The population biology of the parasitic phase of the Haemonchus contortus life-cycle is discussed within the context of a mathematical model of those processes which regulate population density. Parasite death is assumed to be attributable to at least three processes: failure of the 3rd-stage larvae (L3) to exsheath following ingestion, failure to become established following exsheathment (immune exclusion), and mortality of 4th- and 5th-stage worms on the mucosal surface. The proportion of worms which avoid immune exclusion is a declining sigmoidal function of the duration of infection, at least under conditions of continual exposure to L3 larvae. Under the same conditions, the death rate of 4th- and 5thstage worms can be modelled as an increasing linear function of the host's experience of 3rd stage larvae. The model provides a good description of the worm burdens observed during the course of a long-term trickle infection experiment even though a priori arguments indicate that it would be an inadequate representation of events in the field.

We examined dynamical factors that shape the distribution of the number of parasites/host in constant or temporally varying environments, and with or without host-age dependent variation in host susceptibility and parasite mortality. We predict properties of the parasite distribution in the absence of density-dependent factors such as densitydependent mortality or recruitment and parasite-induced host mortality. These properties provide a criterion for the detection of density dependence in temporally variable systems with host-age dependent interactions. We have then introduced methods to estimate and statistically evaluate the effects of host age or size on the distribution of parasites/host. The methods are based on a maximum likelihood protocol for linear and non-linear regression when data are negatively binomially distributed. We have illustrated the use of the theoretical results and statistical methods by re-analysing the data of Halvorsen & Andersen (1984) on cestode infections in Norwegian arctic charr and by analysing new data on nematode infections in Caribbean Anolis lizards.

The data needed to develop analytical models of trypanosomiasis transmission have become available only recently. By making some simplifying assumptions, models of the dynamics of the disease in vector, cattle and wild mammal populations can be constructed in order to determine criteria for successful disease control by mass and targetted chemotherapy, and by vector control. The heterogeneity in transmission due to tsetse fly feeding preferences and the variability of immunological characteristics among the vertebrate hosts account for differences in prevalence of Trypanosoma vivax and T. congolense, and also lead to an increase in the basic reproductive rates of the parasites and a corresponding decrease in the vector population density threshold for disease eradication or persistence. The long life-span of the vectors relative to the duration of the parasites' developmental period lead to high infection rates in the vector and high values of R0. The efficacy of chemotherapeutic regimes depends on the relationship between treatment rate and the duration of prophylaxis conferred by the drugs used. The model's predictions of the effects of vector control are shown to be in broad agreement with published field data for Mkwaja Ranch, Tanzania. Vector control programmes are frequently blighted by reinvasion, and the implications of this are discussed in terms of a model for fly immigration. With immigration of vectors, the disease is always endemic, though the infection rate in the fly population is modified by the effect of differential mortalities inside and outside the controlled area on cohorts of incubating flies. Sensitivity analysis of the model, using Monte-Carlo methods, enables an assessment of the relative importance of the parameters to be made. The results emphasize the need for studies of the wild animal reservoir to be carried out alongside entomological surveys. The relative accuracy with which field measurements need to be made in order to minimize the uncertainty in predictions of trypanosomiasis prevalence is discussed.