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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Reassessing early-age strength development of high-volume fly ash concretes for precast buildings

Increasing beneficial use of fresh or landfilled fly ash as a replacement for Portland cement can be more challenging for the construction of precast buildings or similar applications requiring rapid strength development. Therefore, the framework presented in this paper aims to reassess high-volume fly ash concretes but in the context of facilitating more sustainable precast buildings. More specifically, the framework was used to characterize strength development of concrete mixes with a target minimum 24-hour compressive strength of 24.1 MPa (3500 psi), selected as an example strength development metric to demonstrate the framework, and comprised of 40% Class C, Class F, and landfilled (harvested) fly ash – as a high-volume replacement of Type III or Type IL cement. High-early strength was driven by optimized dosages of commercial grade gypsum and accelerating admixtures, in addition to optimal aggregate packing and mix proportioning strategies. Early-age mechanical properties including compressive strength, modulus of rupture, and modulus of elasticity were reevaluated within 24 hours of batching with respect to common precast production demands. Simple data analyses were then used to highlight cases where currently accepted design provisions for the aforementioned properties are either overly-conservative or unconservative with respect to test data. Furthermore, the framework and demonstration of example mixes presented herein aim to promote confidence for using larger fractions of fresh or landfilled fly ashes for precast buildings to further enhance environmental benefits without sacrificing pertinent early-age structural performance.

42 ENGINEERING↗
DOE OSTI2013

Risk assessment of coccidostatics during feed cross-contamination: Animal and human health aspects

Coccidiosis, an intestinal plasmodium infection, is a major infectious disease in poultry and rabbits. Eleven different coccidiostats are licensed in the EU for the prevention of coccidiosis in these animal species. According to their chemical nature and main biological activity, these compounds can be grouped as ionophoric (monensin, lasalocid sodium, salinomycin, narasin, maduramicin and semduramicin) or non-ionophoric (robenidine, decoquinate, nicarbazin, diclazuril, and halofuginone) substances. Coccidiostats are used as feed additives, mixed upon request into the compounded feed. During the technical process of commercial feed production, cross-contamination of feed batches can result in the exposure of non-target animals and induce adverse health effects in these animals due to a specific sensitivity of mammalian species as compared to poultry. Residue formation in edible tissues of non-target species may result in unexpected human exposure through the consumption of animal products. This review presents recent risk assessments performed by the Scientific Panel on Contaminants in the Food Chain (CONTAM) of the European Food Safety Authority (EFSA). The health risk to non-target species that would result from the consumption of cross-contaminated feed with coccidostats at levels of 2, 5 or 10% was found to be negligible for most animal species with the exception of salinomycin and monensin in horses because of the particular sensitivity for which toxicity may occur when cross-contamination exceeds 2% and 5% respectively. Kinetic data and tissue analyses showed that residues of coccidiostats may occur in the liver and eggs in some cases. However, the level of residues of each coccidiostat in edible animal tissues remained sufficiently low that the aggregate exposure of consumers would not exceed the established acceptable daily intake (ADI) of each coccidiostat. It could be concluded that technical cross-contamination of animal feeds would not be expected to adversely affect the health of consumers.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Structure and stability of CaH 2 surfaces: on the possibility of electron-rich surfaces in metal hydrides for catalysis

Structure, thermodynamic stability, and electronic properties of CaH 2 surfaces in (001),(110), and (111) crystallographic orientations are investigated using ab initio modeling. We show that stoichiometric surfaces terminated with a hydrogen atomic plane are the most energetically favorable and discuss properties of hydrogen vacancies (VH) at these surfaces. The average calculated work function of the most stable pristine surfaces (~5.2 eV) is in agree-ment with experimental data for powder samples. Neutral hydrogen vacancies host localized electrons and induce defect states in the band gap, thereby shifting the effective work function to much lower values of ~2.7 eV. Surface VH are predicted to aggregate into dimers and form electron-rich centers (e - )Ca 2+ (e - ) stable to over 800 K. These results suggest that hydrogen-deficient surfaces of CaH 2 can host a large concentration of localized electrons and, thus, give rise to new catalytic functionalities involving electron transfer between the surface, catalysts supported on it, and reacting species.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Galactic Potential and Dark Matter Density from Angular Stellar Accelerations

