms in swine. Using observed data on pig movements during the initial phase of the PEDv epidemic, we developed a network-based and spatially explicit epidemiological model that simulates the spread of PEDv via both indirect and direct movement-related contact in order to answer unresolved questions concerning factors facilitating between-farm transmission. By modifying the likelihood of each transmission mechanism and fitting this model to observed epidemiological dynamics, our results suggest that between-farm transmission was primarily driven by direct mechanisms related to animal movement and indirect mechanisms related to local spatial spread based on geographic proximity. However, other forms of indirect transmission among farms, including contact via contaminated vehicles and feed, were responsible for high consequence transmission events resulting in the introduction of the virus into new geographic areas. This research is among the first reports of farm-level animal movements in the U.S. swine industry and, to our knowledge, represents the first epidemiological model of commercial U.S. swine using actual data on farm-level animal movement.

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“…It was shown that the new infections were driven by the trading 23 behavior, and that the removing rate determined whether the virus could survive long 24 enough to infect others. Thakur et al [14] developed an agent-based, stochastic, 25 farm-level, state transition computer simulation model with the incorporation of 26 network-structure among the swine farms for the between farm spread of porcine 27 reproductive and respiratory syndrome (PRRS) virus to assess the impact of contact 28 structures on between-farm transmission of PRRS virus. It was shown that scenarios 29 considering only direct contacts resulted in lower overall epidemic sizes than that for 30 scenarios in which both direct contact and indirect contact network were considered.…”

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confidence: 99%

Understanding the effects of contact structures and information sharing on the FMD transmission among beef cattle farms

Yang

Scoglio

2020

Preprint

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Moving infected animals and sharing contaminated equipment and vehicles are considered as the most effective ways for between-farm disease transmission. The objective of this study is to develop a network-based simulation model to investigate the effects of direct contact, indirect contact, and their combination on a hypothetical foot-and-mouth disease spread between beef-cattle farms in southwest Kansas, the US.

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Development of a network based model to simulate the between-farm transmission of the porcine reproductive and respiratory syndrome virus

Cited by 25 publications

References 38 publications

Combining network analysis with epidemiological data to inform risk-based surveillance: Application to hepatitis E virus (HEV) in pigs

Combining network analysis with epidemiological data to inform risk-based surveillance: Application to hepatitis E virus (HEV) in pigs

Role of animal movement and indirect contact among farms in transmission of porcine epidemic diarrhea virus

Understanding the effects of contact structures and information sharing on the FMD transmission among beef cattle farms

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