Engineering topics

Daughton, Ashlynn R.

Publications and source records attributed to Daughton, Ashlynn R..

10 records

DOE OSTI2021

Mining and Validating Social Media Data for COVID-19–Related Human Behaviors Between January and July 2020: Infodemiology Study

Background: Health authorities can minimize the impact of an emergent infectious disease outbreak through effective and timely risk communication, which can build trust and adherence to subsequent behavioral messaging. Monitoring the psychological impacts of an outbreak, as well as public adherence to such messaging, is also important for minimizing long-term effects of an outbreak. Objective: We used social media data from Twitter to identify human behaviors relevant to COVID-19 transmission, as well as the perceived impacts of COVID-19 on individuals, as a first step toward real-time monitoring of public perceptions to inform public health communications. Methods: We developed a coding schema for 6 categories and 11 subcategories, which included both a wide number of behaviors as well codes focused on the impacts of the pandemic (eg, economic and mental health impacts). We used this to develop training data and develop supervised learning classifiers for classes with sufficient labels. Classifiers that performed adequately were applied to our remaining corpus, and temporal and geospatial trends were assessed. We compared the classified patterns to ground truth mobility data and actual COVID-19 confirmed cases to assess the signal achieved here. Results: We applied our labeling schema to approximately 7200 tweets. The worst-performing classifiers had F1 scores of only 0.18 to 0.28 when trying to identify tweets about monitoring symptoms and testing. Classifiers about social distancing, however, were much stronger, with F1 scores of 0.64 to 0.66. We applied the social distancing classifiers to over 228 million tweets. We showed temporal patterns consistent with real-world events, and we showed correlations of up to –0.5 between social distancing signals on Twitter and ground truth mobility throughout the United States. Conclusions: Behaviors discussed on Twitter are exceptionally varied. Twitter can provide useful information for parameterizing models that incorporate human behavior, as well as for informing public health communication strategies by describing awareness of and compliance with suggested behaviors.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

“Thought I’d Share First” and Other Conspiracy Theory Tweets from the COVID-19 Infodemic: Exploratory Study

Background: The COVID-19 outbreak has left many people isolated within their homes; these people are turning to social media for news and social connection, which leaves them vulnerable to believing and sharing misinformation. Health-related misinformation threatens adherence to public health messaging, and monitoring its spread on social media is critical to understanding the evolution of ideas that have potentially negative public health impacts. Objective: The aim of this study is to use Twitter data to explore methods to characterize and classify four COVID-19 conspiracy theories and to provide context for each of these conspiracy theories through the first 5 months of the pandemic. Methods: We began with a corpus of COVID-19 tweets (approximately 120 million) spanning late January to early May 2020. We first filtered tweets using regular expressions (n=1.8 million) and used random forest classification models to identify tweets related to four conspiracy theories. Our classified data sets were then used in downstream sentiment analysis and dynamic topic modeling to characterize the linguistic features of COVID-19 conspiracy theories as they evolve over time. Results: Analysis using model-labeled data was beneficial for increasing the proportion of data matching misinformation indicators. Random forest classifier metrics varied across the four conspiracy theories considered (F1 scores between 0.347 and 0.857); this performance increased as the given conspiracy theory was more narrowly defined. We showed that misinformation tweets demonstrate more negative sentiment when compared to non-misinformation tweets and that theories evolve over time, incorporating details from unrelated conspiracy theories as well as real-world events. Conclusions: Although we focus here on health-related misinformation, this combination of approaches is not specific to public health and is valuable for characterizing misinformation in general, which is an important first step in creating targeted messaging to counteract its spread. Initial messaging should aim to preempt generalized misinformation before it becomes widespread, while later messaging will

5g↗

DOE OSTI2020

Comparison of Social Media, Syndromic Surveillance, and Microbiologic Acute Respiratory Infection Data: Observational Study

Internet data can be used to improve infectious disease models. However, the representativeness and individual-level validity of internet-derived measures are largely unexplored as this requires ground truth data for study. This study sought to identify relationships between Web-based behaviors and/or conversation topics and health status using a ground truth, survey-based dataset. This study leveraged a unique dataset of self-reported surveys, microbiological laboratory tests, and social media data from the same individuals toward understanding the validity of individual-level constructs pertaining to influenza-like illness in social media data. Logistic regression models were used to identify illness in Twitter posts using user posting behaviors and topic model features extracted from users’ tweets. Of 396 original study participants, only 81 met the inclusion criteria for this study. Of these participants’ tweets, we identified only two instances that were related to health and occurred within 2 weeks (before or after) of a survey indicating symptoms. It was not possible to predict when participants reported symptoms using features derived from topic models (area under the curve [AUC]=0.51; P=.38), though it was possible using behavior features, albeit with a very small effect size (AUC=0.53; P≤.001). Individual symptoms were also generally not predictable either. The study sample and a random sample from Twitter are predictably different on held-out data (AUC=0.67; P≤.001), meaning that the content posted by people who participated in this study was predictably different from that posted by random Twitter users. Individuals in the random sample and the GoViral sample used Twitter with similar frequencies (similar @ mentions, number of tweets, and number of retweets; AUC=0.50; P=.19). To our knowledge, this is the first instance of an attempt to use a ground truth dataset to validate infectious disease observations in social media data. The lack of signal, the lack of predictability among behaviors or topics, and the demonstrated volunteer bias in the study population are important findings for the large and growing body of disease surveillance using internet-sourced data.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

