Moving Biosurveillance Beyond Coded Data Using AI for Symptom Detection From Physician Notes: Retrospective Cohort Study.

BACKGROUND: Real-time surveillance of emerging infectious diseases necessitates a dynamically evolving, computable case definition, which frequently incorporates symptom-related criteria. For symptom detection, both population health monitoring platforms and research initiatives primarily depend on structured data extracted from electronic health records. OBJECTIVE: This study sought to validate and test an artificial intelligence (AI)-based natural language processing (NLP) pipeline for detecting COVID-19 symptoms from physician notes in pediatric patients. We specifically study patients presenting to the emergency department (ED) who can be sentinel cases in an outbreak. METHODS: Subjects in this retrospective cohort study are patients who are 21 years of age and younger, who presented to a pediatric ED at a large academic children's hospital between March 1, 2020, and May 31, 2022. The ED notes for all patients were processed with an NLP pipeline tuned to detect the mention of 11 COVID-19 symptoms based on Centers for Disease Control and Prevention (CDC) criteria. For a gold standard, 3 subject matter experts labeled 226 ED notes and had strong agreement (F1-score=0.986; positive predictive value [PPV]=0.972; and sensitivity=1.0). F1-score, PPV, and sensitivity were used to compare the performance of both NLP and the International Classification of Diseases, 10th Revision (ICD-10) coding to the gold standard chart review. As a formative use case, variations in symptom patterns were measured across SARS-CoV-2 variant eras. RESULTS: There were 85,678 ED encounters during the study period, including 4% (n=3420) with patients with COVID-19. NLP was more accurate at identifying encounters with patients that had any of the COVID-19 symptoms (F1-score=0.796) than ICD-10 codes (F1-score =0.451). NLP accuracy was higher for positive symptoms (sensitivity=0.930) than ICD-10 (sensitivity=0.300). However, ICD-10 accuracy was higher for negative symptoms (specificity=0.994) than NLP (specificity=0.917). Congestion or runny nose showed the highest accuracy difference (NLP: F1-score=0.828 and ICD-10: F1-score=0.042). For encounters with patients with COVID-19, prevalence estimates of each NLP symptom differed across variant eras. Patients with COVID-19 were more likely to have each NLP symptom detected than patients without this disease. Effect sizes (odds ratios) varied across pandemic eras. CONCLUSIONS: This study establishes the value of AI-based NLP as a highly effective tool for real-time COVID-19 symptom detection in pediatric patients, outperforming traditional ICD-10 methods. It also reveals the evolving nature of symptom prevalence across different virus variants, underscoring the need for dynamic, technology-driven approaches in infectious disease surveillance.

Applied genomics for identification of virulent biothreats and for disease outbreak surveillance.

Fortifying our preparedness to cope with biological threats by identifying and targeting virulence factors may be a preventative strategy for curtailing infectious disease outbreak. Virulence factors evoke successful pathogenic invasion, and the science and technology of genomics offers a way of identifying them, their agents and evolutionary ancestor. Genomics offers the possibility of deciphering if the release of a pathogen was intentional or natural by observing sequence and annotated data of the causative agent, and evidence of genetic engineering such as cloned vectors at restriction sites. However, to leverage and maximise the application of genomics to strengthen global interception system for real-time biothreat diagnostics, a complete genomic library of pathogenic and non-pathogenic agents will create a robust reference assembly that can be used to screen, characterise, track and trace new and existing strains. Encouraging ethical research sequencing pathogens found in animals and the environment, as well as creating a global space for collaboration will lead to effective global regulation and biosurveillance.

Clade-Specific Biosurveillance of Pseudoperonospora cubensis Using Spore Traps for Precision Disease Management of Cucurbit Downy Mildew.

