Coupled models of genomic surveillance and evolving pandemics with applications for timely public health interventions.

Disease surveillance systems provide early warnings of disease outbreaks before they become public health emergencies. However, pandemics containment would be challenging due to the complex immunity landscape created by multiple variants. Genomic surveillance is critical for detecting novel variants with diverse characteristics and importation/emergence times. Yet, a systematic study incorporating genomic monitoring, situation assessment, and intervention strategies is lacking in the literature. We formulate an integrated computational modeling framework to study a realistic course of action based on sequencing, analysis, and response. We study the effects of the second variant's importation time, its infectiousness advantage and, its cross-infection on the novel variant's detection time, and the resulting intervention scenarios to contain epidemics driven by two-variants dynamics. Our results illustrate the limitation in the intervention's effectiveness due to the variants' competing dynamics and provide the following insights: i) There is a set of importation times that yields the worst detection time for the second variant, which depends on the first variant's basic reproductive number; ii) When the second variant is imported relatively early with respect to the first variant, the cross-infection level does not impact the detection time of the second variant. We found that depending on the target metric, the best outcomes are attained under different interventions' regimes. Our results emphasize the importance of sustained enforcement of Non-Pharmaceutical Interventions on preventing epidemic resurgence due to importation/emergence of novel variants. We also discuss how our methods can be used to study when a novel variant emerges within a population.

Genetics of flight in spongy moths (Lymantria dispar ssp.): functionally integrated profiling of a complex invasive trait.

BACKGROUND: Flight can drastically enhance dispersal capacity and is a key trait defining the potential of exotic insect species to spread and invade new habitats. The phytophagous European spongy moths (ESM, Lymantria dispar dispar) and Asian spongy moths (ASM; a multi-species group represented here by L. d. asiatica and L. d. japonica), are globally invasive species that vary in adult female flight capability-female ASM are typically flight capable, whereas female ESM are typically flightless. Genetic markers of flight capability would supply a powerful tool for flight profiling of these species at any intercepted life stage. To assess the functional complexity of spongy moth flight and to identify potential markers of flight capability, we used multiple genetic approaches aimed at capturing complementary signals of putative flight-relevant genetic divergence between ESM and ASM: reduced representation genome-wide association studies, whole genome sequence comparisons, and developmental transcriptomics. We then judged the candidacy of flight-associated genes through functional analyses aimed at addressing the proximate demands of flight and salient features of the ecological context of spongy moth flight evolution. RESULTS: Candidate gene sets were typically non-overlapping across different genetic approaches, with only nine gene annotations shared between any pair of approaches. We detected an array of flight-relevant functional themes across gene sets that collectively suggest divergence in flight capability between European and Asian spongy moth lineages has coincided with evolutionary differentiation in multiple aspects of flight development, execution, and surrounding life history. Overall, our results indicate that spongy moth flight evolution has shaped or been influenced by a large and functionally broad network of traits. CONCLUSIONS: Our study identified a suite of flight-associated genes in spongy moths suited to exploration of the genetic architecture and evolution of flight, or validation for flight profiling purposes. This work illustrates how complementary genetic approaches combined with phenotypically targeted functional analyses can help to characterize genetically complex traits.

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.

Simulation Study of Surveillance Strategies for Faster Detection of Novel SARS-CoV-2 Variants.

Earlier global detection of novel SARS-CoV-2 variants gives governments more time to respond. However, few countries can implement timely national surveillance, resulting in gaps in monitoring. The United Kingdom implemented large-scale community and hospital surveillance, but experience suggests it might be faster to detect new variants through testing England arrivals for surveillance. We developed simulations of emergence and importation of novel variants with a range of infection hospitalization rates to the United Kingdom. We compared time taken to detect the variant though testing arrivals at England borders, hospital admissions, and the general community. We found that sampling 10%-50% of arrivals at England borders could confer a speed advantage of 3.5-6 weeks over existing community surveillance and 1.5-5 weeks (depending on infection hospitalization rates) over hospital testing. Directing limited global capacity for surveillance to highly connected ports could speed up global detection of novel SARS-CoV-2 variants.

