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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Inferring the spread of COVID-19: the role of time-varying reporting rate in epidemiological modelling

The role of epidemiological models is crucial for informing public health officials during a public health emergency, such as the COVID-19 pandemic. However, traditional epidemiological models fail to capture the time-varying effects of mitigation strategies and do not account for under-reporting of active cases, thus introducing bias in the estimation of model parameters. To infer more accurate parameter estimates and to reduce the uncertainty of these estimates, we extend the SIR and SEIR epidemiological models with two time-varying parameters that capture the transmission rate and the rate at which active cases are reported to health officials. Using two real data sets of COVID-19 cases, we perform Bayesian inference via our SIR and SEIR models with time-varying transmission and reporting rates and via their standard counterparts with constant rates; our approach provides parameter estimates with more realistic interpretation, and 1-week ahead predictions with reduced uncertainty. Furthermore, we find consistent under-reporting in the number of active cases in the data that we consider, suggesting that the initial phase of the pandemic was more widespread than previously reported.

42 ENGINEERING↗
DOE OSTI2023

Lipid-coated mesoporous silica nanoparticles for anti-viral applications via delivery of CRISPR-Cas9 ribonucleoproteins

Abstract Emerging and re-emerging viral pathogens present a unique challenge for anti-viral therapeutic development. Anti-viral approaches with high flexibility and rapid production times are essential for combating these high-pandemic risk viruses. CRISPR-Cas technologies have been extensively repurposed to treat a variety of diseases, with recent work expanding into potential applications against viral infections. However, delivery still presents a major challenge for these technologies. Lipid-coated mesoporous silica nanoparticles (LCMSNs) offer an attractive delivery vehicle for a variety of cargos due to their high biocompatibility, tractable synthesis, and amenability to chemical functionalization. Here, we report the use of LCMSNs to deliver CRISPR-Cas9 ribonucleoproteins (RNPs) that target the Niemann–Pick disease type C1 gene, an essential host factor required for entry of the high-pandemic risk pathogen Ebola virus, demonstrating an efficient reduction in viral infection. We further highlight successful in vivo delivery of the RNP-LCMSN platform to the mouse liver via systemic administration.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Machine learning approaches for influenza A virus risk assessment identifies predictive correlates using ferret model in vivo data

In vivo assessments of influenza A virus (IAV) pathogenicity and transmissibility in ferrets represent a crucial component of many pandemic risk assessment rubrics, but few systematic efforts to identify which data from in vivo experimentation are most useful for predicting pathogenesis and transmission outcomes have been conducted. To this aim, we aggregated viral and molecular data from 125 contemporary IAV (H1, H2, H3, H5, H7, and H9 subtypes) evaluated in ferrets under a consistent protocol. Three overarching predictive classification outcomes (lethality, morbidity, transmissibility) were constructed using machine learning (ML) techniques, employing datasets emphasizing virological and clinical parameters from inoculated ferrets, limited to viral sequence-based information, or combining both data types. Among 11 different ML algorithms tested and assessed, gradient boosting machines and random forest algorithms yielded the highest performance, with models for lethality and transmission consistently better performing than models predicting morbidity. Comparisons of feature selection among models was performed, and highest performing models were validated with results from external risk assessment studies. Our findings show that ML algorithms can be used to summarize complex in vivo experimental work into succinct summaries that inform and enhance risk assessment criteria for pandemic preparedness that take in vivo data into account.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Quantifying Movement Motivations, Demand, and Inflow-Outflow Dynamics in Four Cities (New York, Chicago, Austin, and San Diego) During COVID-19

The COVID-19 pandemic has impacted a wide range of human activities, from food delivery habits to major moving and travel decisions. Results indicate multiple pandemic-related factors have influenced millions of relocation decisions by Americans (e.g., health risk, financial pressures, more space, and employment), and there are various positive economic and social outcomes of this influence (e.g., remote work and education), enabling more affordable living and opportunity. This paper addresses COVID-19 impacts on mobility, especially involving permanent relocations. Survey design and data analysis with U-Haul targeted customers in Austin, New York, San Diego, and Chicago to understand mobility, new moving dynamics, and motivations.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2022

Sustainability, Scalability, and Resiliency of the Town of Innisfil Mobility-on-Demand Experiment: Preliminary Results, Analyses, and Lessons Learned

