From the hospital scale to nationwide: observability and identification of models for the COVID-19 epidemic waves

Two mathematical models of the COVID-19 dynamics are considered as the health system in some country consists in a network of regional hospital centers. The first macroscopic model for the virus dynamics at the level of the general population of the country is derived from a standard SIR model. The second local model refers to a single node of the health system network, i.e. it models the flows of patients with a smaller granularity at the level of a regional hospital care center for COVID-19 infected patients. Daily (low cost) data are easily collected at this level, and are worked out for a fast evaluation of the local health status thanks to control systems methods. Precisely, the identifiability of the parameters of the hospital model is proven and thanks to the availability of clinical data, essential characteristics of the local health status are identified. Those parameters are meaningful not only to alert on some increase of the infection, but also to assess the efficiency of the therapy and health polic

Practical identification of a glucose-insulin dynamics model

Glycemia regulation algorithms which are designed to be implemented in several artificial pancreas projects are often model based control algorithms. However, actual diabetes monitoring is based throughout the world on the so-called Flexible Insulin Therapy (FIT) which does not always cope with current mathematical models. In this paper, we initiate an identification methodology of those FIT parameters from some standard ambulatory clinical data. This issue has an interest per se, or for a further use in any closed-loop regulation system

A nonlinear time-delay realization for gastroparesis in patients with diabetes

International audienceThe realization problem is fundamental in control systems theory and is well understood for linear systems. The situation is quite different for nonlinear systems with or without time delays. New results are given herein either as an abstract characterization of the class of delay–free nonlinear systems which are known to admit a realization, or by characterizing the existence of a realization for a wider class of retarded type or neutral type time delay nonlinear systems. The problem is motivated by the very practical modelling and realization problem of diabetes with or without gastroparesis. Up to 4% to 12% patients with diabetes are affected by the so-called gastroparesis which delays the digestion process. Sev- eral concurrent state space mathematical models do exist for diabetes and require a re-evaluation based on physiologic facts. These are reviewed herein to derive a (minimal) realizatio

Diabetic Gastroparesis Modeling and Observer Design

International audienc

Diabetic Gastroparesis Modeling and Observer Design

International audienc

Clinical assessment of a new biomatical model for decision making in functional insulin therapy

International audienc

A metamodel-based flexible insulin therapy for type 1 diabetes patients subjected to aerobic physical activity

International audiencePatients with type 1 diabetes are subject to exogenous insulin injections, whether manually or through (semi)automated insulin pumps. Basic knowledge of the patient’s characteristics and flexible insulin therapy (FIT) parameters are then needed. Specifically, artificial pancreas-like closed-loop insulin delivery systems are some of the most promising devices for substituting for endogenous insulin secretion in type 1 diabetes patients. However, these devices require self-reported information such as carbohydrates or physical activity from the patient, introducing potential miscalculations and delays that can have life-threatening consequences. Here, we display a metamodel for glucose-insulin dynamics that is subject to carbohydrate ingestion and aerobic physical activity. This metamodel incorporates major existing knowledge-based models. We derive comprehensive and universal definitions of the underlying FIT parameters to form an insulin sensitivity factor ( ISF ). In addition, the relevance of physical activity modelling is assessed, and the FIT is updated to take physical exercise into account. Specifically, we cope with physical activity by using heart rate sensors (watches) with a fully automated closed insulin loop, aiming to maximize the time spent in the glycaemic range (75.5% in the range and 1.3% below the range for hypoglycaemia on a virtual patient simulator).These mathematical parameter definitions are interesting on their own, may be new tools for assessing mathematical models and can ultimately be used in closed-loop artificial pancreas algorithms or to extend distinguished FIT

Clinical assessment of a new biomatical model for decision making in functional insulin therapy

International audienc

Astrocyte-derived adenosine is central to the hypnogenic effect of glucose

International audienceSleep has been hypothesised to maintain a close relationship with metabolism. Here we focus on the brain structure that triggers slow-wave sleep, the ventrolateral preoptic nucleus (VLPO), to explore the cellular and molecular signalling pathways recruited by an increase in glucose concentration. We used infrared videomicroscopy on ex vivo brain slices to establish that glucose induces vasodilations specifically in the VLPO via the astrocytic release of adenosine. Real-time detection by in situ purine biosensors further revealed that the adenosine level doubles in response to glucose, and triples during the wakefulness period. Finally, patch-clamp recordings uncovered the depolarizing effect of adenosine and its A2A receptor agonist, CGS-21680, on sleep-promoting VLPO neurons. Altogether, our results provide new insights into the metabolically driven release of adenosine. We hypothesise that adenosine adjusts the local energy supply to local neuronal activity in response to glucose. This pathway could contribute to sleep-wake transition and sleep intensity

A Markov-switching model of inflation: looking at the future during uncertain times

In this paper, we analyze the dynamic of inflation in Venezuela, during the last eighteen years, through a Markov-switching estimation of a New Keynesian Phillips curve. Estimation is carried out using the EM algorithm. The model´s estimates distinguish between a normal or backward looking regime and a rational expectation regime consistent with episodes of high uncertainty regarding the performance of the economy. This characterization of regimes is based on two elements: the description of the process of formation of inflationary expectations and the main economic events occurred during each regime