Alien Marine Species in the Mediterranean - the 100 ‘Worst Invasives’ and their Impact

A number of marine alien species have been described as invasive or locally invasive in the Mediterranean because of their proliferation, and/or their geographical spread and/or impact on native populations. Based on that information and on the documented impact they have on the biodiversity and socioeconomics of the basin, a preliminary list of the 100 ‘worst’ Invasive Alien Species (IAS) in the Mediterranean has been produced and presented in this work along with details on their impact. Emphasis is given to their impact on socioeconomics (fi sheries/aquaculture, health & sanitation, infrastructure & building), documented for 43 species. Such selection of the ‘worst’ IAS was diffi cult and controversial and is expected to attract much attention and scientifi c criticism since not only can the documentation of the impact of IAS be controversial, but also their inventory can be biased towards the effort and resources devoted to the study of the impact of certain species/taxonomic groups. Thus, while marine plants (phytobenthos and phytoplankton) are fairly well studied, less attention has been paid to the impact of vertebrates and even less to invertebrates. Nevertheless, the list highlights the need for continued research on the issue (monitoring aliens and their impact for an integrated ecosystem based management approach over the entire area). The preliminary list can provide the basis for selecting indicator species within the Mediterranean and thus be the common ground to build cooperation about IAS within countries in the region

Cancer Morbidity Trends and Regional Differences in England - a Bayesian Analysis

Reliable modelling of the dynamics of cancer morbidity risk is important, not least due to its significant impact on healthcare and related policies. We identify morbidity trends and regional differences in England for all-cancer and type-specific incidence between 1981 and 2016. We use Bayesian modelling to estimate cancer morbidity incidence at various age, year, gender, and region levels. Our analysis shows increasing trends in most rates and marked regional variations that also appear to intensify through time in most cases. All-cancer rates have increased significantly, with the highest increase in East, North West and North East. The absolute difference between the rates in the highest- and lowest-incidence region, per 100,000 people, has widened from 39 (95% CI 33-45) to 86 (78-94) for females, and from 94 (85-104) to 116 (105-127) for males. Lung cancer incidence for females has shown the highest increase in Yorkshire and the Humber, while for males it has declined in all regions with the highest decrease in London. The gap between the highest- and lowest-incidence region for females has widened from 47 (42-51) to 94 (88-100). Temporal change in in bowel cancer risk is less manifested, with regional heterogeneity also declining. Prostate cancer incidence has increased with the highest increase in London, and the regional gap has expanded from 33 (30-36) to 76 (69-83). For breast cancer incidence the highest increase has occurred in North East, while the regional variation shows a less discernible increase. The analysis reveals that there are important regional differences in the incidence of all-type and type-specific cancers, and that most of these regional differences become more pronounced over time. A significant increase in regional variation has been demonstrated for most types of cancer examined here, except for bowel cancer where differences have narrowed

HIV with contact-tracing: a case study in Approximate Bayesian Computation

Missing data is a recurrent issue in epidemiology where the infection process may be partially observed. Approximate Bayesian Computation, an alternative to data imputation methods such as Markov Chain Monte Carlo integration, is proposed for making inference in epidemiological models. It is a likelihood-free method that relies exclusively on numerical simulations. ABC consists in computing a distance between simulated and observed summary statistics and weighting the simulations according to this distance. We propose an original extension of ABC to path-valued summary statistics, corresponding to the cumulated number of detections as a function of time. For a standard compartmental model with Suceptible, Infectious and Recovered individuals (SIR), we show that the posterior distributions obtained with ABC and MCMC are similar. In a refined SIR model well-suited to the HIV contact-tracing data in Cuba, we perform a comparison between ABC with full and binned detection times. For the Cuban data, we evaluate the efficiency of the detection system and predict the evolution of the HIV-AIDS disease. In particular, the percentage of undetected infectious individuals is found to be of the order of 40%

