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- Jonathan J Ludlam, Gavin J. Gibson, Wilfred Otten, Christopher Aidan Gilligan
- Journal of the Royal Society, Interface
- 2012

There is increasing interest in the use of the percolation paradigm to analyse and predict the progress of disease spreading in spatially structured populations of animals and plants. The wider utility of the approach has been limited, however, by several restrictive assumptions, foremost of which is a strict requirement for simple nearest-neighbour… (More)

- Gavin J. Gibson, Sammy Siu, Colin Cowan
- IEEE Trans. Signal Processing
- 1991

This paper considers the problem of reconstructing digital signals which have been passed through a dispersive channel and corrupted with additive noise, a problem which is of considerable importance in communications. The problems encountered by linear equalizers under adverse conditions on the signal-to-noise ratio and channel phase are described, and by… (More)

- João A. N. Filipe, Gavin J. Gibson
- Bulletin of mathematical biology
- 2001

Analytical methods for predicting and exploring the dynamics of stochastic, spatially interacting populations have proven to have useful application in epidemiology and ecology. An important development has been the increasing interest in spatially explicit models, which require more advanced analytical techniques than the usual mean-field or mass-action… (More)

- Alex R. Cook, Gavin J. Gibson, Christopher Aidan Gilligan
- Biometrics
- 2008

This article describes a method for choosing observation times for stochastic processes to maximise the expected information about their parameters. Two commonly used models for epidemiological processes are considered: a simple death process and a susceptible-infected (SI) epidemic process with dual sources for infection spreading within and from outwith… (More)

- Sergio Hernandez-Marin, Andrew M. Wallace, Gavin J. Gibson
- IEEE Transactions on Pattern Analysis and Machine…
- 2007

Time-correlated single photon counting and burst illumination laser data can be used for range profiling and target classification. In general, the problem is to analyze the response from a histogram of either photon counts or integrated intensities to assess the number, positions, and amplitudes of the reflected returns from object surfaces. The goal of… (More)

- João A. N. Filipe, W. Otten, Gavin J. Gibson, Christopher Aidan Gilligan
- Bulletin of mathematical biology
- 2004

Spatial interactions are key determinants in the dynamics of many epidemiological and ecological systems; therefore it is important to use spatio-temporal models to estimate essential parameters. However, spatially-explicit data sets are rarely available; moreover, fitting spatially-explicit models to such data can be technically demanding and… (More)

- Gavin J. Gibson
- Phytopathology
- 1997

- Marie Forrester, Anthony N. Pettitt, Gavin J. Gibson
- Biostatistics
- 2007

This paper describes a stochastic epidemic model developed to infer transmission rates of asymptomatic communicable pathogens within a hospital ward. Inference is complicated by partial observation of the epidemic process and dependencies within the data. The epidemic process of nosocomial communicable pathogens can be partially observed by routine swabs… (More)

- Franco M. Neri, Alex R. Cook, Gavin J. Gibson, Tim R. Gottwald, Christopher Aidan Gilligan
- PLoS Computational Biology
- 2014

Outbreaks of infectious diseases require a rapid response from policy makers. The choice of an adequate level of response relies upon available knowledge of the spatial and temporal parameters governing pathogen spread, affecting, amongst others, the predicted severity of the epidemic. Yet, when a new pathogen is introduced into an alien environment, such… (More)

- Matthew Parry, Gavin J. Gibson, +4 authors Christopher Aidan Gilligan
- Proceedings of the National Academy of Sciences…
- 2014

The spread of Huanglongbing through citrus groves is used as a case study for modeling an emerging epidemic in the presence of a control. Specifically, the spread of the disease is modeled as a susceptible-exposed-infectious-detected-removed epidemic, where the exposure and infectious times are not observed, detection times are censored, removal times are… (More)