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. We conduct a series of simulation experiments to illustrate the competitive advantage of our proposal over less specific alternatives. The proposed approach is applied to data collected on the common bottlenose dolphin population inhabiting the Tiber River estuary (Mediterranean Sea). Our results provide novel insights into this population's size and structure, shedding light on some of the ecological processes governing its dynamics.

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. Results of growth curve analysis and MANOVA test revealed that all isolates assimilate a broad range of substrates, generally preferring complex compounds such as monosaccharides related compounds, nitrogen compounds, carboxylic acids and esters. No evidence of competition for nutritional resources have been observed among isolates of this study. As a result, a combination of different isolates could be proposed to obtain a SynCom useful for the practice of phytopathogen biocontrol in forestry. The addition of i-erythritol, adenosine and turanose to a growth substrate could be suggested as stimulating compounds for the growth of the selected Trichoderma isolates.

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. (2) Distinct functional regression by cluster. RESULTS: The LMAs are divided into four clusters: 1 low EM; 2 moderate EM; 3 high EM; and 4 high EM-first wave. Low-Income showed a negative association with EM clusters 1 and 4. Population density and percentage of over 70 did not seem to affect EM significantly. Bed availability positively associates with EM during the first wave. The employment rate positively associates with EM during the first two waves, becoming negatively associated when the vaccination campaign began. CONCLUSIONS: The clustering shows diverse behaviours by geography and time, the impact of socioeconomic characteristics, and local governments and health services' responses. The LMAs allow to draw a clear picture of local characteristics associated with the spread of the virus. The employment rate trend confirmed that essential workers were at risk, especially during the first wave.

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. We asked whether evidence exists for reduced mortality rate following postexposure-vaccine-prophylaxis. To estimate the effectiveness of postexposure-vaccine-prophylaxis, we obtained data from the Israeli Ministry of Health registry. The study population consisted of Israeli residents aged 12 years and older, identified for the first time as PCR-positive for SARS-CoV-2, between December 20th, 2020 (the beginning of the vaccination campaign) and October 7th, 2021. We compared "recently injected" patients-that proved PCR-positive on the same day or on 1 of the 5 consecutive days after first vaccination (representing an unintended postexposure-vaccine-prophylaxis)s-to unvaccinated control group. Among Israeli residents identified PCR-positive for SARS-CoV-2, 11 687 were found positive on the day they received their first vaccine injection (BNT162b2) or on 1 of the 5 days thereafter. In patients over 65 years, 143 deaths occurred among 1412 recently injected (10.13%) compared to 255 deaths among the 1412 unvaccinated (18.06%), odd ratio (OR) 0.51 (95% confidence interval [CI]: 0.41-0.64; p < 0.001). A significant reduction in the death toll was observed among the 55-64 age group, with 8 deaths occurring among the 1320 recently injected (0.61%) compared to 24 deaths among the 1320 unvaccinated control (1.82%), OR 0.33 (95% CI: 0.13-0.76; p = 0.007). Postexposure-vaccine-prophylaxis is effective against death in COVID-19 infection.

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. METHODS: We selected three estimation models: model 1 (Maruotti et al.) is a Negative-Binomial GLMM with seasonal patterns; model 2 (Dorrucci et al.) is a Negative Binomial GLM epidemiological approach; and model 3 (Scortichini et al.) is a quasi-Poisson GLM time-series approach with temperature distributions. We extended the time windows of the original models until December 2021, computing various EM estimates to allow for comparisons. RESULTS: We compared the results with our benchmark, the ISS-ISTAT official estimates. Model 1 was the most consistent, model 2 was almost identical, and model 3 differed from the two. Model 1 was the most stable towards changes in the baseline years, while model 2 had a lower cross-validation RMSE. DISCUSSION: Presently, an unambiguous explanation of EM in Italy is not possible. We provide a range that we consider sound, given the high variability associated with the use of different models. However, all three models accurately represented the spatiotemporal trends of the pandemic waves in Italy.

