parameters estimated for Salmonella typhimurium in swine using susceptible-infectious-resistant m... more parameters estimated for Salmonella typhimurium in swine using susceptible-infectious-resistant models and unobserved sources of uncertainty (cohort effects, diagnostic test sensitivity), so leading to more reliable estimates Correia-Gomes et al. BMC Veterinary Research 2014, 10:101
Uncertainty estimation is crucial to establishing confidence in any data analysis, and this is es... more Uncertainty estimation is crucial to establishing confidence in any data analysis, and this is especially true for Essential Climate Variables, including ocean colour. Methods for deriving uncertainty vary greatly across data types, so a generic statistics-based approach applicable to multiple data types is an advantage to simplify the use and understanding of uncertainty data. Progress towards rigorous uncertainty analysis of ocean colour has been slow, in part because of the complexity of ocean colour processing. Here, we present a general approach to uncertainty characterisation, using a database of satellite-in situ matchups to generate a statistical model of satellite uncertainty as a function of its contributing variables. With an example NASA MODIS-Aqua chlorophyll-a matchups database mostly covering the north Atlantic, we demonstrate a model that explains 67% of the squared error in log(chlorophyll-a) as a potentially correctable bias, with the remaining uncertainty being characterised as standard deviation and standard error at each pixel. The method is quite general, depending only on the existence of a suitable database of matchups or reference values, and can be applied to other sensors and data types such as other satellite observed Essential Climate Variables, empirical algorithms derived from in situ data, or even model data.
ABSTRACTBackground Campylobacteriosis is a major public health concern. Despite evidence that cli... more ABSTRACTBackground Campylobacteriosis is a major public health concern. Despite evidence that climate factors influence the spatio-temporal patterns of the infections; their impact is not fully described and understood. ObjectivesTo examine methods for determining the impact of rainfall and temperature on Campylobacter cases in England and Wales. MethodsReported cases for England and Wales were linked to local temperature and rainfall at laboratory postcode locations in the 30 days before the specimen date. Descriptive, statistical and spatial methods included a novel Comparative Conditional Incidence (CCI), wavelet analysis, hierarchical clustering, generalized additive model (GAM) and generalized structural time series model (GEST).ResultsThe Campylobacter increase in late spring was linked to temperature two weeks prior, with an increase in CCI of 0.175 cases per 100,000 per week for weeks 17 to 24; the relationship was non-linear and changed through the year. GEST with penalized...
MCS diversity and classifier confidence: A Bayesian approach
Bayes' rule is introduced as a coherent averaging strategy for multiclassifier system (MCS) o... more Bayes' rule is introduced as a coherent averaging strategy for multiclassifier system (MCS) output, and as a strategy for eliminating the uncertainty associated with a particular choice of classifier-model parameters. We use a Markov-Chain Monte Carlo method for efficient selection of classifiers to approximate the computationally intractable elements of the Bayesian approach --- the set of classifiers so selected is our MCS. Furthermore we exploit the massive sampling (thousands of classifiers) within the Bayesian framework to encompass an estimate of the confidence to be placed in any classification result --- thus providing a sound basis for rejection of some MCS classification results. We present uncertainty envelopes as one way to derive these confidence estimates from the population of classifiers that constitutes the MCS, and we show that as the diversity among component classifiers increases so does the accuracy of confident classification estimates, but diversity is not...
Microbe associated molecular pattern (MAMP) receptors in plants recognize MAMPs and activate basa... more Microbe associated molecular pattern (MAMP) receptors in plants recognize MAMPs and activate basal defences; however a complete understanding of the molecular and physiological mechanisms conferring immunity remains elusive. Pathogens suppress active defence in plants through the combined action of effector proteins. Here we show that the chloroplast is a key component of early immune responses. MAMP perception triggers the rapid, large-scale suppression of nuclear encoded chloroplast-targeted genes (NECGs). Virulent Pseudomonas syringae effectors reprogramme NECG expression in Arabidopsis, target the chloroplast and inhibit photosynthetic CO 2 assimilation through disruption of photosystem II. This activity prevents a chloroplastic reactive oxygen burst. These physiological changes precede bacterial multiplication and coincide with pathogen-induced abscisic acid (ABA) accumulation. MAMP pretreatment protects chloroplasts from effector manipulation, whereas application of ABA or the herbicide DCMU inhibits the MAMP-induced chloroplastic reactive oxygen burst, and enhances growth of a P. syringae hrpA mutant that fails to secrete effectors.