Epidemiological Data Challenges: Planning for a More Robust Future Through Data Standards

Accessible epidemiological data are of great value for emergency preparedness and response, understanding disease progression through a population, and building statistical and mechanistic disease models that enable forecasting. The status quo, however, renders acquiring and using such data difficult in practice. In many cases, a primary way of obtaining epidemiological data is through the internet, but the methods by which the data are presented to the public often differ drastically among institutions. As a result, there is a strong need for better data sharing practices. This paper identifies, in detail and with examples, the three key challenges one encounters when attempting to acquire and use epidemiological data: (1) interfaces, (2) data formatting, and (3) reporting. These challenges are used to provide suggestions and guidance for improvement as these systems evolve in the future. If these suggested data and interface recommendations were adhered to, epidemiological and public health analysis, modeling, and informatics work would be significantly streamlined, which can in turn yield better public health decision-making capabilities.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

An approach to and web-based tool for infectious disease outbreak intervention analysis

Infectious diseases are a leading cause of death globally. Decisions surrounding how to control an infectious disease outbreak currently rely on a subjective process involving surveillance and expert opinion. However, there are many situations where neither may be available. Modeling can fill gaps in the decision making process by using available data to provide quantitative estimates of outbreak trajectories. Effective reduction of the spread of infectious diseases can be achieved through collaboration between the modeling community and public health policy community. However, such collaboration is rare, resulting in a lack of models that meet the needs of the public health community. Here we show a Susceptible-Infectious-Recovered (SIR) model modified to include control measures that allows parameter ranges, rather than parameter point estimates, and includes a web user interface for broad adoption. We apply the model to three diseases, measles, norovirus and influenza, to show the feasibility of its use and describe a research agenda to further promote interactions between decision makers and the modeling community.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Salivary microbiomes of indigenous Tsimane mothers and infants are distinct despite frequent premastication

Background Premastication, the transfer of pre-chewed food, is a common infant and young child feeding practice among the Tsimane, forager-horticulturalists living in the Bolivian Amazon. Research conducted primarily with Western populations has shown that infants harbor distinct oral microbiota from their mothers. Premastication, which is less common in these populations, may influence the colonization and maturation of infant oral microbiota, including via transmission of oral pathogens. We collected premasticated food and saliva samples from Tsimane mothers and infants (9–24 months of age) to test for evidence of bacterial transmission in premasticated foods and overlap in maternal and infant salivary microbiota. We extracted bacterial DNA from two premasticated food samples and 12 matched salivary samples from maternal-infant pairs. DNA sequencing was performed with MiSeq (Illumina). We evaluated maternal and infant microbial composition in terms of relative abundance of specific taxa, alpha and beta diversity, and dissimilarity distances. Results The bacteria in saliva and premasticated food were mapped to 19 phyla and 400 genera and were dominated by Firmicutes, Proteobacteria, Actinobacteria, and Bacteroidetes. The oral microbial communities of Tsimane mothers and infants who frequently share premasticated food were well-separated in a non-metric multi-dimensional scaling ordination (NMDS) plot. Infant microbiotas clustered together, with weighted Unifrac distances significantly differing between mothers and infants. Infant saliva contained more Firmicutes ( p < 0.01) and fewer Proteobacteria ( p < 0.05) than did maternal saliva. Many genera previously associated with dental and periodontal infections, e.g. Neisseria , Gemella , Rothia , Actinomyces , Fusobacterium , and Leptotrichia , were more abundant in mothers than in infants. Conclusions Salivary microbiota of Tsimane infants and young children up to two years of age do not appear closely related to those of their mothers, despite frequent premastication and preliminary evidence that maternal bacteria is transmitted to premasticated foods. Infant physiology and diet may constrain colonization by maternal bacteria, including several oral pathogens.

