Improving Short-Term QPF Using Geostationary Satellite All-Sky Infrared Radiances: Real-Time Ensemble Data Assimilation and Forecast during the PRECIP 2020 and 2021 Experiments

The Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP) aims to improve our understanding of extreme rainfall processes in the East Asian summer monsoon. A convection-permitting ensemble-based data assimilation and forecast system (the PSU WRF-EnKF system) was run in real time in the summers of 2020–21 in advance of the 2022 field campaign, assimilating all-sky infrared (IR) radiances from the geostationary Himawari-8 and GOES-16 satellites, and providing 48-h ensemble forecasts every day for weather briefings and discussions. This is the first time that all-sky IR data assimilation has been performed in a real-time forecast system at a convection-permitting resolution for several seasons. Compared with retrospective forecasts that exclude all-sky IR radiances, rainfall predictions are statistically significantly improved out to at least 4–6 h for the real-time forecasts, which is comparable to the time scale of improvements gained from assimilating observations from the dense ground-based Doppler weather radars. The assimilation of all-sky IR radiances also reduced the forecast errors of large-scale environments and helped to maintain a more reasonable ensemble spread compared with the counterpart experiments that did not assimilate all-sky IR radiances. The results indicate strong potential for improving routine short-term quantitative precipitation forecasts using these high-spatiotemporal-resolution satellite observations in the future.Significance StatementDuring the summers of 2020/21, the PSU WRF-EnKF data assimilation and forecast system was run in real time in advance of the 2022 Prediction of Rainfall Extremes Campaign In the Pacific (PRECIP), assimilating all-sky (clear-sky and cloudy) infrared radiances from geostationary satellites into a numerical weather prediction model and providing ensemble forecasts. This study presents the first-of-its-kind systematic evaluation of the impacts of assimilating all-sky infrared radiances on short-term qualitative precipitation forecasts using multiyear, multiregion, real-time ensemble forecasts. Results suggest that rainfall forecasts are improved out to at least 4–6 h with the assimilation of all-sky infrared radiances, comparable to the influence of assimilating radar observations, with benefits in forecasting large-scale environments and representing atmospheric uncertainties as well.

From Non‐Believer to Believer: What Leads People to Change Their Climate Views

Studies have highlighted the political, economic, and psychological factors in the debate over anthropogenic climate change—a hegemony approach—but have rarely focused on the stories and possibilities of people's transitions from climate change non‐believer to climate change believer. Based on publicly accessible narratives, this study examines the stories of those who have switched from non‐believer to believer—a narrative approach—and the dilemmas involved in those switches. Our investigation illuminates that a transition to climate change believer is a cultural and moral matter based on changing social relations of knowledge and what people regard as ignorable. We find that narratives of transition commonly describe interrelated shifts in three social relational factors: the narrator's notions of self, material reality, and justice. We term this contextualized transformative experience a relational rupture. Our narrative approach thus contextualizes climate change denialism within a person's web of social relations, not the hegemony of climate change communication alone. Moreover, we suggest that, since public debate and polarization on scientific topics such as climate change, vaccination, and COVID‐19 are socially situated, they may potentially be socially bridged. [ FROM AUTHOR]

The Pediatric Guideline Adherence and Outcomes (PEGASUS Argentina) program in severe traumatic brain injury: study protocol adaptations during the COVID-19 pandemic for a multisite implementation-effectiveness cluster randomized controlled trial.

