Excess Fatal Overdoses in the United States During the COVID-19 Pandemic by Geography and Substance Type: March 2020-August 2021.

Objectives. To assess heterogeneity in pandemic-period excess fatal overdoses in the United States, by location (state, county) and substance type. Methods. We used seasonal autoregressive integrated moving average (SARIMA) models to estimate counterfactual death counts in the scenario that no pandemic had occurred. Such estimates were subtracted from actual death counts to assess the magnitude of pandemic-period excess mortality between March 2020 and August 2021. Results. Nationwide, we estimated 25 668 (95% prediction interval [PI] = 2811, 48 524) excess overdose deaths. Specifically, 17 of 47 states and 197 of 592 counties analyzed had statistically significant excess overdose-related mortality. West Virginia, Louisiana, Tennessee, Kentucky, and New Mexico had the highest rates (20-37 per 100 000). Nationally, there were 5.7 (95% PI = 1.0, 10.4), 3.1 (95% PI = 2.1, 4.2), and 1.4 (95% PI = 0.5, 2.4) excess deaths per 100 000 involving synthetic opioids, psychostimulants, and alcohol, respectively. Conclusions. The steep increase in overdose-related mortality affected primarily the southern and western United States. We identified synthetic opioids and psychostimulants as the main contributors. Public Health Implications. Characterizing overdose-related excess mortality across locations and substance types is critical for optimal allocation of public health resources. (Am J Public Health. 2024;114(6):599-609. https://doi.org/10.2105/AJPH.2024.307618).

A Cross-Domain Approach to Analyzing the Short-Run Impact of COVID-19 on the US Electricity Sector.

The novel coronavirus disease (COVID-19) has rapidly spread around the globe in 2020, with the US becoming the epicenter of COVID-19 cases since late March. As the US begins to gradually resume economic activity, it is imperative for policymakers and power system operators to take a scientific approach to understanding and predicting the impact on the electricity sector. Here, we release a first-of-its-kind cross-domain open-access data hub, integrating data from across all existing US wholesale electricity markets with COVID-19 case, weather, mobile device location, and satellite imaging data. Leveraging cross-domain insights from public health and mobility data, we rigorously uncover a significant reduction in electricity consumption that is strongly correlated with the number of COVID-19 cases, degree of social distancing, and level of commercial activity.

An optimization formulation for characterization of pulsatile cortisol secretion.

Cortisol is released to relay information to cells to regulate metabolism and reaction to stress and inflammation. In particular, cortisol is released in the form of pulsatile signals. This low-energy method of signaling seems to be more efficient than continuous signaling. We hypothesize that there is a controller in the anterior pituitary that leads to pulsatile release of cortisol, and propose a mathematical formulation for such controller, which leads to impulse control as opposed to continuous control. We postulate that this controller is minimizing the number of secretory events that result in cortisol secretion, which is a way of minimizing the energy required for cortisol secretion; this controller maintains the blood cortisol levels within a specific circadian range while complying with the first order dynamics underlying cortisol secretion. We use an ℓ0-norm cost function for this controller, and solve a reweighed ℓ1-norm minimization algorithm for obtaining the solution to this optimization problem. We use four examples to illustrate the performance of this approach: (i) a toy problem that achieves impulse control, (ii) two examples that achieve physiologically plausible pulsatile cortisol release, (iii) an example where the number of pulses is not within the physiologically plausible range for healthy subjects while the cortisol levels are within the desired range. This novel approach results in impulse control where the impulses and the obtained blood cortisol levels have a circadian rhythm and an ultradian rhythm that are in agreement with the known physiology of cortisol secretion. The proposed formulation is a first step in developing intermittent controllers for curing cortisol deficiency. This type of bio-inspired pulse controllers can be employed for designing non-continuous controllers in brain-machine interface design for neuroscience applications.

Quantifying Pituitary-Adrenal Dynamics and Deconvolution of Concurrent Cortisol and Adrenocorticotropic Hormone Data by Compressed Sensing.

Pulsatile release of cortisol from the adrenal glands is governed by pulsatile release of adrenocorticotropic hormone (ACTH) from the anterior pituitary. In return, cortisol has a negative feedback effect on ACTH release. Simultaneous recording of ACTH and cortisol is not typical, and determining the number, timing, and amplitudes of pulsatile events from simultaneously recorded data is challenging because of several factors: 1) stimulator ACTH pulse activity, 2) kinematics of ACTH and cortisol, 3) the sampling interval, and 4) the measurement error. We model ACTH and cortisol secretion simultaneously using a linear differential equations model with Gaussian errors and sparse pulsatile events as inputs to the model. We propose a novel framework for recovering pulses and parameters underlying the interactions between ACTH and cortisol. We recover the timing and amplitudes of pulses using compressed sensing and employ generalized cross validation for determining the number of pulses. We analyze serum ACTH and cortisol levels sampled at 10-min intervals over 24 h from ten healthy women. We recover physiologically plausible timing and amplitudes for these pulses and model the feedback effect of cortisol. We recover 15 to 18 pulses over 24 h, which is highly consistent with the results of another cortisol data analysis approach. Modeling the interactions between ACTH and cortisol allows for accurate quantification of pulsatile events, and normal and pathological states. This could lay the basis for a more physiologically-based approach for administering cortisol therapeutically. The proposed approach can be adapted to deconvolve other pairs of hormones with similar interactions.

