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Forecasts of COVID-19 outcomes play an essential role in alerting public health and government officials to the trajectory of the pandemic. The sudden and critical need for these forecasts spurred both the proliferation of diverse epidemiological transmission models from academia and industry across the United States and efforts to standardize and curate these model outputs. In many scientific domains, ensemble models, where individual forecasts are aggregated into one, have demonstrated smaller forecasting error than the individual models from which they are constructed. Using COVID-19 deaths as an index outcome, we developed and evaluated several ensemble approaches where point forecast models were combined via weighted sums based on historical individual model or ensemble model performance. We found that a simple method that minimized the error of the past performance of individual models and used L2 regularization to encourage broader distribution of weights across models outperformed a baseline mean ensemble and all other tested methods across US states for both absolute error and weighted interval scores. This suggests that performance-based ensembles can produce accurate forecasts despite training on only point forecasts and recent historical data, provided that sufficient regularization and constraints are used to capture uncertainty. Availability of an accurate and explainable ensemble forecast model can increase trust among stakeholders and the general public, thus bettering preparedness and response efforts during the COVID-19 pandemic.

Situational awareness (SA) is critical to mobilizing a rapid, efficient, and effective response to disasters. Limited by time and resources, response agencies must make decisions about rapidly evolving situations, which requires the collection, analysis, and sharing of actionable information across a complex landscape. Emerging technologies, if appropriately applied, can enhance SA and enable responders to make quicker, more accurate decisions. The aim of this systematic review is to identify technologies that can improve SA and assist decision-making across the United States Government and the domestic and international agencies they support during disaster response operations. A total of 1459 articles and 36 after-action reports were identified during literature searches. Following the removal of duplicates and application of inclusion/exclusion criteria, 302 articles and after-action reports were included in the review. Our findings suggest SA is constrained primarily due to unreliable and significantly delayed communications, time-intensive data analysis and visualization, and a lack of interoperable sensor networks and other capabilities providing data to shared platforms. Many of these challenges could be addressed by existing technologies. Bridging the divide between research and development efforts and the operational needs of response agencies should be prioritized.

Early during the COVID-19 pandemic, the Centers for Disease Control and Prevention (CDC) leveraged an existing surveillance system infrastructure to monitor COVID-19 cases and deaths in the United States. Given the time needed to report individual-level (also called line-level) COVID-19 case and death data containing detailed information from individual case reports, CDC designed and implemented a new aggregate case surveillance system to inform emergency response decisions more efficiently, with timelier indicators of emerging areas of concern. We describe the processes implemented by CDC to operationalize this novel, multifaceted aggregate surveillance system for collecting COVID-19 case and death data to track the spread and impact of the SARS-CoV-2 virus at national, state, and county levels. We also review the processes established to acquire, process, and validate the aggregate number of cases and deaths due to COVID-19 in the United States at the county and jurisdiction levels during the pandemic. These processes include time-saving tools and strategies implemented to collect and validate authoritative COVID-19 case and death data from jurisdictions, such as web scraping to automate data collection and algorithms to identify and correct data anomalies. This topical review highlights the need to prepare for future emergencies, such as novel disease outbreaks, by having an event-agnostic aggregate surveillance system infrastructure in place to supplement line-level case reporting for near–real-time situational awareness and timely data.

ObjectiveThe COVID-19 pandemic has significantly stressed healthcare systems. The addition of monoclonal antibody (mAb) infusions, which prevent severe disease and reduce hospitalizations, to the repertoire of COVID-19 countermeasures offers the opportunity to reduce system stress but requires strategic planning and use of novel approaches. Our objective was to develop a web-based decision-support tool to help existing and future mAb infusion facilities make better and more informed staffing and capacity decisions.Materials and MethodsUsing real-world observations from three medical centers operating with federal field team support, we developed a discrete-event simulation model and performed simulation experiments to assess performance of mAb infusion sites under different conditions.Results162,000 scenarios were evaluated by simulations. Our analyses revealed that it was more effective to add check-in staff than to add additional nurses for middle-to-large size sites with ≥ 2 infusion nurses; that scheduled appointments performed better than walk-ins when patient load was not high; and that reducing infusion time was particularly impactful when load on resources was only slightly above manageable levels.DiscussionPhysical capacity, check-in staff, and infusion time were as important as nurses for mAb sites. Health systems can effectively operate an infusion center under different conditions to provide mAb therapeutics even with relatively low investments in physical resources and staff.ConclusionSimulations of mAb infusion sites were used to create a capacity planning tool to optimize resource utility and allocation in constrained pandemic conditions, and more efficiently treat COVID-19 patients at existing and future mAb infusion sites.

