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A discrete-time epidemiological model to quantify selection for disease resistance

Published online by Cambridge University Press:  18 August 2016

K. MacKenzie and
S. C. Bishop
K. MacKenzie
Affiliation:
Roślin Institute (Edinburgh), Roślin, Midlothian EH25 9PS
S. C. Bishop
Affiliation:
Roślin Institute (Edinburgh), Roślin, Midlothian EH25 9PS
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Abstract

A genetic epidemiological model (GEM) for investigating the effect of selection for disease resistance on the epidemiology of infectious diseases is presented and applied to a pig breeding scenario. Fundamental to the model is R0, the basic reproductive ratio. R0 is the expected number of secondary infections caused by a single infection. If R0 is greater than 1, there will be an epidemic. The aim of the model is to quantify the effect of selection on R0 and the consequences this has on disease epidemiology. Two implementations are presented: selection for reduced susceptibility/infectivity to a disease and introgression of a major resistance gene. The results suggest that the effects of selection for reduced susceptibility I infectivity are critically dependent on the infectiousness of the disease. Under the assumptions made in the model, for a disease with a low infection level, it takes approximately 15 years of selection until R0 is less than 1 and the population is safe from epidemics should the infectious agent be present. For a highly infectious disease, this time may be as long as 100 years. For gene introgression, the population is expected to be free from epidemics within 5 years and the time to reduce R0 to less than 1 is largely independent of the disease being considered. With gene introgression, the proportion of the population which needs to be resistant to ensure that R0 is less than one is shown to be a function of the initial R0 for the disease. Although selection, as modelled, results in a linear decline in R0, the reduction in the proportion of animals infected during an epidemic is non-linear. The selection process reduces the amount of infectious material that is in the environment when an infection occurs and this decreases the force of infection on unselected animals. This phenomenon results in a marked interaction between host genotype and disease epidemiology. Thus, the results of the model show that altering the genetics of individual animals affects the epidemiology of the disease at the population level. The model can be applied to any farm structure and any microparasitic infectious disease.

Keywords

disease resistanceepidemiologygeneticspigsR0

Information

Type
Research Article
Information
Animal Science , Volume 69 , Issue 3 , December 1999, pp. 543 - 551
Copyright
Copyright © British Society of Animal Science 1999

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