The processes and mechanisms giving rise to failures in repairable systems such as underground wa... more The processes and mechanisms giving rise to failures in repairable systems such as underground water pipes are quite complex and not quite fully understood yet. Many data sets involving pipe break history are poor in both quantity and quality resulting in data sets in which a significant number of pipes have no failures recorded at all. Conventional point process models such as the NHPP may be inadequately flexible to capture the failure process. In this paper both an NHPP and a zeroinflated version of it are applied on a North American pipe data set, using the cumulative number of failures in the past as an explanatory variable.
The long-term evolution of ozone is influenced by a wide variety of factors that may be broadly s... more The long-term evolution of ozone is influenced by a wide variety of factors that may be broadly separated into radiative, dynamical, transport, chemical and external forcing processes. Many of the processes are also coupled in the sense that, for example, dynamical changes lead ...
One difficulty for real-time tracking of epidemics is related to reporting delay. The reporting d... more One difficulty for real-time tracking of epidemics is related to reporting delay. The reporting delay may be due to laboratory confirmation, logistic problems, infrastructure difficulties and so on. The ability to correct the available information as quickly as possible is crucial, in terms of decision making such as issuing warnings to the public and local authorities. A Bayesian hierarchical modelling approach is proposed as a flexible way of correcting the reporting delays and to quantify the associated uncertainty. Implementation of the model is fast, due to the use of the integrated nested Laplace approximation (INLA). The approach is illustrated on dengue fever incidence data in Rio de Janeiro, and Severe Acute Respiratory Illness (SARI) data in Paran\'a state, Brazil.
• A range of different approaches have been suggested for the multivariate modelling of the geogr... more • A range of different approaches have been suggested for the multivariate modelling of the geographical distribution of different but potentially related diseases. We suggest an addition to these methods which incorporates a discrete mixture of latent factors, as opposed to using CAR or MCAR random effect formulations. Our proposal provides for a potentially richer range of dependency structures than those encompassed in previously used models in that it is capable of representing an enhanced range of correlation structures between diseases at the same time as implicitly allowing for less restrictive spatial correlation structures between geographical units. We illustrate results of using the model on data taken from cancer registries on four carcinomas in some 300 UK geographical areas.
One difficulty for real-time tracking of epidemics is related to reporting delay. The reporting d... more One difficulty for real-time tracking of epidemics is related to reporting delay. The reporting delay may be due to laboratory confirmation, logistical problems, infrastructure difficulties, and so on. The ability to correct the available information as quickly as possible is crucial, in terms of decision making such as issuing warnings to the public and local authorities. A Bayesian hierarchical modelling approach is proposed as a flexible way of correcting the reporting delays and to quantify the associated uncertainty. Implementation of the model is fast due to the use of the integrated nested Laplace approximation. The approach is illustrated on dengue fever incidence data in Rio de Janeiro, and severe acute respiratory infection data in the state of Paraná, Brazil.
Health & social care in the community, Jan 16, 2016
There is a general understanding that socioeconomically disadvantaged people are also disadvantag... more There is a general understanding that socioeconomically disadvantaged people are also disadvantaged with respect to their access to NHS care. Insofar as considerable NHS funding has been targeted at deprived areas, it is important to better understand whether and why socioeconomic variations in access and utilisation exist. Exploring this question with reference to cardiovascular care, our aims were to synthesise and evaluate evidence relating to access to and/or use of English NHS services around (i) different points on the care pathway (i.e. presentation, primary management and specialist management) and (ii) different dimensions of inequality (socioeconomic, age- and gender-related, ethnic or geographical). Restricting our search period from 2004 to 2016, we were concerned to examine whether, compared to earlier research, there has been a change in the focus of research examining inequalities in cardiac care and whether the pro-rich bias reported in the late 1990s and early 2000s...
Recently, a prototype dengue early warning system was developed to produce probabilistic forecast... more Recently, a prototype dengue early warning system was developed to produce probabilistic forecasts of dengue risk three months ahead of the 2014 World Cup in Brazil. Here, we evaluate the categorical dengue forecasts across all microregions in Brazil, using dengue cases reported in June 2014 to validate the model. We also compare the forecast model framework to a null model, based on seasonal averages of previously observed dengue incidence. When considering the ability of the two models to predict high dengue risk across Brazil, the forecast model produced more hits and fewer missed events than the null model, with a hit rate of 57% for the forecast model compared to 33% for the null model. This early warning model framework may be useful to public health services, not only ahead of mass gatherings, but also before the peak dengue season each year, to control potentially explosive dengue epidemics.
