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DOE OSTI2020

Improving five-year survival prediction via multitask learning across HPV-related cancers

Oncology is a highly siloed field of research in which sub-disciplinary specialization has limited the amount of information shared between researchers of distinct cancer types. This can be attributed to legitimate differences in the physiology and carcinogenesis of cancers affecting distinct anatomical sites. However, underlying processes that are shared across seemingly disparate cancers probably affect prognosis. The objective of the current study is to investigate whether multitask learning improves 5-year survival cancer patient survival prediction by leveraging information across anatomically distinct HPV related cancers. Furthermore, data were obtained from the Surveillance, Epidemiology, and End Results (SEER) program database. The study cohort consisted of 29,768 primary cancer cases diagnosed in the United States between 2004 and 2015. Ten different cancer diagnoses were selected, all with a known association with HPV risk. In the analysis, the cancer diagnoses were categorized into three distinct topography groups of varying specificity. The most specific topography grouping consisted of 10 original cancer diagnoses differentiated by the first two digits of the ICD-O-3 topography code. The second topography grouping consisted of cancer diagnoses categorized into six distinct organ groups. Finally, the third topography grouping consisted of just two groups, head-neck cancers and ano-genital cancers. The tasks were to predict 5-year survival for patients within the different topography groups using 14 predictive features which were selected among descriptive variables available in the SEER database. The information from the predictive features was shared between tasks in three different ways, resulting in three distinct predictive models: 1) Information was not shared between patients assigned to different tasks (single task learning); 2) Information was shared between all patients, regardless of task (pooled model); 3) Only relevant information was shared between patients grouped to different tasks (multitask learning). Prediction performance was evaluated with Brier scores. All three models were evaluated against one another on each of the three distinct topography-defined tasks. The results showed that multitask classifiers achieved relative improvement for the majority of the scenarios studied compared to single task learning and pooled baseline methods. In this study, we have demonstrated that sharing information among anatomically distinct cancer types can lead to improved predictive survival models.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

COVID-19 Evidence Accelerator: A parallel analysis to describe the use of Hydroxychloroquine with or without Azithromycin among hospitalized COVID-19 patients

Background: The COVID-19 pandemic remains a significant global threat. However, despite urgent need, there remains uncertainty surrounding best practices for pharmaceutical interventions to treat COVID-19. In particular, conflicting evidence has emerged surrounding the use of hydroxychloroquine and azithromycin, alone or in combination, for COVID-19. The COVID-19 Evidence Accelerator convened by the Reagan-Udall Foundation for the FDA, in collaboration with Friends of Cancer Research, assembled experts from the health systems research, regulatory science, data science, and epidemiology to participate in a large parallel analysis of different data sets to further explore the effectiveness of these treatments. Methods: Electronic health record (EHR) and claims data were extracted from seven separate databases. Parallel analyses were undertaken on data extracted from each source. Each analysis examined time to mortality in hospitalized patients treated with hydroxychloroquine, azithromycin, and the two in combination as compared to patients not treated with either drug. Cox proportional hazards models were used, and propensity score methods were undertaken to adjust for confounding. Frequencies of adverse events in each treatment group were also examined. Results: Neither hydroxychloroquine nor azithromycin, alone or in combination, were significantly associated with time to mortality among hospitalized COVID-19 patients. No treatment groups appeared to have an elevated risk of adverse events. Conclusion: Administration of hydroxychloroquine, azithromycin, and their combination appeared to have no effect on time to mortality in hospitalized COVID-19 patients. Continued research is needed to clarify best practices surrounding treatment of COVID-19.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Analysis of mobility data to build contact networks for COVID-19