GalaxyTrakr: a distributed analysis tool for public health whole genome sequence data accessible to non-bioinformaticians.

BACKGROUND: Processing and analyzing whole genome sequencing (WGS) is computationally intense: a single Illumina MiSeq WGS run produces ~ 1 million 250-base-pair reads for each of 24 samples. This poses significant obstacles for smaller laboratories, or laboratories not affiliated with larger projects, which may not have dedicated bioinformatics staff or computing power to effectively use genomic data to protect public health. Building on the success of the cloud-based Galaxy bioinformatics platform ( http://galaxyproject.org ), already known for its user-friendliness and powerful WGS analytical tools, the Center for Food Safety and Applied Nutrition (CFSAN) at the U.S. Food and Drug Administration (FDA) created a customized 'instance' of the Galaxy environment, called GalaxyTrakr ( https://www.galaxytrakr.org ), for use by laboratory scientists performing food-safety regulatory research. The goal was to enable laboratories outside of the FDA internal network to (1) perform quality assessments of sequence data, (2) identify links between clinical isolates and positive food/environmental samples, including those at the National Center for Biotechnology Information sequence read archive ( https://www.ncbi.nlm.nih.gov/sra/ ), and (3) explore new methodologies such as metagenomics. GalaxyTrakr hosts a variety of free and adaptable tools and provides the data storage and computing power to run the tools. These tools support coordinated analytic methods and consistent interpretation of results across laboratories. Users can create and share tools for their specific needs and use sequence data generated locally and elsewhere. RESULTS: In its first full year (2018), GalaxyTrakr processed over 85,000 jobs and went from 25 to 250 users, representing 53 different public and state health laboratories, academic institutions, international health laboratories, and federal organizations. By mid-2020, it has grown to 600 registered users and processed over 450,000 analytical jobs. To illustrate how laboratories are making use of this resource, we describe how six institutions use GalaxyTrakr to quickly analyze and review their data. Instructions for participating in GalaxyTrakr are provided. CONCLUSIONS: GalaxyTrakr advances food safety by providing reliable and harmonized WGS analyses for public health laboratories and promoting collaboration across laboratories with differing resources. Anticipated enhancements to this resource will include workflows for additional foodborne pathogens, viruses, and parasites, as well as new tools and services.

Using Artificial Intelligence With Natural Language Processing to Combine Electronic Health Record's Structured and Free Text Data to Identify Nonvalvular Atrial Fibrillation to Decrease Strokes and Death: Evaluation and Case-Control Study.

BACKGROUND: Nonvalvular atrial fibrillation (NVAF) affects almost 6 million Americans and is a major contributor to stroke but is significantly undiagnosed and undertreated despite explicit guidelines for oral anticoagulation. OBJECTIVE: The aim of this study is to investigate whether the use of semisupervised natural language processing (NLP) of electronic health record's (EHR) free-text information combined with structured EHR data improves NVAF discovery and treatment and perhaps offers a method to prevent thousands of deaths and save billions of dollars. METHODS: We abstracted 96,681 participants from the University of Buffalo faculty practice's EHR. NLP was used to index the notes and compare the ability to identify NVAF, congestive heart failure, hypertension, age ≥75 years, diabetes mellitus, stroke or transient ischemic attack, vascular disease, age 65 to 74 years, sex category (CHA2DS2-VASc), and Hypertension, Abnormal liver/renal function, Stroke history, Bleeding history or predisposition, Labile INR, Elderly, Drug/alcohol usage (HAS-BLED) scores using unstructured data (International Classification of Diseases codes) versus structured and unstructured data from clinical notes. In addition, we analyzed data from 63,296,120 participants in the Optum and Truven databases to determine the NVAF frequency, rates of CHA2DS2­VASc ≥2, and no contraindications to oral anticoagulants, rates of stroke and death in the untreated population, and first year's costs after stroke. RESULTS: The structured-plus-unstructured method would have identified 3,976,056 additional true NVAF cases (P<.001) and improved sensitivity for CHA2DS2-VASc and HAS-BLED scores compared with the structured data alone (P=.002 and P<.001, respectively), causing a 32.1% improvement. For the United States, this method would prevent an estimated 176,537 strokes, save 10,575 lives, and save >US $13.5 billion. CONCLUSIONS: Artificial intelligence-informed bio-surveillance combining NLP of free-text information with structured EHR data improves data completeness, prevents thousands of strokes, and saves lives and funds. This method is applicable to many disorders with profound public health consequences.