Here, we present an approach to measure the Milky Way (MW) potential using the angular accelerations of stars in aggregate as measured by astrometric surveys like Gaia. Accelerations directly probe the gradient of the MW potential, as opposed to indirect methods using, e.g., stellar velocities. We show that end-of-mission Gaia stellar acceleration data may be used to measure the potential of the MW disk at approximately 3σ significance and, if recent measurements of the solar acceleration are included, the local dark matter density at ~2σ significance. Since the significance of detection scales steeply as t 5/2 for observing time t, future surveys that include angular accelerations in the astrometric solutions may be combined with Gaia to precisely measure the local dark matter density and shape of the density profile.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Self-organization of local magnetoplasma structures in the upper layers of the solar convection zone

Self-organization and evolution of magnetoplasma structures in the upper layers of the solar convection zone are discussed as a process of diffuse aggregation of magnetic flux tubes. Equations describing the tube motion under the action of magnetic interaction forces, hydrodynamic forces, and random forces are written explicitly. The process of aggregation of magnetic flux tubes into magnetic flux clusters of different shapes and dimensions is simulated numerically. The obtained structures are compared with the observed morphological types of sunspot groups. The quantitative comparison with the observational data was performed by comparing the fractal dimensions of the photospheric magnetic structures observed in solar active regions with those of structures obtained in the numerical experiment. The model has the following free parameters: the numbers of magnetic flux tubes with opposite polarities on the considered area element (Nn and Ns), the average radius of the cross section of the magnetic flux tube (a), its effective length (l), the twist factor of the tube field (k), and the absolute value of the average velocity of chaotic tube displacements (d). Variations in these parameters in physically reasonable limits leads to the formation of structures (tube clusters of different morphological types) having different fractal dimensions. Using the NOAA 10488 active region, which appeared and developed into a complicated configuration near the central meridian, as an example, it is shown that good quantitative agreement between the fractal dimensions is achieved at the following parameters of the model: Nn = Ns = 250 ± 50; a = 150 ± 50 km; l ∼ 5000 km, and d = 80 ± 10 m/s. These results do not contradict the observational data and theoretical estimates obtained in the framework of the Parker “spaghetti” model and provide new information on the physical processes resulting in the origin and evolution of local magnetic plasma structures in the near-photospheric layers of the solar convection zone.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

BCL: A Cross-Platform Distributed Data Structures Library

One-sided communication is a useful paradigm for irregular parallel applications, but most one-sided programming environments, including MPI's one-sided interface and PGAS programming languages, lack application-level libraries to support these applications. We present the Berkeley Container Library, a set of generic, cross-platform, high-performance data structures for irregular applications, including queues, hash tables, Bloom filters and more. BCL is written in C++ using an internal DSL called the BCL Core that provides one-sided communication primitives such as remote get and remote put operations. The BCL Core has backends for MPI, OpenSHMEM, GASNet-EX, and UPC++, allowing BCL data structures to be used natively in programs written using any of these programming environments. Along with our internal DSL, we present the BCL ObjectContainer abstraction, which allows BCL data structures to transparently serialize complex data types while maintaining efficiency for primitive types. We also introduce the set of BCL data structures and evaluate their performance across a number of high-performance computing systems, demonstrating that BCL programs are competitive with hand-optimized code, even while hiding many of the underlying details of message aggregation, serialization, and synchronization.

