Modelling of paratuberculosis spread between dairy cattle farms at a regional scale

Beaunée, Gaël; Vergu, Elisabeta; Ezanno, Pauline

doi:10.1186/s13567-015-0247-3

Cited by 39 publications

(36 citation statements)

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“…When assessing the impact of paratuberculosis, its potential for spreading and efficacy of different surveillance and control measures, the problems of the lack of empirical data and scarce resources can be overcome to a certain extent by the use of different mathematical and computational methods. In the case of paratuberculosis, these methods were used to study: the disease dynamics at the herd level (23)(24)(25), between-herd transmission and spread potential (11,26), evaluation of the surveillance systems and control measures (27)(28)(29), and assessment of economic consequences of the disease and control measures (30)(31)(32)(33)(34).…”

Section: Introductionmentioning

confidence: 99%

Implications of Cattle Trade for the Spread and Control of Infectious Diseases in Slovenia

et al. 2020

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Section: Introductionmentioning

confidence: 99%

Implications of Cattle Trade for the Spread and Control of Infectious Diseases in Slovenia

et al. 2020

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“…As such, animal movement data have been increasingly studied using social network analysis (SNA) methods, with farms being considered as nodes, and animal movements between farms as links (Wasserman and Faust, 1994;Bigras-Poulin et al, 2006;Bigras-Poulin et al, 2007;Martínez-López et al, 2009;Natale et al, 2009;Ribbens et al, 2009;Nöremark et al, 2011;Lindstrom et al, 2012;Rautureau et al, 2012;Buttner et al, 2013;Dorjee et al, 2013;Guinat et al, 2016;Thakur et al, 2016). Although in most studies network analyses have been motivated by the consequences of animal trade on the epidemiology of animal diseases (Keeling, 2005;Lloyd-Smith et al, 2005;Bigras-Poulin et al, 2007;Martínez-López et al, 2009;Rautureau et al, 2012;Buttner et al, 2013), the specific role of animal shipments in pathogen transmission and/or exposure has only scarcely been documented and rarely quantified, especially in the swine sector (Ortiz-Pelaez et al, 2006;Green et al, 2008;Martin et al, 2011;Porphyre et al, 2011;Frössling et al, 2012;Nicolas et al, 2013;Beaunee et al, 2015;Lee et al, 2017;Salines et al, 2017b;Sintayehu et al, 2017). Analysing contact patterns related to pig trade could provide new insight into infection dynamics, pathogen spread and risk factors, helping to design risk-based surveillance programmes.…”

Section: Introductionmentioning

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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“…The model of MAP spread accounts only for between‐herds spread through animal trade movements, which represents the main pathway. The regional model consists in coupling numerous (one per farm) stochastic within‐farm epidemiological models through cattle trade movements.…”

Section: Case Studies: Bvdv and Map Spread Simulatorsmentioning

confidence: 99%

A service‐based framework for building and executing epidemic simulation applications in the cloud

Parlavantzas

Pham

Morin

et al. 2019

Concurrency and Computation

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The cloud has emerged as an attractive platform for resource-intensive scientific applications, such as epidemic simulators. However, building and executing such applications in the cloud presents multiple challenges, including exploiting elasticity, handling failures, and simplifying multi-cloud deployment. To address these challenges, this paper proposes a novel service-based framework called DiFFuSE that enables simulation applications with a bag-of-tasks structure to fully exploit cloud platforms. This paper describes how the framework is applied to restructure two legacy applications, simulating the spread of bovine viral diarrhea virus and Mycobacterium avium subspecies paratuberculosis, into elastic cloud-native applications. Experimental results show that the framework enhances application performance and allows exploring different cost-performance trade-offs while supporting automatic failure handling and elastic resource acquisition from multiple clouds. KEYWORDScloud computing, epidemic simulation, high performance computing, simulation models INTRODUCTIONAs global transport networks continue to expand, the risk of infectious disease transmission grows, making research in pathogen spread on humans and animals increasingly important. An essential tool for studying pathogen spread is computer simulation based on mechanistic modeling. However, running epidemic simulations at large scale is time consuming for multiple reasons. First, the simulation models can capture fine-grained dynamics, considering different population scales (individual, population, and meta-population) and accounting for multiple layouts (eg, combining population and infection dynamics with farm management decisions). 1 Second, some processes can be data-driven and exploit large amounts of real-life observed data, mostly concerning the population dynamics of hosts. Third, understanding the underlying biological system requires performing and comparing many simulation scenarios, eg, through a model sensitivity analysis. Finally, producing reliable results demands large numbers of simulation runs when models are stochastic.Speeding up the execution of large-scale simulations requires parallelism. Parallelism can be implemented at the single-computer level using multiple cores and processors as well as the distributed level using multiple computers interconnected through a network. There are currently three main options for obtaining the necessary computing resources for running simulations in parallel. The first option is using dedicated high-end multicore systems or clusters. The limitation is that such infrastructures are typically shared among many local users, resulting in long waiting times. At the same time, extending such infrastructures to increase user satisfaction is time consuming and costly. The second option is grid computing, which allows sharing computers from multiple clusters in multiple sites. The main limitation of grids is the heterogeneity of the software stacks in each cluster, which makes application deployment di...

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Modelling of paratuberculosis spread between dairy cattle farms at a regional scale

Cited by 39 publications

References 50 publications

Implications of Cattle Trade for the Spread and Control of Infectious Diseases in Slovenia

Implications of Cattle Trade for the Spread and Control of Infectious Diseases in Slovenia

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

A service‐based framework for building and executing epidemic simulation applications in the cloud

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