The 2001 Epidemic
Foot and Mouth Disease is one of the most highly transmissible of any livestock disease and therefore the outbreak in 2001 had the potential to catastrophically impact on both the farming community but also the national economy. It was therefore national policy (and EU law) that infected farms should have all their animals destroyed (culled) as should all "Dangerous Contacts" where there is suspicion that the infection may have been transmitted.
It was abundantly clear from preliminary analysis that early control measures were not sufficiently effective in reducing the spread infection and therefore action was required. This took two forms: firstly, culls of infected premises and dangerous contacts, therefore removing potential sources of infection as early as possible; secondly, it was decided that due to the high risk of local transmission of the virus all farms that were contiguous to an infected farm should also have their livestock culled.
By the end of October 2001, nearly five million animals on ten thousand farms had been culled either as a direct result of infection or in an attempt to control the spread. Our models show that the situation could have been far worse; if only livestock on infected premises were culled then more than three times as many farms would have been affected. However, the situation could have been far better, if culls had been preformed rapidly from the start and the CP culling policy introduced earlier then we predict that the impact would only have been reduce to a quarter of that observed.
Our predictions of the scale of the outbreak based on the early location of cases. We clearly identify the main hot-spots of infection.
Our predictions of the time-course of the outbreak (red) compared to the observed cases (black). It is clear that our model captures the main features of the epidemic and the action of increased controls in April.
Predicted scale of the 2001 outbreak if only livestock on infected farms were culled. Only dangerous-control and contiguous-premise culls run the risk of killing un-infected animals, the national-scale impact of leaving these animals is a much larger and longer outbreak.
Figure 1. Foot and Mouth had a devastating impact on the farming and rural communities. It was therefore vitally important that policy decisions were aided by the best scientific advice we were able to provide. While mathematical modelling is able to provide an intergrated population-level view of control, it is only through the use of specialists on the ground that these can be implemented effectively.
Since 2001 our modelling and statistical analysis has continued with three main themes: better statistical inference and analysis; optimal control by vaccination and spatially specific controls; and extending the approach to other countries.
Figure 2: Graphs showing the effect of vaccination had it been implemented during the 2001 outbreak. The success of any vaccination campaign is dependent upon the resources available to implement the campaign and the speed at which any vaccination policy is implemented. Graphs from Tildesley et al. 2006 (Nature).
In 2001, in the heat of the epidemic, the parameter inference was somewhat ad-hoc -- the main aim was to generate useful predictions, not to be too fussy about the statistical methodology. Since 2001, we have re-analysed the methods that were used during the epidemic and compared the accuracy of our models to the data on a farm-by-farm basis. In addition, we have been developing Bayesian MCMC methodology which provides a more rigorous assessment of model parameter values, but also allow us to assess the risk (on a day-by-day basis) of each farm being infected but not yet detected (so-called occult infections). Knowledge of occult infections is a vital step in efficient targeting of surveillance -- this methodology was formulated to aid with the 2007 outbreak in Surrey.
Vaccination obviously has the potential to be a powerful tool in the control of any infection. It was not used in 2001 due to difficulties of distinguishing between infected carriers of foot-and-mouth disease and animals that had been vaccinated, but modern vaccinology tools have overcome this issue. However, one of the major challenges is the delay between injecting the animal and them becoming protected against infection. In 2003 & 2006, we therefore focused in detail on the advantages and disadvantages of vaccinating cattle (vaccinating sheep is not considered cost effective); we showed that a national rapidly implemented prophylactic vaccination campaign could be a viable control measure, and that vaccinating farms in the vacinity of recently detected infections was also highly effective (figure 2).
Foot-and-Mouth outside the UK
Figure 3: Results of simulations for FMD spreading through US counties. Showing the number of subsequent counties infected when the outbreak starts in a particular location.
More recent work has focused on extending this work to Denmark and the USA. Denmark presents interesting challenges and opportunities as they have comparible quality data to the UK (recording farm locations and animal numbers in each farm), but their livestock industry is dominated by pigs.
In contrast, the data in the USA is of far poorer quality, often with just the number of farms and animals in each of the 3000 counties being recorded. Despite this sparsity of data, we have shown that mathematical modelling can still be a useful tool in assessing the scale of control measures that are optimal. We have shown that county-level demographic information is sufficient to characterize disease spread and inform policy at epidemiologically and policy relevant spatial scales in the US.
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- Tildesley, M.J. Smith, G. and Keeling, M.J. (2012) Modeling the spread and control of foot-and-mouth disease in Pennsylvania following its discovery and options for control, Prev Vet Med 104, 224–239
- Carslake, D., Grant, W., Green, L.E., Cave, J., Greaves, J., Keeling, M., McEldowney, J., Weldegebriel, H. and Medley, G.F. (2011) Endemic cattle diseases: comparative epidemiology and governance. Philos. Trans. R. Soc. Lond. B 366 1975-86
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- Carrique-Mas, JJ; Medley, GF; Green, LE (2008) Risks for bovine tuberculosis in British cattle farms restocked after the foot and mouth disease epidemic of 2001. Prev. Vet. Med. 84 85-93
- NJ Savill, DJ Shaw, R Deardon, MJ Tildesley, MJ Keeling, MEJ Woolhouse, SP Brooks, BT Grenfell (2007) Effect of data quality on estimates of farm infectiousness trends in the UK 2001 foot-and-mouth disease epidemic. Journal of the Royal Society Interface 4, 235-241
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- MJ Tildesley, NJ Savill, DJ Shaw, R Deardon, SP Brooks, MEJ Woolhouse, BT Grenfell, MJ Keeling (2006) Optimal reactive vaccination strategies for a foot-and-mouth outbreak in the UK. Nature 440, 83-86
- NJ Savill, DJ Shaw, R Deardon, MJ Tildesley, MJ Keeling, MEJ Woolhouse, SP Brooks, BT Grenfell (2006) Topographic determinants of foot and mouth disease transmission in the UK 2001 epidemic. BMC Veterinary Research 2, art. no. 3
- MJ Keeling (2005). Models of Foot-and-Mouth Disease. Proc. Roy. Soc. Lond. B 272, 1195-1202.
- MJ Keeling, MEJ Woolhouse, RM May, G Davies, BT Grenfell (2003). Modelling Vaccination Strategies Against Foot-and-Mouth Disease. Nature 421, 136-142.
- Green, L.E. and Medley, G.F. (2002) Mathematical modelling of the foot and mouth disease epidemic of 2001: Strengths and weaknesses. Research in Vet. Sci. 73 201-205
- Green, L., Wood, J., Newton, R., Mellor, D., Menzies, F., Kelly, L. and Peeler, E. (2003) Modelling and FMD. Vet. Record 151 394-395
- MJ Keeling, MEJ Woolhouse, DJ Shaw, L Matthews, M Chase-Topping, DT Haydon, SJ Cornell, J Kappey, J Wilesmith, BT Grenfell (2001). Dynamics of the 2001 UK Foot and Mouth Epidemic: Stochastic Dispersal in a Heterogeneous Landscape. Science 294, 813-817.
- M Woolhouse, M Chase-Topping, D Haydon, J Friar, L Matthews, G Hughes, D Shaw, J Wilesmith, A Donaldson, S Cornell, M Keeling & B Grenfell (2001). Foot-and-mouth disease under control in the UK. Nature 414, 258-258.
Funded by: Wellcome Trust, BBSRC, DEFRA, NIH, Dept of Homeland Security
WIDER people involved
Mike Tildesley (Nottingham)
Colleen Webb (Colorado)
Matt Farrari (Penn State)