Optimal social distancing in epidemic control: cost prioritization, adherence and insights into preparedness principles.

The COVID-19 pandemic experience has highlighted the importance of developing general control principles to inform future pandemic preparedness based on the tension between the different control options, ranging from elimination to mitigation, and related costs. Similarly, during the COVID-19 pandemic, social distancing has been confirmed to be the critical response tool until vaccines become available. Open-loop optimal control of a transmission model for COVID-19 in one of its most aggressive outbreaks is used to identify the best social distancing policies aimed at balancing the direct epidemiological costs of a threatening epidemic with its indirect (i.

MPC based optimization applied to treatment of HCV infections.

Background And Objective: The recent introduction of antivirals for the treatment of the hepatitis C virus opens new frontiers but also poses a significant burden on public health systems. This paper presents a simulation study in which model predictive control (MPC) is proposed for optimizing the therapy aiming to obtain a reduction of the costs of therapy, while maintaining the best pharmacological control of the infection.

Methods: A dynamic model describing the evolution of hepatitis C is deployed as internal model for MPC implementation, using nominal values of parameters.

Models for optimally controlling varicella and herpes zoster by varicella vaccination: a comparative study.

The introduction of mass vaccination against Varicella-Zoster-Virus (VZV) is being delayed in many European countries mainly because of the "fear" of a subsequent boom in natural herpes zoster (HZ) incidence in the first decades after the initiation of vaccination, caused by the expected decline in the protective effect of natural immunity boosting due to reduced virus circulation. Optimal control theory has proven to be a successful tool in understanding ways to curtail the spread of infectious diseases by devising the optimal disease intervention strategies. In this paper, we describe how a reduced 'toy' model can extract the essentials of the dynamics of the VZV transmission and reactivation in case of the study of optimal paths of varicella immunization programs.

Effective Synchronization of EEG and EMG for Mobile Brain/Body Imaging in Clinical Settings.

Mobile Brain/Body Imaging (MoBI) is rapidly gaining traction as a new imaging modality to study how cognitive processes support locomotion. Electroencephalogram (EEG) and electromyogram (EMG), due to their time resolution, non-invasiveness and portability are the techniques of choice for MoBI, but synchronization requirements among others restrict its use to high-end research facilities. Here we test the effectiveness of a technique that enables us to achieve MoBI-grade synchronization of EEG and EMG, even when other strategies (such as Lab Streaming Layer (LSL)) cannot be used e.

Perspectives on optimal control of varicella and herpes zoster by mass routine varicella vaccination.

Herpes zoster arises from reactivation of the varicella-zoster virus (VZV), causing varicella in children. As reactivation occurs when cell-mediated immunity (CMI) declines, and there is evidence that re-exposure to VZV boosts CMI, mass varicella immunization might increase the zoster burden, at least for some decades. Fear of this natural zoster boom is the main reason for the paralysis of varicella immunization in Europe.

Procoagulant control strategies for the human blood clotting process.

This paper describes the comparison between two drug control strategies to hemophilia A. To emulate blood clotting and the pathological condition of hemophilia, a mathematical model composed by 14 ordinary differential equations is considered. We adopt a variable structure non-linear PID approach and a Model Predictive Control in order to control the dosage of procoagulant factor used in the treatment of hemophiliac patient.

Pattern recognition with adaptive-thresholds for sleep spindle in high density EEG signals.

Medicine and Surgery, University of Pisa, via Savi 10, 56126, Pisa, Italy Sleep spindles are electroencephalographic oscillations peculiar of non-REM sleep, related to neuronal mechanisms underlying sleep restoration and learning consolidation. Based on their very singular morphology, sleep spindles can be visually recognized and detected, even though this approach can lead to significant mis-detections. For this reason, many efforts have been put in developing a reliable algorithm for spindle automatic detection, and a number of methods, based on different techniques, have been tested via visual validation.

Singular spectrum analysis and adaptive filtering enhance the functional connectivity analysis of resting state fMRI data.

Sources of noise in resting-state fMRI experiments include instrumental and physiological noises, which need to be filtered before a functional connectivity analysis of brain regions is performed. These noisy components show autocorrelated and nonstationary properties that limit the efficacy of standard techniques (i.e.

Brain responses to emotional stimuli during breath holding and hypoxia: an approach based on the independent component analysis.

Voluntary breath holding represents a physiological model of hypoxia. It consists of two phases of oxygen saturation dynamics: an initial slow decrease (normoxic phase) followed by a rapid drop (hypoxic phase) during which transitory neurological symptoms as well as slight impairment of integrated cerebral functions, such as emotional processing, can occur. This study investigated how breath holding affects emotional processing.

Detection and removal of ocular artifacts from EEG signals for an automated REM sleep analysis.