Addressing Biodisaster X Threats With Artificial Intelligence and 6G Technologies: Literature Review and Critical Insights.

BACKGROUND: With advances in science and technology, biotechnology is becoming more accessible to people of all demographics. These advances inevitably hold the promise to improve personal and population well-being and welfare substantially. It is paradoxical that while greater access to biotechnology on a population level has many advantages, it may also increase the likelihood and frequency of biodisasters due to accidental or malicious use. Similar to "Disease X" (describing unknown naturally emerging pathogenic diseases with a pandemic potential), we term this unknown risk from biotechnologies "Biodisaster X." To date, no studies have examined the potential role of information technologies in preventing and mitigating Biodisaster X. OBJECTIVE: This study aimed to explore (1) what Biodisaster X might entail and (2) solutions that use artificial intelligence (AI) and emerging 6G technologies to help monitor and manage Biodisaster X threats. METHODS: A review of the literature on applying AI and 6G technologies for monitoring and managing biodisasters was conducted on PubMed, using articles published from database inception through to November 16, 2020. RESULTS: Our findings show that Biodisaster X has the potential to upend lives and livelihoods and destroy economies, essentially posing a looming risk for civilizations worldwide. To shed light on Biodisaster X threats, we detailed effective AI and 6G-enabled strategies, ranging from natural language processing to deep learning-based image analysis to address issues ranging from early Biodisaster X detection (eg, identification of suspicious behaviors), remote design and development of pharmaceuticals (eg, treatment development), and public health interventions (eg, reactive shelter-at-home mandate enforcement), as well as disaster recovery (eg, sentiment analysis of social media posts to shed light on the public's feelings and readiness for recovery building). CONCLUSIONS: Biodisaster X is a looming but avoidable catastrophe. Considering the potential human and economic consequences Biodisaster X could cause, actions that can effectively monitor and manage Biodisaster X threats must be taken promptly and proactively. Rather than solely depending on overstretched professional attention of health experts and government officials, it is perhaps more cost-effective and practical to deploy technology-based solutions to prevent and control Biodisaster X threats. This study discusses what Biodisaster X could entail and emphasizes the importance of monitoring and managing Biodisaster X threats by AI techniques and 6G technologies. Future studies could explore how the convergence of AI and 6G systems may further advance the preparedness for high-impact, less likely events beyond Biodisaster X.

Real-time laboratory surveillance of Dengue antigen(NS1) - Best proposed early warning and response system for Dengue in Sri Lanka? - Results from a pilot study.

Introduction: During the recent past, dengue fever and associated complications have been the most important concern for health policy makers of Sri Lanka. The current notification system has considerable inevitable delays in preventive measures. Objectives: Implementing a laboratory-based real-time antigen (NS1) surveillance system for notification coupled with a rapid preventive response within the Colombo district as a pilot project and compares the notifications with existing national surveillance systems. Methods: An online notification platform was established with a centralized database. Seven main laboratories of the private sector linked with the notification system, where they can create new notifications at the central database, whenever the NS1 test detected as positive. Relevant Medical Officers of Health should update action implemented to complete the response process. A dashboard was designed to visualize each notification and its status with a predefined colour code. Results: Patients from 14 Medical Officer of Health (MOH) areas out of 15 were captured. The immediate preventive response was recorded from the field preventive staff for 90% of the reporting. All most all attended patients have given health advice on awareness, prevention, and source reduction through premise inspection by trained field staff with 24hrs of notification. Conclusions: Salient features of the novel system are notification of antigen-positive patients, the rapidity of notification (real-time) and response, user-friendliness, access to multiple stakeholders simultaneously without data duplication, early involvement of the field staff, the ability to trace the cases using checklists and a color-coding system from a dashboard.