BACKGROUND: The aim of this protocol is to describe the study protocol changes made and subsequently implemented to the Pediatric Guideline Adherence and Outcomes (PEGASUS) Argentina randomized controlled trial (RCT) for care of children with severe traumatic brain injuries (TBI) imposed by the COVID-19 pandemic. The PEGASUS study group met in spring 2020 to evaluate available literature review guidance and the study design change or pausing options due to the potential interruption of research. METHODS: As a parallel cluster RCT, pediatric patients with severe TBIs are admitted to 8 control (usual care) and 8 intervention (PEGASUS program) hospitals in Argentina, Chile, and Paraguay. PEGASUS is an intervention that aims to increase guideline adherence and best practice care for improving patient outcomes using multi-level implementation science-based approaches. Strengths and weaknesses of proposed options were assessed and resulted in a decision to revert from a stepped wedge to a parallel cluster RCT but to not delay planned implementation. DISCUSSION: The parallel cluster design was considered more robust and flexible to secular interruptions and acceptable and feasible to the local study sites in this situation. Due to the early stage of the study, the team had flexibility to redesign and implement a design more compatible with the conditions of the research landscape in 2020 while balancing analytical methods and power, logistical and implementation feasibility, and acceptability. As of fall 2022, the PEGASUS RCT has been active for nearly 2 years of implementation and data collection, scheduled to be completed in in fall 2023. The experience of navigating research during this period will influence decisions about future research design, strategies, and contingencies. TRIAL REGISTRATION: Pediatric Guideline Adherence and Outcomes-Argentina. Registered with ClinicalTrials.gov Identifier NCT03896789 on April 1, 2019.

Establishing a Health Information Technology for the Vaccination of National Institutes of Health Staff.

Introduction: Healthcare organizations faced unique operational challenges during the COVID-19 pandemic. Assuring the safety of both patients and healthcare workers in hospitals has been the primary focus during the COVID-19 pandemic. Methods: The NIH Vaccine Program (VP) with the Vaccine Management System (VMS) was created based on the commitment of NIH leadership, program leadership, the development team, and the program team; defining Key Performance Indicators (KPIs) of the VP and the VMS; and the NIH Clinical Center's (NIH CC) interdisciplinary approach to deploying the VMS. Results: This article discusses the NIH business requirements of the VP and VMS, the target KPIs of the VP and the VMS, and the NIH CC interdisciplinary approach to deploying an organizational VMS for vaccinating the NIH workforce. The use of the DCRI Spiral-Agile Software Development Life Cycle enabled the development of a system with stakeholder involvement that could quickly adapt to changing requirements meeting the defined KPIs for the program and system. The assessment of the defined KPIs through a survey and comments from the survey support that the VP and VMS were successful. Conclusion: A comprehensive program to maintain a healthy workforce includes asymptomatic COVID testing, symptomatic COVID testing, contact tracing, vaccinations, and policy-driven education. The need to develop systems during the pandemic resulted in changes to build software quickly with the input of many more users and stakeholders then typical in a decreased amount of time.

Provider perceptions of severe pediatric traumatic brain injury care priorities across hospitals in South America before and during the COVID-19 pandemic.

BACKGROUND: To understand provider perceptions of the COVID-19 pandemic on priorities of severe pediatric traumatic brain injury (TBI) care across hospitals in South America. METHODS: Site principal investigators (PIs) from 17 hospitals in South America enrolled in the PEGASUS-Argentina randomized controlled trial completed questionnaires regarding order of tasks performed in the care of a typical pediatric patient with severe TBI before (2019) and during (2021) the COVID-19 pandemic. Acute care processes were examined by quintiles to identify early, mid, and late actions and were categorized and compared. Associations of hospital volume and subspecialty resource availability with prioritization of key process actions were examined. FINDING: Site PIs from 15 and 16 hospitals completed the surveys in 2019 and 2021, respectively, including 14 who completed both. Action category order was stable between 2019 and 2021 and were ranked in priorities as: initial encounter, primary survey, interventions and invasive monitors, diagnostics, medications, staff communication, then disposition (in 2019) or nutrition (in 2021). There was variation in specific action order between hospitals at both timepoints, with only a few initial encounter and disposition actions limited to a single quintile. There was no reported association between hospital volume or subspecialty resource availability with prioritization of key process actions. INTERPRETATION: Despite novel healthcare challenges presented by the COVID-19 pandemic, providers in South America perceived maintaining standard severe pediatric TBI care consistent with BTF guidelines. There was large variability in specific action order between individual hospitals reported.

Emerging technology and management research in the container terminals: Trends and the COVID-19 pandemic impacts.