BackgroundMonoclonal antibodies (mAbs) against SARS-CoV-2 are a promising treatment for limiting the progression of COVID-19 and decreasing strain on hospitals. Their use, however, remains limited, particularly in disadvantaged populations.MethodsElectronic health records were reviewed from SARS-CoV-2 patients at a single medical center in the United States that initiated mAb infusions in January 2021 with the support of the U.S. Department of Health and Human Services’ National Disaster Medical System. Patients who received mAbs were compared to untreated patients from the time period before mAb availability who met eligibility criteria for mAb treatment. We used logistic regression to measure the effect of mAb treatment on the risk of hospitalization or emergency department (E.D.) visit within 30 days of laboratory-confirmed COVID-19.ResultsOf 598 COVID-19 patients, 270 (45%) received bamlanivimab and 328 (55%) were untreated. Two hundred and thirty-one patients (39%) were Hispanic. Among treated patients, 5/270 (1.9%) presented to the E.D. or required hospitalization within 30 days of a positive SARS-CoV-2 test, compared to 39/328 (12%) untreated patients (pConclusionsIn this diverse, real-world COVID-19 patient population, mAb treatment significantly decreased the risk of subsequent E.D. visit or hospitalization. Broader treatment with mAbs, including in disadvantaged patient populations, can decrease the burden on hospitals and should be facilitated in all populations in the United States to ensure health equity.SummaryIn a diverse, real-world COVID-19 patient population, treatment with monoclonal antibodies significantly decreased the risk of subsequent emergency department visit or hospitalization within 30 days of a positive SARS-CoV-2 viral test.

Indexed literature (from 2015 to 2020) on artificial intelligence (AI) technologies and machine learning algorithms (ML) pertaining to disasters and public health emergencies were reviewed. Search strategies were developed and conducted for PubMed and Compendex. Articles that met inclusion criteria were filtered iteratively by title followed by abstract review and full text review. Articles were organized to identify novel approaches and breadth of potential AI applications. A total of 1217 articles were initially retrieved by the search. Upon relevant title review, 1003 articles remained. Following abstract screening, 667 articles remained. Full text review for relevance yielded 202 articles. Articles that met inclusion criteria totaled 56 articles. Those identifying specific roles of AI and ML (17 articles) were grouped by topics highlighting utility of AI and ML in disaster and public health emergency contexts. Development and use of AI and ML have increased dramatically over the past few years. This review discusses and highlights potential contextual applications and limitations of AI and ML in disaster and public health emergency scenarios.

BackgroundThe COVID-19 pandemic caught the globe unprepared without targeted medical countermeasures, such as therapeutics, to target the emerging SARS-CoV-2 virus. However, in recent months multiple monoclonal antibody therapeutics to treat COVID-19 have been authorized by the U.S. Food and Drug Administration (FDA) under Emergency Use Authorization (EUA). Despite these authorizations and promising clinical trial efficacy results, monoclonal antibody therapies are currently underutilized as a treatment for COVID-19 across the U.S. Many barriers exist when deploying a new infused therapeutic during an ongoing pandemic with limited resources and staffing, and it is critical to better understand the process and site requirements of incorporating monoclonal antibody infusions into pandemic response activities.MethodsWe examined the monoclonal antibody infusion site process components, resources, and requirements during the COVID-19 pandemic using data from three initial infusion sites at medical centers in the U.S. supported by the National Disaster Medical System. A descriptive analysis was conducted using process assessment metrics to inform recommendations to strengthen monoclonal antibody infusion site implementation.ResultsThe monoclonal antibody infusion sites varied in physical environment and staffing models due to state polices, infection control mechanisms, and underlying medical system structure, but exhibited a common process workflow. Sites operationalized an infusion process staffing model with at least two nurses per ten infusion patients. Monoclonal antibody implementation success factors included tailoring the infusion process to the patient community, strong engagement with local medical providers, batch preparing the therapy before patient arrival, placing the infusion center in proximity to emergency services, and creating procedures resilient to EUA changes. Infusion process challenges stemmed from confirming patient SARS-CoV-2 positivity, strained staff, scheduling needs, and coordination with the pharmacy for therapy preparation.ConclusionsInfusion site processes are most effective when integrated into the pre-existing pandemic response ecosystems and can be implemented with limited staff and physical resources. As the pandemic and policy tools such as EUAs evolve, monoclonal antibody infusion processes must also remain adaptable, as practice changes directly affect resources, staffing, timing, and workflows. Future use may be aided by incorporating innovative emergency deployment techniques, such as vehicle and home-based therapy administration, and by developing drug delivery mechanisms that alleviate the need for observed intravenous infusions by medically-accredited staff.

