A biological network describing infection with Mycoplasma hyopneumoniae in swine herds

Otto, Lars; Kristensen, Charlotte Sonne

doi:10.1016/j.prevetmed.2004.09.005

Cited by 10 publications

(7 citation statements)

References 8 publications

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“…Bayesian networks have been studied extensively and are being widely applied in human medicine (e.g., Chapman et al, 2005). Applications are not yet common in veterinary science, but they are gaining popularity (e.g., Otto and Kristensen, 2004;Jensen et al, 2009;Steeneveld et al, 2009). The NBN used in this study constitute the simplest type of Bayesian network.…”

Section: Discussionmentioning

confidence: 99%

Discriminating between true-positive and false-positive clinical mastitis alerts from automatic milking systems

Steeneveld

Gaag

Ouweltjes

et al. 2010

Journal of Dairy Science

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Automatic milking systems (AMS) generate alert lists reporting cows likely to have clinical mastitis (CM). Dutch farmers indicated that they use non-AMS cow information or the detailed alert information from the AMS to decide whether to check an alerted cow for CM. However, it is not yet known to what extent such information can be used to discriminate between true-positive and false-positive alerts. The overall objective was to investigate whether selection of the alerted cows that need further investigation for CM can be made. For this purpose, non-AMS cow information and detailed alert information were used. During a 2-yr study period, 11,156 alerts for CM, including 159 true-positive alerts, were collected at one farm in the Netherlands. Non-AMS cow information on parity, days in milk, season of the year, somatic cell count history, and CM history was added to each alert. In addition, 6 alert information variables were defined. These were the height of electrical conductivity, the alert origin (electrical conductivity, color, or both), whether or not a color alert for mastitic milk was given, whether or not a color alert for abnormal milk was given, deviation from the expected milk yield, and the number of alerts of the cow in the preceding 12 to 96 h. Subsequently, naive Bayesian networks (NBN) were constructed to compute the posterior probability of an alert being truly positive based only on non-AMS cow information, based on only alert information, or based on both types of information. The NBN including both types of information had the highest area under the receiver operating characteristic curve (AUC; 0.78), followed by the NBN including only alert information (AUC = 0.75) and the NBN including only non-AMS cow information (AUC = 0.62). By combining the 2 types of information and by setting a threshold on the computed probabilities, the number of false-positive alerts on a mastitis alert list was reduced by 35%, and 10% of the true-positive alerts would not be identified. To detect CM cases at a farm with an AMS, checking all alerts is still the best option but would result in a high workload. Checking alerts based on a single alert information variable would result in missing too many true-positive cases. Using a combination of alert information variables, however, is the best way to select cows that need further investigation. The effect of adding non-AMS cow information on making a distinction between true-positive and false-positive alerts would be minor.

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Section: Discussionmentioning

confidence: 99%

Discriminating between true-positive and false-positive clinical mastitis alerts from automatic milking systems

Steeneveld

Gaag

Ouweltjes

et al. 2010

Journal of Dairy Science

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“…Structure discovery has been employed to explore how mastitis and fertility management influence production in dairy herds [18]; to identify changes in pig behaviour related to early piglets mortality [19]; to investigate the most likely pathogens involved in clinical mastitis in dairy cows [20]; and to identify those farm risk factors associated with bovine viral diarrhoea [23]. Besides these examples, other studies employed graphical models informed using existing/expert knowledge to describe risk factors influencing the prevalence of Mycoplasma hyopneumoniae [21]; and to estimate the risk of leg disorders in finishing pigs [22]. A crucial distinction among the abovementioned papers, is that these two latter studies [21,22] did not use structure discovery to inform structure of the network, but were rather based on published knowledge and expert opinion.…”

Section: Introductionmentioning

confidence: 99%

“…Besides these examples, other studies employed graphical models informed using existing/expert knowledge to describe risk factors influencing the prevalence of Mycoplasma hyopneumoniae [21]; and to estimate the risk of leg disorders in finishing pigs [22]. A crucial distinction among the abovementioned papers, is that these two latter studies [21,22] did not use structure discovery to inform structure of the network, but were rather based on published knowledge and expert opinion. The latter is highly subjective and if, as in this study, extensive data are available, then extracting the co-dependence network structure from observed data provides objective and robust empirical analyses.…”

