Linear-quadratic optimal control under non-Markovian switching

We study a finite-dimensional continuous-time optimal control problem on finite horizon for a controlled diffusion driven by Brownian motion, in the linear-quadratic case. We admit stochastic coefficients, possibly depending on an underlying independent marked point process, so that our model is general enough to include controlled switching systems where the switching mechanism is not required to be Markovian. The problem is solved by means of a Riccati equation, which a backward stochastic differential equation driven by the Brownian motion and by the random measure associated to the marked point process

Prestate of Stress and Fault Behavior During the 2016 Kumamoto Earthquake (M7.3)

Fault behavior during an earthquake is controlled by the state of stress on the fault. Complex coseismic fault slip on large earthquake faults has recently been observed by dense seismic networks, which complicates strong motion evaluations for potential faults. Here we show the three‐dimensional prestress field related to the 2016 Kumamoto earthquake. The estimated stress field reveals a spatially variable state of stress that forced the fault to slip in a direction predicted by the “Wallace and Bott Hypothesis.” The stress field also exposes the pre‐condition of pore fluid pressure on the fault. Large coseismic slip occurred in the low‐pressure part of the fault. However, areas with highly pressured fluid also showed large displacement, indicating that the seismic moment of the earthquake was magnified by fluid pressure. These prerupture data could contribute to improved seismic hazard evaluations

Communication and visiting policies in Italian intensive care units during the first COVID-19 pandemic wave and lockdown: a nationwide survey

Background: During the first coronavirus disease 2019 (COVID-19) pandemic wave, an unprecedented number of patients with respiratory failure due to a new, highly contagious virus needed hospitalization and intensive care unit (ICU) admission. The aim of the present study was to describe the communication and visiting policies of Italian intensive care units (ICUs) during the first COVID-19 pandemic wave and national lockdown and compare these data with prepandemic conditions. Methods: A national web-based survey was conducted among 290 Italian hospitals. Each ICU (active between February 24 and May 31, 2020) was encouraged to complete an individual questionnaire inquiring the hospital/ICU structure/organization, communication/visiting habits and the role of clinical psychology prior to, and during the first COVID-19 pandemic wave. Results: Two hundred and nine ICUs from 154 hospitals (53% of the contacted hospitals) completed the survey (202 adult and 7 pediatric ICUs). Among adult ICUs, 60% were dedicated to COVID-19 patients, 21% were dedicated to patients without COVID-19 and 19% were dedicated to both categories (Mixed). A total of 11,102 adult patients were admitted to the participating ICUs during the study period and only approximately 6% of patients received at least one visit. Communication with family members was guaranteed daily through an increased use of electronic devices and was preferentially addressed to the same family member. Compared to the prepandemic period, clinical psychologists supported physicians more often regarding communication with family members. Fewer patients received at least one visit from family members in COVID and mixed-ICUs than in non-COVID ICUs, l (0 [0–6]%, 0 [0–4]% and 11 [2–25]%, respectively, p < 0.001). Habits of pediatric ICUs were less affected by the pandemic. Conclusions: Visiting policies of Italian ICUs dedicated to adult patients were markedly altered during the first COVID-19 wave. Remote communication was widely adopted as a surrogate for family meetings. New strategies to favor a family-centered approach during the current and future pandemics are warranted

Multivariate Hidden Markov Models for disease progression

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