In 2017, the town of Innisfil, Ontario, launched Innisfil Transit in partnership with Uber, a transportation network company, to provide a subsidized on-demand public mobility service as an alternative to investing in a new fixed-route bus service. The performance of Innisfil Transit is documented in a 2021 Ryerson University report by Sweet, Mitra, and Benaroya, which shows greater cost effectiveness of the mobility provided over the proposed bus alternative. This paper expands on those findings by assessing Innisfil Transit with respect to sustainability, scalability, and resiliency. First, we quantify the energy and emissions of this program relative to traditional transit and driving alone across varying powertrains. We then characterize a conservative first-order estimate of the percentage of US communities that fall within a similar spatial-demographic tier as Innisfil. Replicability also hinges on service cost and performance in comparison to average values for low-density transit in the US. Lastly, most transit agencies experienced a significant drop in demand (as much as 90%) with slowly rebounding ridership since the onset of the COVID-19 pandemic. The resiliency of the Innisfil program to the pressures induced by the pandemic is examined in comparison to other transit operations. The lessons learned across these three dimensions complement prior work to better understand the efficiency and sustainability of on-demand public mobility service for low-density communities like Innisfil.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the United States. Results show high variation in accuracy between and within stand-alone models and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public-health action.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Uncovering heterogeneous intercommunity disease transmission from neutral allele frequency time series

The COVID-19 pandemic has underscored the need for accurate epidemic forecasting to predict pathogen spread, evolution, and evaluate intervention strategies. Forecast reliability hinges on detailed knowledge of disease transmission across population segments, which may be inferred from contact surveys or mobility data. However, these indirect approaches make it difficult to estimate rare transmissions between socially or geographically distant communities. We show that the steep ramp-up of genome sequencing surveillance during the pandemic can be leveraged to directly identify transmission patterns between geographically defined communities. Our approach uses a hidden Markov model to infer the fraction of infections a community imports from others based on how rapidly allele frequencies in the focal community converge to those in the donor communities. Applying this method to SARS-CoV-2 sequencing data from England and the United States, we uncover networks of intercommunity transmission that reflect geographical relationships while exposing significant long-range interactions. The scaling of importation rate with distance is consistent across both countries, yet weaker than expected based on mobility data, highlighting limitations of indirect inference. We show that transmission patterns can change between waves of variants of concern and analyze how the inferred heterogeneity in intercommunity transmission impacts evolutionary forecasts. While applied here to geographically defined communities, our approach could be applied to those defined by other traits (e.g., age, socioeconomic status), provided time-series data can be stratified accordingly. Overall, our study highlights population genomic time series data as a crucial record of epidemiological interactions, which can be deciphered using tree-free inference methods.

Okada, Takashi [Department of Physics; University ↗
DOE OSTI2020

Deducing the N- and O-glycosylation profile of the spike protein of novel coronavirus SARS-CoV-2

The current emergence of the novel coronavirus pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) demands the development of new therapeutic strategies to prevent rapid progress of mortalities. The coronavirus spike (S) protein, which facilitates viral attachment, entry and membrane fusion is heavily glycosylated and plays a critical role in the elicitation of the host immune response. The spike protein is comprised of two protein subunits (S1 and S2), which together possess 22 potential N-glycosylation sites. Herein, we report the glycosylation mapping on spike protein subunits S1 and S2 expressed on human cells through high-resolution mass spectrometry. We have characterized the quantitative N-glycosylation profile on spike protein and interestingly, observed unexpected O-glycosylation modifications on the receptor-binding domain of spike protein subunit S1. Even though O-glycosylation has been predicted on the spike protein of SARS-CoV-2, this is the first report of experimental data for both the site of O-glycosylation and identity of the O-glycans attached on the subunit S1. Our data on the N- and O-glycosylation are strengthened by extensive manual interpretation of each glycopeptide spectra in addition to using bioinformatics tools to confirm the complexity of glycosylation in the spike protein. Here, the elucidation of the glycan repertoire on the spike protein provides insights into the viral binding studies and more importantly, propels research toward the development of a suitable vaccine candidate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Comprehensive characterization of N- and O- glycosylation of SARS-CoV-2 human receptor angiotensin converting enzyme 2

