Fast Evaluation of Viral Emerging Risks (FEVER): A computational tool for biosurveillance, diagnostics, and mutation typing of emerging viral pathogens.

Viral pathogens can rapidly evolve, adapt to novel hosts, and evade human immunity. The early detection of emerging viral pathogens through biosurveillance coupled with rapid and accurate diagnostics are required to mitigate global pandemics. However, RNA viruses can mutate rapidly, hampering biosurveillance and diagnostic efforts. Here, we present a novel computational approach called FEVER (Fast Evaluation of Viral Emerging Risks) to design assays that simultaneously accomplish: 1) broad-coverage biosurveillance of an entire group of viruses, 2) accurate diagnosis of an outbreak strain, and 3) mutation typing to detect variants of public health importance. We demonstrate the application of FEVER to generate assays to simultaneously 1) detect sarbecoviruses for biosurveillance; 2) diagnose infections specifically caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); and 3) perform rapid mutation typing of the D614G SARS-CoV-2 spike variant associated with increased pathogen transmissibility. These FEVER assays had a high in silico recall (predicted positive) up to 99.7% of 525,708 SARS-CoV-2 sequences analyzed and displayed sensitivities and specificities as high as 92.4% and 100% respectively when validated in 100 clinical samples. The D614G SARS-CoV-2 spike mutation PCR test was able to identify the single nucleotide identity at position 23,403 in the viral genome of 96.6% SARS-CoV-2 positive samples without the need for sequencing. This study demonstrates the utility of FEVER to design assays for biosurveillance, diagnostics, and mutation typing to rapidly detect, track, and mitigate future outbreaks and pandemics caused by emerging viruses.

Persistence of Bacillus thuringiensis subsp. kurstaki in Urban Environments following Spraying.

Bacillus thuringiensis subsp. kurstaki is applied extensively in North America to control the gypsy moth, Lymantria dispar. Since B. thuringiensis subsp. kurstaki shares many physical and biological properties with Bacillus anthracis, it is a reasonable surrogate for biodefense studies. A key question in biodefense is how long a biothreat agent will persist in the environment. There is some information in the literature on the persistence of Bacillus anthracis in laboratories and historical testing areas and for Bacillus thuringiensis in agricultural settings, but there is no information on the persistence of Bacillus spp. in the type of environment that would be encountered in a city or on a military installation. Since it is not feasible to release B. anthracis in a developed area, the controlled release of B. thuringiensis subsp. kurstaki for pest control was used to gain insight into the potential persistence of Bacillus spp. in outdoor urban environments. Persistence was evaluated in two locations: Fairfax County, VA, and Seattle, WA. Environmental samples were collected from multiple matrices and evaluated for the presence of viable B. thuringiensis subsp. kurstaki at times ranging from less than 1 day to 4 years after spraying. Real-time PCR and culture were used for analysis. B. thuringiensis subsp. kurstaki was found to persist in urban environments for at least 4 years. It was most frequently detected in soils and less frequently detected in wipes, grass, foliage, and water. The collective results indicate that certain species of Bacillus may persist for years following their dispersal in urban environments.

Development of a Supervised Learning Algorithm for Detection of Potential Disease Reemergence: A Proof of Concept.

Infectious disease reemergence is an important yet ambiguous concept that lacks a quantitative definition. Currently, reemergence is identified without specific criteria describing what constitutes a reemergent event. This practice affects reproducible assessments of high-consequence public health events and disease response prioritization. This in turn can lead to misallocation of resources. More important, early recognition of reemergence facilitates effective mitigation. We used a supervised machine learning approach to detect potential disease reemergence. We demonstrate the feasibility of applying a machine learning classifier to identify reemergence events in a systematic way for 4 different infectious diseases. The algorithm is applicable to temporal trends of disease incidence and includes disease-specific features to identify potential reemergence. Through this study, we offer a structured means of identifying potential reemergence using a data-driven approach.

Analytics for Investigation of Disease Outbreaks: Web-Based Analytics Facilitating Situational Awareness in Unfolding Disease Outbreaks.

BACKGROUND: Information from historical infectious disease outbreaks provides real-world data about outbreaks and their impacts on affected populations. These data can be used to develop a picture of an unfolding outbreak in its early stages, when incoming information is sparse and isolated, to identify effective control measures and guide their implementation. OBJECTIVE: This study aimed to develop a publicly accessible Web-based visual analytic called Analytics for the Investigation of Disease Outbreaks (AIDO) that uses historical disease outbreak information for decision support and situational awareness of an unfolding outbreak. METHODS: We developed an algorithm to allow the matching of unfolding outbreak data to a representative library of historical outbreaks. This process provides epidemiological clues that facilitate a user's understanding of an unfolding outbreak and facilitates informed decisions about mitigation actions. Disease-specific properties to build a complete picture of the unfolding event were identified through a data-driven approach. A method of analogs approach was used to develop a short-term forecasting feature in the analytic. The 4 major steps involved in developing this tool were (1) collection of historic outbreak data and preparation of the representative library, (2) development of AIDO algorithms, (3) development of user interface and associated visuals, and (4) verification and validation. RESULTS: The tool currently includes representative historical outbreaks for 39 infectious diseases with over 600 diverse outbreaks. We identified 27 different properties categorized into 3 broad domains (population, location, and disease) that were used to evaluate outbreaks across all diseases for their effect on case count and duration of an outbreak. Statistical analyses revealed disease-specific properties from this set that were included in the disease-specific similarity algorithm. Although there were some similarities across diseases, we found that statistically important properties tend to vary, even between similar diseases. This may be because of our emphasis on including diverse representative outbreak presentations in our libraries. AIDO algorithm evaluations (similarity algorithm and short-term forecasting) were conducted using 4 case studies and we have shown details for the Q fever outbreak in Bilbao, Spain (2014), using data from the early stages of the outbreak. Using data from only the initial 2 weeks, AIDO identified historical outbreaks that were very similar in terms of their epidemiological picture (case count, duration, source of exposure, and urban setting). The short-term forecasting algorithm accurately predicted case count and duration for the unfolding outbreak. CONCLUSIONS: AIDO is a decision support tool that facilitates increased situational awareness during an unfolding outbreak and enables informed decisions on mitigation strategies. AIDO analytics are available to epidemiologists across the globe with access to internet, at no cost. In this study, we presented a new approach to applying historical outbreak data to provide actionable information during the early stages of an unfolding infectious disease outbreak.

Transport of Bacillus thuringiensis var. kurstaki from an outdoor release into buildings: pathways of infiltration and a rapid method to identify contaminated buildings.

Understanding the fate and transport of biological agents into buildings will be critical to recovery and restoration efforts after a biological attack in an urban area. As part of the Interagency Biological Restoration Demonstration (IBRD), experiments were conducted in Fairfax County, VA, to study whether a biological agent can be expected to infiltrate into buildings following a wide-area release. Bacillus thuringiensis var. kurstaki is a common organic pesticide that has been sprayed in Fairfax County for a number of years to control the gypsy moth. Because the bacterium shares many physical and biological properties with Bacillus anthracis, the results from these studies can be extrapolated to a bioterrorist release. In 2009, samples were collected from inside buildings located immediately adjacent to a spray block. A combined probabilistic and targeted sampling strategy and modeling were conducted to provide insight into likely methods of infiltration. Both the simulations and the experimental results indicate sampling entryways and heating, ventilation, and air conditioning (HVAC) filters are reasonable methods for "ruling in" a building as contaminated. Following a biological attack, this method is likely to provide significant savings in time and labor compared to more rigorous, statistically based characterization. However, this method should never be used to "rule out," or clear, a building.