Han, Cliff S.↗

DOE OSTI2016

Novel use of flu surveillance data: Evaluating potential of sentinel populations for early detection of influenza outbreaks

Influenza causes significant morbidity and mortality each year, with 2–8% of weekly outpatient visits around the United States for influenza-like-illness (ILI) during the peak of the season. Effective use of existing flu surveillance data allows officials to understand and predict current flu outbreaks and can contribute to reductions in influenza morbidity and mortality. Previous work used the 2009–2010 influenza season to investigate the possibility of using existing military and civilian surveillance systems to improve early detection of flu outbreaks. Results suggested that civilian surveillance could help predict outbreak trajectory in local military installations. To further test that hypothesis, we compare pairs of civilian and military outbreaks in seven locations between 2000 and 2013. We find no predictive relationship between outbreak peaks or time series of paired outbreaks. This larger study does not find evidence to support the hypothesis that civilian data can be used as sentinel surveillance for military installations. We additionally investigate the effect of modifying the ILI case definition between the standard Department of Defense definition, a more specific definition proposed in literature, and confirmed Influenza A. We find that case definition heavily impacts results. In conclusion, this study thus highlights the importance of careful selection of case definition, and appropriate consideration of case definition in the interpretation of results.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2015

Draft genome sequence of Thauera sp. strain SWB20, isolated from a Singapore wastewater treatment facility using gel microdroplets

In this study, we report here the genome sequence of Thauera sp. strain SWB20, isolated from a Singaporean wastewater treatment facility using gel microdroplets (GMDs) and single-cell genomics (SCG). This approach provided a single clonal microcolony that was sufficient to obtain a 4.9-Mbp genome assembly of an ecologically relevant Thauera species.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2013

Using phage display selected antibodies to dissect microbiomes for complete de novo genome sequencing of low abundance microbes

Background: Single cell genomics has revolutionized microbial sequencing, but complete coverage of genomes in complex microbiomes is imperfect due to enormous variation in organismal abundance and amplification bias. Empirical methods that complement rapidly improving bioinformatic tools will improve characterization of microbiomes and facilitate better genome coverage for low abundance microbes. Methods: We describe a new approach to sequencing individual species from microbiomes that combines antibody phage display against intact bacteria with fluorescence activated cell sorting (FACS). Single chain (scFv) antibodies are selected using phage display against a bacteria or microbial community, resulting in species-specific antibodies that can be used in FACS for relative quantification of an organism in a community, as well as enrichment or depletion prior to genome sequencing. Results: We selected antibodies against Lactobacillus acidophilus and demonstrate a FACS-based approach for identification and enrichment of the organism from both laboratory-cultured and commercially derived bacterial mixtures. The ability to selectively enrich for L. acidophilus when it is present at a very low abundance (<0.2%) leads to complete (>99.8%) de novo genome coverage whereas the standard single-cell sequencing approach is incomplete (<68%). We show that specific antibodies can be selected against L. acidophilus when the monoculture is used as antigen as well as when a community of 10 closely related species is used demonstrating that in principal antibodies can be generated against individual organisms within microbial communities. Conclusions: The approach presented here demonstrates that phage-selected antibodies against bacteria enable identification, enrichment of rare species, and depletion of abundant organisms making it tractable to virtually any microbe or microbial community. Combining antibody specificity with FACS provides a new approach for characterizing and manipulating microbial communities prior to genome sequencing.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2013

Nearly finished genomes produced using gel microdroplet culturing reveal substantial intraspecies genomic diversity within the human microbiome

The majority of microbial genomic diversity remains unexplored. This is largely due to our inability to culture most microorganisms in isolation, which is a prerequisite for traditional genome sequencing. Single-cell sequencing has allowed researchers to circumvent this limitation. DNA is amplified directly from a single cell using the whole-genome amplification technique of multiple displacement amplification (MDA). However, MDA from a single chromosome copy suffers from amplification bias and a large loss of specificity from even very small amounts of DNA contamination, which makes assembling a genome difficult and completely finishing a genome impossible except in extraordinary circumstances. Gel microdrop cultivation allows culturing of a diverse microbial community and provides hundreds to thousands of genetically identical cells as input for an MDA reaction. We demonstrate the utility of this approach by comparing sequencing results of gel microdroplets and single cells following MDA. Bias is reduced in the MDA reaction and genome sequencing, and assembly is greatly improved when using gel microdroplets. We acquired multiple near-complete genomes for two bacterial species from human oral and stool microbiome samples. A significant amount of genome diversity, including single nucleotide polymorphisms and genome recombination, is discovered. Gel microdroplets offer a powerful and high-throughput technology for assembling whole genomes from complex samples and for probing the pan-genome of naturally occurring populations.

59 BASIC BIOLOGICAL SCIENCES↗