Deconvolution of serum cortisol levels by using compressed sensing.

The pulsatile release of cortisol from the adrenal glands is controlled by a hierarchical system that involves corticotropin releasing hormone (CRH) from the hypothalamus, adrenocorticotropin hormone (ACTH) from the pituitary, and cortisol from the adrenal glands. Determining the number, timing, and amplitude of the cortisol secretory events and recovering the infusion and clearance rates from serial measurements of serum cortisol levels is a challenging problem. Despite many years of work on this problem, a complete satisfactory solution has been elusive. We formulate this question as a non-convex optimization problem, and solve it using a coordinate descent algorithm that has a principled combination of (i) compressed sensing for recovering the amplitude and timing of the secretory events, and (ii) generalized cross validation for choosing the regularization parameter. Using only the observed serum cortisol levels, we model cortisol secretion from the adrenal glands using a second-order linear differential equation with pulsatile inputs that represent cortisol pulses released in response to pulses of ACTH. Using our algorithm and the assumption that the number of pulses is between 15 to 22 pulses over 24 hours, we successfully deconvolve both simulated datasets and actual 24-hr serum cortisol datasets sampled every 10 minutes from 10 healthy women. Assuming a one-minute resolution for the secretory events, we obtain physiologically plausible timings and amplitudes of each cortisol secretory event with R (2) above 0.92. Identification of the amplitude and timing of pulsatile hormone release allows (i) quantifying of normal and abnormal secretion patterns towards the goal of understanding pathological neuroendocrine states, and (ii) potentially designing optimal approaches for treating hormonal disorders.

Broad range of neural dynamics from a time-varying FitzHugh-Nagumo model and its spiking threshold estimation.

We study the use of the FitzHugh-Nagumo (FHN) model for capturing neural spiking. The FHN model is a widely used approximation of the Hodgkin-Huxley model that has significant limitations. In particular, it cannot produce the key spiking behavior of bursting. We illustrate that by allowing time-varying parameters for the FHN model, these limitations can be overcome while retaining its low-order complexity. This extension has applications in modeling neural spiking behaviors in the thalamus and the respiratory center. We demonstrate the use of the FHN model from an estimation perspective by presenting a novel parameter estimation method that exploits its multiple time-scale properties, and compare the performance of this method with the extended Kalman filter in several illustrative examples. We demonstrate that the dynamics of the spiking threshold can be recovered even in the absence of complete specifications for the system.

A feedback control model for cortisol secretion.

Existing mathematical models for cortisol secretion do not describe the entire cortisol secretion process, from the neural firing of corticotropin releasing hormone (CRH) in the hypothalamus to cortisol concentration in the plasma. In this paper, we lay the groundwork to construct a more comprehensive model, relating CRH, Adrenocorticotropic hormone (ACTH), and cortisol. We start with an existing mathematical model for cortisol secretion, and combine it with a simplified neural firing model that describes CRH and ACTH release. This simplified neural firing model is obtained using the extended FitzHugh-Nagumo (FHN) model, which includes a time-varying spiking threshold [3]. A key feature of our model is the presence of a feedback loop from cortisol secretion to ACTH secretion.

The Fitzhugh-Nagumo model: Firing modes with time-varying parameters & parameter estimation.

In this paper, we revisit the issue of the utility of the FitzHugh-Nagumo (FHN) model for capturing neuron firing behaviors. It has been noted (e.g., see [6]) that the FHN model cannot exhibit certain interesting firing behaviors such as bursting. We illustrate that, by allowing time-varying parameters for the FHN model, one could overcome such limitations while still retaining the low order complexity of the FHN model. We also highlight the utility of the FHN model from an estimation perspective by presenting a novel parameter estimation method that exploits the multiple time scale feature of the FHN model, and compare the performance of this method with the Extended Kalman Filter through illustrative examples.

Modeling the contribution of lamina 5 neuronal and network dynamics to low frequency EEG phenomena.

The Electroencephalogram (EEG) is an important clinical and research tool in neurophysiology. With the advent of recording techniques, new evidence is emerging on the neuronal populations and wiring in the neocortex. A main challenge is to relate the EEG generation mechanisms to the underlying circuitry of the neocortex. In this paper, we look at the principal intrinsic properties of neocortical cells in layer 5 and their network behavior in simplified simulation models to explain the emergence of several important EEG phenomena such as the alpha rhythms, slow-wave sleep oscillations, and a form of cortical seizure. The models also predict the ability of layer 5 cells to produce a resonance-like neuronal recruitment known as the augmenting response. While previous models point to deeper brain structures, such as the thalamus, as the origin of many EEG rhythms (spindles), the current model suggests that the cortical circuitry itself has intrinsic oscillatory dynamics which could account for a wide variety of EEG phenomena.