The emergence of the coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created the need for development of new therapeutic strategies. Understanding the mode of viral attachment, entry and replication has become a key aspect of such interventions. The coronavirus surface features a trimeric spike (S) protein that is essential for viral attachment, entry and membrane fusion. The S protein of SARS-CoV-2 binds to human angiotensin converting enzyme 2 (hACE2) for entry. Herein, we describe glycomic and glycoproteomic analysis of hACE2 expressed in HEK293 cells. We observed high glycan occupancy (73.2 to 100%) at all seven possible N-glycosylation sites and surprisingly detected one novel O-glycosylation site. To deduce the detailed structure of glycan epitopes on hACE2 that may be involved in viral binding, we have characterized the terminal sialic acid linkages, the presence of bisecting GlcNAc and the pattern of N-glycan fucosylation. We have conducted extensive manual interpretation of each glycopeptide and glycan spectrum, in addition to using bioinformatics tools to validate the hACE2 glycosylation. Our elucidation of the site-specific glycosylation and its terminal orientations on the hACE2 receptor, along with the modeling of hACE2 glycosylation sites can aid in understanding the intriguing virus-receptor interactions and assist in the development of novel therapeutics to prevent viral entry. Here, the relevance of studying the role of ACE2 is further increased due to some recent reports about the varying ACE2 dependent complications with regard to age, sex, race and pre-existing conditions of COVID-19 patients.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

The temperature-dependent conformational ensemble of SARS-CoV-2 main protease (M pro )

The COVID-19 pandemic, instigated by the SARS-CoV-2 coronavirus, continues to plague the globe. The SARS-CoV-2 main protease, or M pro , is a promising target for the development of novel antiviral therapeutics. Previous X-ray crystal structures of M pro were obtained at cryogenic temperature or room temperature only. Here we report a series of high-resolution crystal structures of unliganded M pro across multiple temperatures from cryogenic to physiological, and another at high humidity. We interrogate these data sets with parsimonious multiconformer models, multi-copy ensemble models, and isomorphous difference density maps. Our analysis reveals a perturbation-dependent conformational landscape for M pro , including a mobile zinc ion interleaved between the catalytic dyad, mercurial conformational heterogeneity at various sites including a key substrate-binding loop, and a far-reaching intramolecular network bridging the active site and dimer interface. Our results may inspire new strategies for antiviral drug development to aid preparation for future coronavirus pandemics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

An antibody class with a common CDRH3 motif broadly neutralizes sarbecoviruses

The devastation caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has made clear the importance of pandemic preparedness. To address future zoonotic outbreaks due to related viruses in the sarbecovirus subgenus, we identified a human monoclonal antibody, 10-40, that neutralized or bound all sarbecoviruses tested in vitro and protected against SARS-CoV-2 and SARS-CoV in vivo. Comparative studies with other receptor-binding domain (RBD)–directed antibodies showed 10-40 to have the greatest breadth against sarbecoviruses, suggesting that 10-40 is a promising agent for pandemic preparedness. Moreover, structural analyses on 10-40 and similar antibodies not only defined an epitope cluster in the inner face of the RBD that is well conserved among sarbecoviruses but also uncovered a distinct antibody class with a common CDRH3 motif. Our analyses also suggested that elicitation of this class of antibodies may not be overly difficult, an observation that bodes well for the development of a pan-sarbecovirus vaccine.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Development of learning objectives to support undergraduate virology curriculum guidelines

It has become increasingly important for microbiology educators to help students learn critical concepts of the discipline. This is particularly true in virology, where current challenges include increasing rates of vaccine hesitancy, misinformation about the COVID-19 pandemic, and controversy surrounding research on pathogens with pandemic potential. Having students learn virology can attract more people to the field and increase the number of people who can engage in meaningful discourse about issues relating to the discipline. However, the limited number of virologists who teach undergraduates, combined with the fact that many institutions lack stand-alone virology courses, results in virology often being taught as a limited number of lectures within an undergraduate microbiology course (if it is covered at all), which may or may not be taught by an individual trained as a virologist. To provide a framework to teach virology to undergraduate students, a team of virology educators, with support from the American Society for Virology (ASV), developed curriculum guidelines for use in a stand-alone undergraduate virology course or a virology section within another course. These guidelines are available at the ASV website (https://asv.org/curriculum-guidelines/). To assist educators in implementing these guidelines, we created examples of measurable learning objectives. This perspective provides details about the virology curriculum guidelines and learning objectives and accompanies the perspective by Boury et al. in this issue of the Journal of Microbiology & Biology Education about the recent revision of the microbiology curriculum guidelines overseen by the American Society for Microbiology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A protective and broadly binding antibody class engages the influenza virus hemagglutinin head at its stem interface

