SEARCH · Engineering Papers
Results for “Influenza”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Virus strain influenced the interspecies transmission of influenza D virus between calves and pigs
Not Available
Canonical features of human antibodies recognizing the influenza hemagglutinin trimer interface
Explore the source record for details and available documents.
Fast and accurate influenza forecasting in the United States with Inferno
Infectious disease forecasting is an emerging field and has the potential to improve public health through anticipatory resource allocation, situational awareness, and mitigation planning. By way of exploring and operationalizing disease forecasting, the U.S. Centers for Disease Control and Prevention (CDC) has hosted FluSight since the 2013/14 flu season, an annual flu forecasting challenge. Since FluSight’s onset, forecasters have developed and improved forecasting models in an effort to provide more timely, reliable, and accurate information about the likely progression of the outbreak. While improving the predictive performance of these forecasting models is often the primary objective, it is also important for a forecasting model to run quickly, facilitating further model development and improvement while providing flexibility when deployed in a real-time setting. In this vein I introduce Inferno, a fast and accurate flu forecasting model inspired by Dante, the top performing model in the 2018/19 FluSight challenge. When pseudoprospectively compared to all models that participated in FluSight 2018/19, Inferno would have placed 2nd in the national and regional challenge as well as the state challenge, behind only Dante. Inferno, however, runs in minutes and is trivially parallelizable, while Dante takes hours to run, representing a significant operational improvement with minimal impact to performance. Forecasting challenges like FluSight should continue to monitor and evaluate how they can be modified and expanded to incentivize the development of forecasting models that benefit public health.
Big-Data-Driven Geo-Spatiotemporal Correlation Analysis between Precursor Pollen and Influenza and its Implication to Novel Coronavirus Outbreak.
Abstract not provided.
Influenza Vaccination Timing
Explore the source record for details and available documents.
Structure of avian influenza hemagglutinin in complex with a small molecule entry inhibitor
Abstract not provided
Generation and characterization of interferon-beta-resistant H1N1 influenza A virus
Not provided.
Duration of Highly Pathogenic Avian Influenza Virus and Newcastle Disease Virus Infectivity in Dried Ornithologic Study Skins
Not provided.
What is Influenza-Like Illness?
Explore the source record for details and available documents.
EBSD seed LDRD project: Does Corona Virus – 2019 (COVID-19) and Seasonal Flu have similar meteorology and air quality controls driving their spread?
Seasonal influenza and Influenza like Illnesses (ILI) pose a serious public health risk and in turn affect the economy. Various factors affect ILI cases and mortality, including the pathogen and its interaction with the host, as well as environmental and socioeconomic factors such as meteorology, household structure, air pollution, urbanization, and population. Despite the growing number of studies on influenza and ILI, challenges remain in forecasting the timing of seasonal onset, outbreak patterns, and key factors affecting transmission. In particular, the impacts of meteorology and air quality on ILI have been challenging to understand, with linear regression studies focused on different geographic regions producing contradictory results. For example, influenza seasonality has been associated with cold-dry conditions in temperate mid-latitudes, but with humid-rainy conditions in tropical climates. These apparently contradictory results imply that the relationships between influenza cases and atmospheric variables may be too complex to be captured by linear regression models. In this seed project, we analyze meteorology and air quality variables from numerical models to determine which atmospheric variables are most helpful in predicting weekly changes in recorded flu cases. In contrast to most previous studies that relied on linear regression analysis to predict the timing of the flu onset or peak, we employ a robust machine learning algorithm to evaluate the contribution of atmospheric variables to weekly changes in recorded ILI cases. These results may also be relevant to the spread of other respiratory illnesses such as Corona Virus Disease – 2019 (COVID-19).
Trends in the administration of COVID-19 vaccines with other vaccines in the United States reported to V-safe during December 14, 2020—May 19, 2023
Introduction COVID-19 vaccines may be administered with other vaccines during the same healthcare visit. COVID-19 monovalent (Fall 2021) and bivalent (Fall 2022) vaccine recommendations coincided with annual seasonal influenza vaccination. Data describing the frequency of the co-administration of COVID-19 vaccines with other vaccines are limited. Methods We used V-safe, a voluntary smartphone-based U.S. safety surveillance system established by the CDC, to describe trends in the administration of COVID-19 vaccines with other vaccines reported to V-safe during December 14, 2020 – May 19, 2023. Results Of the 21 million COVID-19 vaccinations reported to V-safe, 2.2% (459,817) were administered with at least 1 other vaccine. Co-administration most frequently occurred during the first week of October 2023 (27,092; 44.1%). Most reports of co-administration included influenza vaccine (393,003; 85.5%). Co-administration was most frequently reported for registrants aged 6 months-6 years (4,872; 4.4%). Conclusion Reports of co-administration to V-safe peaked during October 2023, when influenza vaccination most often occurs, possibly reflecting increased opportunities for multiple vaccinations and greater acceptability of the co-administration of COVID-19 vaccine with other vaccines, especially influenza vaccine.
