Accuracy of models for the 2001 foot-and-mouth epidemic

Tildesley, Michael J.; Deardon, Rob; Savill, Nicholas J.; Bessell, Paul R.; Brooks, Stephen; Woolhouse, Mark; Grenfell, Bryan T.; Keeling, Matthew James

doi:10.1098/rspb.2008.0006

Cited by 72 publications

(91 citation statements)

References 17 publications

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“…Here, we refer to these distributions as spatial kernels because that term has been used in both epidemiological (e.g. [6,10]) and ecological (e.g. [11,12]) studies.…”

Section: Introductionmentioning

confidence: 99%

The shape of the spatial kernel and its implications for biological invasions in patchy environments

2010

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Ecological and epidemiological invasions occur in a spatial context. We investigated how these processes correlate to the distance dependence of spread or dispersal between spatial entities such as habitat patches or epidemiological units. Distance dependence is described by a spatial kernel, characterized by its shape (kurtosis) and width (variance). We also developed a novel method to analyse and generate point-pattern landscapes based on spectral representation. This involves two measures: continuity, which is related to autocorrelation and contrast, which refers to variation in patch density. We also analysed some empirical data where our results are expected to have implications, namely distributions of trees (Quercus and Ulmus) and farms in Sweden. Through a simulation study, we found that kernel shape was not important for predicting the invasion speed in randomly distributed patches. However, the shape may be essential when the distribution of patches deviates from randomness, particularly when the contrast is high. We conclude that the speed of invasions depends on the spatial context and the effect of the spatial kernel is intertwined with the spatial structure. This implies substantial demands on the empirical data, because it requires knowledge of shape and width of the spatial kernel, and spatial structure.

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“…Here, we refer to these distributions as spatial kernels because that term has been used in both epidemiological (e.g. [6,10]) and ecological (e.g. [11,12]) studies.…”

Section: Introductionmentioning

confidence: 99%

The shape of the spatial kernel and its implications for biological invasions in patchy environments

2010

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“…For the 2001 FMD outbreak the power parameters for cattle numbers have been found to vary regionally between 0.2 and 0.44 [24]. We chose p = q = 0.2 for all US regions which corresponds to the minimal amount of demographic dependence within the inferred range (p = q = 0 recovers the demography-free model (1)).…”

Section: Farm Infection Model and Fsr Simulation With Demographymentioning

confidence: 99%

“…We follow Tildesley et al [24,42] in extending the basic spatial force of infection (1) to include a non-linear dependency on the number of cattle (N i ) in both susceptible and infectious farms,…”

Section: Farm Infection Model and Fsr Simulation With Demographymentioning

confidence: 99%

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Rapid simulation of spatial epidemics: A spectral method

Brand

Tildesley

Keeling

2015

Journal of Theoretical Biology

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Spatial structure and hence the spatial position of host populations plays a vital role in the spread of infection. In the majority of situations, it is only possible to predict the spatial spread of infection using simulation models, which can be computationally demanding especially for large population sizes. Here we develop an approximation method that vastly reduces this computational burden. We assume that the transmission rates between individuals or subpopulations are determined by a spatial transmission kernel. This kernel is assumed to be isotropic, such that the transmission rate is simply a function of the distance between susceptible and infectious individuals; as such this provides the ideal mechanism for modelling localised transmission in a spatial environment. We show that the spatial force of infection acting on all susceptibles can be represented as a spatial convolution between the transmission kernel and a spatially extended 'image' of the infection state. This representation allows the rapid calculation of stochastic rates of infection using fast-Fourier transform (FFT) routines, which greatly improves the computational e ciency of spatial simulations. We demonstrate the e ciency and accuracy of this fast spectral rate recalculation (FSR) method with two examples: an idealised scenario simulating an SIR-type epidemic outbreak amongst N habitats distributed across a two-dimensional plane; the spread of infection between US cattle farms, illustrating that the FSR method makes continental-scale outbreak forecasting feasible with desktop processing power. The latter model demonstrates which areas of the US are at consistently high risk for cattle-infections, although predictions of epidemic size are highly dependent on assumptions about the tail of the transmission kernel. Highlights• Kernel-based spatial transmission models are computationally expensive for large population sizes.• We present an e cient and accurate alternative simulation methodology (dubbed FSR simulation) based upon a spectral representation of the infection rate.• FSR simulation also permits the accelerated likelihood calculation for more e cient parameter inference.• Using FSR simulation it was possible to perform detailed outbreak forecasting for a generic cattle infection at the continental-scale.

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“…The real-time modeling during the 2001 foot-and-mouth epidemic in the UK (Keeling et al 2003, Tildesley et al 2008 contributed substantially to decisions to restrict animal movement and cull livestock populations. These actions have been credited with helping to control the outbreak, and more generally successes like these have contributed to progress of disease modeling toward a more predictive science (Tildesley et al 2008).…”

Section: Infectious Diseasementioning

confidence: 99%

The role of data assimilation in predictive ecology

et al. 2014

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Abstract. In this rapidly changing world, improving the capacity to predict future dynamics of ecological systems and their services is essential for better stewardship of the earth system. Prediction relies on models that describe our understanding of the major processes that underlie system dynamics and data about these processes and the present state of ecosystems. Prediction becomes more effective when models are well informed by data. A technological revolution in the capacity to collect data now provides very different opportunities to test hypotheses and project future dynamics than when many standard statistical tests were first developed. Data assimilation is an emerging statistical approach to combine models with data in a rigorous way to constrain model parameters and system states, identify model error, and improve ecological prediction. In this paper, we illustrate how data assimilation can improve ecological prediction to support decision-making by reviewing applications of data assimilation across four different research fields: (1) emerging infectious disease, (2) fisheries, (3) fire, and (4) the terrestrial carbon cycle. Across these fields, data assimilation substantially improves prediction accuracy, highlighting its important role in enabling predictive ecology. Data assimilation with regional and global models faces major challenges, such as the large number of parameters to be estimated, high computational demands, the need to integrate multiple and heterogeneous data sets, and complex social-ecological interactions. Nevertheless, data assimilation provides an important statistical approach that has great potential to enhance the predictive capacity of ecological models in a changing climate.

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Accuracy of models for the 2001 foot-and-mouth epidemic

Cited by 72 publications

References 17 publications

The shape of the spatial kernel and its implications for biological invasions in patchy environments

The shape of the spatial kernel and its implications for biological invasions in patchy environments

Rapid simulation of spatial epidemics: A spectral method

The role of data assimilation in predictive ecology

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