Your browser doesn't support javascript.
Mostrar: 20 | 50 | 100
Resultados 1 - 6 de 6
Más filtros

Base de datos
Intervalo de año de publicación
Prev Vet Med ; 175: 104860, 2019 Nov 30.
Artículo en Inglés | MEDLINE | ID: mdl-31812850


Identifying and understanding the risk factors for endemic bovine tuberculosis (TB) in cattle herds is critical for the control of this disease. Exploratory machine learning techniques can uncover complex non-linear relationships and interactions within disease causation webs, and enhance our knowledge of TB risk factors and how they are interrelated. Classification tree analysis was used to reveal associations between predictors of TB in England and each of the three surveillance risk areas (High Risk, Edge, and Low Risk) in 2016, identifying the highest risk herds. The main classifying predictor for farms in England overall related to the TB prevalence in the 100 nearest cattle herds. In the High Risk and Edge areas it was the number of slaughterhouse destinations and in the Low Risk area it was the number of cattle tested in surveillance tests. How long ago the last confirmed incident was resolved was the most frequent classifier in trees; if within two years, leading to the highest risk group of herds in the High Risk and Low Risk areas. At least two different slaughterhouse destinations led to the highest risk group of herds in England, whereas in the Edge area it was a combination of no contiguous low-risk neighbours (i.e. in a 1 km radius) and a minimum proportion of 6-23 month-old cattle in November. A threshold value of prevalence in 100 nearest neighbours increased the risk in all areas, although the value was specific to each area. Having low-risk contiguous neighbours reduced the risk in the Edge and High Risk areas, whereas high-risk ones increased the risk in England overall and in the Edge area specifically. The best classification tree models informed multivariable binomial logistic regression models in each area, adding statistical inference outputs. These two approaches showed similar predictive performance although there were some disparities regarding what constituted high-risk predictors. Decision tree machine learning approaches can identify risk factors from webs of causation: information which may then be used to inform decision making for disease control purposes.

PLoS One ; 14(12): e0225250, 2019.
Artículo en Inglés | MEDLINE | ID: mdl-31869335


Vector borne diseases are a continuing global threat to both human and animal health. The ability of vectors such as mosquitos to cover large distances and cross country borders undetected provide an ever-present threat of pathogen spread. Many diseases can infect multiple vector species, such that even if the climate is not hospitable for an invasive species, indigenous species may be susceptible and capable of transmission such that one incursion event could lead to disease establishment in these species. Here we present a consensus modelling methodology to estimate the habitat suitability for presence of mosquito species in the UK deemed competent for Rift Valley fever virus (RVF) and demonstrate its application in an assessment of the relative risk of establishment of RVF virus in the UK livestock population. The consensus model utilises observed UK mosquito surveillance data, along with climatic and geographic prediction variables, to inform six independent species distribution models; the results of which are combined to produce a single prediction map. As a livestock host is needed to transmit RVF, we then combine the consensus model output with existing maps of sheep and cattle density to predict the areas of the UK where disease is most likely to establish in local mosquito populations. The model results suggest areas of high suitability for RVF competent mosquito species across the length and breadth of the UK. Notable areas of high suitability were the South West of England and coastal areas of Wales, the latter of which was subsequently predicted to be at higher risk for establishment of RVF due to higher livestock densities. This study demonstrates the applicability of outputs of species distribution models to help predict hot-spots for risk of disease establishment. While there is still uncertainty associated with the outputs we believe that the predictions are an improvement on just using the raw presence points from a database alone. The outputs can also be used as part of a multidisciplinary approach to inform risk based disease surveillance activities.

Distribución Animal , Ganado/virología , Modelos Teóricos , Mosquitos Vectores/virología , Fiebre del Valle del Rift/epidemiología , Virus de la Fiebre del Valle del Rift , Animales , Clima , Brotes de Enfermedades , Vectores de Enfermedades , Reino Unido
Travel Med Infect Dis ; 17: 35-42, 2017.
Artículo en Inglés | MEDLINE | ID: mdl-28456684


BACKGROUND: We describe trends of malaria in London (2000-2014) in order to identify preventive opportunities and we estimated the cost of malaria admissions (2009/2010-2014/2015). METHODS: We identified all cases of malaria, resident in London, reported to the reference laboratory and obtained hospital admissions from Hospital Episode Statistics. RESULTS: The rate of malaria decreased (19.4[2001]-9.1[2014] per 100,000). Males were over-represented (62%). Cases in older age groups increased overtime. The rate was highest amongst people of Black African ethnicity followed by Indian, Pakistani, Bangladeshi ethnicities combined (103.3 and 5.5 per 100,000, respectively). The primary reason for travel was visiting friends and relatives (VFR) in their country of origin (69%), mostly sub-Saharan Africa (92%). The proportion of cases in VFRs increased (32%[2000]-50%[2014]) and those taking chemoprophylaxis decreased (36%[2000]-14%[2014]). The overall case fatality rate was 0.3%. We estimated the average healthcare cost of malaria admissions to be just over £1 million per year. CONCLUSION: Our study highlighted that people of Black African ethnicity, travelling to sub-Saharan Africa to visit friends and relatives in their country of origin remain the most affected with also a decline in chemoprophylaxis use. Malaria awareness should focus on this group in order to have the biggest impact but may require new approaches.

Malaria , Viaje/estadística & datos numéricos , Adolescente , Adulto , África del Sur del Sahara/etnología , Antimaláricos/uso terapéutico , Quimioprevención/estadística & datos numéricos , Femenino , Hospitalización/estadística & datos numéricos , Humanos , Londres/epidemiología , Malaria/tratamiento farmacológico , Malaria/economía , Malaria/epidemiología , Malaria/etnología , Masculino , Persona de Mediana Edad , Adulto Joven
Fungal Genet Biol ; 41(1): 89-101, 2004 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-14643262


In this study, genes of the Schizophyllum commune Balpha and Bbeta mating-type loci are shown to be within a few kilobases of each other. The region between the nearest Balpha and Bbeta genes contains many short direct repeats. Predicted amino acid sequences and activity spectra of three pheromones encoded in the Balpha3 mating-type specificity are presented along with a re-evaluation of pheromone activity of many previously reported S. commune lipopeptide pheromones. This analysis showed that S. commune pheromones belong to five subtypes. Several pheromones activate both a Bbeta receptor and a Balpha receptor, a phenomenon previously unrecognized. Clues from mating tests and DNA hybridization led to the cloning of bar8, the gene encoding the Balpha8 pheromone receptor, Bar8. Bar8 is similar in sequence to Bbr1, the Bbeta1 pheromone receptor, and functionally identical to it. These data begin to elucidate the enigmatic recombination patterns previously encountered at the B mating-type complex.

Genes Fúngicos , Genes del Tipo Sexual de los Hongos , Feromonas/fisiología , Schizophyllum/genética , Secuencia de Bases , Mapeo Cromosómico , ADN de Hongos/análisis , Datos de Secuencia Molecular , Schizophyllum/química , Schizophyllum/fisiología , Homología de Secuencia de Ácido Nucleico