As social distancing policies and recommendations went into effect in response to COVID-19, people made rapid changes to the places they visit. These changes are clearly seen in mobility data, which records foot traffic using location trackers in cell phones. While mobility data is often used to extract the number of customers that visit a particular business or business type, it is the frequency and duration of concurrent occupancy at those sites that governs transmission. Understanding the way people interact at different locations can help target policies and inform contact tracing and prevention strategies. This paper outlines methods to extract interactions from mobility data and build networks that can be used in epidemiological models. Several measures of interaction are extracted: interactions between people, the cumulative interactions for a single person, and cumulative interactions that occur at particular businesses. Network metrics are computed to identify structural trends which show clear changes based on the timing of stay-at-home orders. Measures of interaction and structural trends in the resulting networks can be used to better understand potential spreading events, the percent of interactions that can be classified as close contacts, and the impact of policy choices to control transmission.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Misclassification of causes of death among a small all-autopsied group of former nuclear workers: Death certificates vs. autopsy reports

The U.S. Transuranium and Uranium Registries performs autopsies on each of its deceased Registrants as a part of its mission to follow up occupationally-exposed individuals. This provides a unique opportunity to explore death certificate misclassification errors, and the factors that influence them, among this small population of former nuclear workers. Underlying causes of death from death certificates and autopsy reports were coded using the 10 th revision of the International Classification of Diseases (ICD-10). These codes were then used to quantify misclassification rates among 268 individuals for whom both full autopsy reports and death certificates with legible underlying causes of death were available. When underlying causes of death were compared between death certificates and autopsy reports, death certificates correctly identified the underlying cause of death’s ICD-10 disease chapter in 74.6% of cases. The remaining 25.4% of misclassified cases resulted in over-classification rates that ranged from 1.2% for external causes of mortality to 12.2% for circulatory disease, and under-classification rates that ranged from 7.7% for external causes of mortality to 47.4% for respiratory disease. Neoplasms had generally lower misclassification rates with 4.3% over-classification and 13.3% under-classification. A logistic regression revealed that the odds of a match were 2.8 times higher when clinical history was mentioned on the autopsy report than when it was not. Similarly, the odds of a match were 3.4 times higher when death certificates were completed using autopsy findings than when autopsy findings were not used. This analysis excluded cases where it could not be determined if autopsy findings were used to complete death certificates. The findings of this study are useful to investigate the impact of death certificate misclassification errors on radiation risk estimates and, therefore, improve the reliability of epidemiological studies.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

DOE OSTI2024

Environmental associations of Ophidiomyces ophidiicola , the causative agent of ophidiomycosis in snakes

Emerging pathogenic fungi have become a topic of conservation concern due to declines observed in several host taxa. One emerging fungal pathogen, Ophidiomyces ophidiicola, is well documented as the causative agent of ophidiomycosis, otherwise known as snake fungal disease (SFD). O. ophidiicola has been found to cause disease in a variety of snake species across the United States, including the eastern massasauga (Sistrurus catenatus), a federally threatened rattlesnake species. Most work to date has involved detecting O. ophidiicola for diagnosis of infection through direct sampling of snakes, and attempts to detect O. ophidiicola in the abiotic environment to better understand its distribution, seasonality, and habitat associations are lacking. We collected topsoil and groundwater samples from four macrohabitat types across multiple seasons in northern Michigan at a site where Ophidiomyces infection has been confirmed in eastern massasauga. Using a quantitative PCR (qPCR) assay developed for O. ophidiicola, we detected Ophidiomyces DNA in topsoil but observed minimal to no detection in groundwater samples. Detection frequency did not differ between habitats, but samples grouped seasonally showed higher detection during mid-summer. We found no relationships of detection with hypothesized environmental correlates such as soil pH, temperature, or moisture content. Furthermore, the distribution of Ophidiomyces positive samples across the site was not linked to estimated space use of massasaugas. Our data suggests that season has some effect on the presence of Ophidiomyces. Differences in presence between habitats may exist but are likely more dependent on the time of sampling and currently uninvestigated soil or biotic parameters. These findings build on our understanding of Ophidiomyces ecology and epidemiology to help inform where and when snakes may be exposed to the fungus in the environment.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Using intrahost single nucleotide variant data to predict SARS-CoV-2 detection cycle threshold values

