Surge Capacity in the COVID-19 Era: a Natural Experiment of Neurocritical Care in General Critical Care.

Background: COVID-19 surges led to significant challenges in ensuring critical care capacity. In response, some centers leveraged neurocritical care (NCC) capacity as part of the surge response, with neurointensivists providing general critical care for patients with COVID-19 without neurologic illness. The relative outcomes of NCC critical care management of patients with COVID-19 remain unclear and may help guide further surge planning and provide broader insights into general critical care provided in NCC units.

Assessment of Dentist Participation in Public Insurance Programs for Children in the US.

Importance: Evaluating the availability of dentists to provide dental care services to children is important for identifying interventions for improving access.

Objective: To assess dental care availability for children in the US by public insurance participation, rural-urban setting, and dentist taxonomy (general, pediatric, or specialized).

Design, Setting, And Participants: This cross-sectional study analyzed the availability of dentists from matching 3 data sets: the 2020 National Plan and Provider Enumeration System, the 2019-2020 State Board of Dentistry information acquired from each state, and the 2019 InsureKidsNow.

COVID-19 forecasts using Internet search information in the United States.

As the COVID-19 ravaging through the globe, accurate forecasts of the disease spread are crucial for situational awareness, resource allocation, and public health decision-making. Alternative to the traditional disease surveillance data collected by the United States (US) Centers for Disease Control and Prevention (CDC), big data from Internet such as online search volumes also contain valuable information for tracking infectious disease dynamics such as influenza epidemic. In this study, we develop a statistical model using Internet search volume of relevant queries to track and predict COVID-19 pandemic in the United States.

Critical success factors for routine immunization performance: A case study of Zambia 2000 to 2018.

Introduction: The essential components of a vaccine delivery system are well-documented, but robust evidence on how and why the related processes and implementation strategies prove effective at driving coverage is not well-established. To address this gap, we identified critical success factors associated with advancing key policies and programs that may have led to the substantial changes in routine childhood immunization coverage in Zambia between 2000 and 2018.

Methods: We identified Zambia as an exemplar in the delivery of childhood vaccines through analysis of DTP1 and DTP3 coverage data.

A vaccination model for COVID-19 in Gauteng, South Africa.

The COVID-19 pandemic provides an opportunity to explore the impact of government mandates on movement restrictions and non-pharmaceutical interventions on a novel infection, and we investigate these strategies in early-stage outbreak dynamics. The rate of disease spread in South Africa varied over time as individuals changed behavior in response to the ongoing pandemic and to changing government policies. Using a system of ordinary differential equations, we model the outbreak in the province of Gauteng, assuming that several parameters vary over time.

COVID-19 hospitalizations forecasts using internet search data.

As the COVID-19 spread over the globe and new variants of COVID-19 keep occurring, reliable real-time forecasts of COVID-19 hospitalizations are critical for public health decisions on medical resources allocations. This paper aims to forecast future 2 weeks national and state-level COVID-19 new hospital admissions in the United States. Our method is inspired by the strong association between public search behavior and hospitalization admissions and is extended from a previously-proposed influenza tracking model, AutoRegression with GOogle search data (ARGO).

Identifying and Characterizing Medical Advice-Seekers on a Social Media Forum for Buprenorphine Use.

Background: Online communities such as Reddit can provide social support for those recovering from opioid use disorder. However, it is unclear whether and how advice-seekers differ from other users. Our research addresses this gap by identifying key characteristics of r/suboxone users that predict advice-seeking behavior.

Selective visual attention skills differentially predict decoding and reading comprehension performance across reading ability profiles.

Background: The purpose of this study was to examine the relationship between selective visual attention (SVA), reading decoding, listening comprehension and reading comprehension in children with and without a reading disorder.

Methods: We used longitudinal data from the Avon Longitudinal Study of Parents and Children. We split children into four groups: Typical Readers, Dyslexics, Poor Comprehenders and Comorbid Reading Disorder.

The balancing role of distribution speed against varying efficacy levels of COVID-19 vaccines under variants.

During a pandemic, vaccination plays an important role in reducing the infection spread or adverse outcomes such as hospitalizations and deaths. However, a vaccine's overall public health impact depends not only on its initial efficacy, but also its efficacy against emerging variants and ease and speed of distribution. For example, mutations in SARS-CoV-2 raised concerns about diminishing vaccine effectiveness against COVID-19 caused by particular variants.

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.

Evaluating scenarios for school reopening under COVID19.

Background: Thousands of school systems have struggled with the decisions about how to deliver education safely and effectively amid the COVID19 pandemic. This study evaluates the public health impact of various school reopening scenarios (when, and how to return to in-person instruction) on the spread of COVID19.

Methods: An agent-based simulation model was adapted and used to project the impact of various school reopening strategies on the number of infections, hospitalizations, and deaths in the state of Georgia during the study period, i.

Students' preferences for returning to colleges and universities during the COVID-19 pandemic: A discrete choice experiment.

Importance: When an emerging infectious disease outbreak occurs, such as COVID-19, institutions of higher education (IHEs) must weigh decisions about how to operate their campuses. These decisions entail whether campuses should remain open, how courses should be delivered (in-person, online, or a mixture of the two), and what safety plans should be enacted for those on campus. These issues have weighed heavily on campus administrators during the on-going COVID-19 pandemic.

