Markov chain approach to analyze the dynamics of pathogen fecal shedding—Example of Listeria monocytogenes shedding in a herd of dairy cattle

Ivanek, Renata; Gröhn, Yrjö T.; Ho, Alphina; Wiedmann, Martin

doi:10.1016/j.jtbi.2006.09.031

Cited by 18 publications

(23 citation statements)

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“…Previous studies of fecal shedding and immune response dynamics have used survival analysis and logistic regression models to find risk factors associated with these processes [37]–[41]. Only one previous study investigated the effect of time-varying covariates on L. monocytogenes fecal shedding using a Markov Chain modeling approach [35]. Our study demonstrated that multistate Markov models are a useful statistical tool for the analysis of longitudinal fecal shedding and immune response data.…”

Section: Discussionmentioning

confidence: 86%

“…The [34] study modeled recurrence of infectiousness through fecal shedding by inclusion of the carrier state into the model of the S. Typhimurium transmission within a pig herd. Similarly, the [35] study modeled the transition of cattle between the states of shedding and non-shedding of L. monocytogenes in feces. However, to our knowledge, the study described here is unique in the way it explicitly separated the shedder animals into those that are in the continuous and intermittent shedding states, which has important implications for infection detection and transmission, as already discussed.…”

Section: Discussionmentioning

confidence: 99%

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Salmonella Fecal Shedding and Immune Responses are Dose- and Serotype- Dependent in Pigs

et al. 2012

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Despite the public health importance of Salmonella infection in pigs, little is known about the associated dynamics of fecal shedding and immunity. In this study, we investigated the transitions of pigs through the states of Salmonella fecal shedding and immune response post-Salmonella inoculation as affected by the challenge dose and serotype. Continuous-time multistate Markov models were developed using published experimental data. The model for shedding had four transient states, of which two were shedding (continuous and intermittent shedding) and two non-shedding (latency and intermittent non-shedding), and one absorbing state representing permanent cessation of shedding. The immune response model had two transient states representing responses below and above the seroconversion level. The effects of two doses [low (0.65×106 CFU/pig) and high (0.65×109 CFU/pig)] and four serotypes (Salmonella Yoruba, Salmonella Cubana, Salmonella Typhimurium, and Salmonella Derby) on the models' transition intensities were evaluated using a proportional intensities model. Results indicated statistically significant effects of the challenge dose and serotype on the dynamics of shedding and immune response. The time spent in the specific states was also estimated. Continuous shedding was on average 10–26 days longer, while intermittent non-shedding was 2–4 days shorter, in pigs challenged with the high compared to low dose. Interestingly, among pigs challenged with the high dose, the continuous and intermittent shedding states were on average up to 10–17 and 3–4 days longer, respectively, in pigs infected with S. Cubana compared to the other three serotypes. Pigs challenged with the high dose of S. Typhimurium or S. Derby seroconverted on average up to 8–11 days faster compared to the low dose. These findings highlight that Salmonella fecal shedding and immune response following Salmonella challenge are dose- and serotype-dependent and that the detection of specific Salmonella strains and immune responses in pigs are time-sensitive.

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Section: Discussionmentioning

confidence: 86%

Section: Discussionmentioning

confidence: 99%

Salmonella Fecal Shedding and Immune Responses are Dose- and Serotype- Dependent in Pigs

et al. 2012

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“…However, we propose that farmers' management decisions could also be optimized for the production of microbiologically safer foods by linking precision farming with models that can predict microbial presence in a location based on the site-specific environmental, meteorological, and management factors (similar to the one proposed here). Similarly, predictive models of microbial presence in the environment could be coupled with a predictive model of pathogen fecal shedding (19) and be used by farmers that use GIS-based livestock production (2, 50) to predict when fields may have an increased risk of animal exposure if used for grazing.…”

Section: Vol 75 2009mentioning

confidence: 99%

Modeling of Spatially Referenced Environmental and Meteorological Factors Influencing the Probability ofListeriaSpecies Isolation from Natural Environments

