What is Influenza-Like Illness?
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Spencer, Julie Allison↗
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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).
60 APPLIED LIFE SCIENCES↗
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.
60 APPLIED LIFE SCIENCES↗
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.
59 BASIC BIOLOGICAL SCIENCES↗
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.
60 APPLIED LIFE SCIENCES↗
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.
54 ENVIRONMENTAL SCIENCES↗
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.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Isolation and characterization of IgG3 glycan-targeting antibodies with exceptional cross-reactivity for diverse viral families
Broadly reactive antibodies that target sequence-diverse antigens are of interest for vaccine design and monoclonal antibody therapeutic development because they can protect against multiple strains of a virus and provide a barrier to evolution of escape mutants. Using LIBRA-seq (linking B cell receptor to antigen specificity through sequencing) data for the B cell repertoire of an individual chronically infected with human immunodeficiency virus type 1 (HIV-1), we identified a lineage of IgG3 antibodies predicted to bind to HIV-1 Envelope (Env) and influenza A Hemagglutinin (HA). Two lineage members, antibodies 2526 and 546, were confirmed to bind to a large panel of diverse antigens, including several strains of HIV-1 Env, influenza HA, coronavirus (CoV) spike, hepatitis C virus (HCV) E protein, Nipah virus (NiV) F protein, and Langya virus (LayV) F protein. We found that both antibodies bind to complex glycans on the antigenic surfaces. Antibody 2526 targets the stem region of influenza HA and the N-terminal domain (NTD) region of SARS-CoV-2 spike. A crystal structure of 2526 Fab bound to mannose revealed the presence of a glycan-binding pocket on the light chain. Antibody 2526 cross-reacted with antigens from multiple pathogens and displayed no signs of autoreactivity. These features distinguish antibody 2526 from previously described glycan-reactive antibodies. Further study of this antibody class may aid in the selection and engineering of broadly reactive antibody therapeutics and can inform the development of effective vaccines with exceptional breadth of pathogen coverage.
Microbiology↗
RSVP - Flu Like Illness and Respiratory Syndromes COVID-19 Syndromic Reporting Tool Prototype
Individuals infected with SARS-CoV-2, the virus that causes COVID-19, may be infectious between 1-3 days prior to symptom onset. People may delay seeking medical care after symptom development due to multiple determinants of health seeking behavior like availability of testing, accessibility of providers, and ability to pay. Therefore, understanding symptoms in the general public is important to better predict and inform resource management plans and engage in reopening. As the influenza season looms, the ability to differentiate between clinical presentation of COVID-19 and seasonal influenza will also be important to health providers and public health response efforts. This project has developed an algorithm that when used with captured syndromic trends can help provide both differentiation to various influenza-like illnesses (ILI) as well as provide public health decision makers a better understanding regarding spatial and temporal trends. This effort has also developed a web-based tool to allow for the capturing of generalized syndromic trends and provide both spatial and temporal outputs on these trends. This page left blank
59 BASIC BIOLOGICAL SCIENCES↗
Human anti-N1 monoclonal antibodies elicited by pandemic H1N1 virus infection broadly inhibit HxN1 viruses in vitro and in vivo
Neuraminidase (NA) is one of the two influenza virus surface glycoproteins, and antibodies that target it are an independent correlate of protection. However, our current understanding of NA antigenicity is incomplete. Here, in this work, we describe human monoclonal antibodies (mAbs) from a patient with a pandemic H1N1 virus infection in 2009. Two mAbs exhibited broad reactivity and inhibited NA enzyme activity of seasonal H1N1 viruses circulating before and after 2009, as well as viruses with avian or swine N1s. The mAbs provided robust protection from lethal challenge with human H1N1 and avian H5N1 viruses in mice, and both target an epitope on the lateral face of NA. In summary, we identified two broadly protective NA antibodies that share a novel epitope, inhibited NA activity, and provide protection against virus challenge in mice. Our work reaffirms that NA should be included as a target in future broadly protective or universal influenza virus vaccines.
