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A useful parametric specification for the expected value of an epidemiological process is revived, and its statistical and empirical efficacy are explored. The Richards' curve is flexible enough to adapt to several growth phenomena, including recent epidemics and outbreaks. Here, two different estimation methods are described. The first, based on likelihood maximisation, is particularly useful when the outbreak is still ongoing and the main goal is to obtain sufficiently accurate estimates in negligible computational run-time. The second is fully Bayesian and allows for more ambitious modelling attempts such as the inclusion of spatial and temporal dependence, but it requires more data and computational resources. Regardless of the estimation approach, the Richards' specification properly characterises the main features of any growth process (e.g. growth rate, peak phase etc.), leading to a reasonable fit and providing good short- to medium-term predictions. To demonstrate such flexibility, we show different applications using publicly available data on recent epidemics where the data collection processes and transmission patterns are extremely heterogeneous, as well as benchmark datasets widely used in the literature as illustrative. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• We provide an easy-to-compute summary measure of the severity of COVID-19 over time. • Intensive care unit admissions and deaths are summarized into a severity-generalized index. • Even on different scales, indicators can be further added to the analysis. • Results from Italy on the severity of the Alpha, Delta, and Omicron variants are discussed. • The estimated severity of the Alpha and Delta variant is about 55% higher than the Omicron variant. [ABSTRACT FROM AUTHOR]

We propose a semi-parametric spatio-temporal Hawkes process with periodic components to model the occurrence of car accidents in a given spatio-temporal window. The overall intensity is split into the sum of a background component capturing the spatio-temporal varying intensity and an excitation component accounting for the possible triggering effect between events. The spatial background is estimated and evaluated on the road network, allowing the derivation of accurate risk maps of road accidents. We constrain the spatio-temporal excitation to preserve an isotropic behaviour in space, and we generalize it to account for the effect of covariates. The estimation is pursued by maximizing the expected complete data log-likelihood using a tailored version of the stochastic-reconstruction algorithm that adopts ad hoc boundary correction strategies. An original application analyses the car accidents that occurred on the Rome road network in the years 2019, 2020, and 2021. Results highlight that car accidents of different types exhibit varying degrees of excitation, ranging from no triggering to a 10% chance of triggering further events. [ABSTRACT FROM AUTHOR]

Art experience is not solely the observation of artistic objects, but great relevance is also placed on the environment in which the art experience takes place, often in museums and galleries. Interestingly, in the last few years, the introduction of some forms of virtual reality (VR) in museum contexts has been increasing. This has solicited enormous research interest in investigating any eventual differences between looking at the same artifact either in a real context (e.g. a museum) and in VR. To address such a target, a neuroaesthetic study was performed in which electroencephalography (EEG) and autonomic signals (heart rate and skin conductance) were recorded during the observation of the Etruscan artifact "Sarcophagus of the Spouses", both in the museum and in a VR reproduction. Results from EEG analysis showed a higher level of the Workload Index during observation in the museum compared to VR (p = 0.04), while the Approach–Withdrawal Index highlighted increased levels during the observation in VR compared to the observation in the museum (p = 0.03). Concerning autonomic indices, the museum elicited a higher Emotional Index response than the VR (p = 0.03). Overall, preliminary results suggest a higher engagement potential of the museum compared to VR, although VR could also favour higher embodiment than the museum. [ABSTRACT FROM AUTHOR]

Simple Summary: Photo-identification is a non-invasive method of identifying individual animals from natural markings present on one or more parts of the body. The dorsal fin of a dolphin is the portion that contains individually distinctive marks and the most commonly visible when an animal surfaces to breathe. The camera captures an image of the dorsal fin whenever it is sighted and based on the number of times an image is captured, a statistical method called capture–recapture is used to estimate the site fidelity and the size of the population in a defined area. Here, we applied these methods to study the bottlenose dolphins (Tursiops truncatus) between 2017 and 2020 at the Tiber estuary (Mediterranean Sea, Rome, Italy), a polluted major river supplying organic material to the Capitoline (Roman) coastal area and nearby regions. We identified 347 unique individuals, with different patterns of residency (i.e., the tendency of dolphins to remain in, or return to, the study area): 42 were labeled resident, 73 part-time, and 232 transient. Estimating a total population size of 529 individuals and highlighting the presence of animals with a strong site-fidelity, this study suggests developing conservation plans for this vulnerable top-predator species not only focused on areas close to the Tiber River mouths but also extended to cover a broader scale of area. 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. [ABSTRACT FROM AUTHOR]