Meta-analysis of the parasitic phase traits of Haemonchus contortus infection in sheep

Background: the parasitic nematode haemonchus contortus shows highly variable life history traits. this highlights the need to have an average estimate and a quantification of the variation around it to calibrate epidemiological models. methods: this paper aimed to quantify the main life history traits of h. contortus and to identify explanatory factors affecting these traits using a powerful method based on a systematic review and meta-analysis of current literature. the life history traits considered are: (i) the establishment rate of ingested larvae; (ii) the adult mortality rate; (iii) the fertility (i.e. the number of eggs laid/female/day); and (iv) fecundity of female worms (i.e. the number of eggs per gram of faeces). results: a total of 37 papers that report single experimental infection with h. contortus in sheep and published from 1960 to 2015,were reviewed and collated in this meta-analysis. this encompassed 115 experiments on 982 animals. each trait was analysed using a linear model weighted by its inverse variance. the average (± se) larval establishment rate was 0.24 ± 0.02,which decreased as a function of the infection dose and host age. an average adult mortality rate of 0.021 ± 0.002) was estimated from the literature. this trait varied as a function of animal age,breed and protective response due to prior exposure to the parasite. average female fertility was 1295.9 ± 280.4 eggs/female/day and decreased in resistant breeds and previously infected hosts. average faecal egg count at necropsy was 908.5 ± 487.1 eggs per gram of faeces and varied as a function of infection duration and host resistance. the average sex ratio of h. contortus was 0.51 ± 0.006. conclusion: this work is the first systematic review to summarise the available information on the parasitic phase of h. contortus in sheep. the results of the meta-analysis provide robust estimates of life history traits for parametrization of epidemiological models,their expected variation according to experimental factors,and provides correlations between these. © 2017 the author(s).