Abstract:
A previously developed multiple regression algorithm was used as the basis of a
stochastic model to simulate worm burdens in sheep naturally infected with Haemonchus
contortus over five consecutive Haemonchus seasons (November to January/February) on
a farm in the summer rainfall region in South Africa, although only one season is
discussed. The algorithm associates haemoglobin levels with worm counts in individual
animals. Variables were represented by distributions based on FAMACHA© scores and
body weights of sheep, and Monte Carlo sampling was used to simulate worm burdens.
Under conditions of high disease risk, defined as the sampling event during the worm
season with the lowest relative mean haemoglobin level for a class of sheep, the model
provided a distribution function for mean class H. contortus burdens and the probability
of these occurring.
A mean H. contortus burden for ewes (n=130 per sample) of approximately 1 000
(range 51 to 28 768) and 2 933 (range 78 to 44 175) for rams (n = 120 per sample) was
predicted under these conditions. At the beginning of the worm season when the risk of
disease was lowest (i.e. when both classes had their highest estimated mean haemoglobin
levels), a mean worm burden of 525 (range 39 to 4910) for ewes and 651 (range 37 to
17260) for rams was predicted. Model indications were that despite being selectively
drenched according to FAMACHA© evaluation, 72% of the ewes would maintain their
mean worm burden below an arbitrarily selected threshold of 1 000 even when risk of
disease was at its highest. In contrast, far fewer rams (27%) remained below this
threshold, especially towards the end of the worm season.
The model was most sensitive to changes in haemoglobin value, and thus by
extrapolation, the haematocrit, which is used as the gold standard for validating the
FAMACHA© system. The mean class haemoglobin level at which there was a 50%
probability of worm burdens being ≤1000 worms was 7.05 g/dl in ewes and 7.92 g/dl in
rams.
