Abstract Infection with the abomasal nematode Haemonchus contortus is responsible for considerable production loss in small ruminants globally, and especially in warm, summer-rainfall regions. Previous attempts to predict infection levels have followed the traditional framework for macroparasite models, i.e. tracking parasite population sizes as a function of host and climatic factors. Targeted treatment strategies, in which patho-physiological indices are used to identify the individuals most affected by parasites, could provide a foundation for alternative, incidence-based epidemiological models. In this paper, an elaboration of the classic susceptible-infected-recovered (SIR) model framework for microparasites was adapted to haemonchosis and used to predict disease in Merino sheep on a commercial farm in South Africa. Incidence was monitored over a single grazing season using the FAMACHA scoring system for conjunctival mucosal coloration, which indicates high burdens of H. contortus, and used to fit the model by estimating transmission parameters. The model predicted force of infection (FOI) between sequential FAMACHA monitoring events in groups of dry, pregnant and lactating ewes, and related FOI to factors including climate (temperature, rainfall and rainfall entropy), using a random effects model with reproductive status group as the cluster variable. Temperature and rainfall in the seven days prior to monitoring significantly predicted the interval FOI (p ≤ 0.002), while rainfall entropy did not (p = 0.289). Differences across the three groups accounted for approximately 90% of the variability in the interval FOI over the period of investigation. Maintained FOI during targeted treatment of cases of haemonchosis suggests strong underlying transmission from sub-clinically infected animals, and/or limited impact on pre-existing pasture contamination by removal of clinical worm burdens later in the grazing season. The model has the potential to contribute to the sustainable management of H. contortus by predicting periods of heightened risk, and hence to focus and optimise limited resources for monitoring and treatment. SIR-type model frameworks are an alternative to classic abundance-based compartmental models of macroparasite epidemiology, and could be useful where incidence data are available. Significant challenges remain, however, in the ability to calibrate such models to field data at spatial and temporal scales that are useful for decision support at farm level.
- Force of infection, Targeted selective treatment, Refugia, FAMACHA, Model, Climate, Epidemiology, Haemonchus contortus