Rapid eye movements (REMs) are a prominent feature of REM sleep, and their distribution and time density over the night represent important physiological and clinical parameters. At the same time, REMs produce substantial distortions on the electroencephalographic (EEG) signals, which strongly affect the significance of normal REM sleep quantitative study. In this work a new procedure for a complete and automated analysis of REM sleep is proposed, which includes both a REMs detection algorithm and an ocular artifact removal system.

Combining pharmacological therapy and vaccination in Chronic Myeloid Leukemia via model predictive control.

This paper describes a simulation study which aims at optimizing the therapy for the control of Chronic Myeloid Leukemia according to the following objectives: the reduction of the administered drug and vaccine amounts, the establishment of a auto-immune response and the long-term control of disease without reducing the effective of therapy with respect to the full treatment. A therapy optimization method is developed defining and solving a Model Predictive Control algorithm, preceded by an accurate Initial Guess search based on Monte-Carlo like approach. Simulation results show that the suggested procedure achieves the proposed goals.

Adaptive filtering and random variables coefficient for analyzing functional magnetic resonance imaging data.

Functional magnetic resonance imaging (fMRI) is used to study brain functional connectivity (FC) after filtering the physiological noise (PN). Herein, we employ: adaptive filtering for removing nonstationary PN; random variables (RV) coefficient for FC analysis. Comparisons with standard techniques were performed by quantifying PN filtering and FC in neural vs.

ErpICASSO: a tool for reliability estimates of independent components in EEG event-related analysis.

Independent component analysis and blind source separation methods are steadily gaining popularity for separating individual brain and non-brain source signals mixed by volume conduction in electroencephalographic data. Despite the advancements on these techniques, determining the number of embedded sources and their reliability are still open issues. In particular to date no method takes into account trial-to-trial variability in order to provide a reliability measure of independent components extracted in Event Related Potentials (ERPs) studies.

Serum insulin-like growth factor-1 concentrations are reduced in severely obese women and raise after weight loss induced by laparoscopic adjustable gastric banding.

Background: Obesity is associated with abnormalities of the growth hormone/insulin-like growth factor-1 (GH/IGF-1) axis. The role of serum IGF-1 measurement for recognition of hypothalamic-pituitary diseases in obesity is still a matter of debate.

Methods: This study evaluated the serum levels of IGF-1 in a population of severely obese women before and after long-term weight loss obtained by laparoscopic adjustable gastric banding (LAGB).

Innovative model to simulate exhalation phase in human respiratory system.

In this paper, we present a mathematical model, which mimics the bronchial resistances of human's lung in an expiratory act. The model is implemented in Matlab. The inputs that are used in this model derive from spirometry test.

Artificial neural networks in the outcome prediction of adjustable gastric banding in obese women.

Background: Obesity is unanimously regarded as a global epidemic and a major contributing factor to the development of many common illnesses. Laparoscopic Adjustable Gastric Banding (LAGB) is one of the most popular surgical approaches worldwide. Yet, substantial variability in the results and significant rate of failure can be expected, and it is still debated which categories of patients are better suited to this type of bariatric procedure.

A model predictive control strategy toward optimal structured treatment interruptions in anti-HIV therapy.

In this paper, model predictive control (MPC) strategies are applied to the control of human immunodeficiency virus infection, with the final goal of implementing an optimal structured treatment interruptions protocol. The MPC algorithms proposed in this paper use a dynamic model recently developed in order to mimic both transient responses and ultimate behavior, and to describe accordingly the different effect of commonly used drugs in highly active antiretroviral therapy (HAART). Simulation studies show that the proposed methods achieve the goal of reducing the drug consumption (thus minimizing the severe side effects of HAART drugs) while respecting the desired constraints on CD4+ cells and free virions concentration.

Functional structure of spontaneous sleep slow oscillation activity in humans.

Background: During non-rapid eye movement (NREM) sleep synchronous neural oscillations between neural silence (down state) and neural activity (up state) occur. Sleep Slow Oscillations (SSOs) events are their EEG correlates. Each event has an origin site and propagates sweeping the scalp.

Likeness-based detection of sleep slow oscillations in normal and altered sleep conditions: application on low-density EEG recordings.

Sleep slow oscillation (SSO) is a common EEG pattern of spontaneous activity during nonrapid eye movement sleep. A new method for detecting SSOs is presented and compared to previous canonical methods. The main result of this research is that for the first time an extensive SSO analysis is applied to clinical EEG montages, based on low-density EEG recordings.

Modelling and control of HIV dynamics.

Various models of HIV infection and evolution have been considered in the literature. This paper considers a variant of the Wodarz and Nowak mathematical model, adding "aggressiveness" as a new state variable in order to quantify the strength of the virus and its response to drugs. Although the model proposed is relatively simple, simulation results suggest that it may be useful in predicting the impact of the effectiveness of therapy on HIV dynamics.