During an Ebola virus disease (EVD) outbreak, calculating the exposure window of a confirmed case can assist field investigators in identifying the source of infection and establishing chains of transmission. However, field investigators often have difficulty calculating this window. We developed a bilingual (English/French), smartphone-based field application to assist field investigators in determining the exposure window of an EVD case. The calculator only requires the reported date of symptoms onset and the type of symptoms present at onset or the date of death. Prior to the release of this application, there was no similar electronic capability to enable consistent calculation of EVD exposure windows for field investigators. The Democratic Republic of the Congo Ministry of Health endorsed the application and incorporated it into trainings for field staff. Available for Apple and Android devices, the calculator continues to be downloaded even as the eastern DRC outbreak resolved. We rapidly developed and implemented a smartphone application to estimate the exposure window for EVD cases in an outbreak setting

2020 saw the continuation of the second largest outbreak of Ebola virus disease (EVD) in history. Determining epidemiological links between cases is a key part of outbreak control. However, due to the large quantity of data and subsequent data entry errors, inconsistencies in potential epidemiological links are difficult to identify. We present chainchecker, an online and offline shiny application which visualises, curates and verifies transmission chain data. The application includes the calculation of exposure windows for individual cases of EVD based on user defined incubation periods and user specified symptom profiles. It has an upload function for viral hemorrhagic fever data and utility for additional entries. This data may then be visualised as a transmission tree with inconsistent links highlighted. Finally, there is utility for cluster analysis and the ability to highlight nosocomial transmission. chainchecker is a R shiny application which has an offline version for use with VHF (viral hemorrhagic fever) databases or linelists. The software is available at https://shiny.dide.imperial.ac.uk/chainchecker which is a web-based application that links to the desktop application available for download and the github repository, https://github.com/imperialebola2018/chainchecker.

Initiatives to improve hypertension control within academic medical centers and closed health systems have been extensively studied, but large community-wide quality improvement (QI) initiatives have been both less common and less successful in the United States. The authors examined a community-wide QI initiative across 226 843 patients from 198 practices in nine counties across upstate New York to improve hypertension control and reduce disparities. The QI initiative focused on (a) providing population and practice-level comparative data, (b) community engagement, especially in underserved communities, and (c) practice-level quality improvement assistance, but was not designed to examine causality of specific components. Across the nine counties, hypertension control rates improved from 61.9% in 2011 to 69.5% in 2016. Improvements were greatest among whites (73.7%-81.5%) and more modest among black patients (58.8%-64.7%). The authors noted a considerable improvement in BP within the group of patients with the highest risk (defined as a BP ≥ 160/100) and a decrease in disparities within this group. The quality collaborative identified five key lessons to help guide future community initiatives: (a) anticipate a plateauing of response; (b) distinguish the needs of disparate populations and create subpopulation-specific strategies to address and reduce disparities; (c) recognize the variation across low SES practices; (d) remain open to the refinement of outcome measures; and (e) continually seek best practices and barriers to success. Overall, a large community-wide QI initiative, involving multiple different stakeholders, was associated with improvements in BP control and modest reductions in some targeted disparities.

