Utilizing stochastic genetic epidemiological models to quantify the impact of selection for resistance to infectious diseases in domestic livestock.

MacKenzie, Katrin; Bishop, Stephen

doi:10.2527/2001.7982057x

Cited by 18 publications

(13 citation statements)

References 14 publications

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“…The concept that infectious disease may have a genetic component is, of course, not new. Many agricultural geneticists make their livings by breeding disease resistance into both plants and animals [82,83]. One of the founders of behavioral genetics, Franz Kallmann [84], showed genetic factors influenced acquiring tuberculosis (DZ concordance = 26%, MZ concordance = 87%), an observation that was confirmed in modern times [85,86].…”

Section: Discussionmentioning

confidence: 99%

Theories of schizophrenia: a genetic-inflammatory-vascular synthesis

2005

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Background: Schizophrenia, a relatively common psychiatric syndrome, affects virtually all brain functions yet has eluded explanation for more than 100 years. Whether by developmental and/or degenerative processes, abnormalities of neurons and their synaptic connections have been the recent focus of attention. However, our inability to fathom the pathophysiology of schizophrenia forces us to challenge our theoretical models and beliefs. A search for a more satisfying model to explain aspects of schizophrenia uncovers clues pointing to genetically mediated CNS microvascular inflammatory disease.

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Section: Discussionmentioning

confidence: 99%

Theories of schizophrenia: a genetic-inflammatory-vascular synthesis

2005

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“…Innocent, Morrison, Brownlie & Gettingby (1997) tested effects of different management practices on disease incidence in a closed dairy herd, and Stärk, Pfeiffer & Morris (2000) determined disease spread between farms stochastically. MacKenzie & Bishop (2001b)) developed a stochastic model of hypothetical epidemics caused by microparasites in a pig farm and later applied the same model to quantify the impact of selection for disease resistance by adapting host genotype in the model (MacKenzie & Bishop 2001b). However, the weakness of many of these models is that they do not have robust data against which to parameterise or test their model.…”

Section: Discussionmentioning

confidence: 99%

“…The stochastic setting of the compartmental model is described below for the SLIR model. There are two components in the stochastic models: the event type (Table 1) and the inter‐event time (Renshaw 1991; MacKenzie & Bishop 2001a,b). The time periods between events may be considered random, with probability of specific events defined by the components of the model as described by Renshaw (1991).…”

Section: Methodsmentioning

confidence: 99%

A stochastic modelling approach to describing the dynamics of an experimental furunculosis epidemic in Chinook salmon, Oncorhynchus tshawytscha (Walbaum)

Öğüt

Bishop

2007

Journal of Fish Diseases

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A susceptible-infected-removed (SIR) stochastic model was compared to a susceptible-latent-infectious-removed (SLIR) stochastic model in terms of describing and capturing the variation observed in replicated experimental furunculosis epidemics, caused by Aeromonas salmonicida. The epidemics had been created by releasing a single infectious fish into a group of susceptible fish (n = 43) and progress of the epidemic was observed for 10 days. This process was replicated in 70 independent groups. The two stochastic models were run 5000 times and after every run and every 100 runs, daily mean values of each compartment were compared to the observed data. Both models, the SIR model (R(2) = 0.91), and the SLIR model (R(2) = 0.90) were successful in predicting the number of fish in each category at each time point in the experimental data. Moreover, between-replicate variability in the stochastic model output was similar to between-replicate variability in the experimental data. Generally, there was little change in the goodness of fit (R(2)) after 200 runs in the SIR model whereas 500 runs were necessary to have stable predictions with the SLIR model. In the SIR model, on an individual replicate basis, approximately 80% of 5000 simulated replicates had R(2) = 0.7 and above, whereas this ratio was slightly higher (82%) with the SLIR model. In brief, both models were equally effective in predicting the observed data and its variance but the SLIR model was advantageous because it differentiated the latent, i.e. infected but not having the ability to discharge pathogen, from the infectious fish.

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“…The models used are simple insofar as the only epidemiological parameters included are transmission coefficient and recovery rate. However, other parameters can easily be incorporated, as demonstrated by MacKenzie and Bishop (2001). Similarly, the farm parameters, such as contact rate, can be modified to inves- tigate the effect of farm structure on disease epidemiology.…”

Section: Discussionmentioning

confidence: 99%

Developing stochastic epidemiological models to quantify the dynamics of infectious diseases in domestic livestock.

MacKenzie

Bishop²

2001

Journal of Animal Science

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A stochastic model describing disease transmission dynamics for a microparasitic infection in a structured domestic animal population is developed and applied to hypothetical epidemics on a pig farm. Rational decision making regarding appropriate control strategies for infectious diseases in domestic livestock requires an understanding of the disease dynamics and risk profiles for different groups of animals. This is best achieved by means of stochastic epidemic models. Methodologies are presented for 1) estimating the probability of an epidemic, given the presence of an infected animal, whether this epidemic is major (requires intervention) or minor (dies out without intervention), and how the location of the infected animal on the farm influences the epidemic probabilities; 2) estimating the basic reproductive ratio, R0 (i.e., the expected number of secondary cases on the introduction of a single infected animal) and the variability of the estimate of this parameter; and 3) estimating the total proportion of animals infected during an epidemic and the total proportion infected at any point in time. The model can be used for assessing impact of altering farm structure on disease dynamics, as well as disease control strategies, including altering farm structure, vaccination, culling, and genetic selection.

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Utilizing stochastic genetic epidemiological models to quantify the impact of selection for resistance to infectious diseases in domestic livestock.

Cited by 18 publications

References 14 publications

Theories of schizophrenia: a genetic-inflammatory-vascular synthesis

Theories of schizophrenia: a genetic-inflammatory-vascular synthesis

A stochastic modelling approach to describing the dynamics of an experimental furunculosis epidemic in Chinook salmon, Oncorhynchus tshawytscha (Walbaum)

Developing stochastic epidemiological models to quantify the dynamics of infectious diseases in domestic livestock.

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