Improving probabilistic infectious disease forecasting through coherence
With an estimated $10.4 billion in medical costs and 31.4 million outpatient visits each year, influenza poses a serious burden of disease in the United States. To provide insights and advance warning into the spread of influenza, the U.S. Centers for Disease Control and Prevention (CDC) runs a challenge for forecasting weighted influenza-like illness (wILI) at the national and regional level. Many models produce independent forecasts for each geographical unit, ignoring the constraint that the national wILI is a weighted sum of regional wILI, where the weights correspond to the population size of the region. We propose a novel algorithm that transforms a set of independent forecast distributions to obey this constraint, which we refer to as probabilistically coherent. Enforcing probabilistic coherence led to an increase in forecast skill for 79% of the models we tested over multiple flu seasons, highlighting the importance of respecting the forecasting system’s geographical hierarchy.
A Coarse-Grained Model of Affinity Maturation Indicates the Importance of B-Cell Receptor Avidity in Epitope Subdominance
The elicitation of broadly neutralizing antibodies (bnAbs) is a major goal in the design of vaccines against rapidly-mutating viruses. In the case of influenza, many bnAbs that target conserved epitopes on the stem of the hemagglutinin protein (HA) have been discovered. However, these antibodies are rare, are not boosted well upon reinfection, and often have low neutralization potency, compared to strain-specific antibodies directed to the HA head. Different hypotheses have been proposed to explain this phenomenon. We use a coarse-grained computational model of the germinal center reaction to investigate how B-cell receptor binding valency affects the growth and affinity maturation of competing B-cells. We find that receptors that are unable to bind antigen bivalently, and also those that do not bind antigen cooperatively, have significantly slower rates of growth, memory B-cell production, and, under certain conditions, rates of affinity maturation. The corresponding B-cells are predicted to be outcompeted by B-cells that bind bivalently and cooperatively. We use the model to explore strategies for a universal influenza vaccine, e.g., how to boost the concentrations of the slower growing cross-reactive antibodies directed to the stem. The results suggest that, upon natural reinfections subsequent to vaccination, the protectiveness of such vaccines would erode, possibly requiring regular boosts. Collectively, our results strongly support the importance of bivalent antibody binding in immunodominance, and suggest guidelines for developing a universal influenza vaccine.
Transboundary determinants of avian zoonotic infectious diseases: challenges for strengthening research capacity and connecting surveillance networks
As the climate changes, global systems have become increasingly unstable and unpredictable. This is particularly true for many disease systems, including subtypes of highly pathogenic avian influenzas (HPAIs) that are circulating the world. Ecological patterns once thought stable are changing, bringing new populations and organisms into contact with one another. Wild birds continue to be hosts and reservoirs for numerous zoonotic pathogens, and strains of HPAI and other pathogens have been introduced into new regions via migrating birds and transboundary trade of wild birds. With these expanding environmental changes, it is even more crucial that regions or counties that previously did not have surveillance programs develop the appropriate skills to sample wild birds and add to the understanding of pathogens in migratory and breeding birds through research. For example, little is known about wild bird infectious diseases and migration along the Mediterranean and Black Sea Flyway (MBSF), which connects Europe, Asia, and Africa. Focusing on avian influenza and the microbiome in migratory wild birds along the MBSF, this project seeks to understand the determinants of transboundary disease propagation and coinfection in regions that are connected by this flyway. Through the creation of a threat reduction network for avian diseases (Avian Zoonotic Disease Network, AZDN) in three countries along the MBSF (Georgia, Ukraine, and Jordan), this project is strengthening capacities for disease diagnostics; microbiomes; ecoimmunology; field biosafety; proper wildlife capture and handling; experimental design; statistical analysis; and vector sampling and biology. Here, we cover what is required to build a wild bird infectious disease research and surveillance program, which includes learning skills in proper bird capture and handling; biosafety and biosecurity; permits; next generation sequencing; leading-edge bioinformatics and statistical analyses; and vector and environmental sampling. Creating connected networks for avian influenzas and other pathogen surveillance will increase coordination and strengthen biosurveillance globally in wild birds.
A Size-Selectively Biomolecule-Immobilized Nanoprobe-Based Chemiluminescent Lateral Flow Immunoassay for Detection of Avian-Origin Viruses
Here, a signal-amplifiable nanoprobe-based chemiluminescent lateral flow immunoassay (CL-LFA) was developed to detect avian influenza viruses (AIV) and other contagious and fatal viral avian-origin diseases worldwide. Signal-amplifiable nanoprobes are capable of size-selective immobilization of antibodies (binding receptors) and enzymes (signal transducers) on sensitive paper-based sensor platforms. Particle structure designs and conjugation pathways conducive for antigen accessibility to maximum amounts of immobilized enzymes and antibodies have advanced. The detection limit of the CL-LFA using the signal-amplifiable nanoprobe for the nucleoprotein of the H3N2 virus was 5 pM. Sensitivity tests for low pathogenicity avian influenza H9N2, H1N1, and high pathogenicity avian influenza H5N9 viruses were conducted, and the detection limits of CL-LFA were found to be 10 3.5 50% egg infective dose (EID 50 )/mL, 10 2.5 EID 50 /mL, and 10 4 EID 50 /mL, respectively, which is 20 to 100 times lower than that of a commercial AIV rapid test kit. Moreover, CL-LFA demonstrated high sensitivity and specificity against 37 clinical samples. The signal-amplifiable probe designed in this study is a potential diagnostic probe with ultrahigh sensitivity for applications in the field of clinical diagnosis, which requires sensitive antigen detection as evidenced by enhanced signaling capacity and sensitivity of the LFAs.