Monitoring measles infections using flight passenger dynamics in Europe: A data-driven approach.

This paper presents an open-access repository collecting information on measles virus infections and flight passenger movements in European countries from 2011 to 2023. It provides a comprehensive overview of reported measles cases and measles-mumps-rubella (MMR) vaccination coverage from authoritative organizations such as the World Health Organization (WHO) and the European Centre for Disease Prevention and Control (ECDC). In addition, the dataset includes detailed data on passenger movements between countries, facilitating analysis of cross-border disease transmission.

Understanding excess mortality in 2022: The dual impact of COVID-19 and heatwaves on the Italian elderly population.

In this study, we employ a comprehensive approach to model the concurrent effects of the COVID-19 epidemic and heatwaves on all-cause excess mortality. Our investigation uncovers distinct peaks in excess mortality, notably among individuals aged 80 years and older, revealing a strong positive correlation with excess temperatures (ET) during the summer of 2022 in Italy. Furthermore, we identify a notable role played by COVID-19 hospitalizations, exhibiting regional disparities, particularly during the winter months.

Terrain, oceanographic, and biological factors underlying the development of Mediterranean coastal animal forests.

Marine Animal Forests (MAFs) form three-dimensional seascapes and provide substrate and shelter for a variety of species. We investigated the fine-scale distribution pattern of three habitat-forming species of the coastal Mediterranean MAFs: Eunicella cavolini, E. singularis and Paramuricea clavata, and assessed the influence of terrain, oceanographic, and biological factors on their distribution and the formation of MAFs in the central-northern Tyrrhenian Sea.

Green infrastructure design for the containment of biological invasions. Insights from a peri-urban case study in Rome, Italy.

Secondary shrublands and transitional woodland/shrub formations are recognised to be particularly susceptible to plant invasions, one of the main global threats to biodiversity, especially in dynamic peri-urban landscapes. Urban fringes are in fact often the place for the sprawl of artificial surfaces, fragmentation of habitats, and complex land transitions (including both agriculture intensification and abandonment), which in turn increase propagule pressure of exotic species over residual semi-natural ecosystems. Within this framework, the present study was aimed at analysing i) how landscape composition and configuration affect the richness of woody exotic species in shrubland and transitional woodland/shrub patches, and ii) how this threat can be addressed by means of green infrastructure design in a peri-urban case study (Metropolitan City of Rome, Italy).

Finite mixtures in capture-recapture surveys for modeling residency patterns in marine wildlife populations.

This work aims to show how prior knowledge about the structure of a heterogeneous animal population can be leveraged to improve the abundance estimation from capture-recapture survey data. We combine the Open Jolly-Seber model with finite mixtures and propose a parsimonious specification tailored to the residency patterns of the common bottlenose dolphin. We employ a Bayesian framework for our inference, discussing the appropriate choice of priors to mitigate label-switching and nonidentifiability issues, commonly associated with finite mixture models.

Characterization of Trichoderma species from forest ecosystems by high-throughput phenotypic microarray.

The use of beneficial organisms for the biocontrol of soil-borne pathogens in forestry is still poor explored. In this work, the nutritional demands of 10 previously selected isolates of Trichoderma for the biocontrol of forest soil-borne pathogens have been tested by Phenotype Microarray technology, to investigate about their C-source utilization and exploring the possibility to obtain a microbial consortia (SynCom), an innovative strategy for the biocontrol of plant disease. All Trichoderma isolates tested in this study showed a high spore germination percentage within 3 d and evidenced nutritional preference regardless of the species they belong to, and unrelated to their soil of origin.

Predicting acute kidney injury with an artificial intelligence-driven model in a pediatric cardiac intensive care unit.

Background: Acute kidney injury (AKI) is among the most common complications following cardiac surgery in adult and pediatric patients, significantly affecting morbidity and mortality. Artificial Intelligence (AI) with Machine Learning (ML) can be used to predict outcomes. AKI diagnosis anticipation may be an ideal target of these methods.

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations.

Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022.

Assessment of Excess Mortality in Italy in 2020-2021 as a Function of Selected Macro-Factors.

Background: Excess mortality (EM) can reliably capture the impact of a pandemic, this study aims at assessing the numerous factors associated with EM during the COVID-19 pandemic in Italy.

Methods: Mortality records (ISTAT 2015-2021) aggregated in the 610 Italian Labour Market Areas (LMAs) were used to obtain the EM P-scores to associate EM with socioeconomic variables. A two-step analysis was implemented: (1) Functional representation of EM and clustering.

Two years of COVID-19 pandemic: The Italian experience of Statgroup-19.

The amount and poor quality of available data and the need of appropriate modeling of the main epidemic indicators require specific skills. In this context, the statistician plays a key role in the process that leads to policy decisions, starting with monitoring changes and evaluating risks. The "what" and the "why" of these changes represent fundamental research questions to provide timely and effective tools to manage the evolution of the epidemic.

Assessing COVID-19-Related Excess Mortality Using Multiple Approaches-Italy, 2020-2021.

