Search

The Covid-19 pandemic has spawned numerous dynamic modeling attempts aimed at estimation, prediction, and ultimately control. The predictive power of these attempts has varied, and there remains a lack of consensus regarding the mechanisms of virus spread and the effectiveness of various non-pharmaceutical interventions that have been enforced regionally as well as nationally. Setting out in data available in the spring of 2020, and with a now-famous model by Imperial College researchers as example, we employ an information-theoretical approach to shed light on why the predictive power of early modeling approaches have remained disappointingly poor.

Existing working heart models for ex vivo functional evaluation of donor hearts often use cardiac afterloads made up of discrete resistive and compliant elements. This approach limits the practicality of independently controlling systolic and diastolic aortic pressure to safely test the heart under multiple loading conditions. We present and investigate a novel afterload concept designed to enable such control.Six ∼70 kg pig hearts were evaluated in vivo, then ex vivo in left-ventricular working mode using the presented afterload. Both in vivo and ex vivo, the hearts were evaluated at two exertion levels: at rest and following a 20 μg adrenaline bolus, while measuring aortic pressure and flow, left ventricular pressure and volume, and left atrial pressure.The afterload gave aortic pressure waveforms that matched the general shape of the in vivo measurements. A wide range of physiological systolic pressures (93 to 160 mm Hg) and diastolic pressures (73 to 113 mm Hg) were generated by the afterload.With the presented afterload concept, multiple physiological loading conditions could be tested ex vivo, and compared with the corresponding in vivo data. An additional control loop from the set pressure limits to the measured systolic and diastolic aortic pressure is proposed to address discrepancies observed between the set limits and the measured pressures.

AimTo estimate the COVID-19 infection-to-fatality ratio (IFR), infection-to-case ratio (ICR), and infection-to-ICU admission ratio (IIAR) in Sweden; to suggest methods for time series reconstruction and prediction.MethodsWe optimize a set of simple finite impulse response (FIR) models comprising of a scaling factor and time-delay between officially reported cases, ICU admissions and deaths time series using the least squares method. Combined with randomized PCR study results, we utilize this simple model to estimate the total number of infections in Sweden, and the corresponding IFR.ResultsThe model class provides a good fit between ICU admissions and deaths throughout 2020. Cases fit consistently from July 2020, by when PCR tests had become broadly available. We observe a diminished IFR in late summer as well as a strong decline during 2021, following the launch of a nation-wide vaccination program. The total number of infections during 2020 is estimated to 1.3 million.ConclusionsA FIR model with a delta filter function describes the evolution of epidemiological data in Sweden well. The fact that we found IFR, ICR and IIAR constant over large parts of 2020 is in contrast with claims of healthcare adaptation or mutated virus variants importantly affecting these ratios. The model allows us to retrospectively estimate the COVID-19 epidemiological trajectory, and conclude that Sweden was far from herd immunity by the end of 2020.

There is a large variability between individuals in the response to anesthetic drugs, that seriously limits the achievable performance of closed-loop controlled drug dosing. Full individualization of patient models based on early clinical response data has been suggested as a means to improve performance with maintained robustness (safety). We use estimation theoretic analysis and realization theory to characterize practical identifiability of the standard pharmacological model structure from anesthetic induction phase data and conclude that such approaches are not practically feasible.

Automatic tuning of PID controllers using relay feedback experiments has received attention on and off since it was first proposed and industrially implemented in a control system in the 1980s. While optimal experiment design and modern system identification easily outperform the original automatic tuner, they rely on computational resources that are not always available in industrial control systems. Here we present a combination of experiment and subsequent output-error identification of continuoustime first-order time-delayed (FOTD) system models, that requires very little in terms of computations and memory. The method has been extensively evaluated in simulation, and a prototype has been implemented for the ABB AC 800M controller family. (Less)

