Enhancing the Management of Deployment-Limiting Medical Conditions in the U.S. Department of Navy Leveraging Electronic Health Records and Predictive Analytics Methods.

Introduction: Deployment-limiting medical conditions (DLMCs) such as debilitating injuries and conditions may interfere with the ability of military service members (SMs) to deploy. SMs in the United States (U.S.

Correction: Inferring school district learning modalities during the COVID-19 pandemic with a hidden Markov model.

[This corrects the article DOI: 10.1371/journal.pone.

Inferring school district learning modalities during the COVID-19 pandemic with a hidden Markov model.

During the COVID-19 pandemic, many public schools across the United States shifted from fully in-person learning to alternative learning modalities such as hybrid and fully remote learning. In this study, data from 14,688 unique school districts from August 2020 to June 2021 were collected to track changes in the proportion of schools offering fully in-person, hybrid and fully remote learning over time. These data were provided by Burbio, MCH Strategic Data, the American Enterprise Institute's Return to Learn Tracker and individual state dashboards.

Correlations and Timeliness of COVID-19 Surveillance Data Sources and Indicators - United States, October 1, 2020-March 22, 2023.

When the U.S. COVID-19 public health emergency declaration expires on May 11, 2023, national reporting of certain categories of COVID-19 public health surveillance data will be transitioned to other data sources or will be discontinued; COVID-19 hospitalization data will be the only data source available at the county level (1).

Gecko: A time-series model for COVID-19 hospital admission forecasting.

During the COVID-19 pandemic, concerns about hospital capacity in the United States led to a demand for models that forecast COVID-19 hospital admissions. These short-term forecasts were needed to support planning efforts by providing decision-makers with insight about future demands for health care capacity and resources. We present a SARIMA time-series model called Gecko developed for this purpose.

Trends in Disease Severity and Health Care Utilization During the Early Omicron Variant Period Compared with Previous SARS-CoV-2 High Transmission Periods - United States, December 2020-January 2022.

The B.1.1.

Pediatric COVID-19 Cases in Counties With and Without School Mask Requirements - United States, July 1-September 4, 2021.

Consistent and correct mask use is a critical strategy for preventing the transmission of SARS-CoV-2, the virus that causes COVID-19 (1). CDC recommends that schools require universal indoor mask use for students, staff members, and others in kindergarten through grade 12 (K-12) school settings (2). As U.

COVID-19-Related School Closures and Learning Modality Changes - United States, August 1-September 17, 2021.

Beginning in January 2021, the U.S. government prioritized ensuring continuity of learning for all students during the COVID-19 pandemic (1).

Can subjective pain be inferred from objective physiological data? Evidence from patients with sickle cell disease.

Patients with sickle cell disease (SCD) experience lifelong struggles with both chronic and acute pain, often requiring medical interventMaion. Pain can be managed with medications, but dosages must balance the goal of pain mitigation against the risks of tolerance, addiction and other adverse effects. Setting appropriate dosages requires knowledge of a patient's subjective pain, but collecting pain reports from patients can be difficult for clinicians and disruptive for patients, and is only possible when patients are awake and communicative.

Continuous Pain Assessment Using Ensemble Feature Selection from Wearable Sensor Data.

Sickle cell disease (SCD) is a red blood cell disorder complicated by lifelong issues with pain. Management of SCD related pain is particularly challenging due to its subjective nature. Hence, the development of an objective automatic pain assessment method is critical to pain management in SCD.

Model reconstruction from temporal data for coupled oscillator networks.

In a complex system, the interactions between individual agents often lead to emergent collective behavior such as spontaneous synchronization, swarming, and pattern formation. Beyond the intrinsic properties of the agents, the topology of the network of interactions can have a dramatic influence over the dynamics. In many studies, researchers start with a specific model for both the intrinsic dynamics of each agent and the interaction network and attempt to learn about the dynamics of the model.

Chaos in Kuramoto oscillator networks.

Kuramoto oscillators are widely used to explain collective phenomena in networks of coupled oscillatory units. We show that simple networks of two populations with a generic coupling scheme, where both coupling strengths and phase lags between and within populations are distinct, can exhibit chaotic dynamics as conjectured by Ott and Antonsen [Chaos 18, 037113 (2008)]. These chaotic mean-field dynamics arise universally across network size, from the continuum limit of infinitely many oscillators down to very small networks with just two oscillators per population.

Chimera states in two populations with heterogeneous phase-lag.

The simplest network of coupled phase-oscillators exhibiting chimera states is given by two populations with disparate intra- and inter-population coupling strengths. We explore the effects of heterogeneous coupling phase-lags between the two populations. Such heterogeneity arises naturally in various settings, for example, as an approximation to transmission delays, excitatory-inhibitory interactions, or as amplitude and phase responses of oscillators with electrical or mechanical coupling.

Chimera states in networks of phase oscillators: The case of two small populations.

Chimera states are dynamical patterns in networks of coupled oscillators in which regions of synchronous and asynchronous oscillation coexist. Although these states are typically observed in large ensembles of oscillators and analyzed in the continuum limit, chimeras may also occur in systems with finite (and small) numbers of oscillators. Focusing on networks of 2N phase oscillators that are organized in two groups, we find that chimera states, corresponding to attracting periodic orbits, appear with as few as two oscillators per group and demonstrate that for N>2 the bifurcations that create them are analogous to those observed in the continuum limit.

Chimera states on the surface of a sphere.

A chimera state is a spatiotemporal pattern in which a network of identical coupled oscillators exhibits coexisting regions of asynchronous and synchronous oscillation. Two distinct classes of chimera states have been shown to exist: "spots" and "spirals." Here we study coupled oscillators on the surface of a sphere, a single system in which both spot and spiral chimera states appear.

Symmetry breaking in optimal timing of traffic signals on an idealized two-way street.

Simple physical models based on fluid mechanics have long been used to understand the flow of vehicular traffic on freeways; analytically tractable models of flow on an urban grid, however, have not been as extensively explored. In an ideal world, traffic signals would be timed such that consecutive lights turned green just as vehicles arrived, eliminating the need to stop at each block. Unfortunately, this "green-wave" scenario is generally unworkable due to frustration imposed by competing demands of traffic moving in different directions.

Chimera states on a flat torus.

Chimera states are surprising spatiotemporal patterns in which regions of coherence and incoherence coexist. Initially observed numerically, these mathematical oddities were recently reproduced in a laboratory setting, sparking a flurry of interest in their properties. Here we use asymptotic methods to derive the conditions under which two-dimensional "spot" and "stripe" chimeras (similar to those observed in experiments) can exist in a periodic space.

A model balancing cooperation and competition can explain our right-handed world and the dominance of left-handed athletes.

An overwhelming majority of humans are right-handed. Numerous explanations for individual handedness have been proposed, but this population-level handedness remains puzzling. Here, we present a novel mathematical model and use it to test the idea that population-level hand preference represents a balance between selective costs and benefits arising from cooperation and competition in human evolutionary history.