Brock, Benjamin↗
DOE OSTI2018

Deploy threading in Nalu solver stack

The goal of the ExaWind project is to enable predictive simulations of wind farms composed of many MW-scale turbines situated in complex terrain. Predictive simulations will require computational fluid dynamics (CFD) simulations for which the mesh resolves the geometry of the turbines, and captures the rotation and large deflections of blades. Whereas such simulations for a single turbine are arguably petascale class, multi-turbine wind farm simulations will require exascale-class resources. The primary code in the ExaWind project is Nalu, which is an unstructured-grid solver for the acousticallyincompressible Navier-Stokes equations, and mass continuity is maintained through pressure projection. The model consists of the mass-continuity Poisson-type equation for pressure and a momentum equation for the velocity. For such modeling approaches, simulation times are dominated by linear-system setup and solution for the continuity and momentum systems. For the ExaWind challenge problem, the moving meshes greatly affect overall solver costs as re-initialization of matrices and re-computation of preconditioners is required at every time step In this Milestone, we examine the effect of threading on the solver stack performance against flat-MPI results obtained from previous milestones using Haswell performance data full-turbine simulations. Whereas the momentum equations are solved only with the Trilinos solvers, we investigate two algebraic-multigrid preconditioners for the continuity equations: Trilinos/Muelu and HYPRE/BoomerAMG. These two packages embody smoothed-aggregation and classical Ruge-Stiiben AMG methods, respectively. In our FY18 Q2 report, we described our efforts to improve setup and solve of the continuity equations under flat-MPI parallelism. While significant improvement was demonstrated in the solve phase, setup times remained larger than expected. Starting with the optimized settings described in the Q2 report, we explore here simulation performance where OpenMP threading is employed in the solver stack. For Trilinos, threading is acheived through the Kokkos abstraction where, whereas HYPRE/BoomerAMG employs straight OpenMP. We examined results for our mid-resolution baseline turbine simulation configuration (229M DOF). Simulations on 2048 Haswell cores explored the effect of decreasing the number of MPI ranks while increasing the number of threads. Both HYPRE and Trilinos exhibited similar overal solution times, and both showed dramatic increases in simulation time in the shift from MPI ranks to OpenMP threads. This increase is attributed to the large amount of work per MPI rank starting at the single-thread configuration. Decreasing MPI ranks, while increasing threads, may be increasing simulation time due to thread synchronization and start-up overhead contributing to the latency and serial time in the model. These result showed that an MPI+OpenMP parallel decomposition will be more effective as the amount per MPI rank computation per MPI rank decreases and the communication latency increases. This idea was demonstrated in a strong scaling study of our low-resolution baseline model (29M DOF) with the Trilinos-HYPRE configuration. While MPI-only results showed scaling improvement out to about 1536 cores, engaging threading carried scaling improvements out to 4128 cores — roughly 7000 DOF per core. This is an important result as improved strong scaling is needed for simulations to be executed over sufficiently long simulated durations (i.e., for many timesteps). In addition to threading work described above, the team examined solver-performance improvements by exploring communication-overhead in the HYPRE-GMRES implementation through a communicationoptimal- GMRE algorithm (CO-GMRES), and offloading compute-intensive solver actions to GPUs. To those ends, a HYPRE mini-app was allow us to easily test different solver approaches and HYPRE parameter settings without running the entire Nalu code. With GPU acceleration on the Summitdev supercomputer, a 20x speedup was achieved for the overall preconditioner and solver execution time for the mini-app. A study on Haswell processors showed that CO-GMRES provides benefits as one increases MPI ranks.

17 WIND ENERGY↗
DOE OSTI2025

Network-Aware and Welfare-Maximizing Dynamic Pricing for Energy Sharing

The proliferation of behind-the-meter (BTM) distributed energy resources (DER) within the electrical distribution network presents significant supply and demand flexibilities, but also introduces operational challenges such as voltage spikes and reverse power flows. In response, this paper proposes a network-aware dynamic pricing framework tailored for energy-sharing coalitions that aggregate small, but ubiquitous, BTM DER downstream of a distribution system operator's (DSO) revenue meter that adopts a generic net energy metering (NEM) tariff. By formulating a Stackelberg game between the energy-sharing market leader and its prosumers, we show that the dynamic pricing policy induces the prosumers toward a network-safe operation and decentrally maximizes the energysharing social welfare. The dynamic pricing mechanism involves a combination of a locational ex-ante dynamic price and an ex-post allocation, both of which are functions of the energy sharing's BTM DER. The ex-post allocation is proportionate to the price differential between the DSO NEM price and the energy-sharing locational price. Simulation results using real DER data and the IEEE 13-bus test systems illustrate the dynamic nature of network-aware pricing at each bus, and its impact on voltage.

aggregates↗
DOE OSTI2016

Estimating the National Carbon Abatement Potential of City Policies: A Data- Driven Approach

