Biomedical Advanced Research and Development Authority

About: Biomedical Advanced Research and Development Authority is a government organization based out in Washington D.C., District of Columbia, United States. It is known for research contribution in the topics: Vaccination & Influenza vaccine. The organization has 82 authors who have published 70 publications receiving 4279 citations. The organization is also known as: BARDA & Office of the Biomedical Advanced Research and Development Authority.

Discovery, research, and development of new antibiotics: the WHO priority list of antibiotic-resistant bacteria and tuberculosis.

Abstract: Summary Background The spread of antibiotic-resistant bacteria poses a substantial threat to morbidity and mortality worldwide. Due to its large public health and societal implications, multidrug-resistant tuberculosis has been long regarded by WHO as a global priority for investment in new drugs. In 2016, WHO was requested by member states to create a priority list of other antibiotic-resistant bacteria to support research and development of effective drugs. Methods We used a multicriteria decision analysis method to prioritise antibiotic-resistant bacteria; this method involved the identification of relevant criteria to assess priority against which each antibiotic-resistant bacterium was rated. The final priority ranking of the antibiotic-resistant bacteria was established after a preference-based survey was used to obtain expert weighting of criteria. Findings We selected 20 bacterial species with 25 patterns of acquired resistance and ten criteria to assess priority: mortality, health-care burden, community burden, prevalence of resistance, 10-year trend of resistance, transmissibility, preventability in the community setting, preventability in the health-care setting, treatability, and pipeline. We stratified the priority list into three tiers (critical, high, and medium priority), using the 33rd percentile of the bacterium's total scores as the cutoff. Critical-priority bacteria included carbapenem-resistant Acinetobacter baumannii and Pseudomonas aeruginosa , and carbapenem-resistant and third-generation cephalosporin-resistant Enterobacteriaceae. The highest ranked Gram-positive bacteria (high priority) were vancomycin-resistant Enterococcus faecium and meticillin-resistant Staphylococcus aureus . Of the bacteria typically responsible for community-acquired infections, clarithromycin-resistant Helicobacter pylori , and fluoroquinolone-resistant Campylobacter spp, Neisseria gonorrhoeae , and Salmonella typhi were included in the high-priority tier. Interpretation Future development strategies should focus on antibiotics that are active against multidrug-resistant tuberculosis and Gram-negative bacteria. The global strategy should include antibiotic-resistant bacteria responsible for community-acquired infections such as Salmonella spp, Campylobacter spp, N gonorrhoeae , and H pylori . Funding World Health Organization.

Safety and Efficacy of Single-Dose Ad26.COV2.S Vaccine against Covid-19.

Abstract: Background The Ad26.COV2.S vaccine is a recombinant, replication-incompetent human adenovirus type 26 vector encoding full-length severe acute respiratory syndrome coronavirus 2 (SARS-CoV-...

A Randomized, Controlled Trial of Ebola Virus Disease Therapeutics.

Abstract: Background Although several experimental therapeutics for Ebola virus disease (EVD) have been developed, the safety and efficacy of the most promising therapies need to be assessed in the ...

Enhancing disease surveillance with novel data streams: challenges and opportunities

Abstract: Novel data streams (NDS), such as web search data or social media updates, hold promise for enhancing the capabilities of public health surveillance. In this paper, we outline a conceptual framework for integrating NDS into current public health surveillance. Our approach focuses on two key questions: What are the opportunities for using NDS and what are the minimal tests of validity and utility that must be applied when using NDS? Identifying these opportunities will necessitate the involvement of public health authorities and an appreciation of the diversity of objectives and scales across agencies at different levels (local, state, national, international). We present the case that clearly articulating surveillance objectives and systematically evaluating NDS and comparing the performance of NDS to existing surveillance data and alternative NDS data is critical and has not sufficiently been addressed in many applications of NDS currently in the literature.

Influenza forecasting in human populations: a scoping review.

Abstract: Forecasts of influenza activity in human populations could help guide key preparedness tasks. We conducted a scoping review to characterize these methodological approaches and identify research gaps. Adapting the PRISMA methodology for systematic reviews, we searched PubMed, CINAHL, Project Euclid, and Cochrane Database of Systematic Reviews for publications in English since January 1, 2000 using the terms “influenza AND (forecast* OR predict*)”, excluding studies that did not validate forecasts against independent data or incorporate influenza-related surveillance data from the season or pandemic for which the forecasts were applied. We included 35 publications describing population-based (N = 27), medical facility-based (N = 4), and regional or global pandemic spread (N = 4) forecasts. They included areas of North America (N = 15), Europe (N = 14), and/or Asia-Pacific region (N = 4), or had global scope (N = 3). Forecasting models were statistical (N = 18) or epidemiological (N = 17). Five studies used data assimilation methods to update forecasts with new surveillance data. Models used virological (N = 14), syndromic (N = 13), meteorological (N = 6), internet search query (N = 4), and/or other surveillance data as inputs. Forecasting outcomes and validation metrics varied widely. Two studies compared distinct modeling approaches using common data, 2 assessed model calibration, and 1 systematically incorporated expert input. Of the 17 studies using epidemiological models, 8 included sensitivity analysis. This review suggests need for use of good practices in influenza forecasting (e.g., sensitivity analysis); direct comparisons of diverse approaches; assessment of model calibration; integration of subjective expert input; operational research in pilot, real-world applications; and improved mutual understanding among modelers and public health officials.