Livestock disease can be tackled through a number of routes including on farm vaccination programs, biosecurity, herd health schemes (which restrict animal movements to between accredited farms), testing of animals when they move between farms, and quarantining of new arrivals on farms.

Designing the most effective strategies benefits from accurate assessment of the herd-to-herd spread of disease. However, the capacity for disease transmission with populations is frequently measured using the basic reproduction number, R0, which provides a poor measure of disease spread in a highly structured population, for example a population of cattle herds, and says little about between-group transmission dynamics.

Here we discuss the properties of an analogous value for between-group transmission, Rpop, which is the expected number of secondary groups infected while the disease persists in the primary group. An important result is that when disease transmission is driven by movement of infective individuals, rather than by between-group contact, then the movement acts to deplete numbers of infectives in the primary group, and hasten its recovery. This feature distinguishes our formulation from the usual phenomenological models of group-to-group transmission. Moreover, it has important implications for movement-based control options (e.g. testing and treating, or quarantining animals when they move between herds) and therefore for optimal disease control strategies.

Using structured metapopulation models, we show that Rpop peaks at intermediate movement rates. In combination with testing and treating prior to movement, or post movement quarantine; this can lead to islands of movement rates that permit the disease to persist. Under such control schemes, a reduction in movement rates can, counter-intuitively, lead to an increase in disease prevalence. Examining the effects of heterogeneity in herd size, herd movement rates, and individual infectiousness, we show that metapopulation disease spread is dependent on within-farm dynamics.  We explore the implications of these results for disease problems (e.g. BVD, bTB, E. coli O157) with different characteristic R0, infectious periods and movement rates.

modelling, metapopulation, disease intervention