Comparison of the performance of an amplicon sequencing assay based on Oxford Nanopore technology to real-time PCR assays for detecting bacterial biodefense pathogens.

BACKGROUND: The state-of-the-art in nucleic acid based biodetection continues to be polymerase chain reaction (PCR), and many real-time PCR assays targeting biodefense pathogens for biosurveillance are in widespread use. These assays are predominantly singleplex; i.e. one assay tests for the presence of one target, found in a single organism, one sample at a time. Due to the intrinsic limitations of such tests, there exists a critical need for high-throughput multiplex assays to reduce the time and cost incurred when screening multiple targets, in multiple pathogens, and in multiple samples. Such assays allow users to make an actionable call while maximizing the utility of the small volumes of test samples. Unfortunately, current multiplex real-time PCR assays are limited in the number of targets that can be probed simultaneously due to the availability of fluorescence channels in real-time PCR instruments. RESULTS: To address this gap, we developed a pipeline in which the amplicons produced by a 14-plex end-point PCR assay using spiked samples were subsequently sequenced using Nanopore technology. We used bar codes to sequence multiple samples simultaneously, leading to the generation and subsequent analysis of sequence data resulting from a short sequencing run time (< 10 min). We compared the limits of detection (LoD) of real-time PCR assays to Oxford Nanopore Technologies (ONT)-based amplicon sequencing and estimated the sample-to-answer time needed for this approach. Overall, LoDs determined from the first 10 min of sequencing data were at least one to two orders of magnitude lower than real-time PCR. Given enough time, the amplicon sequencing approach is approximately 100 times more sensitive than real-time PCR, with detection of amplicon specific reads even at the lowest tested spiking concentration (around 2.5-50 Colony Forming Units (CFU)/ml). CONCLUSIONS: Based on these results, we propose amplicon sequencing assay as a viable alternative to replace the current real-time PCR based singleplex assays for higher throughput biodefense applications. We note, however, that targeted amplicon specific reads were not detectable even at the highest tested spike concentrations (2.5 X 104-5.0 X105 CFU/ml) without an initial amplification step, indicating that PCR is still necessary when utilizing this protocol.

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.

Using DNA-barcoded Malaise trap samples to measure impact of a geothermal energy project on the biodiversity of a Costa Rican old-growth rain forest.

We report one year (2013-2014) of biomonitoring an insect community in a tropical old-growth rain forest, during construction of an industrial-level geothermal electricity project. This is the first-year reaction by the species-rich insect biodiversity; six subsequent years are being analyzed now. The site is on the margin of a UNESCO Natural World Heritage Site, Área de Conservación Guanacaste (ACG), in northwestern Costa Rica. This biomonitoring is part of Costa Rica's ongoing efforts to sustainably retain its wild biodiversity through biodevelopmental integration with its societies. Essential tools are geothermal engineering needs, entomological knowledge, insect species-rich forest, government-NGO integration, common sense, DNA barcoding for species-level identification, and Malaise traps. This research is tailored for integration with its society at the product level. We combine an academic view with on-site engineering decisions. This biomonitoring requires alpha-level DNA barcoding combined with centuries of morphology-based entomological taxonomy and ecology. Not all desired insect community analyses are performed; they are for data from subsequent years combined with this year. We provide enough analysis to be used by both guilds now. This biomonitoring has shown, for the first year, that the geothermal project impacts only the biodiversity within a zone less than 50 m from the project margin.

[Feasibility evaluation of estimating the 24-hour urinary sodium excretion in Chinese population with three spot urine methods].