Pseudoperonospora cubensis is an obligate oomycete and cause of cucurbit downy mildew (CDM), the most destructive foliar disease affecting cucurbit hosts. Annual epidemics develop throughout the United States as windborne sporangia travel great distances and survive prolonged exposure to solar radiation. Recent genomic evidence suggests that P. cubensis isolates display host adaptation based on their respective clade. Early detection is key for fungicide application timing, and identification of the host-adapted clade provides information on the risk of infection for specific cucurbit crops. In this study, a multiplex quantitative PCR assay was developed based on species- and clade-specific nuclear genomic markers. The assay detected as few as 10 sporangia or DNA at 100 fg/ml for both clades and was validated in the field by deploying rotorod spore samplers in cucurbit sentinel plots located at two research stations in North Carolina. Using this assay, sporangia DNA was detected in spore trap sampling rods before signs of P. cubensis or CDM symptoms were observed in the sentinel plots. Both clade 1 and clade 2 DNA were detected in late-season cucumber and watermelon plots but only clade 2 DNA was detected in the early-season cucumber plots. These results will significantly improve disease management of CDM by monitoring inoculum levels to determine the cucurbit crops at risk of infection throughout each growing season.

A big data approach to metagenomics for all-food-sequencing.

BACKGROUND: All-Food-Sequencing (AFS) is an untargeted metagenomic sequencing method that allows for the detection and quantification of food ingredients including animals, plants, and microbiota. While this approach avoids some of the shortcomings of targeted PCR-based methods, it requires the comparison of sequence reads to large collections of reference genomes. The steadily increasing amount of available reference genomes establishes the need for efficient big data approaches. RESULTS: We introduce an alignment-free k-mer based method for detection and quantification of species composition in food and other complex biological matters. It is orders-of-magnitude faster than our previous alignment-based AFS pipeline. In comparison to the established tools CLARK, Kraken2, and Kraken2+Bracken it is superior in terms of false-positive rate and quantification accuracy. Furthermore, the usage of an efficient database partitioning scheme allows for the processing of massive collections of reference genomes with reduced memory requirements on a workstation (AFS-MetaCache) or on a Spark-based compute cluster (MetaCacheSpark). CONCLUSIONS: We present a fast yet accurate screening method for whole genome shotgun sequencing-based biosurveillance applications such as food testing. By relying on a big data approach it can scale efficiently towards large-scale collections of complex eukaryotic and bacterial reference genomes. AFS-MetaCache and MetaCacheSpark are suitable tools for broad-scale metagenomic screening applications. They are available at https://muellan.github.io/metacache/afs.html (C++ version for a workstation) and https://github.com/jmabuin/MetaCacheSpark (Spark version for big data clusters).

A new approach to molecular biosurveillance of invasive species using DNA metabarcoding.

Non-indigenous species (NIS) reach every corner of the world, at times wreaking havoc on ecosystems and costing the global economy billions of dollars. A rapid and accurate biosurveillance tool tailored to a particular biogeographic region is needed to detect NIS when they are first introduced into an area as traditional detection methods are expensive and require specialized expertise. Metabarcoding of environmental and community DNA meets those biosurveillance requirements; a novel tool tailored to the Northwest Pacific Ocean is presented here using an approach that could revolutionize early detection of NIS. Eight newly designed genetic markers for multiple gene regions were implemented to meet the stringent taxonomic requirements for the detection of NIS across four major marine phyla. The tool was considered highly successful because it identified 12 known NIS in the study area and a further seven species representing potential new records. Overall community composition detected here was statistically different between substrate types; zooplankton sampling accounted for significantly higher species richness than filtered sea water in most cases, but this was dominated by mollusk and arthropod species. Both substrate types sampled were required to identify the wide taxonomic breadth of known NIS in the study area. Intensive sampling is known to be paramount for the detection of rare species, including new incursions of NIS, thus it is recommended to include diverse DNA sampling protocols based on species' life-history characteristics for broad detection capacity. Application of a metabarcoding-based molecular biosurveillance tool optimized for biogeographic regions enables rapid and accurate early detection across a wide taxonomic range to allow quick implementation of eradication or control efforts and potentially mitigate some of the devastating effects of NIS worldwide.

Next-Generation Sequencing for Biodefense: Biothreat Detection, Forensics, and the Clinic.