parameters estimated for Salmonella typhimurium in swine using susceptible-infectious-resistant m... more parameters estimated for Salmonella typhimurium in swine using susceptible-infectious-resistant models and unobserved sources of uncertainty (cohort effects, diagnostic test sensitivity), so leading to more reliable estimates Correia-Gomes et al. BMC Veterinary Research 2014, 10:101
Uncertainty estimation is crucial to establishing confidence in any data analysis, and this is es... more Uncertainty estimation is crucial to establishing confidence in any data analysis, and this is especially true for Essential Climate Variables, including ocean colour. Methods for deriving uncertainty vary greatly across data types, so a generic statistics-based approach applicable to multiple data types is an advantage to simplify the use and understanding of uncertainty data. Progress towards rigorous uncertainty analysis of ocean colour has been slow, in part because of the complexity of ocean colour processing. Here, we present a general approach to uncertainty characterisation, using a database of satellite-in situ matchups to generate a statistical model of satellite uncertainty as a function of its contributing variables. With an example NASA MODIS-Aqua chlorophyll-a matchups database mostly covering the north Atlantic, we demonstrate a model that explains 67% of the squared error in log(chlorophyll-a) as a potentially correctable bias, with the remaining uncertainty being characterised as standard deviation and standard error at each pixel. The method is quite general, depending only on the existence of a suitable database of matchups or reference values, and can be applied to other sensors and data types such as other satellite observed Essential Climate Variables, empirical algorithms derived from in situ data, or even model data.
ABSTRACTBackground Campylobacteriosis is a major public health concern. Despite evidence that cli... more ABSTRACTBackground Campylobacteriosis is a major public health concern. Despite evidence that climate factors influence the spatio-temporal patterns of the infections; their impact is not fully described and understood. ObjectivesTo examine methods for determining the impact of rainfall and temperature on Campylobacter cases in England and Wales. MethodsReported cases for England and Wales were linked to local temperature and rainfall at laboratory postcode locations in the 30 days before the specimen date. Descriptive, statistical and spatial methods included a novel Comparative Conditional Incidence (CCI), wavelet analysis, hierarchical clustering, generalized additive model (GAM) and generalized structural time series model (GEST).ResultsThe Campylobacter increase in late spring was linked to temperature two weeks prior, with an increase in CCI of 0.175 cases per 100,000 per week for weeks 17 to 24; the relationship was non-linear and changed through the year. GEST with penalized...
MCS diversity and classifier confidence: A Bayesian approach
Bayes' rule is introduced as a coherent averaging strategy for multiclassifier system (MCS) o... more Bayes' rule is introduced as a coherent averaging strategy for multiclassifier system (MCS) output, and as a strategy for eliminating the uncertainty associated with a particular choice of classifier-model parameters. We use a Markov-Chain Monte Carlo method for efficient selection of classifiers to approximate the computationally intractable elements of the Bayesian approach --- the set of classifiers so selected is our MCS. Furthermore we exploit the massive sampling (thousands of classifiers) within the Bayesian framework to encompass an estimate of the confidence to be placed in any classification result --- thus providing a sound basis for rejection of some MCS classification results. We present uncertainty envelopes as one way to derive these confidence estimates from the population of classifiers that constitutes the MCS, and we show that as the diversity among component classifiers increases so does the accuracy of confident classification estimates, but diversity is not...
Microbe associated molecular pattern (MAMP) receptors in plants recognize MAMPs and activate basa... more Microbe associated molecular pattern (MAMP) receptors in plants recognize MAMPs and activate basal defences; however a complete understanding of the molecular and physiological mechanisms conferring immunity remains elusive. Pathogens suppress active defence in plants through the combined action of effector proteins. Here we show that the chloroplast is a key component of early immune responses. MAMP perception triggers the rapid, large-scale suppression of nuclear encoded chloroplast-targeted genes (NECGs). Virulent Pseudomonas syringae effectors reprogramme NECG expression in Arabidopsis, target the chloroplast and inhibit photosynthetic CO 2 assimilation through disruption of photosystem II. This activity prevents a chloroplastic reactive oxygen burst. These physiological changes precede bacterial multiplication and coincide with pathogen-induced abscisic acid (ABA) accumulation. MAMP pretreatment protects chloroplasts from effector manipulation, whereas application of ABA or the herbicide DCMU inhibits the MAMP-induced chloroplastic reactive oxygen burst, and enhances growth of a P. syringae hrpA mutant that fails to secrete effectors.
