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.e., societal level) costs arising from enduring control measures. In particular, we analyse how optimal social distancing varies according to three key policy factors, namely, the degree of prioritization of indirect costs, the adherence to control measures, and the timeliness of intervention. As the prioritization of indirect costs increases, (i) the corresponding optimal distancing policy suddenly switches from elimination to suppression and, finally, to mitigation; (ii) the "effective" mitigation region-where hospitals' overwhelming is prevented-is dramatically narrow and shows multiple control waves; and (iii) a delicate balance emerges, whereby low adherence and lack of timeliness inevitably force ineffective mitigation as the only accessible policy option. The present results show the importance of open-loop optimal control, which is traditionally absent in public health preparedness, for studying the suppression-mitigation trade-off and supplying robust preparedness guidelines.

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. Different closed-loop simulations are presented both in nominal and in mismatch conditions. In addition, a more easily implementable treatment is proposed, which is based on a discrete dosage approach, where days on/off therapy are considered instead of continuous therapy modulation. RESULTS: Results show that therapy modulation allows one to achieve the same infection evolution as with full therapy, with a reduction of drug consumption between 10% and 40%. The alternative discrete dosage approach shows similar results achieved with therapy modulation, both in terms of therapy effectiveness and drug consumption reduction. CONCLUSIONS: The proposed model predictive control therapy optimization strategies appear to be effective, implementable and robust to model errors. It therefore represents a potentially useful approach to alleviate the burden of HCV therapy cost on national health systems.

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. Results obtained using different optimization approaches are compared with the ones of a more realistic age-structured model. The reduced model shows some unreliable predictions in regards of model time scales about herpes zoster dynamic; nevertheless, it is able to reproduce the main qualitative dynamic of the more realistic model to the different optimization problems, while requiring a minimal number of parameters to be identified. Graphical abstract ᅟ.

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.g., due to the unavailability of proprietary Application Programming Interfaces (APIs), which is often the case in clinical settings. The proposed strategy is that of aligning several spikes at the beginning and end of the session. We delivered a train of spikes to the EEG amplifier and EMG electrodes every 2 s over a 10-min time period. We selected a variable number of spikes (from 1 to 10) both at the beginning and end of the time series and linearly resampled the data so as to align them. We then compared the misalignment of the "middle" spikes over the whole recording to test for jitter and synchronization drifts, highlighting possible nonlinearities (due to hardware filters) and estimated the maximum length of the recording to achieve a [-5 to 5] ms misalignment range. We demonstrate that MoBI-grade synchronization can be achieved within 10-min recordings with a 1.7 ms jitter and [-5 5] ms misalignment range. We show that repeated spike delivery can be used to test online synchronization options and to troubleshoot synchronization issues over EEG and EMG. We also show that synchronization cannot rely only on the equipment sampling rate advertised by manufacturers. The synchronization strategy described can be used virtually in every clinical environment, and may increase the interest among a broader spectrum of clinicians and researchers in the MoBI framework, ultimately leading to a better understanding of the brain processes underlying locomotion control and the development of more effective rehabilitation approaches.

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. We apply optimal control to a realistically parametrized age-structured model for VZV transmission and reactivation to investigate whether feasible varicella immunization paths that are optimal in controlling both varicella and zoster exist. We analyse the optimality system numerically focusing on the role of the cost functional, of the relative zoster-varicella cost and of the planning horizon length. We show that optimal programmes will mostly be unfeasible for public health owing to their complex temporal profiles. This complexity is the consequence of the intrinsically antagonistic nature of varicella immunization programmes when aiming to control both varicella and zoster. However, we show that gradually increasing-hence feasible-vaccination schedules can perform better than routine programmes with constant vaccine uptake. Finally, we show the optimal profiles of feasible programmes targeting mitigation of the post-immunization natural zoster boom with priority.