ABSTRACT Influenza infection and vaccination impart strain-specific immunity that protects against neither seasonal antigenic variants nor the next pandemic. However, antibodies directed to conserved sites can confer broad protection. Here, we identify and characterize a class of human antibodies that engage a previously undescribed, conserved epitope on the influenza hemagglutinin (HA) protein. Prototype antibody S8V1-157 binds at the normally occluded interface between the HA head and stem. Antibodies to this HA head–stem interface epitope are non-neutralizingin vitrobut protect against lethal influenza infection in mice. These antibodies bind to most influenza A subtypes and seasonal human variants, and are present at low frequencies in the memory B cell populations of multiple human donors. Vaccines designed to elicit these antibodies might contribute to “universal” influenza immunity. IMPORTANCE Antibodies to the influenza virus hemagglutinin (HA) protein confer the strongest protection against infection. Human antibodies elicited by infection and/or vaccination fail to protect against antigenically novel animal, pandemic, or human seasonal viruses. Improved vaccines are needed. We identify a novel class of antibodies that bind most divergent HA subtypes and all seasonal human HA antigenic variants tested. These antibodies confer protection from lethal influenza challenge in animal models. The corresponding epitope on the HA head is occluded by its interaction with the stem and is inaccessible in the well-resolved prefusion state. The immunogenicity of this head–stem interface indicates that poorly understood conformations of HA presenting widely conserved surfaces are explored in biochemical, cell-based, andin vivoassays. Head–stem interface antibodies warrant further investigation as an avenue to improve influenza vaccines and therapeutics.

Microbiology↗
DOE OSTI2022

Characterization of a 2016-2017 Human Seasonal H3 Influenza A Virus Spillover Now Endemic to U.S. Swine

In 2017, the Iowa State University Veterinary Diagnostic Laboratory detected a reverse-zoonotic transmission of a human seasonal H3 influenza A virus into swine (IAV-S) in Oklahoma. Pairwise comparison between the recently characterized human seasonal H3 IAV-S (H3.2010.2) hemagglutinin (HA) sequences detected in swine and the most similar 2016–2017 human seasonal H3 revealed 99.9% nucleotide identity. To elucidate the origin of H3.2010.2 IAV-S, 45 HA and 27 neuraminidase (NA) sequences from 2017 to 2020 as well as 11 whole-genome sequences (WGS) were genetically characterized. Time to most recent common human ancestor was estimated between August and September 2016. The N2 NA was of human origin in all but one strain from diagnostic submissions with NA sequences, and the internal gene segments from WGS consisted of matrix genes originating from the 2009 pandemic H1N1 and another 5 internal genes of triple reassortant swine origin (TTTTPT). Pigs experimentally infected with H3.2010.2 demonstrated efficient nasal shedding and replication in the lungs, mild pneumonia, and minimal microscopic lung lesions and transmitted the virus to indirect contact swine. Antigenically, H3.2010.2 viruses were closer to a human seasonal vaccine strain, A/Hong Kong/4801/2014, than to the H3.2010.1 human seasonal H3 viruses detected in swine in 2012. This was the second sustained transmission of a human seasonal IAV into swine from the 2010 decade after H3.2010.1. Monitoring the spillover and detection of novel IAV from humans to swine may help vaccine antigen selection and could impact pandemic preparedness.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Comprehensive compartmental model and calibration algorithm for the study of clinical implications of the population-level spread of COVID-19: a study protocol