Background and Objective: New proposals to improve the regulation of hypnosis in anaesthesia based on the development of advanced control structures emerge continuously. However, a fair study to analyse the real benefits of these structures compared to simpler clinically validated PID-based solutions has not been presented so far. The main objective of this work is to analyse the performance limitations associated with using a filtered PID controller, as compared to a high-order controller, represented through a Youla parameter. Methods: The comparison consists of a two-steps methodology. First, two robust optimal filtered PID controllers, considering the effect of the inter-patient variability, are synthesised. A set of 47 validated paediatric pharmacological models, identified from clinical data, is used to this end. This model set provides representative inter-patient variability Second, individualised filtered PID and Youla controllers are synthesised for each model in the set. For fairness of comparison, the same performance objective is optimised for all designs, and the same robustness constraints are considered. Controller synthesis is performed utilising convex optimisation and gradient-based methods relying on algebraic differentiation. The worst-case performance over the patient model set is used for the comparison. Results: Two robust filtered PID controllers for the entire model set, as well as individual-specific PID and Youla controllers, were optimised. All considered designs resulted in similar frequency response characteristics. The performance improvement associated with the Youla controllers was not significant compared to the individually tuned filtered PID controllers. The difference in performance between controllers synthesized for the model set and for individual models was significantly larger than the performance difference between the individual-specific PID and Youla controllers. The different controllers were evaluated in simulation. Although all of them showed clinically acceptable results, the robust solutions provided slower responses. Conclusion: Taking the same clinical and technical considerations into account for the optimisation of the different controllers, the design of individual-specific solutions resulted in only marginal differences in performance when comparing an optimal Youla parameter and its optimal filtered PID counterpart. The inter-patient variability is much more detrimental to performance than the limitations imposed by the simple structure of the filtered PID controller.

Case isolation is a strategy with the potential to curb infectious disease epidemics. Expressions for the stability boundary of a case isolation scheme defined through the proportion of the infectious population that it isolates with a given delay have recently been established. Here we quantify how this stability boundary moves when heterogeneity of the inter-individual contact network increases, and explain the underlying mechanism through insightful examples.

Purpose Ischemic myocardial contracture (IMC) or “stone heart” is a condition with rapid onset following circulatory death. It inhibits transplantability of hearts donated upon circulatory death (DCD). We investigate the effectiveness of hemodynamic normalization upon withdrawal of life-sustaining therapy (WLST) in a large-animal controlled DCD model, with the hypothesis that reduction in cardiac work delays the onset of IMC. Methods A large-animal study was conducted comprising of a control group ($$n=6$$ n = 6 ) receiving no therapy upon WLST, and a test group ($$n=6$$ n = 6 ) subjected to a protocol for fully automated computer-controlled hemodynamic drug administration. Onset of IMC within 1 h following circulatory death defined the primary end-point. Cardiac work estimates based on pressure-volume loop concepts were developed and used to provide insight into the effectiveness of the proposed computer-controlled therapy. Results No test group individual developed IMC within $${1} \text { h}$$ 1 h , whereas all control group individuals did (4/6 within $${30}{\text { min}}$$ 30 min ). Conclusion Automatic dosing of hemodynamic drugs in the controlled DCD context has the potential to prevent onset of IMC up to $${1}{\text { h}}$$ 1 h , enabling ethical and medically safe organ procurement. This has the potential to increase the use of DCD heart transplantation, which has been widely recognized as a means of meeting the growing demand for donor hearts.

The role of non-pharmaceutical interventions (NPIs) on the spread of SARS-CoV-2 has drawn significant attention, both scientific and political. Particularly, an article by the Imperial College COVID-19 Response Team (ICCRT), published online in Nature on June 8, 2020, evaluates the efficiency of 5 NPIs. Based on mortality data up to early May, it concludes that only one of the interventions, lockdown, has been efficient in 10 out of 11 studied European countries.We show, via simulations using the ICCRT model code, that conclusions regarding the effectiveness of individual NPIs are not justified. Our analysis focuses on the 11th country, Sweden, an outlier in that no lockdown was effectuated. The new simulations show that estimated NPI efficiencies across all 11 countries change drastically unless the model is adapted to give the Swedish data special treatment. While stated otherwise in the Nature article, such adaptation has been done in the model code reproducing its results: An ungrounded country-specific parameter said to have been introduced in all 11 countries, is in the code only activated for Sweden. This parameter de facto provides a new NPI category, only present in Sweden, and with an impact comparable to that of a lockdown.While the considered NPIs have unarguably contributed to reduce virus spread, our analysis reveals that their individual efficiency cannot be reliably quantified by the ICCRT model, provided mortality data up to early May.