Ivanek

Gröhn

Wells

et al. 2009

Appl Environ Microbiol

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Many pathogens have the ability to survive and multiply in abiotic environments, representing a possible reservoir and source of human and animal exposure. Our objective was to develop a methodological framework to study spatially explicit environmental and meteorological factors affecting the probability of pathogen isolation from a location. Isolation of Listeria spp. from the natural environment was used as a model system. Logistic regression and classification tree methods were applied, and their predictive performances were compared. Analyses revealed that precipitation and occurrence of alternating freezing and thawing temperatures prior to sample collection, loam soil, water storage to a soil depth of 50 cm, slope gradient, and cardinal direction to the north are key predictors for isolation of Listeria spp. from a spatial location. Different combinations of factors affected the probability of isolation of Listeria spp. from the soil, vegetation, and water layers of a location, indicating that the three layers represent different ecological niches for Listeria spp. The predictive power of classification trees was comparable to that of logistic regression. However, the former were easier to interpret, making them more appealing for field applications. Our study demonstrates how the analysis of a pathogen's spatial distribution improves understanding of the predictors of the pathogen's presence in a particular location and could be used to propose novel control strategies to reduce human and animal environmental exposure.The transmission cycle of many pathogens involves biotic hosts and abiotic environments. After infection of a host with a pathogen like Listeria monocytogenes, Bacillus anthracis, enterohemorrhagic Escherichia coli, Salmonella spp., or Toxoplasma gondii, large numbers of the pathogen may be shed into the environment where, under favorable conditions, they may survive, multiply, and infect new hosts, including humans (6,11,13,30,37). It is important to identify spatially explicit environmental and meteorological factors that favor a pathogen's presence in a particular environmental location. That information could be used to design novel measures to reduce the presence of the pathogen in the environment and prevent exposure and infection of animal and human hosts. For analysis of pathogens' spatial distribution in the environment, geographic information systems (GIS) integrated with standard statistical and epidemiological methods provide tremendous opportunities (5).Detection of pathogens in environmental samples is usually based on culturing methods without enumeration, resulting in presence/absence data. For such data, a standard statistical approach to predict microbial presence as influenced by covariates would be logistic regression (LR). However, classification trees (CT) have recently been suggested as a powerful yet simple alternative to LR in ecological studies (7,48). It is therefore of interest to contrast the performance of the CT with that of the standard LR approach in predict...

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“…L. monocytogenes is carried asymptomatically in silage-fed livestock and is dropped in feces [16]. Shedding of the pathogen by a variety of domestic animals like sheep and cattle has been reported [16][17][18][19][20]. A possible route of infection into the human food chain, for instance, is through the ingestion of uncooked food plants grown on soils irrigated with contaminated water or via the consumption of vegetables cultivated on manure treated fields [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Permanent colonization of creek sediments, creek water and limnic water plants by fourListeriaspecies in low population densities

Lang-Halter

Schober

Scherer

2016

Zeitschrift Für Naturforschung C

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During a 1-year longitudinal study, water, sediment and water plants from two creeks and one pond were sampled monthly and analyzed for the presence of Listeria species. A total of 90 % of 30 sediment samples, 84 % of 31 water plant samples and 67 % of 36 water samples were tested positive. Generally, most probable number counts ranged between 1 and 40 g were detected. Species differentiation based on FT-IR spectroscopy and multiplex PCR of a total of 1220 isolates revealed L. innocua (46 %), L. seeligeri (27 %), L. monocytogenes (25 %) and L. ivanovii (2 %). Titers and species compositions were similar during all seasons. While the species distributions in sediments and associated Ranunculus fluitans plants appeared to be similar in both creeks, RAPD typing did not provide conclusive evidence that the populations of these environments were connected. It is concluded that (i) the fresh-water sediments and water plants are year-round populated by Listeria, (ii) no clear preference for growth in habitats as different as sediments and water plants was found and (iii) the RAPD-based intraspecific biodiversity is high compared to the low population density.Keywords: biodiversity; fresh water; Listeria monocytogenes; Listeria spp.; sediment; water plants.Dedication: This work is dedicated to the memory of Professor Peter Böger. His continuous support and thoughtful guidance over many years encouraged the senior author to aim at an academic career in microbial ecology and evolutionary biology.

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Markov chain approach to analyze the dynamics of pathogen fecal shedding—Example of Listeria monocytogenes shedding in a herd of dairy cattle

Cited by 18 publications

References 18 publications

Salmonella Fecal Shedding and Immune Responses are Dose- and Serotype- Dependent in Pigs

Salmonella Fecal Shedding and Immune Responses are Dose- and Serotype- Dependent in Pigs

Modeling of Spatially Referenced Environmental and Meteorological Factors Influencing the Probability ofListeriaSpecies Isolation from Natural Environments

Permanent colonization of creek sediments, creek water and limnic water plants by fourListeriaspecies in low population densities

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