Novel approaches to improving disaster response have begun to include the use of big data and information and communication technology (ICT). However, there remains a dearth of literature on the use of these technologies in disasters. We have conducted an integrative literature review on the role of ICT and big data in disasters. Included in the review were 113 studies that met our predetermined inclusion criteria. Most studies used qualitative methods (39.8%, n=45) over mixed methods (31%, n=35) or quantitative methods (29.2%, n=33). Nearly 80% (n=88) covered only the response phase of disasters and only 15% (n=17) of the studies addressed disasters in low- and middle-income countries. The 4 most frequently mentioned tools were geographic information systems, social media, patient information, and disaster modeling. We suggest testing ICT and big data tools more widely, especially outside of high-income countries, as well as in nonresponse phases of disasters (eg, disaster recovery), to increase an understanding of the utility of ICT and big data in disasters. Future studies should also include descriptions of the intended users of the tools, as well as implementation challenges, to assist other disaster response professionals in adapting or creating similar tools. (Disaster Med Public Health Preparedness. 2019;13:353–367)

BACKGROUND Advancements in the quality and availability of highly sensitive analytical instrumentation and methodologies have led to increased interest in the use of microsamples. Among microsamples, dried blood spots (DBS) are the most well-known. Although there have been a variety of review papers published on DBS, there has been no attempt at describing the full range of analytes measurable in DBS, or any systematic approach published for characterizing the strengths and weaknesses associated with adoption of DBS analyses. CONTENT A scoping review of reviews methodology was used for characterizing the state of the science in DBS. We identified 2018 analytes measured in DBS and found every common analytic method applied to traditional liquid samples had been applied to DBS samples. Analytes covered a broad range of biomarkers that included genes, transcripts, proteins, and metabolites. Strengths of DBS enable its application in most clinical and laboratory settings, and the removal of phlebotomy and the need for refrigeration have expanded biosampling to hard-to-reach and vulnerable populations. Weaknesses may limit adoption in the near term because DBS is a nontraditional sample often requiring conversion of measurements to plasma or serum values. Opportunities presented by novel methodologies may obviate many of the current limitations, but threats around the ethical use of residual samples must be considered by potential adopters. SUMMARY DBS provide a wide range of potential applications that extend beyond the reach of traditional samples. Current limitations are serious but not intractable. Technological advancements will likely continue to minimize constraints around DBS adoption.

We introduce a deep learning approach to perform fine-grained population estimation for displacement camps using high-resolution overhead imagery. We train and evaluate our approach on drone imagery cross-referenced with population data for refugee camps in Cox's Bazar, Bangladesh in 2018 and 2019. Our proposed approach achieves 7.02% mean absolute percent error on sequestered camp imagery. We believe our experiments with real-world displacement camp data constitute an important step towards the development of tools that enable the humanitarian community to effectively and rapidly respond to the global displacement crisis., Comment: Fixed typo in abstract

ObjectiveWe aimed to quantitatively gauge local public health workers’ perceptions toward disaster recovery role expectations among jurisdictions in New Jersey and Maryland affected by Hurricane Sandy.MethodsAn online survey was made available in 2014 to all employees in 8 Maryland and New Jersey local health departments whose jurisdictions had been impacted by Hurricane Sandy in October 2012. The survey included perceptions of their actual disaster recovery involvement across 3 phases: days to weeks, weeks to months, and months to years. The survey also queried about their perceptions about future involvement and future available support.ResultsSixty-four percent of the 1047 potential staff responded to the survey (n=669). Across the 3 phases, 72% to 74% of the pre-Hurricane Sandy hires knew their roles in disaster recovery, 73% to 75% indicated confidence in their assigned roles (self-efficacy), and 58% to 63% indicated that their participation made a difference (response efficacy). Of the respondents who did not think it likely that they would be asked to participate in future disaster recovery efforts (n=70), 39% indicated a willingness to participate.ConclusionThe marked gaps identified in local public health workers’ awareness of, sense of efficacy toward, and willingness to participate in disaster recovery efforts after Hurricane Sandy represent a significant infrastructural concern of policy and programmatic relevance. (Disaster Med Public Health Preparedness. 2016;10:371–377)

Beyond the structural drivers such as distance from the road, rural/urban divide or demographic profiles, not much is known about the spatial relationship between HIV and social covariates. Spatial relations between social covariates and HIV infection of persons above 15 years were explored and mapped using geographically weighted regression model using data from a national HIV household survey conducted in 2008 and comprising 23 369 individuals from approximately 1000 enumeration areas that were randomly selected from the national census. The maps show spatial non-stationarity in relationship between local patterns of HIV prevalence and the social covariates across South Africa. The high prevalence districts have very homogeneous population defined by the following characteristics: Black origin, unfavorable sex ratio (high proportion of females), low socioeconomic status, being single or low marriage rates, multiple sexual partners and intergenerational sex. Markedly, intergenerational sex compounds the risk of acquiring HIV infection for females in poor districts. Identification of key social drivers of HIV and how they vary from location to location can help to effectively guide and focus intervention programs to areas of particular need.