Parallel Subgradient Algorithm with Block Dual Decomposition for Large-scale Optimization.

This paper studies computational approaches for solving large-scale optimization problems using a Lagrangian dual reformulation, solved by parallel sub-gradient methods. Since there are many possible reformulations for a given problem, an important question is: Which reformulation leads to the fastest solution time? One approach is to detect a block diagonal structure in the constraint matrix, and reformulate the problem by dualizing the constraints outside of the blocks; the approach is defined herein as block dual decomposition. Main advantage of such a reformulation is that the Lagrangian relaxation has a block diagonal constraint matrix, thus decomposable into smaller sub-problems that can solved in parallel.

Global Sensitivity Analysis via a Statistical Tolerance Approach.

Sensitivity analysis and multiparametric programming in optimization modeling study variations of optimal value and solutions in the presence of uncertain input parameters. In this paper, we consider simultaneous variations in the inputs of the objective and constraint (jointly called the ), where the uncertainty is represented as a multivariate probability distribution. We introduce a tolerance approach based on principal component analysis, which obtains a tolerance region that is suited to the given distribution and can be considered a confidence set for the random input parameters.

Revolutionizing Membrane Design Using Machine Learning-Bayesian Optimization.

Polymeric membrane design is a multidimensional process involving selection of membrane materials and optimization of fabrication conditions from an infinite candidate space. It is impossible to explore the entire space by trial-and-error experimentation. Here, we present a membrane design strategy utilizing machine learning-based Bayesian optimization to precisely identify the optimal combinations of unexplored monomers and their fabrication conditions from an infinite space.

Recovery of Critical Metals from Aqueous Sources.

Critical metals, identified from supply, demand, imports, and market factors, include rare earth elements (REE), platinum group metals, precious metals, and other valuable metals such as lithium, cobalt, nickel, and uranium. Extraction of metals from U.S.

Resiliency of on-demand multimodal transit systems during a pandemic.

During the COVID-19 pandemic, the collapse of the public transit ridership led to significant budget deficits due to dramatic decreases in fare revenues. Additionally, public transit agencies are facing challenges of reduced vehicle capacity due to social distancing requirements, additional costs of cleaning and protective equipment, and increased downtime for vehicle cleaning. Due to these constraints on resources and budgets, many transit agencies have adopted essential service plans with reduced service hours, number of routes, or frequencies.

Resource allocation for different types of vaccines against COVID-19: Tradeoffs and synergies between efficacy and reach.

Objective: Vaccine shortage and supply-chain challenges have caused limited access by many resource-limited countries during the COVID-19 pandemic. One of the primary decisions for a vaccine-ordering decision-maker is how to allocate the limited resources between different types of vaccines effectively. We studied the tradeoff between efficacy and reach of the two vaccine types that become available at different times.

Repetitive Low-level Blast Exposure and Neurocognitive Effects in Army Ranger Mortarmen.

Introduction: Occupational exposure to repetitive, low-level blasts in military training and combat has been tied to subconcussive injury and poor health outcomes for service members. Most low-level blast studies to date have focused on explosive breaching and firing heavy weapon systems; however, there is limited research on the repetitive blast exposure and physiological effects that mortarmen experience when firing mortar weapon systems. Motivated by anecdotal symptoms of mortarmen, the purpose of this paper is to characterize this exposure and its resulting neurocognitive effects in order to provide preliminary findings and actionable recommendations to safeguard the health of mortarmen.

Real-world data analyses unveiled the immune-related adverse effects of immune checkpoint inhibitors across cancer types.

Immune checkpoint inhibitors have demonstrated significant survival benefits in treating many types of cancers. However, their immune-related adverse events (irAEs) have not been systematically evaluated across cancer types in large-scale real-world populations. To address this gap, we conducted real-world data analyses using nationwide insurance claims data with 85.

Safety and health perceptions of location-based augmented reality gaming app and their implications.

This study seeks to understand the potential safety and health implications of location-based augmented reality gaming apps ("LAR apps") through studying people perception of Pokémon GO, a popular LAR gaming app. These perceptions can affect app usage behavior, app retention rate, and market share which can be critical to policymakers and app developers. An online survey is conducted to capture the impacts of Pokémon GO regarding: (i) perceived risk of using the app and opinion of prohibiting its usage while driving and cycling, (ii) frequency of app-related distracted driving and cycling, (iii) frequency of app-induced driving and potentially unsafe driving behavior, (iv) average daily steps before and after using the app, and (v) perceived physical and mental health benefits.

Combined Heat and Power May Conflict with Decarbonization Goals-Air Emissions of Natural Gas Combined Cycle Power versus Combined Heat and Power Systems for Commercial Buildings.

This study compares the environmental impacts of a centralized natural gas combined cycle (NGCC) and a distributed natural gas-fired combined heat and power (CHP) energy system in the United States. We develop an energy-balance model in which each energy system supplies the electric, heating, and cooling demands of 16 commercial building types in 16 climate zones of the United States. We assume a best-case scenario where all the CHP's heat and power are allocated toward building demands to ensure robust results.