Despite the significant fluctuations in global trend due to the rising trade friction and the COVID19 pandemic, the container terminals are continuously working in three technology areas including automation, electrification and digitalization. This study reviewed recent technology trends as well as relevant research topics related to the container terminals, and investigated how the trends and topics would facilitate the terminals to achieve their strategic objectives. We also studied the trends in the container terminal industry before and after the pandemic outbreak. Recent progress shows that generally the long-term plans remain unchanged while there are some changes in timeline and priorities. The findings suggest that despite the common interest in long-term plans, gaps are still identified between academia and industry interests. Future directions are discussed for these technology areas, particularly in the context of the post-pandemic world, where the limited resources should be invested to the most urgent areas.

The Temporal Association of the COVID-19 Pandemic and Pediatric Cardiopulmonary Resuscitation Quality and Outcomes.

OBJECTIVES: The COVID-19 pandemic resulted in adaptations to pediatric resuscitation systems of care. The objective of this study was to determine the temporal association between the pandemic and pediatric in-hospital cardiac arrest (IHCA) process of care metrics, cardiopulmonary resuscitation (cardiopulmonary resuscitation) quality, and patient outcomes. DESIGN: Multicenter retrospective analysis of a dataset comprising observations of IHCA outcomes pre pandemic (March 1, 2019 to February 29, 2020) versus pandemic (March 1, 2020 to February 28, 2021). SETTING: Data source was the ICU-RESUScitation Project ("ICU-RESUS;" NCT028374497), a prospective, multicenter, cluster randomized interventional trial. PATIENTS: Children (≤ 18 yr) who received cardiopulmonary resuscitation while admitted to the ICU and were enrolled in ICU-RESUS. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Among 429 IHCAs meeting inclusion criteria, occurrence during the pandemic period was associated with higher frequency of hypotension as the immediate cause of arrest. Cardiac arrest physiology, cardiopulmonary resuscitation quality metrics, and postarrest physiologic and quality of care metrics were similar between the two periods. Survival with favorable neurologic outcome (Pediatric Cerebral Performance Category score 1-3 or unchanged from baseline) occurred in 102 of 195 subjects (52%) during the pandemic compared with 140 of 234 (60%) pre pandemic ( p = 0.12). Among survivors, occurrence of IHCA during the pandemic period was associated with a greater increase in Functional Status Scale (FSS) (i.e., worsening) from baseline (1 [0-3] vs 0 [0-2]; p = 0.01). After adjustment for confounders, IHCA survival during the pandemic period was associated with a greater increase in FSS from baseline (+1.19 [95% CI, 0.35-2.04] FSS points; p = 0.006) and higher odds of a new FSS-defined morbidity (adjusted odds ratio, 1.88 [95% CI, 1.03-3.46]; p = 0.04). CONCLUSIONS: Using the ICU-RESUS dataset, we found that relative to the year prior, pediatric IHCA during the first year of the COVID-19 pandemic was associated with greater worsening of functional status and higher odds of new functional morbidity among survivors.

Multisystem Inflammatory Syndrome of Children: Subphenotypes, Risk Factors, Biomarkers, Cytokine Profiles, and Viral Sequencing.

OBJECTIVE: To assess demographic, clinical, and biomarker features distinguishing patients with multisystem inflammatory syndrome in children (MIS-C); compare MIS-C sub-phenotypes; identify cytokine biosignatures; and characterize viral genome sequences. STUDY DESIGN: We performed a prospective observational cohort study of 124 children hospitalized and treated under the institutional MIS-C Task Force protocol from March to September 2020 at Children's National, a quaternary freestanding children's hospital in Washington, DC. Of this cohort, 63 of the patients had the diagnosis of MIS-C (39 confirmed, 24 probable) and 61 were from the same cohort of admitted patients who subsequently had an alternative diagnosis (controls). RESULTS: Median age and sex were similar between MIS-C and controls. Black (46%) and Latino (35%) children were over-represented in the MIS-C cohort, with Black children at greatest risk (OR 4.62, 95% CI 1.151-14.10; P = .007). Cardiac complications were more frequent in critically ill patients with MIS-C (55% vs 28%; P = .04) including systolic myocardial dysfunction (39% vs 3%; P = .001) and valvular regurgitation (33% vs 7%; P = .01). Median cycle threshold was 31.8 (27.95-35.1 IQR) in MIS-C cases, significantly greater (indicating lower viral load) than in primary severe acute respiratory syndrome coronavirus 2 infection. Cytokines soluble interleukin 2 receptor, interleukin [IL]-10, and IL-6 were greater in patients with MIS-C compared with controls. Cytokine analysis revealed subphenotype differences between critically ill vs noncritically ill (IL-2, soluble interleukin 2 receptor, IL-10, IL-6); polymerase chain reaction positive vs negative (tumor necrosis factor-α, IL-10, IL-6); and presence vs absence of cardiac abnormalities (IL-17). Phylogenetic analysis of viral genome sequences revealed predominance of GH clade originating in Europe, with no differences comparing patients with MIS-C with patients with primary coronavirus disease 19. Treatment was well tolerated, and no children died. CONCLUSIONS: This study establishes a well-characterized large cohort of MIS-C evaluated and treated following a standardized protocol and identifies key clinical, biomarker, cytokine, viral load, and sequencing features. Long-term follow-up will provide opportunity for future insights into MIS-C and its sequelae.