Introduction: Excess mortality (EM) is a valid indicator of COVID-19's impact on public health. Several studies regarding the estimation of EM have been conducted in Italy, and some of them have shown conflicting values. We focused on three estimation models and compared their results with respect to the same target population, which allowed us to highlight their strengths and limitations.

Spatio-temporal modelling of COVID-19 incident cases using Richards' curve: An application to the Italian regions.

We introduce an extended generalised logistic growth model for discrete outcomes, in which spatial and temporal dependence are dealt with the specification of a network structure within an Auto-Regressive approach. A major challenge concerns the specification of the network structure, crucial to consistently estimate the canonical parameters of the generalised logistic curve, e.g.

Postexposure-vaccine-prophylaxis against COVID-19.

During the COVID-19 pandemic, postexposure-vaccine-prophylaxis is not a practice. Following exposure, only patient isolation is imposed. Moreover, no therapeutic prevention approach is applied.

COVID-19-induced excess mortality in Italy during the Omicron wave.

Background: The existing literature estimates a significantly reduced odds of hospitalisation and death among individuals. However, though less severe than other variants, the Omicron variant may still lead to excess mortality compared to pre-pandemic years.

Methods: A generalized linear mixed model is introduced to model weekly mortality from 2011 to 2019, taking into account seasonal patterns and year-specific trends.

Stratification of hospitalized COVID-19 patients into clinical severity progression groups by immuno-phenotyping and machine learning.

Quantitative or qualitative differences in immunity may drive clinical severity in COVID-19. Although longitudinal studies to record the course of immunological changes are ample, they do not necessarily predict clinical progression at the time of hospital admission. Here we show, by a machine learning approach using serum pro-inflammatory, anti-inflammatory and anti-viral cytokine and anti-SARS-CoV-2 antibody measurements as input data, that COVID-19 patients cluster into three distinct immune phenotype groups.

Estimating COVID-19-induced excess mortality in Lombardy, Italy.

We compare the expected all-cause mortality with the observed one for different age classes during the pandemic in Lombardy, which was the epicenter of the epidemic in Italy. The first case in Italy was found in Lombardy in early 2020, and the first wave was mainly centered in Lombardy. The other three waves, in Autumn 2020, March 2021 and Summer 2021 are also characterized by a high number of cases in absolute terms.

Seagrass restoration monitoring and shallow-water benthic habitat mapping through a photogrammetry-based protocol.

Seagrasses rank among the most productive yet highly threatened ecosystems on Earth. Loss of seagrass habitat because of anthropogenic disturbances and evidence of their limited resilience have provided the impetus for investigating and monitoring habitat restoration through transplantation programmes. Although Structure from Motion (SfM) photogrammetry is becoming a more and more relevant technique for mapping underwater environments, no standardised methods currently exist to provide 3-dimensional high spatial resolution and accuracy cartographic products for monitoring seagrass transplantation areas.

Exploring methods for the assessment of temporal trends in mortality and hospitalization in Italian industrially contaminated sites.

Introduction: The Italian contaminated sites of interest for remediation are monitored by SENTIERI, an epidemiological surveillance system describing the health status of populations living nearby these sites. There is an increasing concern on how to assess temporal changes in the health status of these populations.

Methods: A sequence of three statistical techniques was adopted to analyse temporal trends of mortality and hospitalization, by using different indicators and reference populations, in a sample of 36 sites with industrial sources of contamination monitored by SENTIERI.

A new robust Bayesian small area estimation via -stable model for estimating the proportion of athletic students in California.

In the last few years, diabetes mellitus and obesity revealed to be one of the fastest-growing chronic diseases in youth in the United States. The number of new diabetes cases is dramatically increasing, and, for the moment, effective therapy does not exist. Experts believe that one of the causes of this increase is the decline in exercise behavior.

Nowcasting COVID-19 incidence indicators during the Italian first outbreak.

A novel parametric regression model is proposed to fit incidence data typically collected during epidemics. The proposal is motivated by real-time monitoring and short-term forecasting of the main epidemiological indicators within the first outbreak of COVID-19 in Italy. Accurate short-term predictions, including the potential effect of exogenous or external variables are provided.

Capitoline Dolphins: Residency Patterns and Abundance Estimate of at the Tiber River Estuary (Mediterranean Sea).

Periodic assessments of population status and trends to detect natural influences and human effects on coastal dolphin are often limited by lack of baseline information. Here, we investigated for the first time the site-fidelity patterns and estimated the population size of bottlenose dolphins () at the Tiber River estuary (central Mediterranean, Tyrrhenian Sea, Rome, Italy) between 2017 and 2020. We used photo-identification data and site-fidelity metrics to study the tendency of dolphins to remain in, or return to, the study area, and capture-recapture models to estimate the population abundance.

An ensemble approach to short-term forecast of COVID-19 intensive care occupancy in Italian regions.

The availability of intensive care beds during the COVID-19 epidemic is crucial to guarantee the best possible treatment to severely affected patients. In this work we show a simple strategy for short-term prediction of COVID-19 intensive care unit (ICU) beds, that has proved very effective during the Italian outbreak in February to May 2020. Our approach is based on an optimal ensemble of two simple methods: a generalized linear mixed regression model, which pools information over different areas, and an area-specific nonstationary integer autoregressive methodology.