Objective: To evaluate the feasibility of three spot urine methods (Kawasaki, INTERSALT and Tanaka) for estimating the 24 h urinary sodium excretion in the Chinese population. Methods: In 2017, 1 499 participants aged 18 to 69 years old were selected from Yiwu City, Haining City, Taishun County, Yinzhou District of Ningbo City and Liandu District of Lishui City of Zhejiang Province by using the multistage random sampling method. Sociodemographic information of the subjects was collected with questionnaires and physical measurements were performed. 24 h urine was collected and urinary volume was recorded. The concentrations of urinary sodium, potassium and creatinine were also measured. Kawasaki, INTERSALT and Tanaka spot urine methods were applied to estimate the 24 h urinary sodium excretion and compared with actual values among 1 426 participants who passed urine integrity test. Results: The age of participants was (46.71±14.04) years old, including 700 males, accounting for 49.1%. The actual value of 24 h urinary sodium excretion was (167.10±74.70) mmol, but Kawasaki method overestimated it as (184.61±57.10) mmol, and INTERSALT and Tanaka methods underestimated it as(134.62±39.21) and (143.20±35.66) mmol. Estimated difference (95%CI) (mmol) from small to large was Kawasaki method [17.51 (13.54, 21.47)], Tanaka method [-23.90 (-27.60, -20.20)] and INTERSALT method [-32.48 (-36.29, -28.67)]. With the increase of 24 h sodium intake, all estimation methods changed from the overestimation to underestimation. In those with 24 h sodium intake <9.0 g, the estimated difference (95%CI) of the INTERSALT method was the smallest as 43.15 (37.73, 48.57) and 1.26 (-2.10, 4.63) mmol for <6.0 and 6.0-8.9 g groups, respectively. In those with 24 h sodium intake≥9.0 g, the Kawasaki method had the smallest estimated difference (95%CI) as -12.50 (-18.14, -6.86) and -53.73 (-61.25, -46.22) for 9.0-11.9 g and ≥ 12.0 g group, respectively. The consistency analysis of the Bland-Altman method showed that the Kawasaki method had the best consistency with actual measured value and it had the least number of points outside the range (69 points accounting for 4.84%). Conclusion: Among the three spot urine methods, the Kawasaki method has better applicability in predicting the excretion of 24 h urine sodium in the Chinese population.

Southeast Asia Strategic Multilateral Dialogue on Biosecurity.

A strategic multilateral dialogue related to biosecurity risks in Southeast Asia, established in 2014, now includes participants from Singapore, Malaysia, Indonesia, Thailand, Philippines, and the United States. This dialogue is conducted at the nonministerial level, enabling participants to engage without the constraints of operating in their official capacities. Participants reflect on mechanisms to detect, mitigate, and respond to biosecurity risks and highlight biosecurity issues for national leadership. Participants have also identified factors to improve regional and global biosecurity, including improved engagement and collaboration across relevant ministries and agencies, sustainable funding for biosecurity programs, enhanced information sharing for communicable diseases, and increased engagement in international biosecurity forums.

Bacterial diversity in the waterholes of the Kruger National Park: an eDNA metabarcoding approach 1.

Bacteria are essential components of natural environments. They contribute to ecosystem functioning through roles as mutualists and pathogens for larger species, and as key components of food webs and nutrient cycles. Bacterial communities respond to environmental disturbances, and the tracking of these communities across space and time may serve as indicators of ecosystem health in areas of conservation concern. Recent advances in DNA sequencing of environmental samples allow for rapid and culture-free characterization of bacterial communities. Here we conduct the first metabarcoding survey of bacterial diversity in the waterholes of the Kruger National Park, South Africa. We show that eDNA can be amplified from waterholes and find strongly structured microbial communities, likely reflecting local abiotic conditions, animal ecology, and anthropogenic disturbance. Over timescales from days to weeks we find increased turnover in community composition, indicating bacteria may represent host-associated taxa of large vertebrates visiting the waterholes. Through taxonomic annotation we also identify pathogenic taxa, demonstrating the utility of eDNA metabarcoding for surveillance of infectious diseases. These samples serve as a baseline survey of bacterial diversity in the Kruger National Park, and in the future, spatially distinct microbial communities may be used as markers of ecosystem disturbance, or biotic homogenization across the park.

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.

Choosing the best algorithm for event detection based on the intended application: A conceptual framework for syndromic surveillance.

