Modelling the initial spread of foot-and-mouth disease through animal movements

Abstract

Livestock movements in Great Britain (GB) are well recorded and are a unique record of the network of connections among livestock-holding locations. These connections can be critical for disease spread, as in the 2001 epidemic of foot-and-mouth disease (FMD) in the UK. Here, the movement data are used to construct an individual-farm-based model of the initial spread of FMD in GB and determine the susceptibility of the GB livestock industry to future outbreaks under the current legislative requirements. Transmission through movements is modelled, with additional local spread unrelated to the known movements. Simulations show that movements can result in a large nationwide epidemic, but only if cattle are heavily involved, or the epidemic occurs in late summer or early autumn. Inclusion of random local spread can considerably increase epidemic size, but has only a small impact on the spatial extent of the disease. There is a geographical bias in the epidemic size reached, with larger epidemics originating in Scotland and the north of England than elsewhere.

Interventions for avian influenza A (H5N1) risk management in live bird market networks

“…Network analysis carried out in other livestock production systems has confirmed livestock markets as the main hubs for livestock movements (25,26) and their contamination as a prerequisite for large epidemics (45). However, some farms also might act as bridges connecting markets.…”

Investigating the potential of reported cattle mortality data in Switzerland for syndromic surveillance

“…These computerised, databased systems were designed to restore consumer faith in food safety, by enabling the tracing of cattle suspected of having BSE from the slaughterhouse back to their various holdings of origin. Since then, they have proven valuable for other types of epidemiological investigations, for example tracing the movements of animals potentially infected with other agents such as bovine viral diarrhoea virus (Presi et al, 2011), estimating population dynamics for modelling disease transmission (Green et al, 2006) and designing cost-effective disease control and monitoring programs (Blickenstorfer et al, 2011;Schärrer et al, 2014).…”

A Contact-Network-Based Formulation of a Preferential Mixing Model

“…This implies that all individuals are equally likely to contact each other and, therefore, if infected, are equally likely to infect susceptible members of the population. The availability of more accurate data at the individual level, the collection of which has been partially driven by the epidemics of HIV/AIDS (Liljeros et al, 2001;Jones and Handcock, 2003;Catania et al, 1992;Anderson et al, 1990;Gupta et al, 1989), SARS (Lipsitch et al, 2003;Hufnagel et al, 2004;Meyers et al, 2005), Foot-and-Mouth Disease (FMD; Ferguson et al, 2001;Keeling et al, 2001;Kiss et al, 2005Kiss et al, , 2006aGreen et al, 2006;, and the possibility of a world wide Pandemic Influenza (Eubank et al, 2004;Ferguson et al, 2005), has highlighted the important role played by contact heterogeneity, spatial structure, and connectivity correlations. Differential-equation-based models can be adapted to capture such properties and are amenable to a variety of powerful analytical tools .…”