Purpose – This paper aims to create a terminal area operations (TAO) analysis software that can accurately appreciate the nuances of hybrid electric distributed propulsion (HEDP), including unique failure modes and powered-lift effects. Design/methodology/approach – The program was written in Visual Basic with a user interface in Microsoft Excel. It integrates newly defined force components over time using a fourth order Runge-Kutta scheme. Findings – Powered-lift, HEDP failure modes and electrical component thermal limitations play significant roles on the performance of aircraft during TAO. Thoughtful design may yield better efficiency; however, care must be given to address negative implications. Reliability and performance can be improved during component failure scenarios. Research limitations/implications – This program has and will support the investigation of novel propulsion system architectures and aero-propulsive relationships through accurate TAO performance prediction. Practical implications – Powered-lift and HEDP architectures can be employed to improve takeoff and climb performance, both during nominal and component failure scenarios, however, reliance on powered-lift may result in faster approach speeds. High-lift and system failure behavior may also allow new approaches to design and sizing requirements. Originality/value – This program is unique in both the public and private sectors in its broad capabilities for TAO analysis of aircraft with HEDP systems and powered-lift.

Treatment of type 2 diabetes mellitus (T2DM) in older patients is complicated by a variety of factors and requires an individualized approach. Benefits of intensive glycemic control must be weighed against associated risks. Dipeptidyl peptidase-4 inhibitors, glucagon-like peptide-1 receptor agonists, and sodium-glucose cotransporter-2 inhibitors are newer antihyperglycemic agents that effectively lower glycated hemoglobin levels, with a low risk of hypoglycemia, and have a neutral or beneficial effect on weight. The purpose of this review is to discuss challenges in treating older patients with T2DM, and the efficacy and safety of these newer classes based on clinical trials in older populations.

Patients suffering from chronic pain present frequently to emergency departments for acute and chronic pain issues. Chronic pain patients represent a distinct challenge for emergency physicians, who encounter significant barriers in providing appropriate pain management. Physician biases, lack of training in pain management, addiction risk, and patient behaviors frequently contribute to these difficulties. In order to provide appropriate care, emergency physicians need to address goals of management, make attempts to elucidate the underlying pathology or issues contributing to the acute exacerbation, and decide on pain management treatment. The treatment possibilities for acute exacerbations of chronic pain are multiple and include provision of opioid medications, non-opioid medications, and complimentary therapies. A clinical approach is presented to help provide a framework when approaching chronic pain patients in the emergency department with the aim to provide optimal care and minimize suffering to this patient population.

Background. Chronic pain (CP) accounts for 10–16% of emergency department (ED) visits, contributing to ED overcrowding and leading to adverse events.Objectives. To describe patients with CP attending the ED and identify factors contributing to their visit.Methods. We used a mixed-method design combining interviews and questionnaires addressing pain, psychological distress, signs of opioid misuse, and disability. Participants were adults who attended the EDs of a large academic tertiary care center for their CP problem.Results. Fifty-eight patients (66% women; mean age 46.5, SD = 16.9) completed the study. The most frequently cited reason (60%) for ED visits was inability to cope with pain. Mental health problems were common, including depression (61%) and anxiety (45%). Participants had questions about the etiology of their pain, concerns about severe pain-related impairment, and problems with medication renewals or efficacy and sometimes felt invalidated in the ED. Although most participants had a primary care physician, the ED was seen as the only or best option when pain became unmanageable.Conclusions. Patients with CP visiting the ED often present with complex difficulties that cannot be addressed in the ED. Better access to interdisciplinary pain treatment is needed to reduce the burden of CP on the ED.

