Foot-and-mouth is one of the world's most economically important livestock diseases. We developed an individual farm–based stochastic model of the current UK epidemic. The fine grain of the epidemiological data reveals the infection dynamics at an unusually high spatiotemporal resolution. We show that the spatial distribution, size, and species composition of farms all influence the observed pattern and regional variability of outbreaks. The other key dynamical component is long-tailed stochastic dispersal of infection, combining frequent local movements with occasional long jumps. We assess the history and possible duration of the epidemic, the performance of control strategies, and general implications for disease dynamics in space and time.

Significance: When multiple pathogens cocirculate this can lead to competitive or cooperative forms of pathogen–pathogen interactions. It is believed that such interactions occur among cold and flu viruses, perhaps through broad-acting immunity, resulting in interlinked epidemiological patterns of infection. However, to date, quantitative evidence has been limited. We analyzed a large collection of diagnostic reports collected over multiple years for 11 respiratory viruses. Our analyses provide strong statistical support for the existence of interactions among respiratory viruses. Using computer simulations, we found that very short-lived interferences may explain why common cold infections are less frequent during flu seasons. Improved understanding of how the epidemiology of viral infections is interlinked can help improve disease forecasting and evaluation of disease control interventions.

Identification of the relative importance of within- and between-host variability in infectiousness and the impact of these heterogeneities on the transmission dynamics of infectious agents can enable efficient targeting of control measures. Cattle, a major reservoir host for the zoonotic pathogenEscherichia coliO157, are known to exhibit a high degree of heterogeneity in bacterial shedding densities. By relating bacterial count to infectiousness and fitting dynamic epidemiological models to prevalence data from a cross-sectional survey of cattle farms in Scotland, we identify a robust pattern: ≈80% of the transmission arises from the 20% most infectious individuals. We examine potential control options under a range of assumptions about within- and between-host variability in infection dynamics. Our results show that the within-herd basic reproduction ratio,R₀, could be reduced to <1 with targeted measures aimed at preventing infection in the 5% of individuals with the highest overall infectiousness. Alternatively, interventions such as vaccination or the use of probiotics that aim to reduce bacterial carriage could produce dramatic reductions inR₀by preventing carriage at concentrations corresponding to the top few percent of the observed range of counts. We conclude that a greater understanding of the cause of the heterogeneity in bacterial carriage could lead to highly efficient control measures to reduce the prevalence ofE. coliO157.

The case–reproduction ratio for the spread of an infectious disease is a critically important concept for understanding dynamics of epidemics and for evaluating impact of control measures on spread of infection. Reliable estimation of this ratio is a problem central to epidemiology and is most often accomplished by fitting dynamic models to data and estimating combinations of parameters that equate to the case–reproduction ratio. Here, we develop a novel parameter–free method that permits direct estimation of the history of transmission events recoverable from detailed observation of a particular epidemic. From these reconstructed ‘epidemic trees’, case–reproduction ratios can be estimated directly. We develop a bootstrap algorithm that generates percentile intervals for these estimates that shows the procedure to be both precise and robust to possible uncertainties in the historical reconstruction. Identifying and ‘pruning’ branches from these trees whose occurrence might have been prevented by implementation of more stringent control measures permits estimation of the possible efficacy of these alternative measures. Examination of the cladistic structure of these trees as a function of the distance of each case from its infection source reveals useful insights about the relationship between long-distance transmission events and epidemic size. We demonstrate the utility of these methods by applying them to data from the 2001 foot–and–mouth disease outbreak in the UK.

SUMMARY: The prevalence ofEscherichia coliO157 displays striking variability across the Scottish cattle population. On 78% of farms, in a cross-sectional survey of 952, no shedding ofE. coliO157 was detected, but on a small proportion, ∼2%, very high prevalences of infection were found (with 90–100% of pats sampled being positive). We ask whether this variation arises from the inherent stochasticity in transmission dynamics or whether it is a signature of underlying heterogeneities in the cattle population. A novel approach is taken whereby the cross-sectional data are viewed as providing independent snapshots of a dynamic process. Using maximum-likelihood methods to fit time-dependent epidemiological models to the data we obtain estimates for the rates of immigration and transmission ofE. coliO157 infection – parameters which have not been previously quantified in the literature. A comparison of alternative model fits reveals that the variation in the prevalence data is best explained when a proportion of the cattle are assumed to transmit infection at much higher levels than the rest – the so-called super-shedders. Analysis of a second dataset, comprising samples taken from 32 farms at monthly intervals over a period of 1 year, additionally yields an estimate for the rate of recovery from infection. The pattern of prevalence displayed in the second dataset also strongly supports the super-shedder hypothesis.