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. Based on the 2019 year-specific conditional best linear unbiased predictions, excess of mortality is estimated. Results: In Italy, 14 and 11 regions suffered from relevant excess mortality in January and February, respectively. However, the situation is far from being as critical as during previous waves. Conclusions: We can conclude that no matter which variant (or multiple inter-variant recombination) we are facing, excess mortality will appear in correspondence of any incidence peak.

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. A generalized linear mixed model is introduced to model weekly mortality from 2011 to 2019, taking into account seasonal patterns and year-specific trends. Based on the 2019 year-specific conditional best linear unbiased predictions, a significant excess of mortality is estimated in 2020, leading to approximately 35000 more deaths than expected, mainly arising during the first wave. In 2021, instead, the excess mortality is not significantly different from zero, for the 85+ and 15-64 age classes, and significant reductions with respect to the 2020 estimated excess mortality are estimated for other age classes.

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. Answers to such questions need appropriate statistical models and visualization tools. Here, we give an overview of the role played by Statgroup-19, an independent Italian research group born in March 2020. The group includes seven statisticians from different Italian universities, each with different backgrounds but with a shared interest in data analysis, statistical modeling, and biostatistics. Since the beginning of the COVID-19 pandemic the group has interacted with authorities and journalists to support policy decisions and inform the general public about the evolution of the epidemic. This collaboration led to several scientific papers and an accrued visibility across various media, all made possible by the continuous interaction across the group members that shared their unique expertise.

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. RESULTS: Positive temporal trends in health risks are detected reflecting mainly long term effects of industrial activities. The adopted methodology identifies multiple factors influencing the temporal patterns: type of health outcomes, type of disease, and its link with gender and type of emission sources. CONCLUSIONS: Reliable methods to assess health profile changes in local populations attributable to contaminations are key elements to measure the impact of remediation activities.

A new robust Bayesian small area estimation via α -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. The California Education Code requires local educational agencies (LEAs) to administer the FITNESSGRAM, the Physical Fitness Test (PFT), to Californian students of public schools. This test evaluates six fitness areas, and experts defined that a passing result on all six areas of the test represents a fitness level that offers some protection against the diseases associated with physical inactivity. We consider 2015-2016 data provided by the California Department of Education (CDE): for each Californian county ( m=57 ), we aim at estimating the county-level proportion of students with a score equal to six. To account for the heterogeneity of the phenomenon and the presence of outlying counties, we extend the standard area-level model by specifying the random effects as a symmetric α -stable (S α S) distribution that can accommodate different types of outlying observations. The model can accurately estimate the county-level proportion of students with a score equal to six. Results highlight some interesting relationships with social and economic situations in each county. The performance of the proposed model is also investigated through an extensive simulation study.

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. This ensures to accurately predict important characteristics of the epidemic (e.g., peak time and height), allowing for a better allocation of health resources over time. Parameter estimation is carried out in a maximum likelihood framework. All computational details required to reproduce the approach and replicate the results are provided.

Capitoline Dolphins: Residency Patterns and Abundance Estimate of Tursiops truncatus 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 (Tursiops truncatus) 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. In all, 347 unique individuals were identified. The hierarchical cluster analysis highlighted 3 clusters, labeled resident (individuals encountered at least five times, in three different months, over three distinct years; n = 42), part-time (individuals encountered at least on two occasions in a month, in at least two different years; n = 73), and transient (individuals encountered on more than one occasion, in more than 1 month, none of them in more than 1 year; n = 232), each characterized by site-fidelity metrics. Open POPAN modeling estimated a population size of 529 individuals (95% CI: 456-614), showing that the Capitoline (Roman) coastal area and nearby regions surrounding the Tiber River estuary represent an important, suitable habitat for bottlenose dolphins, despite their proximity to one of the major urban centers in the world (the city of Rome). Given the high number of individuals in the area and the presence of resident individuals with strong site fidelity, we suggest that conservation plans should not be focused only close to the Tiber River mouths but extended to cover a broader scale of area.

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. Optimal weights are estimated using a leave-last-out rationale. The approach has been set up and validated during the first epidemic wave in Italy. A report of its performance for predicting ICU occupancy at regional level is included.