Cities are increasingly taking actions such as building code enforcement, urban planning, and public transit expansion to reduce emissions of carbon dioxide in their communities and municipal operations. However, many cities lack the quantitative information needed to estimate policy impacts and prioritize city actions in terms of carbon abatement potential and cost effectiveness. This report fills this research gap by providing methodologies to assess the carbon abatement potential of a variety of city actions. The methodologies are applied to an energy use data set of 23,458 cities compiled for the U.S. Department of Energy’s City Energy Profile tool. The analysis estimates the national carbon abatement potential of the most commonly implemented actions in six specific policy areas. The results of this analysis suggest that, in aggregate, cities could reduce nationwide carbon emissions by about 210 million metric tons of carbon dioxide (MMT CO 2 ) per year in a "moderate abatement scenario" by 2035 and 480 MMT CO 2 /year in a "high abatement scenario" by 2035 through these common actions typically within a city’s control in the six policy areas. The aggregate carbon abatement potential of these specific areas equates to a reduction of 3%-7% relative to 2013 U.S. emissions. At the city level, the results suggest the average city could reduce carbon emissions by 7% (moderate) to 19% (high) relative to current city-level emissions. City carbon abatement potential is sensitive to national and state policies that affect the carbon intensity of electricity and transportation. Specifically, the U.S. Clean Power Plan and further renewable energy cost reductions could reduce city carbon emissions overall, helping cities achieve their carbon reduction goals.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Cardinal: Seismic and Geoacoustic Array Processing

Data collected via seismic and infrasound array deployments are leveraged in the geosciences to detect and characterize a myriad of natural and anthropogenic sources. These deployments consist of numerous sensors placed in a predetermined configuration to amplify signal strength and improve the efficacy of array processing techniques used to measure signal directionality and waveform coherence. High‐fidelity feature extraction is often predicated on interstation distance as well as the frequency content and wavelength of an incident signal. Numerous array processing softwares analyze data in sequential frequency bands to obtain a more detailed characterization of a signal. However, current algorithms are limited in their ability to determine optimal array configuration for each band. We introduce an open‐source Python code, called Cardinal, to process seismic and infrasound array data in discretized time–frequency space with the option of applying an adaptive array design to determine optimal subarray configuration for each frequency band. To reduce computational time, the array processing step can be run in parallel using multithreading. Furthermore, the software has the capability to aggregate array processing results from different time–frequency pixels to produce separate sets of detections, or families, with added utility via the application of an adaptive semblance threshold, which aids in isolating signals‐of‐interest from coherent background noise. Upon appropriate configuration, Cardinal exhibits the potential to combine distinct seismic and infrasound phases into separate families.

Adaptive Array↗
DOE OSTI2024

Estimating irrigation water use from remotely sensed evapotranspiration data: Accuracy and uncertainties at field, water right, and regional scales

Irrigated agriculture is the dominant user of water globally, but most water withdrawals are not monitored or reported. As a result, it is largely unknown when, where, and how much water is used for irrigation. Here, we evaluated the ability of remotely sensed evapotranspiration (ET) data, integrated with other datasets, to calculate irrigation water withdrawals and applications in an intensively irrigated portion of the United States. We compared irrigation calculations based on an ensemble of satellite-driven ET models from OpenET with reported groundwater withdrawals from hundreds of farmer irrigation application records and a statewide flowmeter database at three spatial scales (field, water right group, and management area). At the field scale, we found that ET-based calculations of irrigation agreed best with reported irrigation when the OpenET ensemble mean was aggregated to the growing season timescale (bias = 1.6–4.9%, R 2 = 0.53–0.74), and agreement between calculated and reported irrigation was better for multi-year averages than for individual years. At the water right group scale, linking pumping wells to specific irrigated fields was the primary source of uncertainty. At the management area scale, calculated irrigation exhibited similar temporal patterns as flowmeter data but tended to be positively biased with more interannual variability. Disagreement between calculated and reported irrigation was strongly correlated with annual precipitation, and calculated and reported irrigation agreed more closely after statistically adjusting for annual precipitation. The selection of an ET model was also an important consideration, as variability across ET models was larger than the potential impacts of conservation measures employed in the region. From these results, we suggest key practices for working with ET-based irrigation data that include accurately accounting for changes in soil moisture, deep percolation, and runoff; careful verification of irrigated area and well-field linkages; and conducting application-specific evaluations of uncertainty.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Assessment of the importance of spatial scale in long-term land use modelling of the Midwestern United States

The purpose of this study is to assess the value of enhanced spatial resolution in the agriculture and land use component of an integrated assessment model. While these models have historically represented land use decisions at the scale of geopolitical regions, the present generation uses regions defined by climatic or environmental characteristics, to better represent the spatial heterogeneity of land productivity and potential uses. However, increasing a model’s spatial resolution incurs costs, including input data processing, structural modifications, increased run time, and increased complexity of results. This study uses the Global Change Assessment Model (GCAM) to analyze land use in the Midwestern United States in three levels of sub-regional aggregation and three scenarios of future climate mitigation. We demonstrate benefits of enhanced spatial resolution, and to facilitate the assessment of higher resolution model output we apply non-metric multidimensional scaling on a pair-wise distance matrix for visualization and analysis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Stochastic Simulation of Isotopic Exchange Mechanisms for Fe(II)-Catalyzed Recrystallization of Goethite