BACKGROUND: Next-generation sequencing (NGS) is revolutionizing a variety of molecular biology fields including bioforensics, biosurveillance, and infectious disease diagnostics. For pathogen detection, the ability to sequence all nucleic acids in a sample allows near limitless multiplexability, free from a priori knowledge regarding an etiologic agent as is typically required for targeted molecular assays such as real-time PCR. Furthermore, sequencing capabilities can generate in depth genomic information, allowing detailed molecular epidemiological studies and bioforensics analysis, which is critical for source agent identification in a biothreat outbreak. However, lack of analytical specificity, inherent to NGS, presents challenges for regulated applications such as clinical diagnostics and molecular attribution. CONTENT: Here, we discuss NGS applications in the context of preparedness and biothreat readiness. Specifically, we investigate current and future applications of NGS technologies to affect the fields of biosurveillance, bioforensics, and clinical diagnostics with specific focus on biodefense. SUMMARY: Overall, there are many advantages to the implementation of NGS for preparedness and readiness against biowarfare agents, from forensics to diagnostics. However, appropriate caveats must be associated with any technology. This includes NGS. While NGS is not the panacea replacing all molecular techniques, it will greatly enhance the ability to detect, characterize, and diagnose biowarfare agents, thus providing an excellent addition to the biodefense toolbox of biosurveillance, bioforensics, and biothreat diagnosis.

A new statistical early outbreak detection method for biosurveillance and performance comparisons.

Biosurveillance for rapid detection of epidemics of diseases is a challenging area of endeavor in many respects. Hence, this area is in need of development of methodology and opens to novel methods of detection. In this study, a new simple statistical early outbreak detection approach is proposed to detect outbreaks of diseases in real time. The new approach is called LWMAT since it is based on linearly weighted moving average. Furthermore, it does not require a long baseline and partly takes into account of likely features of biosurveillance data such as nonstationary and overdispersion to some extent. Moreover, this newly proposed method is easily adapted to automated use in public health surveillance systems to monitor simultaneously large number time series of indicators associated with the relevant diseases. To compare the performance of the new method with those of some well-known outbreak detection methods, semisynthetic data with outbreaks of various magnitudes and durations are simulated by considering the weekly number of outpatient visits for influenza-like illness for the influenza seasons 2014-2015 through 2017-2018 at Centers for Disease Control and Prevention (CDC) in the United States. Under the conditions of the simulation studies, Serfling regression and Farrington flexible seem to be preferable methods for monitoring the weekly influenza data at CDC in terms of early identification of influenza outbreaks with a high probability. In addition, the newly proposed LWMAT-type methods appear to be promising and useful methods in the case of small magnitude outbreaks with a short duration.

Poisoning, Overdoses, Toxic Exposures.

The national poison center movement originated in the Midwest with actions of the American Academy of Pediatrics in Chicago, Illinois, in 1972. The Missouri Poison Center (MPC) was established in 1974. The MPC and other regional poison centers are essential to the public health locally and nationally. Trends in serious poisoning outbreaks such as the release of synthetic cannabinoids have been detected by real-time electronic surveillance by specialists in poison information and medical toxicologists.

An extensible framework and database of infectious disease for biosurveillance.

Biosurveillance, a relatively young field, has recently increased in importance because of increasing emphasis on global health. Databases and tools describing particular subsets of disease are becoming increasingly common in the field. Here, we present an infectious disease database that includes diseases of biosurveillance relevance and an extensible framework for the easy expansion of the database.

Sigma: strain-level inference of genomes from metagenomic analysis for biosurveillance.