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. This work aims at improving current pattern recognition procedures for sleep spindles detection by taking into account their physiological sources of variability. We provide a method as a synthesis of the current state of art that, improving dynamic threshold adaptation, is able to follow modification of spindle characteristics as a function of sleep depth and inter-subjects variability. The algorithm has been applied to physiological data recorded by a high density EEG in order to perform a validation based on visual inspection and on evaluation of expected results from normal night sleep in healthy subjects.

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. The two control actions are sampled for a practical application. Finally, we discuss and compare the results of the two control approaches, introducing a suited control index (eINR).

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. time filtering and general linear model). Herein we describe a novel approach based on the combination of singular spectrum analysis and adaptive filtering, which allows a greater noise reduction and yields better connectivity estimates between regions at rest, providing a new feasible procedure to analyze fMRI data.

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. To this aim we characterized the modulation of event-related potentials (ERPs) evoked by emotional-laden pictures as a function of breath holding time course. We recorded ERPs during free breathing and breath holding performed in air by elite apnea divers. We modeled brain responses during free breathing with four independent components distributed over different brain areas derived by an approach based on the independent component analysis (ICASSO). We described ERP changes during breath holding by estimating amplitude scaling and time shifting of the same components (component adaptation analysis). Component 1 included the main EEG features of emotional processing, had a posterior localization and did not change during breath holding; component 2, localized over temporo-frontal regions, was present only in unpleasant stimuli responses and decreased during breath holding, with no differences between breath holding phases; component 3, localized on the fronto-central midline regions, showed phase-independent breath holding decreases; component 4, quite widespread but with frontal prevalence, decreased in parallel with the hypoxic trend. The spatial localization of these components was compatible with a set of processing modules that affects the automatic and intentional controls of attention. The reduction of unpleasant-related ERP components suggests that the evaluation of aversive and/or possibly dangerous situations might be altered during breath holding.

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.

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. The two steps, based respectively on Wavelet Transform and adaptive filtering, are fully integrated and their performance is evaluated using REM simulated signals. Thanks to the integration with the detection algorithm, the proposed artifact removal system shows an enhanced accuracy in the recovering of the true EEG signal, compared to a system based on the adaptive filtering only. Finally the artifact removal system is applied to physiological data and an estimation of the actual distortion induced by REMs on EEG signals is supplied.

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. non-neural regions. As a result, adaptive filtering plus RV coefficient showed a greater suppression of PN and higher connectivity in neural regions, representing a novel effective approach to analyze fMRI data.

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). Eighty obese women with body mass index (BMI) of more than 34 kg/m(2) and 80 unrelated age-matched lean controls were enrolled. IGF-1 serum levels were measured together with BMI, liver volume, and intra-abdominal fat thickness assessed by ultrasound. Evaluation was repeated 2 years after LAGB. RESULTS: Our results showed that mean IGF-1 levels in obese subjects before LAGB were significantly lower (p < 0.001) than that observed in age-matched controls. Age and BMI were independent predictors of serum IGF-1 values, overall accounting for 39 % of IGF-1 variability. The mean IGF-1 concentration significantly increased 2 years after LAGB. BMI reduction was independently associated with IGF-1 increase (r = -0.29, p < 0.001). For each point of BMI reduction, the mean increase of serum IGF-1 was 4.39 ng/mL. CONCLUSIONS: (1) Severely obese women have low IGF-1 serum levels with respect to normal weight age-matched controls; (2) the extent of IGF-1 deficiency is proportional to increased BMI; (3) after LAGB a spontaneous raise of serum IGF-1 occurs, proportional to the extent of weight reduction; and (4) serum IGF-1 in severely obese subjects may have a limited value for detection of hypothalamic-pituitary diseases.

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. In this work we present ErpICASSO, a new method which modifies a data-driven approach named ICASSO for the analysis of trials (epochs). In addition to ICASSO the method enables the user to estimate the number of embedded sources, and provides a quality index of each extracted ERP component by combining trial-to-trial bootstrapping and CCA projection. We applied ErpICASSO on ERPs recorded from 14 subjects presented with unpleasant and neutral pictures. We separated potentials putatively related to different systems and identified the four primary ERP independent sources. Standing on the confidence interval estimated by ErpICASSO, we were able to compare the components between neutral and unpleasant conditions. ErpICASSO yielded encouraging results, thus providing the scientific community with a useful tool for ICA signal processing whenever dealing with trials recorded in different conditions.