New model diagnostics for spatio-temporal systems in epidemiology and ecology

A cardinal challenge in epidemiological and ecological modelling is to develop effective and easily deployed tools for model assessment. The availability of such methods would greatly improve understanding, prediction and management of disease and ecosystems. Conventional Bayesian model assessment tools such as Bayes factors and the deviance information criterion (DIC) are natural candidates but suffer from important limitations because of their sensitivity and complexity. Posterior predictive checks, which use summary statistics of the observed process simulated from competing models, can provide a measure of model fit but appropriate statistics can be difficult to identify. Here, we develop a novel approach for diagnosing mis-specifications of a general spatio-temporal transmission model by embedding classical ideas within a Bayesian analysis. Specifically, by proposing suitably designed non-centred parametrization schemes, we construct latent residuals whose sampling properties are known given the model specification and which can be used to measure overall fit and to elicit evidence of the nature of mis-specifications of spatial and temporal processes included in the model. This model assessment approach can readily be implemented as an addendum to standard estimation algorithms for sampling from the posterior distributions, for example Markov chain Monte Carlo. The proposed methodology is first tested using simulated data and subsequently applied to data describing the spread of Heracleum mantegazzianum (giant hogweed) across Great Britain over a 30-year period. The proposed methods are compared with alternative techniques including posterior predictive checking and the DIC. Results show that the proposed diagnostic tools are effective in assessing competing stochastic spatio-temporal transmission models and may offer improvements in power to detect model mis-specifications. Moreover, the latent-residual framework introduced here extends readily to a broad range of ecological and epidemiological models

Indicators for the Sea-floor Integrity of the Hellenic Seas under the European Marine Strategy Framework Directive: establishing the thresholds and standards for Good Environmental Status

A data set of 625 samples of benthic macroinvertebrates collected from the Hellenic Seas (Ionian and Aegean) was used to establish thresholds and reference standards for two of the indicators addressing the descriptors of Sea-floor Integrity under the Marine Strategy Framework Directive (MSFD): species diversity and richness and the ratio of sensitive species to tolerant species. The dataset was categorised according to the baseline ecological status assessment of the respective water bodies under the Water Framework Directive (WFD). Species diversity and richness were characterised using the Shannon diversity and species richness indices, respectively, and were analysed for three pre-defined substrate types, three depth zones and three sample-size categories, and the significant categories were statistically validated. Good Environmental Status (GEnS) threshold and reference values were established for the valid combinations of categories denoted as ‘ecotypes’ through the use of a boxplot and an analysis of variance. The limitations and specifications for an overall GEnS assessment using the above indices are highlighted based on the WFD experience. For the ratio of sensitive species to tolerant species, the BENTIX index classification scale is proposed for GEnS assessment, and an integrated approach to the assessment of diversity and species richness is suggested. Finally, the regionality of the tested indices in relation to the two Mediterranean sub-regions, including the Hellenic area, was tested

The effect of the COVID-19 health disruptions on breast cancer mortality for older women: A semi-Markov modelling approach

We propose a methodology to quantify the impact on breast cancer mortality of diagnostic delays caused by public health measures introduced as a response to the COVID-19 pandemic. These measures affected cancer pathways by halting cancer screening, delaying diagnostic tests, and reducing the numbers of patients starting treatment. We introduce a semi-Markov model, to quantify the impact of the pandemic based on publicly available population data for women age 65{89 years in England and relevant medical literature. We quantify age-specific excess deaths, for a period up to 5 years, along with years of life expectancy lost and change in cancer mortality by cancer stage. Our analysis suggests a 3-6% increase in breast cancer deaths, corresponding to more than 40 extra deaths, per 100,000 women, after age 65 years old over 5 years, and a 4-6% increase in registrations of advanced (Stage 4) breast cancer. Our modelling approach exhibits consistent results in sensitivity analyses, providing a model that can account for changes in breast cancer diagnostic and treatment services