MOTIVATION: Metagenomic sequencing of clinical samples provides a promising technique for direct pathogen detection and characterization in biosurveillance. Taxonomic analysis at the strain level can be used to resolve serotypes of a pathogen in biosurveillance. Sigma was developed for strain-level identification and quantification of pathogens using their reference genomes based on metagenomic analysis. RESULTS: Sigma provides not only accurate strain-level inferences, but also three unique capabilities: (i) Sigma quantifies the statistical uncertainty of its inferences, which includes hypothesis testing of identified genomes and confidence interval estimation of their relative abundances; (ii) Sigma enables strain variant calling by assigning metagenomic reads to their most likely reference genomes; and (iii) Sigma supports parallel computing for fast analysis of large datasets. The algorithm performance was evaluated using simulated mock communities and fecal samples with spike-in pathogen strains. AVAILABILITY AND IMPLEMENTATION: Sigma was implemented in C++ with source codes and binaries freely available at http://sigma.omicsbio.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bayesian mixture analysis for metagenomic community profiling.

MOTIVATION: Deep sequencing of clinical samples is now an established tool for the detection of infectious pathogens, with direct medical applications. The large amount of data generated produces an opportunity to detect species even at very low levels, provided that computational tools can effectively profile the relevant metagenomic communities. Data interpretation is complicated by the fact that short sequencing reads can match multiple organisms and by the lack of completeness of existing databases, in particular for viral pathogens. Here we present metaMix, a Bayesian mixture model framework for resolving complex metagenomic mixtures. We show that the use of parallel Monte Carlo Markov chains for the exploration of the species space enables the identification of the set of species most likely to contribute to the mixture. RESULTS: We demonstrate the greater accuracy of metaMix compared with relevant methods, particularly for profiling complex communities consisting of several related species. We designed metaMix specifically for the analysis of deep transcriptome sequencing datasets, with a focus on viral pathogen detection; however, the principles are generally applicable to all types of metagenomic mixtures. AVAILABILITY AND IMPLEMENTATION: metaMix is implemented as a user friendly R package, freely available on CRAN: http://cran.r-project.org/web/packages/metaMix CONTACT: sofia.morfopoulou.10@ucl.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bionformatics online.

ORBiT: Oak Ridge biosurveillance toolkit for public health dynamics.

BACKGROUND: The digitization of health-related information through electronic health records (EHR) and electronic healthcare reimbursement claims and the continued growth of self-reported health information through social media provides both tremendous opportunities and challenges in developing effective biosurveillance tools. With novel emerging infectious diseases being reported across different parts of the world, there is a need to build systems that can track, monitor and report such events in a timely manner. Further, it is also important to identify susceptible geographic regions and populations where emerging diseases may have a significant impact. METHODS: In this paper, we present an overview of Oak Ridge Biosurveillance Toolkit (ORBiT), which we have developed specifically to address data analytic challenges in the realm of public health surveillance. In particular, ORBiT provides an extensible environment to pull together diverse, large-scale datasets and analyze them to identify spatial and temporal patterns for various biosurveillance-related tasks. RESULTS: We demonstrate the utility of ORBiT in automatically extracting a small number of spatial and temporal patterns during the 2009-2010 pandemic H1N1 flu season using claims data. These patterns provide quantitative insights into the dynamics of how the pandemic flu spread across different parts of the country. We discovered that the claims data exhibits multi-scale patterns from which we could identify a small number of states in the United States (US) that act as "bridge regions" contributing to one or more specific influenza spread patterns. Similar to previous studies, the patterns show that the south-eastern regions of the US were widely affected by the H1N1 flu pandemic. Several of these south-eastern states act as bridge regions, which connect the north-east and central US in terms of flu occurrences. CONCLUSIONS: These quantitative insights show how the claims data combined with novel analytical techniques can provide important information to decision makers when an epidemic spreads throughout the country. Taken together ORBiT provides a scalable and extensible platform for public health surveillance.

Practical comparison of aberration detection algorithms for biosurveillance systems.