Over the last four years, each successive wave of the COVID-19 pandemic has been caused by variants with mutations that improve the transmissibility of the virus. Despite this, we still lack tools for predicting clinically important features of the virus. In this study, we show that it is possible to predict the PCR cycle threshold (Ct) values from clinical detection assays using sequence data. Ct values often correspond with patient viral load and the epidemiological trajectory of the pandemic. Using a collection of 36,335 high quality genomes, we built models from SARS-CoV-2 intrahost single nucleotide variant (iSNV) data, computing XGBoost models from the frequencies of A, T, G, C, insertions, and deletions at each position relative to the Wuhan-Hu-1 reference genome. Our best model had an R 2 of 0.604 [0.593–0.616, 95% confidence interval] and a Root Mean Square Error (RMSE) of 5.247 [5.156–5.337], demonstrating modest predictive power. Overall, we show that the results are stable relative to an external holdout set of genomes selected from SRA and are robust to patient status and the detection instruments that were used. This study highlights the importance of developing modeling strategies that can be applied to publicly available genome sequence data for use in disease prevention and control.

COVID19↗

DOE OSTI2024

Calibration verification for stochastic agent-based disease spread models

Accurate disease spread modeling is crucial for identifying the severity of outbreaks and planning effective mitigation efforts. To be reliable when applied to new outbreaks, model calibration techniques must be robust. However, current methods frequently forgo calibration verification (a stand-alone process evaluating the calibration procedure) and instead use overall model validation (a process comparing calibrated model results to data) to check calibration processes, which may conceal errors in calibration. In this work, we develop a stochastic agent-based disease spread model to act as a testing environment as we test two calibration methods using simulation-based calibration, which is a synthetic data calibration verification method. The first calibration method is a Bayesian inference approach using an empirically-constructed likelihood and Markov chain Monte Carlo (MCMC) sampling, while the second method is a likelihood-free approach using approximate Bayesian computation (ABC). Simulation-based calibration suggests that there are challenges with the empirical likelihood calculation used in the first calibration method in this context. These issues are alleviated in the ABC approach. Despite these challenges, we note that the first calibration method performs well in a synthetic data model validation test similar to those common in disease spread modeling literature. We conclude that stand-alone calibration verification using synthetic data may benefit epidemiological researchers in identifying model calibration challenges that may be difficult to identify with other commonly used model validation techniques.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Stochastic parametric skeletal dosimetry model for humans: Anatomical-morphological basis and parameter evaluation

Radiation exposure of the hematopoietic system that results in a radiation dose to bone marrow of more than 100 mGy leads to an increase in the risk of leukemia in humans. Excess relative risk of leukemia was observed in cohorts whose members lived in the territories of the Southern Urals that were radioactively contaminated in the 1950s. As part of the dosimetric support of epidemiological studies of these cohorts, an original methodology for stochastic bone dosimetric modeling was developed, termed the Stochastic Parametric Skeletal Dosimetry (SPSD) model. The purpose of this work was to present the anatomical and morphological bases of the SPSD model, which includes an assessment of the parameters of the microstructure of the trabecular bone in the hematopoietic areas of the human skeleton, as well as a description of the macrostructural division (segmentation) of hematopoietic areas into simple bone segments. As a result, an anatomical-morphological basis of the SPSD model was created based on published data. Data collection work included the analysis of original articles, atlases, manuals, monographs and the formation of primary data files. Data on the duration of hematopoiesis in various parts of the skeleton; and data on age-related changes in the microstructure and linear dimensions of human bones and their segments were analyzed. The paper describes the full set of parameters to be used for the dosimetric model for newborns, children aged 1, 5 and 10 years, as well as for adolescents aged 15 years and adults; for the latter, sex differences in bone size were considered. In total, the SPSD model includes 289 unique basic (bone) phantom segments, each of which is described by 7 or more parameters describing the microstructure, thickness of the cortical layer and linear dimensions. Population variability was estimated for each parameter. The approach to SPSD modeling, i.e., the use of simple geometric shapes, was successfully verified using independent datasets on bone masses and volumes.