The processes and mechanisms giving rise to failures in repairable systems such as underground wa... more The processes and mechanisms giving rise to failures in repairable systems such as underground water pipes are quite complex and not quite fully understood yet. Many data sets involving pipe break history are poor in both quantity and quality resulting in data sets in which a significant number of pipes have no failures recorded at all. Conventional point process models such as the NHPP may be inadequately flexible to capture the failure process. In this paper both an NHPP and a zeroinflated version of it are applied on a North American pipe data set, using the cumulative number of failures in the past as an explanatory variable.
The long-term evolution of ozone is influenced by a wide variety of factors that may be broadly s... more The long-term evolution of ozone is influenced by a wide variety of factors that may be broadly separated into radiative, dynamical, transport, chemical and external forcing processes. Many of the processes are also coupled in the sense that, for example, dynamical changes lead ...
One difficulty for real-time tracking of epidemics is related to reporting delay. The reporting d... more One difficulty for real-time tracking of epidemics is related to reporting delay. The reporting delay may be due to laboratory confirmation, logistic problems, infrastructure difficulties and so on. The ability to correct the available information as quickly as possible is crucial, in terms of decision making such as issuing warnings to the public and local authorities. A Bayesian hierarchical modelling approach is proposed as a flexible way of correcting the reporting delays and to quantify the associated uncertainty. Implementation of the model is fast, due to the use of the integrated nested Laplace approximation (INLA). The approach is illustrated on dengue fever incidence data in Rio de Janeiro, and Severe Acute Respiratory Illness (SARI) data in Paran\'a state, Brazil.
• A range of different approaches have been suggested for the multivariate modelling of the geogr... more • A range of different approaches have been suggested for the multivariate modelling of the geographical distribution of different but potentially related diseases. We suggest an addition to these methods which incorporates a discrete mixture of latent factors, as opposed to using CAR or MCAR random effect formulations. Our proposal provides for a potentially richer range of dependency structures than those encompassed in previously used models in that it is capable of representing an enhanced range of correlation structures between diseases at the same time as implicitly allowing for less restrictive spatial correlation structures between geographical units. We illustrate results of using the model on data taken from cancer registries on four carcinomas in some 300 UK geographical areas.
One difficulty for real-time tracking of epidemics is related to reporting delay. The reporting d... more One difficulty for real-time tracking of epidemics is related to reporting delay. The reporting delay may be due to laboratory confirmation, logistical problems, infrastructure difficulties, and so on. The ability to correct the available information as quickly as possible is crucial, in terms of decision making such as issuing warnings to the public and local authorities. A Bayesian hierarchical modelling approach is proposed as a flexible way of correcting the reporting delays and to quantify the associated uncertainty. Implementation of the model is fast due to the use of the integrated nested Laplace approximation. The approach is illustrated on dengue fever incidence data in Rio de Janeiro, and severe acute respiratory infection data in the state of Paraná, Brazil.
Health & social care in the community, Jan 16, 2016
There is a general understanding that socioeconomically disadvantaged people are also disadvantag... more There is a general understanding that socioeconomically disadvantaged people are also disadvantaged with respect to their access to NHS care. Insofar as considerable NHS funding has been targeted at deprived areas, it is important to better understand whether and why socioeconomic variations in access and utilisation exist. Exploring this question with reference to cardiovascular care, our aims were to synthesise and evaluate evidence relating to access to and/or use of English NHS services around (i) different points on the care pathway (i.e. presentation, primary management and specialist management) and (ii) different dimensions of inequality (socioeconomic, age- and gender-related, ethnic or geographical). Restricting our search period from 2004 to 2016, we were concerned to examine whether, compared to earlier research, there has been a change in the focus of research examining inequalities in cardiac care and whether the pro-rich bias reported in the late 1990s and early 2000s...
Recently, a prototype dengue early warning system was developed to produce probabilistic forecast... more Recently, a prototype dengue early warning system was developed to produce probabilistic forecasts of dengue risk three months ahead of the 2014 World Cup in Brazil. Here, we evaluate the categorical dengue forecasts across all microregions in Brazil, using dengue cases reported in June 2014 to validate the model. We also compare the forecast model framework to a null model, based on seasonal averages of previously observed dengue incidence. When considering the ability of the two models to predict high dengue risk across Brazil, the forecast model produced more hits and fewer missed events than the null model, with a hit rate of 57% for the forecast model compared to 33% for the null model. This early warning model framework may be useful to public health services, not only ahead of mass gatherings, but also before the peak dengue season each year, to control potentially explosive dengue epidemics.
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Papers by Trevor Bailey