IntroductionA series of modelling reports that quantify the effect of non-pharmaceutical interventions (NPIs) on the spread of the SARS-CoV-2 virus have been made available prior to external scientific peer-review. The aim of this study was to investigate the method used by the Imperial College COVID-19 Research Team (ICCRT) for estimation of NPI effects from the system theoretical viewpoint of model identifiability.MethodsAn input-sensitivity analysis was performed by running the original software code of the systems model that was devised to estimate the impact of NPIs on the reproduction number of the SARS-CoV-2 infection and presented online by ICCRT in Report 13 on March 30 2020. An empirical investigation was complemented by an analysis of practical parameter identifiability, using an estimation theoretical framework.ResultsDespite being simplistic with few free parameters, the system model was found to suffer from severe input sensitivities. Our analysis indicated that the model lacks practical parameter identifiability from data. The analysis also showed that this limitation is fundamental, and not something readily resolved should the model be driven with data of higher reliability.DiscussionReports based on system models have been instrumental to policymaking during the SARS-CoV-2 pandemic. With much at stake during all phases of a pandemic, we conclude that it is crucial to thoroughly scrutinise any SARS-CoV-2 effect analysis or prediction model prior to considering its use as decision support in policymaking. The enclosed example illustrates what such a review might reveal.

Modeling is fundamental to both feed-forward and feedback control. Within automated anesthesia the two paradigms are usually referred to as target-controlled infusion (TCI) and closed-loop drug delivery, respectively. In both cases, the objective is to control a system with anesthetic drug infusion rate as input, and (measured) clinical effect as output. The input is related to the output through the pharmacokinetics (PK) and pharmacodynamics (PD) of the patient. This chapter gives an introduction to PKPD modeling in automated anesthesia management, intended to be accessible to both anesthesiology and (control) engineering researchers. The following topics are discussed: the role of modeling; the classic PKPD structure used in clinical pharmacology; anesthesia modeling and identification for closed-loop control; inter-patient variability and model uncertainty; disturbance, noise and equipment models. The chapter emphasizes electroencephalogram-guided control of propofol. (Less)

First responders to cardiac arrest depend on cardiopulmonary resuscitation to keep patients alive. A new ventilation method, phase-controlled intermittent insufflation of oxygen, was previously shown to improve heart perfusion during cardiopulmonary resuscitation in a large-animal study, outperforming the best currently known ventilation method. This paper investigates whether the advantage of the new method can be explained using standard linear lumped-parameter models of respiratory mechanics. The simple models were able to qualitatively capture the improvement.

Relay autotuning has proven very successful for single-input single-output systems. This paper proposes an identification method for relay autotuning of systems with two inputs and two outputs (TITO systems). The combination of asymmetric relay feedback and output error identification admits short tuning time, without the need for limit cycle convergence. The method is successfully demonstrated on relevant system models, including the Wood-Berry distillation column.

A method for optimization of PID controller parameters and measurement filter time constant is presented. The method differs from the traditional approach in that the controller and filter parameters are simultaneously optimized, as opposed to standard, sequential, design. Control performance is maximized through minimization of the integrated absolute error (IAE) caused by a unit step load disturbance. Robustness is achieved through Hinf constraints on sensitivity and complementary sensitivity. At the same time, noise attenuation is enforced by limiting either the H2 or Hinf norm of the transfer function from measurement noise to control signal. The use of exact gradients makes the synthesis method faster and more numerically robust than previously proposed alternatives. (Less)

Purpose: It has previously been shown that continuous intratracheal insufflation of oxygen (CIO) is superior to intermittent positive pressure ventilation (IPPV) regarding gas exchange and haemodynamics. The purpose of this study was to investigate gas exchanged and haemodynamics with a new technique of phase-controlled intermittent insufflation of oxygen (PIIO) compared to CIO. Method: Twenty (20) pigs were used, stratified into two groups (CIO, PIIO), with 10 animals each. Upon induction of ventricular fibrillation, standard ventilator support was replaced by either of CIO or PIIO ventilation. Chest compressions were delivered by the LUCAS I mCPR device. Following 20 min of CPR in normothermia, defibrillation was attempted. Results: Return of spontaneous circulation (ROSC) occurrence was not significantly higher (P