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. This model is able to study a physiologic condition, a pathologic one and the patient's follow up after drug treatment. We split our study into two parts. The first one focuses the analysis on the gas fluido dynamic inside of the respiratory pathways. The second part takes care of the pressure equilibrium in the exchange zone. We use the outputs that derive from the second subsystem to solve the Bernoulli's equation of the first part. The model was validated with data provided from "Clinical Physiology Institute" of CNR and G. Monasterio Foundation of Pisa.

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. The aim of this study was to build a statistical model based on both psychological and physical data to predict weight loss in obese patients treated by LAGB, and to provide a valuable instrument for the selection of patients that may benefit from this procedure. METHODOLOGY/PRINCIPAL FINDINGS: The study population consisted of 172 obese women, with a mean ± SD presurgical and postsurgical Body Mass Index (BMI) of 42.5 ± 5.1 and 32.4 ± 4.8 kg/m(2), respectively. Subjects were administered the comprehensive test of psychopathology Minnesota Multiphasic Personality Inventory-2 (MMPI-2). Main goal of the study was to use presurgical data to predict individual therapeutical outcome in terms of Excess Weight Loss (EWL) after 2 years. Multiple linear regression analysis using the MMPI-2 scores, BMI and age was performed to determine the variables that best predicted the EWL. Based on the selected variables including age, and 3 psychometric scales, Artificial Neural Networks (ANNs) were employed to improve the goodness of prediction. Linear and non linear models were compared in their classification and prediction tasks: non linear model resulted to be better at data fitting (36% vs. 10% variance explained, respectively) and provided more reliable parameters for accuracy and mis-classification rates (70% and 30% vs. 66% and 34%, respectively). CONCLUSIONS/SIGNIFICANCE: ANN models can be successfully applied for prediction of weight loss in obese women treated by LAGB. This approach may constitute a valuable tool for selection of the best candidates for surgery, taking advantage of an integrated multidisciplinary approach.

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. Such promising results are obtained with realistic assumptions of infrequent (possibly noisy) measurements of a subset of model state variables. Furthermore, the control objectives are achieved even in the presence of mismatch between the dynamics of true patients and that of the MPC model.

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. The proposed method gives positive indications about its effectiveness also for altered sleep, extending the SSO analysis to extreme cases, and thus, opening a new front for investigating pathophysiological correlates of sleep disorders.

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. While recent findings suggest a SSO key role in memory consolidation processes, the structure and the propagation of individual SSO events, as well as their modulation by sleep stages and cortical areas have not been well characterized so far. METHODOLOGY/PRINCIPAL FINDINGS: We detected SSO events in EEG recordings and we defined and measured a set of features corresponding to both wave shapes and event propagations. We found that a typical SSO shape has a transition to down state, which is steeper than the following transition from down to up state. We show that during SWS SSOs are larger and more locally synchronized, but less likely to propagate across the cortex, compared to NREM stage 2. Also, the detection number of SSOs as well as their amplitudes and slopes, are greatest in the frontal regions. Although derived from a small sample, this characterization provides a preliminary reference about SSO activity in healthy subjects for 32-channel sleep recordings. CONCLUSIONS/SIGNIFICANCE: This work gives a quantitative picture of spontaneous SSO activity during NREM sleep: we unveil how SSO features are modulated by sleep stage, site of origin and detection location of the waves. Our measures on SSOs shape indicate that, as in animal models, onsets of silent states are more synchronized than those of neural firing. The differences between sleep stages could be related to the reduction of arousal system activity and to the breakdown of functional connectivity. The frontal SSO prevalence could be related to a greater homeostatic need of the heteromodal association cortices.

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.