National syndromic surveillance systems require optimal anomaly detection methods. For method performance comparison, we injected multi-day signals stochastically drawn from lognormal distributions into time series of aggregated daily visit counts from the U.S. Centers for Disease Control and Prevention's BioSense syndromic surveillance system. The time series corresponded to three different syndrome groups: rash, upper respiratory infection, and gastrointestinal illness. We included a sample of facilities with data reported every day and with median daily syndromic counts ⩾1 over the entire study period. We compared anomaly detection methods of five control chart adaptations, a linear regression model and a Poisson regression model. We assessed sensitivity and timeliness of these methods for detection of multi-day signals. At a daily background alert rate of 1% and 2%, the sensitivities and timeliness ranged from 24 to 77% and 3.3 to 6.1days, respectively. The overall sensitivity and timeliness increased substantially after stratification by weekday versus weekend and holiday. Adjusting the baseline syndromic count by the total number of facility visits gave consistently improved sensitivity and timeliness without stratification, but it provided better performance when combined with stratification. The daily syndrome/total-visit proportion method did not improve the performance. In general, alerting based on linear regression outperformed control chart based methods. A Poisson regression model obtained the best sensitivity in the series with high-count data.

All-Food-Seq (AFS): a quantifiable screen for species in biological samples by deep DNA sequencing.

BACKGROUND: DNA-based methods like PCR efficiently identify and quantify the taxon composition of complex biological materials, but are limited to detecting species targeted by the choice of the primer assay. We show here how untargeted deep sequencing of foodstuff total genomic DNA, followed by bioinformatic analysis of sequence reads, facilitates highly accurate identification of species from all kingdoms of life, at the same time enabling quantitative measurement of the main ingredients and detection of unanticipated food components. RESULTS: Sequence data simulation and real-case Illumina sequencing of DNA from reference sausages composed of mammalian (pig, cow, horse, sheep) and avian (chicken, turkey) species are able to quantify material correctly at the 1% discrimination level via a read counting approach. An additional metagenomic step facilitates identification of traces from animal, plant and microbial DNA including unexpected species, which is prospectively important for the detection of allergens and pathogens. CONCLUSIONS: Our data suggest that deep sequencing of total genomic DNA from samples of heterogeneous taxon composition promises to be a valuable screening tool for reference species identification and quantification in biosurveillance applications like food testing, potentially alleviating some of the problems in taxon representation and quantification associated with targeted PCR-based approaches.

Use of syndromic surveillance at local health departments: movement toward more effective systems.

CONTEXT: Syndromic surveillance systems enhance public health practice in both large and small population settings. However, data from these systems are typically monitored by state and federal agencies and less frequently used by small public health agencies, such as local health departments (LHDs). Syndromic surveillance system modifications may facilitate use by LHDs. OBJECTIVE: To describe syndromic surveillance system modifications and survey LHD staff to assess subsequent changes in system use. DESIGN: Pre- and postintervention cross-sectional analysis. SETTING: North Carolina (NC) LHDs, 2009 and 2012. PARTICIPANTS: LHD nursing and preparedness staff. MAIN OUTCOME MEASURES: Use of syndromic surveillance data by LHDs for outbreak response, seasonal event response, program management, and stakeholder reports. RESULTS: In NC, syndromic surveillance system modifications made between 2009 and 2012 included implementation of LHD-specific data "dashboards" and increased distribution of LHD-specific surveillance information by the state public health agency. Users of LHD syndromic surveillance system increased from 99 in 2009 to 175 in 2012. Twenty-seven of 28 (96%) and 62 of 72 (86%) respondents completed the 2009 and 2012 surveys, respectively. Among respondents, 23% used syndromic surveillance data for outbreak response in 2009, compared with 25% in 2012. In 2009, 46% of respondents used these data for seasonal event response, compared with 57% in 2012. Syndromic surveillance data were used for program management by 25% of respondents in 2009 (compared with 30% in 2012) and for stakeholder reports by 23% of respondents in 2009 (compared with 33% in 2012). CONCLUSIONS: Syndromic surveillance system changes supported modest increases in LHD use of syndromic surveillance information. Because use of syndromic surveillance information at smaller LHD is rare, these modest increases indicate effective modification of the NC syndromic surveillance system.

Response to Genovesi et al.: Ant biosurveillance should come before invasion.

Menchetti et al. respond to the letter of Genovesi et al. and contribute new records of the red imported fire ant in Sicily.