Science & Technology - Other Topics↗

DOE OSTI2026

Semi-Analytical Hierarchical Bayesian Inference of Nonlinear Model Structure in Stochastic Dynamics: Applied to Compartmental Models of Infectious Diseases

A Bayesian computational framework for parsimonious inference in stochastic nonlinear dynamical systems is presented. This framework enables the concurrent estimation of system states, time-varying parameters, time-invariant parameters, and the optimal sparsity structure of the model parameters. Because differential equation-based models are often simplified mechanistic or phenomenological representations, robust inference from noisy measurement data requires explicit treatment of model error and uncertainty. Model error and time-varying parameters can be represented as random processes, enabling inference while making minimal assumptions about the underlying sources of discrepancy and variability. Adopting stochastic differential equation representations affords the model significant flexibility, but can also render it susceptible to overfitting during statistical inversion, where the inferred model may track noise rather than the underlying signal. To alleviate the effects of overfitting and to enable the discovery of the optimal sparse representation of the time-invariant parameters, a Bayesian sparse learning algorithm is embedded within the framework. This sparse learning framework adopts an approximate hierarchical Bayesian setting defined by a series of semi-analytical expressions. The model structure inference framework is validated using a stochastic compartmental model for tracking and forecasting active cases of an infectious disease. Compartmental models describe population-level infectious disease dynamics through interactions among population fractions grouped by disease state. Mathematically, such models consist of a system of coupled ordinary differential equations. This example adopts an expressive compartmental model that includes multiple possible interactions between disease states, motivated by early uncertainty surrounding COVID-19 reinfection dynamics and their implications for long-term epidemic forecasting. The sparse learning exercise permits the inference of a priori unknown epidemiological dynamics from simulated public health data, discovering the nested compartmental model that optimizes the trade-off between average data-fit and model complexity. It is shown that inducing sparsity among the model parameters eliminates redundant interactions between compartments, equivalently revealing the optimal coupling structure between differential equations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Lineage frequency time series reveal elevated levels of genetic drift in SARS-CoV-2 transmission in England

Genetic drift in infectious disease transmission results from randomness of transmission and host recovery or death. The strength of genetic drift for SARS-CoV-2 transmission is expected to be high due to high levels of superspreading, and this is expected to substantially impact disease epidemiology and evolution. However, we don’t yet have an understanding of how genetic drift changes over time or across locations. Furthermore, noise that results from data collection can potentially confound estimates of genetic drift. To address this challenge, we develop and validate a method to jointly infer genetic drift and measurement noise from time-series lineage frequency data. Our method is highly scalable to increasingly large genomic datasets, which overcomes a limitation in commonly used phylogenetic methods. We apply this method to over 490,000 SARS-CoV-2 genomic sequences from England collected between March 2020 and December 2021 by the COVID-19 Genomics UK (COG-UK) consortium and separately infer the strength of genetic drift for pre-B.1.177, B.1.177, Alpha, and Delta. We find that even after correcting for measurement noise, the strength of genetic drift is consistently, throughout time, higher than that expected from the observed number of COVID-19 positive individuals in England by 1 to 3 orders of magnitude, which cannot be explained by literature values of superspreading. Our estimates of genetic drift suggest low and time-varying establishment probabilities for new mutations, inform the parametrization of SARS-CoV-2 evolutionary models, and motivate future studies of the potential mechanisms for increased stochasticity in this system.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

The Effect of Prostate-Specific Antigen (PSA) Test on Radiation Risk Estimate for Prostate Cancer Incidence among Atomic-Bomb Survivors