There is an extensive list of methods available for the early detection of an epidemic signal in syndromic surveillance data. However, there is no commonly accepted classification system for the statistical methods used for event detection in syndromic surveillance. Comparing and choosing appropriate event detection algorithms is an increasingly challenging task. Although lists of selection criteria, and statistical methods used for signal detection have been reported, selection criteria are rarely linked to a specific set of appropriate statistical methods. The paper presents a practical approach for guiding surveillance practitioners to make an informed choice from among the most popular event detection algorithms based on the intended application of the algorithm. We developed selection criteria by mapping the assumptions and performance characteristics of event detection algorithms directly to important characteristics of the time series used in syndromic surveillance. We also considered types of epidemics that may be expected and other characteristics of the surveillance system. These guidelines will provide decisions makers, data analysts, public health practitioners, and researchers with a comprehensive but practical overview of the domain, which may reduce the technical barriers to the development and implementation of syndromic surveillance systems in animal and human health. The classification scheme was restricted to univariate and temporal methods because they are the most commonly used algorithms in syndromic surveillance.

National Minimum Data Set for Antimicrobial Resistance Management: Toward Global Surveillance System.

BACKGROUND: Success of infection treatment depends on the availability of accurate, reliable, and comprehensive data, information, and knowledge at the point of therapeutic decision-making. The identification of a national minimum data set will support the development and implementation of an effective surveillance system. The goal of this study was to develop a national antimicrobial resistance surveillance minimum data set. METHODS: In this cross-sectional and descriptive study, data were collected from selected pioneering countries and organizations which have national or international antimicrobial resistance surveillance systems. A minimum data set checklist was extracted and validated. The ultimate data elements of the minimum data set were determined by applying the Delphi technique. RESULTS: Through the Delphi technique, we obtained 80 data elements in 8 axes. The resistance data categories comprised basic, clinical, electronic reporting, infection control, microbiology, pharmacy, World Health Organization-derived, and expert-recommended data. Relevance coding was extracted based on the Iranian electronic health record coding system. CONCLUSION: This study provides a set of data elements and a schematic framework for the implementation of an antimicrobial resistance surveillance system. A uniform minimum data set was created based on key informants' opinions to cover essential needs in the early implementation of a global antimicrobial resistance surveillance system in Iran.

Implementation and Initial Analysis of a Laboratory-Based Weekly Biosurveillance System, Provence-Alpes-Côte d'Azur, France.

We describe the implementation of an automated infectious disease surveillance system that uses data collected from 210 microbiologic laboratories throughout the Provence-Alpes-Côte d'Azur region in France. Each week, these facilities report bacterial species that have been isolated from patients in their area. An alarm is triggered whenever the case count for a bacterial species infection exceeds 2 SDs of the historical mean for that species at the participating laboratory. At its inception in July 2013, the system monitored 611 bacterial species. During July 1, 2013-March 20, 2016, weekly analyses of incoming surveillance data generated 34 alarms signaling possible infectious disease outbreaks; after investigation, 14 (41%) of these alarms resulted in health alerts declared by the regional health authority. We are currently improving the system by developing an Internet-based surveillance platform and extending our surveillance to include more laboratories in the region.

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.

Pathogen metadata platform: software for accessing and analyzing pathogen strain information.

BACKGROUND: Pathogen metadata includes information about where and when a pathogen was collected and the type of environment it came from. Along with genomic nucleotide sequence data, this metadata is growing rapidly and becoming a valuable resource not only for research but for biosurveillance and public health. However, current freely available tools for analyzing this data are geared towards bioinformaticians and/or do not provide summaries and visualizations needed to readily interpret results. RESULTS: We designed a platform to easily access and summarize data about pathogen samples. The software includes a PostgreSQL database that captures metadata useful for disease outbreak investigations, and scripts for downloading and parsing data from NCBI BioSample and BioProject into the database. The software provides a user interface to query metadata and obtain standardized results in an exportable, tab-delimited format. To visually summarize results, the user interface provides a 2D histogram for user-selected metadata types and mapping of geolocated entries. The software is built on the LabKey data platform, an open-source data management platform, which enables developers to add functionalities. We demonstrate the use of the software in querying for a pathogen serovar and for genome sequence identifiers. CONCLUSIONS: This software enables users to create a local database for pathogen metadata, populate it with data from NCBI, easily query the data, and obtain visual summaries. Some of the components, such as the database, are modular and can be incorporated into other data platforms. The source code is freely available for download at https://github.com/wchangmitre/bioattribution .