Space-time monitoring of coastal pollution in the Gulf of Gaeta, Italy, using δ15N values of Ulva lactuca, landscape hydromorphology, and Bayesian Kriging modelling.

We investigated the space-time dynamics of N pollution in a Mediterranean gulf (Gulf of Gaeta) by means of δ15N variation in seaweed fronds (Ulva lactuca) previously collected from an unpolluted habitat. We used a comprehensive deployment grid that enabled the generation of isotopic seascapes (isoseascapes) describing the topography of N pollution in coastal waters and identifying N input hotspots and their pathways of dispersion at sea. The δ15N values of U. lactuca increased during 48h of exposure to the gulf waters, indicating anthropogenic N inputs from wastewater-derived sources. Comparison of the isoseascapes between two years differing in terms of rainfall identified coastal and offshore areas that were vulnerable to freshwater-transported nutrients, consistent with terrestrial hydromorphology and sea surface-water circulation. Isoseacapes were robust enough to reduce deployment effort, representing a powerful tool for monitoring and management strategies and useful for Environmental Protection Agencies, the main target audience of applied ecological research.

Functional exploratory data analysis for high-resolution measurements of urban particulate matter.

In this work we propose the use of functional data analysis (FDA) to deal with a very large dataset of atmospheric aerosol size distribution resolved in both space and time. Data come from a mobile measurement platform in the town of Perugia (Central Italy). An OPC (Optical Particle Counter) is integrated on a cabin of the Minimetrò, an urban transportation system, that moves along a monorail on a line transect of the town. The OPC takes a sample of air every six seconds and counts the number of particles of urban aerosols with a diameter between 0.28 µm and 10 µm and classifies such particles into 21 size bins according to their diameter. Here, we adopt a 2D functional data representation for each of the 21 spatiotemporal series. In fact, space is unidimensional since it is measured as the distance on the monorail from the base station of the Minimetrò. FDA allows for a reduction of the dimensionality of each dataset and accounts for the high space-time resolution of the data. Functional cluster analysis is then performed to search for similarities among the 21 size channels in terms of their spatiotemporal pattern. Results provide a good classification of the 21 size bins into a relatively small number of groups (between three and four) according to the season of the year. Groups including coarser particles have more similar patterns, while those including finer particles show a more different behavior according to the period of the year. Such features are consistent with the physics of atmospheric aerosol and the highlighted patterns provide a very useful ground for prospective model-based studies.

Spatiotemporal analysis of the PM10 concentration over the Taranto area.

In this paper, an analysis of air quality data is provided for the municipal area of Taranto (southern Italy) characterized by high environmental risks as formally decreed by the Italian government in the 1990s with two administrative measures. This is due to the massive presence of industrial sites with elevated environmental impact activities along the NW boundary of the city conurbation. The aforementioned activities have effects on the environment and on public health, as a number of epidemiological researches concerning this area reconfirm. The present study is focused on particulate matter as measured by PM10 concentrations at 13 monitoring stations, equipped with analogous instruments based on the Beta absorption technology, either reporting hourly, two-hourly, or daily measurements. Daily estimates of the PM10 concentration surfaces are obtained in order to identify areas of higher concentration (hot spots), possibly related to specific anthropic activities. Preliminary analysis involved addressing several data problems: (1) due to the use of two different validation techniques, a calibration procedure was devised to allow for data comparability; (2) imputation techniques were considered to cope with the large number of missing data, due to both different working periods and occasional malfunctions of PM10 sensors; and (3) reliable weather covariates (wind speed and direction, pressure, temperature, etc.) were obtained and considered within the analysis. Spatiotemporal modelling was addressed by a Bayesian kriging-based model proposed by Le and Zidek (2006) characterized by the use of time varying covariates and a semiparametric covariance structure. Advantages and disadvantages of the model are highlighted and assessed in terms of fit and performance. Estimated daily PM10 concentration surfaces are suitable for the interpretation of time trends and for identifying concentration peaks within the urban area.