The complex dynamics of the coronavirus disease 2019 (COVID-19) pandemic has made obtaining reliable long-term forecasts of the disease progression difficult. Simple mechanistic models with deterministic parameters are useful for short-term predictions but have ultimately been unsuccessful in extrapolating the trajectory of the pandemic because of unmodelled dynamics and the unrealistic level of certainty that is assumed in the predictions. We propose a 22-compartment epidemiological model that includes compartments not previously considered concurrently, to account for the effects of vaccination, asymptomatic individuals, inadequate access to hospital care, post-acute COVID-19 and recovery with long-term health complications. Additionally, new connections between compartments introduce new dynamics to the system and provide a framework to study the sensitivity of model outputs to several concurrent effects, including temporary immunity, vaccination rate and vaccine effectiveness. Subject to data availability for a given region, we discuss a means by which population demographics (age, comorbidity, socioeconomic status, sex and geographical location) and clinically relevant information (different variants, different vaccines) can be incorporated within the 22-compartment framework. Considering a probabilistic interpretation of the parameters allows the model's predictions to reflect the current state of uncertainty about the model parameters and model states. We propose the use of a sparse Bayesian learning algorithm for parameter calibration and model selection. This methodology considers a combination of prescribed parameter prior distributions for parameters that are known to be essential to the modelled dynamics and automatic relevance determination priors for parameters whose relevance is questionable. This is useful as it helps prevent overfitting the available epidemiological data when calibrating the parameters of the proposed model. Population-level administrative health data will serve as partial observations of the model states.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Characterizing the Spread of COVID-19 from Human Mobility Patterns and SocioDemographic Indicators

Mobility is an indicator of human movement through space and time. With the increasing availability of geolocated data (from GPS, accelerometers, etc.), it is now possible to examine individual as well as group human mobility patterns. Human mobility is influenced by both intrinsic (i.e. personal motivations) and extrinsic (i.e., events like natural hazards or a pandemic like the COVID-19) factors. However, the intricate relationships between human mobility patterns and sociodemographic characteristics in the context of a pandemic are yet to be fully explored. Our goal is to overcome this gap by using human mobility data at the census block group level from mobile phones and combining those with social vulnerability indicators to examine the overall spread of COVID-19 at local spatial scales. We used 585,878 weekly visits to 37,871 points of interests (POIs) from Safegraph to quantify mobility indices and social distancing metrics in 2,820 census block groups in the city of Los Angeles (LA) - before and during lockdown as well as during the phase1 and phase 2 reopening. Finally, using supervised machine learning algorithms, we classified the census block groups in LA into High, Medium and Low categories that represented the vulnerability of these block groups based on the cumulative number of occurrences of COVID-19 cases till July 24, 2020. Our results indicate that the tree-based classifiers performed well in comparison to the Support Vector Machines and Multinomial Logit models. Gradient Boosting had the highest classification accuracy of 97.4% COVID-19 with an AUC score of 0.987. The block groups with high COVID-19 cases also had a high concentration of socially vulnerable populations, high human mobility index and a low social distancing index.

Roy, Avipsa↗
DOE OSTI2022

Methodology to Compare Twitter Reaction Trends between Disinformation Communities, to COVID related Campaign Events at Different Geospatial Granularities

With still ongoing COVID pandemic, there is an immediate need for a deeper understanding of how Twitter discussions (or chatters) in disinformation spreading communities get triggered. More specifically, the value is in monitoring how such trigger events in Twitter discussion do align with the timelines of relevant influencing events in the society (indicated in this work as campaign events). For campaign events in regards to COVID pandemic, we consider both NPI (Nonpharmaceutical Interventions) campaigns and disinformation spreading campaigns together. In this short paper we have presented a novel methodology to quantify, compare and relate two Twitter disinformation communities, in terms of their reaction patterns to the timelines of major campaign events. We have also analyzed these campaigns at their three geospatial granularity contexts: local county, state, and country/ federal. We have conducted a novel dataset collection on campaigns (NPI + Disinformation) at these different geospatial granularities. Then, with collected dataset on Twitter disinformation communities, we have performed a case study to validate our proposed methodology.

De, Debraj↗
DOE OSTI2022

Language models for the prediction of SARS-CoV-2 inhibitors

The COVID-19 pandemic highlights the need for computational tools to automate and accelerate drug design for novel protein targets. We leverage deep learning language models to generate and score drug candidates based on predicted protein binding affinity. We pre-trained a deep learning language model (BERT) on ∼9.6 billion molecules and achieved peak performance of 603 petaflops in mixed precision. Our work reduces pre-training time from days to hours, compared to previous efforts with this architecture, while also increasing the dataset size by nearly an order of magnitude. For scoring, we fine-tuned the language model using an assembled set of thousands of protein targets with binding affinity data and searched for inhibitors of specific protein targets, SARS-CoV-2 Mpro and PLpro. We utilized a genetic algorithm approach for finding optimal candidates using the generation and scoring capabilities of the language model. Our generalizable models accelerate the identification of inhibitors for emerging therapeutic targets.

Blanchard, Andrew E.↗