Predicting and forecasting the impact of local outbreaks of COVID-19: use of SEIR-D quantitative epidemiological modelling for healthcare demand and capacity.

BACKGROUND: The world is experiencing local/regional hotspots and spikes in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19 disease. We aimed to formulate an applicable epidemiological model to accurately predict and forecast the impact of local outbreaks of COVID-19 to guide the local healthcare demand and capacity, policy-making and public health decisions. METHODS: The model utilized the aggregated daily COVID-19 situation reports (including counts of daily admissions, discharges and bed occupancy) from the local National Health Service (NHS) hospitals and COVID-19-related weekly deaths in hospitals and other settings in Sussex (population 1.7 million), Southeast England. These data sets corresponded to the first wave of COVID-19 infections from 24 March to 15 June 2020. A novel epidemiological predictive and forecasting model was then derived based on the local/regional surveillance data. Through a rigorous inverse parameter inference approach, the model parameters were estimated by fitting the model to the data in an optimal sense and then subsequent validation. RESULTS: The inferred parameters were physically reasonable and matched up to the widely used parameter values derived from the national data sets by Biggerstaff M, Cowling BJ, Cucunubá ZM et al. (Early insights from statistical and mathematical modeling of key epidemiologic parameters of COVID-19, Emerging infectious diseases. 2020;26(11)). We validate the predictive power of our model by using a subset of the available data and comparing the model predictions for the next 10, 20 and 30 days. The model exhibits a high accuracy in the prediction, even when using only as few as 20 data points for the fitting. CONCLUSIONS: We have demonstrated that by using local/regional data, our predictive and forecasting model can be utilized to guide the local healthcare demand and capacity, policy-making and public health decisions to mitigate the impact of COVID-19 on the local population. Understanding how future COVID-19 spikes/waves could possibly affect the regional populations empowers us to ensure the timely commissioning and organization of services. The flexibility of timings in the model, in combination with other early-warning systems, produces a time frame for these services to prepare and isolate capacity for likely and potential demand within regional hospitals. The model also allows local authorities to plan potential mortuary capacity and understand the burden on crematoria and burial services. The model algorithms have been integrated into a web-based multi-institutional toolkit, which can be used by NHS hospitals, local authorities and public health departments in other regions of the UK and elsewhere. The parameters, which are locally informed, form the basis of predicting and forecasting exercises accounting for different scenarios and impacts of COVID-19 transmission.

Severe Coronavirus Disease-2019 in Children and Young Adults in the Washington, DC, Metropolitan Region.

Despite worldwide spread of severe acute respiratory syndrome coronavirus-2, few publications have reported the potential for severe disease in the pediatric population. We report 177 infected children and young adults, including 44 hospitalized and 9 critically ill patients, with a comparison of patient characteristics between infected hospitalized and nonhospitalized cohorts, as well as critically ill and noncritically ill cohorts. Children <1 year and adolescents and young adults >15 years of age were over-represented among hospitalized patients (P = .07). Adolescents and young adults were over-represented among the critically ill cohort (P = .02).