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Effective site characterization in highly urbanized coastal lowlands requires accurate stratigraphic and geophysical investigations. In these regions, which typically host shallowly buried paleovalley systems formed in response to Quaternary glacio‐eustatic fluctuations, the marked lithologic contrast between soft sediment paleovalley fills and the adjacent, stiff substrate has the potential to modify earthquake motions, and assessment of critical parameters, such as shear wave velocities (VS) and resonance frequencies (f), should be coupled with detailed stratigraphic architecture. To evaluate the potential of the microtremor horizontal‐to‐vertical spectral ratio (mHVSR) for paleovalley recognition and mapping, we performed mHVSR measurements along the Adriatic coastal plain of Italy, where two paleovalley systems (Pescara and Manfredonia) have been recently identified. In both areas, we detected rapid lateral variations in resonance frequencies and highlighted laterally continuous impedance contrasts. Relying on a robust stratigraphic framework, we carefully evaluated the relation between geological and geophysical data and identified the stratigraphic surfaces responsible for the observed resonances. We derived VS models for the sediment fill, reconstructing the geometry of the two buried paleovalleys. We address the importance of evaluating the geological context when designing microzonation studies, for a reliable interpretation of changes in resonance frequencies. Plain Language Summary: When earthquakes occur, buildings shake differently based on several factors, including seismic wave velocity, natural resonance frequencies, and local geological characteristics. Beneath modern coastal lowlands, the presence of paleovalley systems can significantly modify the ground motion. Identification of these buried bodies is therefore essential to assess and reduce seismic hazard. Paleovalleys are shallow incisions formed under periods of fluvial erosion in response to Quaternary climate fluctuations, and subsequently filled with very soft clay. These bodies are found worldwide, and do not have any geomorphological evidence, making their recognition challenging. Geologists typically use expensive sediment core analysis to identify paleovalleys, but this method can only provide spotty information. Geophysical exploration techniques that rely on microtremors (small vibrations on the Earth) can complement mapping of these buried bodies. In this work, we tested this technique in Pescara and Mafredonia (Adriatic coastal plain, Italy), providing dense information about paleovalley geometries and geophysical parameters crucial for predicting how the ground will shake during an earthquake. This study also highlights the importance of integrating disciplines to improve our understanding of subsoil and to design future studies to mitigate seismic hazards. Key Points: Paleovalley fills are key sediment bodies made up of soft clay, tens of m thick and few km wide, buried beneath coastal lowlands worldwideMicrotremor‐based paleovalley profiles and stratigraphic cross‐sections exhibit strong similarityMicrotremor can provide shear wave velocities and resonance frequencies of paleovalleys, key parameters for seismic hazard mitigation [ABSTRACT FROM AUTHOR]

Disease progression models are a powerful tool for understanding the development of a disease, given some clinical measurements obtained from longitudinal events related to a sample of patients. These models are able to give some insights about the disease progression through the analysis of patients histories and can be also used to predict the future course of the disease for an individual. In particular, Hidden Markov Models are suitable for disease progression since they model the latent unobservable states of the disease. In this work, we propose a HMM where the outcome is multivariate and its components are not independent; to accomplish our aim, since we do not make any usual normality assumptions, we model the outcome using copulas. We first test the performance of our model in a simulation setting and show the validity of the method. Then, we study the course of Heart Failure, applying our model to an administrative dataset from Lombardia Region in Italy, showing how episodes of hospitalization can give information about the disease status of a patient. [ABSTRACT FROM AUTHOR]