Understanding Fe(II)-catalyzed transformations of Fe(III)- (oxyhydr)oxides is critical for correctly interpreting stable isotopic distributions and for predicting the fate of metal ions in the environment. Recent Fe isotopic tracer experiments have shown that goethite undergoes rapid recrystallization without phase change when exposed to aqueous Fe(II). The proposed explanation is oxidation of sorbed Fe(II) and reductive Fe(II) release coupled 1:1 by electron conduction through crystallites. Given the availability of two tracer exchange data sets that explore pH and particle size effects (e.g., Handler et al. Environ. Sci. Technol. 2014, 48, 11302-11311; Joshi and Gorski Environ. Sci. Technol. 2016, 50, 7315-7324), we developed a stochastic simulation that exactly mimics these experiments, while imposing the 1:1 constraint. We find that all data can be represented by this model, and unifying mechanistic information emerges. At pH 7.5 a rapid initial exchange is followed by slower exchange, consistent with mixed surface- and diffusion-limited kinetics arising from prominent particle aggregation. At pH 5.0 where aggregation and net Fe(II) sorption are minimal, that exchange is quantitatively proportional to available particle surface area and the density of sorbed Fe(II) is more readily evident. Our analysis reveals a fundamental atom exchange rate of ~10-5 Fe nm-2 s-1, commensurate with some of the reported reductive dissolution rates of goethite, suggesting Fe(II) release is the rate-limiting step in the conduction mechanism during recrystallization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Cluster dynamics models of irradiation damage accumulation in ferritic iron. I. Trap mediated interstitial cluster diffusion

The microstructure that develops under low temperature irradiation in ferritic alloys is dominated by a high density of small (2–5 nm) defects. These defects have been widely observed to move via occasional discrete hops during in situ thin film irradiation experiments. Cluster dynamics models are used to describe the formation of these defects as an aggregation process of smaller clusters created as primary damage. Multiple assumptions regarding the mobility of these damage features are tested in the models, both with and without explicit consideration of such irradiation induced hops. Comparison with experimental data regarding the density of these defects demonstrates the importance of including such motions in a valid model. In particular, discrete hops inform the limited dependence of defect density on irradiation temperature observed in experiments, which the model was otherwise incapable of producing.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Size distribution of possible dust carriers for the extended red emission

Power-law size distributions expected to be applicable to possible carriers of extended red emission (ERE) have been examined using Monte Carlo (MC) simulations. Si nanoparticles and some polycyclic aromatic hydrocarbon complexes such as oligoacene and oligorylenes with energy gaps close to 2 eV have been considered. In the simplest case of unit quantum efficiency, the MC-generated size distributions are used to obtain photoluminescence (PL) spectra that are then corrected for dust extinction and reddening effects for comparison with observed ERE spectra. It is shown that a power-law size distribution with a decay exponent of α = 7/2, which closely agrees with starlight extinction data, fails to produce an ERE-like spectrum. However, size distributions with decay exponents of α = 19/12 and 3/2 are found to lead to acceptable spectra. Results indicate that energetic photon-induced breakup and competing aggregation effects dominate collisional effects in producing the observed steady-state mass distribution. It is shown that the peak wavelength of emission critically depends on the band gap, rather than cluster mass, which for oligoacenes and oligorylenes is widely different. The peak wavelength is also shown to be insensitive to dust attenuation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Behind-the-Meter Solar Generation Disaggregation using Consumer Mixture Models

To facilitate deep penetration of solar energy in smart grids, we need high observability of solar generation at the edges of the grid. Current advanced metering infrastructures (AMI) only monitor the aggregated measurements from net-metered households, but disaggregated consumption and solar generation components are required for grid optimizations. We propose an unsupervised disaggregation model for disaggregating solar generation from AMI measurements without the need of training data. The model requires only AMI measurements from consumers in a region and the solar irradiance as input, and models the consumption of consumers by neighboring households without rooftop photovoltaics (PV) to perform the disaggregation. We evaluate our results on a real life dataset from Austin, Texas. We show that our model is able to disaggregate consumption and solar generation measurements with 42.24% and 31.67% less mean squared error, respectively, in comparison to a baseline technique that uses supervised learning. This shows that our model is capable of disaggregating historical data even if the dataset has no training data and only contains minimal exogenous data.