Section: Introductionmentioning

confidence: 99%

Identifying associations between pig pathologies using a multi-dimensional machine learning methodology

et al. 2012

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BackgroundAbattoir detected pathologies are of crucial importance to both pig production and food safety. Usually, more than one pathology coexist in a pig herd although it often remains unknown how these different pathologies interrelate to each other. Identification of the associations between different pathologies may facilitate an improved understanding of their underlying biological linkage, and support the veterinarians in encouraging control strategies aimed at reducing the prevalence of not just one, but two or more conditions simultaneously.ResultsMulti-dimensional machine learning methodology was used to identify associations between ten typical pathologies in 6485 batches of slaughtered finishing pigs, assisting the comprehension of their biological association. Pathologies potentially associated with septicaemia (e.g. pericarditis, peritonitis) appear interrelated, suggesting on-going bacterial challenges by pathogens such as Haemophilus parasuis and Streptococcus suis. Furthermore, hepatic scarring appears interrelated with both milk spot livers (Ascaris suum) and bacteria-related pathologies, suggesting a potential multi-pathogen nature for this pathology.ConclusionsThe application of novel multi-dimensional machine learning methodology provided new insights into how typical pig pathologies are potentially interrelated at batch level. The methodology presented is a powerful exploratory tool to generate hypotheses, applicable to a wide range of studies in veterinary research.

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“…The model described here is complementary to an earlier study [35]. The latter focused on the veterinarian’s view on the severity of EP and how this view changes with the increased availability of consistent scientific evidence, whereas our model focuses on the course of infection and the impact of intervention strategies.…”

Section: Discussionmentioning

confidence: 99%

Modelling the within-herd transmission of Mycoplasma hyopneumoniae in closed pig herds

Fournié

Wieland²,

Pfeiffer

et al. 2016

Porc Health Manag

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BackgroundA discrete time, stochastic, compartmental model simulating the spread of Mycoplasma hyopneumoniae within a batch of industrially raised pigs was developed to understand infection dynamics and to assess the impact of a range of husbandry practices. A ‘disease severity’ index was calculated based on the ratio between the cumulative numbers of acutely and chronically diseased and infectious pigs per day in each age category, divided by the length of time that pigs spent in this age category. This is equal to the number of pigs per day, either acutely or chronically infectious and diseased, divided by the number of all pigs per all days in the model. The impact of risk and protective factors at batch level was examined by adjusting ‘acclimatisation of gilts’, ‘length of suckling period’, ‘vaccination of suckling pigs against M. hyopneumoniae’, ‘contact between fattening pigs of different age during restocking of compartments’ and ‘co-infections in fattening pigs’.ResultsThe highest ‘disease severity’ was predicted, when gilts do not have contact with live animals during their acclimatisation, suckling period is 28 days, no vaccine is applied, fatteners have contact with pigs of other ages and are suffering from co-infections. Pigs in this scenario become diseased/infectious for 26.1 % of their lifetime. Logistic regression showed that vaccination of suckling pigs was influential for ‘disease severity’ in growers and finishers, but not in suckling and nursery pigs. Lack of contact between gilts and other live pigs during the acclimatisation significantly influenced the ‘disease severity’ in suckling pigs but had less impact in growing and finishing pigs. The length of the suckling period equally affected the severity of the disease in all age groups with the strongest association in nursery pigs. The contact between fatteners of different groups influenced the course of infection among finishers, but not among other pigs. Finally, presence of co-infections was relevant in growers and finishers, but not in younger pigs.ConclusionThe developed model allows comparison of different prevention programmes and strategies for controlling transmission of M. hyopneumoniae.Electronic supplementary materialThe online version of this article (doi:10.1186/s40813-016-0026-1) contains supplementary material, which is available to authorized users.

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A biological network describing infection with Mycoplasma hyopneumoniae in swine herds

Cited by 10 publications

References 8 publications

Discriminating between true-positive and false-positive clinical mastitis alerts from automatic milking systems

Discriminating between true-positive and false-positive clinical mastitis alerts from automatic milking systems

Identifying associations between pig pathologies using a multi-dimensional machine learning methodology

Modelling the within-herd transmission of Mycoplasma hyopneumoniae in closed pig herds

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