Abstract. We analyse a model for macro-parasites in an age-structured host population, with infections of hosts occurring in clumps of parasites. The resulting model is an infinite system of partial differential equations of the first order, with non-local boundary conditions. We establish a condition for the parasite-free equilibrium to be asymptotically stable, in terms of R 0 < 1, where R 0 is a quantity interpreted as the reproduction number of parasites. To show this, we prove that, where B is a positive operator, and A generates a positive semigroup of negative type. Finally, we discuss how R 0 depends on the parameters of the system, especially on the mean size of infecting clumps.1. Introduction. A basic concept in models for micro-parasites (bacteria, viruses, . . .) is the basic reproductive number R 0 , the expected number of infected hosts produced by a single infected host in a completely susceptible host population [7]: in fact, in most epidemic models, R 0 > 1 is a necessary and sufficient condition for the instability of the disease-free equilibrium, and a sufficient condition for the persistence of pathogens.A similar concept (see, for instance, [24]) has been introduced in several models for macro-parasites (mainly helminths), but it has been difficult to obtain general results, because the basic models for macro-parasites consist of an infinite system of differential equations, whose first root can be traced to Kostizin [15]. Such systems have proved very difficult to analyse (see, however, [10, 17, 18, 22, 23, 20]) and much of the analysis, including the formulation of thresholds for parasite persistence in terms of R 0 , has been performed using simplified models consisting of few ordinary differential equations.Anderson and May [1] introduced in the infinite model by Kostizin the assumption that parasite distributions were, at each time, negative binomial, obtaining simplified models consisting of few ordinary differential equations. The negative binomial distribution has been routinely used to fit empirical data on parasite