ORCID Profile
0000-0002-6190-4762
Current Organisation
University of Nottingham
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Publisher: Elsevier BV
Date: 06-2019
DOI: 10.1016/J.HEARES.2019.02.017
Abstract: The heterogeneity of tinnitus is substantial. Its numerous pathophysiological mechanisms and clinical manifestations have h ered fundamental and treatment research significantly. A decade ago, the Tinnitus Research Initiative introduced the Tinnitus S le Case History Questionnaire, a case history instrument for standardised collection of information about the characteristics of the tinnitus patient. Since then, a number of studies have been published which characterise in iduals and groups using data collected with this questionnaire. However, its use has been restricted to a clinical setting and to the evaluation of people with tinnitus only. In addition, it is limited in the ability to capture relevant comorbidities and evaluate their temporal relationship with tinnitus. Here we present a new case history instrument which is comprehensive in scope and can be answered by people with and without tinnitus alike. This 'European School for Interdisciplinary Tinnitus Research Screening Questionnaire' (ESIT-SQ) was developed with specific attention to questions about potential risk factors for tinnitus (including demographics, lifestyle, general medical and otological histories), and tinnitus characteristics (including perceptual characteristics, modulating factors, and associations with co-existing conditions). It was first developed in English, then translated into Dutch, German, Italian, Polish, Spanish, and Swedish, thus having broad applicability and supporting international collaboration. With respect to better understanding tinnitus profiles, we anticipate the ESIT-SQ to be a starting point for comprehensive multi-variate analyses of tinnitus. Data collected with the ESIT-SQ can allow establishment of patterns that distinguish tinnitus from non-tinnitus, and definition of common sets of tinnitus characteristics which might be indicated by the presence of otological or comorbid systemic diseases for which tinnitus is a known symptom.
Publisher: Elsevier BV
Date: 09-2009
DOI: 10.1016/J.PREVETMED.2009.05.019
Abstract: Mathematical simulation modelling of epidemic processes has recently become a popular tool in guiding policy decisions for potential disease outbreaks. Such models all rely on various parameters in order to specify quantities such as transmission and detection rates. However, the values of these parameters are peculiar to an in idual outbreak, and estimating them in advance of an epidemic has been the major difficulty in the predictive credibility of such approaches. The obstruction to classical approaches in estimating model parameters has been that of missing data: (i) an infected in idual is only detected after the onset of clinical signs, we never observe the time of infection directly (ii) if we wish to make inference on an epidemic while it is in progress (in order to predict how it might unfold in the future), we must take into account the fact that there may be in iduals who are infected but not yet detected. In this paper we apply a reversible-jump Markov chain Monte Carlo algorithm to a combined spatial and contact network model constructed in a Bayesian context to provide a real-time risk prediction during an epidemic. Using the ex le of a potential Avian H5N1 epidemic in the UK poultry industry, we demonstrate how such a technique can be used to give real-time predictions of quantities such as the probability of in idual poultry holdings becoming infected, the risk that in idual holdings pose to the population if they become infected, and the number and whereabouts of infected, but not yet detected, holdings. Since the methodology generalises easily to many epidemic situations, we anticipate its use as a real-time decision-support tool for targetting disease control to critical transmission processes, and for monitoring the efficacy of current control policy.
Publisher: Frontiers Media SA
Date: 12-01-2018
Publisher: Cold Spring Harbor Laboratory
Date: 22-02-2022
DOI: 10.1101/2022.02.22.481441
Abstract: Waste from dairy production is one of the world’s largest sources of contamination from antimicrobial resistant bacteria (ARB) and genes (ARGs). However, studies to date do not provide necessary evidence to inform antimicrobial resistance (AMR) countermeasures. We undertook a detailed, interdisciplinary, longitudinal analysis of dairy slurry waste. The slurry contained a population of ARB and ARGs, with resistances to current, historical and never-used on-farm antibiotics resistances were associated with Gram-negative and Gram-positive bacteria and mobile elements (IS Ecp1 , Tn 916 , Tn 21 -family transposons). Modelling and experimental work suggested that these populations are in dynamic equilibrium, with microbial death balanced by fresh input. Consequently, storing slurry without further waste input for at least 60 days was predicted to reduce ARB spread onto land, with % reduction in cephalosporin resistant Escherichia coli . The model also indicated that for farms with low antibiotic use, further reductions are unlikely to reduce AMR further. We conclude that the slurry tank is a critical point for prevalence and control of AMR, and that measures to limit the spread of AMR from dairy waste should combine responsible antibiotic use, including low total quantity, avoidance of human critical antibiotics, and choosing antibiotics with shorter half-lives, coupled with appropriate slurry storage.
Publisher: The Royal Society
Date: 07-2018
Abstract: Functional response models are important in understanding predator–prey interactions. The development of functional response methodology has progressed from mechanistic models to more statistically motivated models that can account for variance and the over-dispersion commonly seen in the datasets collected from functional response experiments. However, little information seems to be available for those wishing to prepare optimal parameter estimation designs for functional response experiments. It is worth noting that optimally designed experiments may require smaller s le sizes to achieve the same statistical outcomes as non-optimally designed experiments. In this paper, we develop a model-based approach to optimal experimental design for functional response experiments in the presence of parameter uncertainty (also known as a robust optimal design approach). Further, we develop and compare new utility functions which better focus on the statistical efficiency of the designs these utilities are generally applicable for robust optimal design in other applications (not just in functional response). The methods are illustrated using a beta-binomial functional response model for two published datasets: an experiment involving the freshwater predator Notonecta glauca (an aquatic insect) preying on Asellus aquaticus (a small crustacean), and another experiment involving a ladybird beetle ( Propylea quatuordecimpunctata L.) preying on the black bean aphid ( Aphis fabae Scopoli). As a by-product, we also derive necessary quantities to perform optimal design for beta-binomial regression models, which may be useful in other applications.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Theodore Kypraios.