14 SOLAR ENERGY↗
DOE OSTI2021

RAVIS: Resource Forecast and Ramp Visualization for Situational Awareness - An Introduction to the Open-Source Tool and Use Cases

The Resource Forecast and Ramp Visualization for Situational Awareness (RAVIS) is an open-source tool for visualizing variable renewable resource forecasts and ramp alerts for significant up/down ramps in renewable resource and the consequent net-load. The modular dashboard of RAVIS contains configurable panes for viewing- probabilistic time series forecasts, ramp event alerts on the look-ahead timeline, spatially resolved resource sites and forecasts, and system simulation and market clearing data such as transmission lines utilization, nodal prices and available generation flexibility. RAVIS uses a technology suite that is assembled to provide optimum visualization facility while maintaining a wide pool of potential deployment and client environments. The tool is designed to take advantage of web application technologies, open source visualization libraries and tooling. Utilizing this technology will enable deployment in any environment, using any operating system, and is scalable to much higher spatial and temporal scales of visualization. As a prototype of the tool and demonstrating a use case of variable renewable integration, RAVIS currently integrates site-specific solar power forecasts in the California Independent System Operator (CAISO) and Mid-continent ISO (MISO) footprint from the IBM WattSun forecasting platform, and also superimposes market simulation data for CAISO footprint from as in-house NREL market clearing tool. The tool has the ability to alert the viewer for excessive up or down ramps for both individual solar sites as well as regionally aggregated net-load ramps, and alerts can also be qualified with respect to available flexible generation. This report will provide an introduction to the RAVIS tool, and summarize the above mentioned capabilities, typical use cases and possible extensions of the tool. The RAVIS development team believes there are likely to be high economic and reliability benefits of integrating probabilistic forecasts of variable renewables into control center visualizations and improved ramp events situational awareness for system operators and forecasting teams in the ISOs and electric utilities in comparison with their business as usual practices.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2018

Analytics for Investigation of Disease Outbreaks (AIDO)

Objective: Analytics for the Investigation of Disease Outbreaks (AIDO) is a web-based tool designed to enhance a user’s understanding of unfolding infectious disease events. A representative library of over 650 outbreaks across a wide selection of diseases allows similar outbreaks to be matched to the conditions entered by the user. These historic outbreaks contain detailed information on how the disease progressed as well as what measures were implemented to control its spread, allowing for a better understanding within the context of other outbreaks.Introduction: Situational awareness, or the understanding of elemental components of an event with respect to both time and space, is critical for public health decision-makers during an infectious disease outbreak. AIDO is a web-based tool designed to contextualize incoming infectious disease information during an unfolding event for decision-making purposes.Methods: Public health analysts of the Biology Division at Los Alamos National Laboratory curated a diverse library of historic disease outbreaks from publicly available official reports and peer reviewed literature to serve as a representation of the range of potential outbreak scenarios for a given disease. Available outbreak metadata are used to identify properties that relate to the magnitude and/or duration of the outbreak. Properties vary by disease, as they are related to disease-specific characteristics like transmission, disease manifestation, risk factors related to disease severity, and environmental factors specific to the given location. These properties are then incorporated into a similarity algorithm (s in Figure 1) to identify outbreaks that are similar to user inputs.Results: AIDO currently includes libraries for 39 diseases that are diverse across pathogen type (viral, bacterial and parasitic) as well as transmission type (vectorborne (e.g., Dengue, Malaria), foodborne (e.g., Salmonella, Campylobacteriosis), waterborne (e.g., Cholera), and person-to-person transmitted (e.g., Measles)). In addition to providing a similarity score to the user’s outbreak, we provide aggregated comparisons to multiple historical outbreaks, descriptive statistics to show the distribution of property values for each disease, and extensive contextual information about each outbreak.Conclusions: The analytics provided by AIDO allow users to interact with a unique data set of historic outbreaks and the associated metadata to contextualize incoming information and generate hypotheses about appropriate decisions. The tool is continually updated with new functionalities and additional data.

Daughton, Ashlynn↗