60 APPLIED LIFE SCIENCES↗
Peptoid-Based Nanosheets Exhibiting Broad Antiviral Activity Against Enveloped RNA Viruses
Enveloped RNA viruses, such as Influenza A (H1N1) and Sindbis virus, pose persistent global health threats due to their high mutation rates, efficient transmission, and frequent drug resistance. By mimicking host cell membrane receptors, multivalent virus inhibitors can block viral attachment, making them promising broad-spectrum antiviral agents. However, most of existing antivirals are often limited by strain specificity, short-lived efficacy, and toxicity. Here, we introduce a broad-spectrum antiviral platform based on highly tunable and biocompatible two-dimensional nanomembranes (2DNMs) self-assembled from amphiphilic peptoids, operating via a non-genomic, mutation-insensitive mechanism. By varying peptoid sequence, we design and synthesize over twenty different 2DNMs with various surface charge and high density of viral-attachment ligands (VALs). The self-assembled architecture of these stable 2DNMs provides cooperative noncovalent multivalent binding to virus particles that result in effective inhibition of viral infection. Screening of variants identified three leads that potently suppressed Influenza A (H1N1) and Sindbis virus infection across median tissue culture infectious dose (TCID50), plaque, RT–qPCR, and immunofluorescence assays, while maintaining >90% cell viability. These nanosheets significantly reduced infectious titers, viral RNA replication, and intracellular viral protein expression, indicating inhibition at early stages of viral entry and propagation. The sequence programmability, chemical robustness, and mutation-insensitive antiviral activity distinguish 2DNMs from traditional antivirals and positions them as a versatile materials platform for antiviral coatings, protective barriers, and prophylactic biomedical applications.
Influenza A virus↗
The predictive skill of convolutional neural networks models for disease forecasting
In this paper we investigate the utility of one-dimensional convolutional neural network (CNN) models in epidemiological forecasting. Deep learning models, in particular variants of recurrent neural networks (RNNs) have been studied for ILI (Influenza-Like Illness) forecasting, and have achieved a higher forecasting skill compared to conventional models such as ARIMA. In this study, we adapt two neural networks that employ one-dimensional temporal convolutional layers as a primary building block—temporal convolutional networks and simple neural attentive meta-learners—for epidemiological forecasting. We then test them with influenza data from the US collected over 2010-2019. We find that epidemiological forecasting with CNNs is feasible, and their forecasting skill is comparable to, and at times, superior to, plain RNNs. Thus CNNs and RNNs bring the power of nonlinear transformations to purely data-driven epidemiological models, a capability that heretofore has been limited to more elaborate mechanistic/compartmental disease models.
59 BASIC BIOLOGICAL SCIENCES↗
Citywide indoor air sampling mirrors wastewater and clinical for environmental surveillance of respiratory viruses
Wastewater surveillance of respiratory pathogens can provide timely estimates of viral activity and disease trends in a population. Indoor air surveillance could be used similarly with some advantages but remains largely unvalidated at the community -scale. Here, an indoor air surveillance program was employed as part of public health environmental surveillance in Chicago, Illinois, USA. Ten air samplers were placed in healthcare and congregate living settings across the city. Weekly air samples were evaluated for influenza A, influenza B, respiratory syncytial virus, and SARS -CoV-2 over two respiratory virus seasons (2023 -2025). Citywide, aggregated air sample positivity and viral load were closely correlated with local clinical case and wastewater surveillance data across all respiratory viruses. Virus trends in air data often preceded clinical and wastewater, although this varied across pathogens and respiratory virus seasons. Further, whole -genome sequencing of SARS -CoV-2 showed close correlation of variant proportions across all datasets. At the building -scale, air samples obtained from a single sampling device provided efficient respiratory virus surveillance, with respiratory pathogen levels mirroring citywide clinical surveillance data. These data demonstrate that air surveillance can provide respiratory virus case and variant trend data at a building or community -scale, serving as an alternative or complementary tool for public health environmental surveillance.
Wilton, Rosemarie↗
Pollen antigens and atmospheric circulation driven seasonal respiratory viral outbreak and its implication to the Covid-19 pandemic
Abstract The patterns of respiratory virus illness are expressed differently between temperate and tropical climates. Tropical outbreaks often peak in wet seasons. Temperate outbreaks typically peak during the winter. The prevailing causal hypotheses focus on sunlight, temperature and humidity variations. Yet no consistent factors have been identified to sufficiently explain seasonal virus emergence and decline at any latitude. Here we demonstrate close connections among global-scale atmospheric circulations, IgE antibody enhancement through seasonal pollen inhalation, and respiratory virus patterns at any populated latitude, with a focus on the US. Pollens emerge each Spring, and the renewed IgE titers in the population are argued to terminate each winter peak of respiratory illness. Globally circulated airborne viruses are postulated to subsequently deposit across the Southern US during lower zonal geostrophic winds each late Summer. This seasonally refreshed viral load is postulated to trigger a new influenza outbreak, once the existing IgE antibodies diminish to a critical value each Fall. Our study offers a new and consistent explanation for the seasonal diminishment of respiratory viral illnesses in temperate climates, the subdued seasonal signature in the tropics, the annually circulated virus phenotypes, and the northerly migration of influenza across the US every year. Our integrated geospatial and IgE hypothesis provides a new perspective for prediction, mitigation and prevention of the outbreak and spread of seasonal respiratory viruses including Covid-19 pandemic.
