Abstract. The basic reproduction number, Ro, can be used to determine factors important in the ability of a disease to invade or persist. We show how this number can be derived or estimated for vector-borne diseases with different complicating factors. African horse sickness is a viral disease transmitted mainly by the midge Culicoides imicola. We use this as an example of such a vector-transmitted disease where latent periods, seasonality in vector populations, and multiple host types may be important. The effect of vector population dynamics which are dependent on either hostor vector density are also addressed. If density-dependent constraints on vector population density are less severe, Ro is more sensitive to vector mortality and the virus development rate. Host-dependent vector dynamics change the relationship between Ro and host population size. Seasonality can either increase or decrease the estimate of Ro, depending on the lag between the peak of the midge population and the infective host population. The relative abundance of two host types is a factor in the ability of a disease to invade, but the strength of this factor depends on the differences between the hosts in recovery from infection, mortality and transmission. Removal of a reservoir host may increase Ro.

Recent research has shown that many parasite populations are made up of a number of epidemiologically distinct strains or genotypes. The implications of strain structure or genetic diversity for parasite population dynamics are still uncertain, partly because there is no coherent framework for the interpretation of ¢eld data. Here, we present an analysis of four published data sets for vector-borne microparasite infections where strains or genotypes have been distinguished: serotypes of African horse sickness (AHS) in zebra; types of Nannomonas trypanosomes in tsetse £ies; parasite-induced erythrocyte surface antigen (PIESA) based isolates of Plasmodium falciparum malaria in humans, and the merozoite surface protein 2 gene (MSP-2) alleles of P. falciparum in humans and in anopheline mosquitoes. For each data set we consider the distribution of strains or types among hosts and any pairwise associations between strains or types. Where host age data are available we also compare age^prevalence relationships and estimates of the force of infection. Multiple infections of hosts are common and for most data sets infections have an aggregated distribution among hosts with a tendency towards positive associations between certain strains or types. These patterns could result from interactions (facilitation) between strains or types, or they could re£ect patterns of contact between hosts and vectors. We use a mathematical model to illustrate the impact of host^vector contact patterns, ¢nding that even if contact is random there may still be signi¢cant aggregation in parasite distributions. This e¡ect is enhanced if there is non-random contact or other heterogeneities between hosts, vectors or parasites. In practice, di¡erent strains or types also have di¡erent forces of infection. We anticipate that aggregated distributions and positive associations between microparasite strains or types will be extremely common.