Following our previous report on the radiation doseresponse for prostate cancer incidence rates in the Life Span Study (LSS) cohort of atomic bomb survivors, we reevaluated the radiation-related risk adjusting for differences in baseline cancer incidence rates among three subsets of the LSS cohort defined by the timing of their first participation in biennial health examinations offered to the Adult Health Study (AHS) sub-cohort members and prostate-specificantigen (PSA) testing status for AHS participants: 1. nonAHS participants, 2. AHS participants before receiving PSA test, and 3. AHS participants after receiving PSA test. We found a 2.9-fold increase in the baseline incidence rates among AHS participants after receiving PSA test. After adjusting for the PSA-testing-status effects on the baseline rates the estimated excess relative risk (ERR) per Gy was 0.54 (95% CI: 0.15, 1.05), which was almost identical to the previously reported unadjusted ERR estimate (0.57, 95% CI: 0.21, 1.00). The current results confirmed that, while the PSA testing among AHS participants increased the baseline incidence rates, it did not impact the radiation risk estimate, strengthening the previously reported dose-response relationship for prostate cancer incidence in the LSS. As the use of PSA tests continue in screening and medical settings, analyses of possible effects of PSA testing should be an important aspect of future epidemiological studies of the association between radiation exposure and prostate cancer.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2023

FrESCO: Framework for Exploring Scalable Computational Oncology

The National Cancer Institute (NCI) monitors population level cancer trends as part of its Surveillance, Epidemiology, and End Results (SEER) program. This program consists of state or regional level cancer registries which collect, analyze, and annotate cancer pathology reports. From these annotated pathology reports, each individual registry aggregates cancer phenotype information from electronic health records. This data is then used to create summary statistics about cancer incidence and mortality to facilitate population health monitoring. Extracting phenotypic information from these reports is a labor intensive task, requiring specialized knowledge about the reports and cancer. Automating the information extraction process from cancer pathology reports has the potential to improve data quality by extracting information in a consistent manner across registries. It can also improve patient outcomes by reducing the time from diagnosis, enabling rapid case ascertainment for clinical trials. Here we present FrESCO, a modular deep-learning natural language processing (NLP) library initially designed for extracting pathology information from clinical text documents. This repository is not solely limited to clinical medical text, but may also be used by researchers just getting started with NLP methods and those looking for a robust solution for their classification problems.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Simulated Night- Shift Schedule Disrupts the Plasma Lipidome and Reveals Early Markers of Cardiovascular Disease Risk

Introduction: The circadian system coordinates daily rhythms in lipid metabolism, storage and utilization. Disruptions of internal circadian rhythms due to altered sleep/wake schedules, such as in night-shift work, have been implicated in increased risk of cardiovascular disease and metabolic disorders. To determine the impact of a night-shift schedule on the human blood plasma lipidome, an in-laboratory simulated shift work study was conducted. Methods: Fourteen healthy young adults were assigned to 3 days of either a simulated day or night-shift schedule, followed by a 24-h constant routine protocol with fixed environmental conditions, hourly isocaloric snacks, and constant wakefulness to investigate endogenous circadian rhythms. Blood plasma samples collected at 3-h intervals were subjected to untargeted lipidomics analysis. Results: More than 400 lipids were identified and quantified across 21 subclasses. Focusing on lipids with low between-subject variation per shift condition, alterations in the circulating plasma lipidome revealed generally increased mean triglyceride levels and decreased mean phospholipid levels after night-shift relative to day-shift. The circadian rhythms of triglycerides containing odd chain fatty acids peaked earlier during constant routine after night-shift. Regardless of shift condition, triglycerides tended to either peak or be depleted at 16:30 h, with chain-specific differences associated with the direction of change. Discussion: The simulated night-shift schedule was associated with altered temporal patterns in the lipidome. This may be premorbid to the elevated cardiovascular risk that has been found epidemiologically in night-shift workers.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