60 APPLIED LIFE SCIENCES↗
Predictive Skill of Deep Learning Models Trained on Limited Sequence Data
In this report we investigate the utility of one-dimensional convolutional neural network (CNN) models in epidemiological forecasting. Deep learning models, especially variants of recurrent neural networks (RNNs) have been studied for influenza forecasting, and have achieved higher forecasting skill compared to conventional models such as ARIMA models. In this study, we adapt two neural networks that employ one-dimensional temporal convolutional layers as a primary building block temporal convolutional networks and simple neural attentive meta-learner for epidemiological forecasting and test them with influenza data from the US collected over 2010-2019. We find that epidemiological forecasting with CNNs is feasible, and their forecasting skill is comparable to, and at times, superior to, RNNs. Thus CNNs and RNNs bring the power of nonlinear transformations to purely data-driven epidemiological models, a capability that heretofore has been limited to more elaborate mechanistic/compartmental disease models.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Amplification-free nucleic acid detection with a fluorescence-based waveguide biosensor
Early detection of pathogens using nucleic acids in clinical samples often requires sensitivity at the single-copy level, which currently necessitates time-consuming and expensive nucleic acid amplification. Here, we describe 1) a redesigned flow cell in the shape of a trapezoid-subtracted geometric stadium, and 2) modified experimental procedures that allow for the measurement of sub-attomolar analytes in microliter quantities on a fluorescence-based waveguide biosensor. We verified our instrumental sensitivity with a 200-μL sample of a fluorescent streptavidin conjugate at 100 zM (100 zeptomolar, or 100·10 −21 mol L −1 ) and theoretically explored the applicability of this modified sensing platform in a sandwich immunoassay format using a Langmuir adsorption model. We present assays that demonstrate specific detection of synthetic influenza A DNA (in buffer) and RNA (in saliva) oligonucleotides at the single-copy level (200 μL at 10 zM) using a fluorescent molecular beacon. Lastly, we demonstrate detection of isolated genomic influenza A RNA at a clinically relevant concentration. This work constitutes a sensitivity improvement of over twelve orders of magnitude compared to our previous nucleic acid detection work, illustrating the significant enhancements that can be gained with optimized experimental design.
59 BASIC BIOLOGICAL SCIENCES↗
Challenges and Solutions for Leave-One-Out Biosensor Design in the Context of a Rugged Fitness Landscape
The leave-one-out (LOO) green fluorescent protein (GFP) approach to biosensor design combines computational protein design with split protein reconstitution. LOO-GFPs reversibly fold and gain fluorescence upon encountering the target peptide, which can be redefined by computational design of the LOO site. Such an approach can be used to create reusable biosensors for the early detection of emerging biological threats. Enlightening biophysical inferences for nine LOO-GFP biosensor libraries are presented, with target sequences from dengue, influenza, or HIV, replacing beta strands 7, 8, or 11. An initially low hit rate was traced to components of the energy function, manifesting in the over-rewarding of over-tight side chain packing. Also, screening by colony picking required a low library complexity, but designing a biosensor against a peptide of at least 12 residues requires a high-complexity library. This double-bind was solved using a “piecemeal” iterative design strategy. Also, designed LOO-GFPs fluoresced in the unbound state due to unwanted dimerization, but this was solved by fusing a fully functional prototype LOO-GFP to a fiber-forming protein, Drosophila ultrabithorax, creating a biosensor fiber. One influenza hemagglutinin biosensor is characterized here in detail, showing a shifted excitation/emission spectrum, a micromolar affinity for the target peptide, and an unexpected photo-switching ability.
Chemistry↗
Tailored design of protein nanoparticle scaffolds for multivalent presentation of viral glycoprotein antigens
Multivalent presentation of viral glycoproteins can substantially increase the elicitation of antigen-specific antibodies. To enable a new generation of anti-viral vaccines, we designed self-assembling protein nanoparticles with geometries tailored to present the ectodomains of influenza, HIV, and RSV viral glycoprotein trimers. We first de novo designed trimers tailored for antigen fusion, featuring N-terminal helices positioned to match the C termini of the viral glycoproteins. Trimers that experimentally adopted their designed configurations were incorporated as components of tetrahedral, octahedral, and icosahedral nanoparticles, which were characterized by cryo-electron microscopy and assessed for their ability to present viral glycoproteins. Electron microscopy and antibody binding experiments demonstrated that the designed nanoparticles presented antigenically intact prefusion HIV-1 Env, influenza hemagglutinin, and RSV F trimers in the predicted geometries. This work demonstrates that antigen-displaying protein nanoparticles can be designed from scratch, and provides a systematic way to investigate the influence of antigen presentation geometry on the immune response to vaccination.
59 BASIC BIOLOGICAL SCIENCES↗