Xenopus laevis oocytes were injected with synthetic mRNA coding for a rat VMAT2 mutant (rVMAT2-I483A/L484A) shown previously to be retained on the plasma membrane as a result of a presumed reduction of endocytosis. Binding of the specific VMAT inhibitor [3H]dihydrotetrabenazine indicated that expression did occur at a level of approximately 3 fmol per oocyte. To determine if rVMAT2-I483A/L484A expressed in oocytes was capable of substrate transport, oocytes were placed in buffer at pH 6.0, dopamine substrate was injected into the cell, and egress of substrate was monitored by fast scan cyclic voltammetry using a carbon fiber microelectrode. Under these conditions, transport by oocytes injected with RNA coding for rVMAT2-I483A/L484A ranged from approximately 0.5 to more than 2.5 pmol/min. Water-injected and uninjected control oocytes did not exhibit appreciable transport activity. Transport by rVMAT2-I483A/L484A-injected oocytes was reduced to control levels by tetrabenazine, a known inhibitor of VMAT transport activity. Comparison of subtracted voltammograms obtained from transport assays with those for calibration experiments confirmed that the transported species was dopamine. These results suggest that expression of VMATs in oocytes may provide a useful model system for mechanistic and regulatory studies that would not be feasible using traditional methods.

Interlaminar epidural steroid injections (ILESIs) are commonly employed in the management of patients with symptomatic degenerative lumbar spinal canal stenosis despite little experimental evidence to guide technique optimization. One untested performance parameter is the intervertebral level at which the ILESI should be performed for maximum patient relief.This study randomized patients with symptomatic degenerative lumbar spinal canal stenosis to receive an ILESI at the level of maximal spinal canal stenosis or at a normal/less stenotic intervertebral site 2 intervertebral levels cephalad to the level of maximal stenosis. Pain with ambulation and Roland Morris Disability Questionnaire scores were collected prior to the procedure and at 1-, 4-, and 12-week follow-ups.Fifty-seven patients were enrolled. Thirty patients (Group 1) received an ILESI at the level of maximal stenosis; 27 patients (Group 2) received an ILESI at a less stenotic level. The mean baseline preprocedural maximal pain with ambulation and disability scores for the 2 groups were not significantly different (P=0.94 and P=0.13, respectively). Patients' pain with ambulation scores were significantly lower in Group 1 compared to Group 2 at 1 and 4 weeks postinjection, but they were not significantly lower at 12 weeks (1 week, P=0.045; 4 weeks, P=0.049; 12 weeks, P=0.08). The mean Roland Morris Disability Questionnaire scores at 1, 4, and 12 weeks postinjection were significantly lower in Group 1 as compared to Group 2 (P=0.001, P=0.009, P=0.003, respectively).Results suggest that patient symptom improvement is optimized when the ILESI is performed at the intervertebral level of maximal stenosis.

The Health Insurance Portability and Accountability Act (HIPAA) seems to be as useful as is capitalism to the medically uninsured (or perhaps, as necessary as another leukotriene inhibitor for asthma). Is the emergency medical community doing enough? Can we improve on HIPAA to increase privacy in the emergency department? HIPAA regulations are reviewed in all their wondrous complexity and simplified so that even your medical director can understand them.

The purpose of this study was to compare CPP (a surrogate for aortic elasticity) in a population of 59 obese pts whose BMI was >30kg/sqm. These pts were divided into hypertensive obese (HTNO) 35 pts and normotensive obese (NTO) 24 pts. Both groups were also divided according to age to the following groups: 30-39, 40-49, 50-59, and over 60 years of age. We also compared both groups, to a group of 100 volunteers (Control group, CTRL) that were normotensive and nonobese. CPP was determined using the Sphygmocor System. The mean CPP for HTNO pts was 48.8 15 mmHg. Mean CPP for NTO pts was 45.5 13 mmHg. Mean CPP for CTRL pts was 44.0 7.7 mmHg. The mean CPP difference between HTNO pts and CTRL pts was significant with a p 1⁄4 0.03; the mean CPP difference between NTO pts and CTRL pts was nonsignificant with a p 1⁄4 0.198. These results are shown graphically in figure #1. We conclude that even in a small population of HTNO and NTO pts, CPP was able to show a significant difference between HTNO compared to CTRL. This finding may suggest that hypertensive obese pts may have a higher vascular risk than NTO because of impaired aortic elasticity. Our preliminary finding should be confirmed by studying a larger population of obese pts and including pulse wave velocity.