RETRO RX

RETRO Rx is an R&D 100 Gold Award-winning suite of tools that provides a new class of analytics in the space between traditional epidemiological models and unanalyzed spreadsheets of data. Unlike traditional models, these tools provide analysts, scientists, practitioners, and decision makers a way to easily and effectively do sophisticated analyses using a simple user interface. AIDO guides users at the early stages of a disease outbreak by comparing the current parameters to historic outbreaks. RED Alert constructs a broad historical picture of disease incidence (recent outbreak included) to detect disease reemergence at its earliest stages, locally and globally. RETRO Rx compiles data and presents analysis in a clear, concise visual format to support disease mitigation and prevention planning. Both are available currently as web-based apps. A prototype of the RED Alert mobile app with enhanced functionality is available for beta testing and AIDO mobile app development is planned.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Uncertainty analysis of Resource Demand Model for Covid-19

As part of the Department of Energy response to the novel coronavirus pandemic of 2020, a modeling effort was sponsored by the DOE Office of Science. One task of this modeling effort at Sandia was to develop a model to predict medical resource needs given various patient arrival scenarios. Resources needed include personnel resources (nurses, ICU nurses, physicians, respiratory therapists), fixed resources (regular or ICU beds and ventilators), and consumable resources (masks, gowns, gloves, face shields, sedatives). This report documents the uncertainty analysis that was performed on the resource model. The uncertainty analysis involved sampling 26 input parameters to the model. The sampling was performed conditional on the patient arrival streams that also were inputs to the model. These patient arrival streams were derived from various epidemiology models and had a significant effect on the projected resource needs. In this report, we document the sampling approach, the parameter ranges used, and the computational workflow necessary to perform large-scale uncertainty studies for every county and state in the United States.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Predicting Future Disease Burden in a Rapidly Changing Climate

The interplay of a rapidly changing climate and infectious disease occurrence is emerging as a critical topic, requiring investigation of possible direct, as well as indirect, connections between disease processes and climate-related variation and phenomena. First, we introduce and overview three infectious disease exemplars (dengue, influenza, valley fever) representing different transmission classes (insect-vectored, human-to-human, environmentally-transmitted) to illuminate the complex and significant interplay between climate disease processes, as well as to motivate discussion of how Sandia can transform the field, and change our understanding of climate-driven infectious disease spread. We also review state-of-the-art epidemiological and climate modeling approaches, together with data analytics and machine learning methods, potentially relevant to climate and infectious disease studies. We synthesize the modeling and disease exemplars information, suggesting initial avenues for research and development (R&D) in this area, and propose potential sponsors for this work. Whether directly or indirectly, it is certain that a rapidly changing climate will alter global disease burden. The trajectory of climate change is an important control on this burden, from local, to regional and global scales. The efforts proposed herein respond to the National Research Councils call for the creation of a multidisciplinary institute that would address critical aspects of these interlocking, cascading crises.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Global Disease Modeling & Forecasting Center [PowerPoint]

The Global Disease Modeling & Forecasting Center works in union with academia, industry, the US Government and global partners to develop, validate, and operationalize epidemiological models to combat infectious diseases. It's mission is to Apply multi-scale modeling and advanced data science to help the world detect, understand, forecast, prevent, and provide decision support in response to emerging and re-emerging disease outbreaks. The presentation explains LANL's role in more detail.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

NVBL (National Virtual Biotechnology Laboratory) Overview

This virtual symposium was held to highlight the impact the U.S. Department of Energy’s (DOE) National Virtual Biotechnology Laboratory (NVBL) has had utilizing the unique capabilities of the DOE to tackle the science and technology challenges associated with COVID-19, and to discuss areas in which the NVBL can have impact in the future. With perspectives from Chris Fall, Director, Office of Science, DOE, and William A. Bookless, Principal Deputy Administrator of the National Nuclear Security Administration, the event featured Presentations from lead investigators reporting progress in: Epidemiological modeling, Therapeutics, Testing, Understanding transport of the virus, and Solving issues around supply chain challenges. In addition, keynote speakers from outside the NVBL discussed the upcoming science and technology needs in computing, testing and surveillance, and vaccines. The event was aimed at the S&T community, media, and general public.

Buchanan, Michelle V.↗