Web-based Decision-Support Tools for Investigating Infectious Disease Threats [Slides]
This presentation describes various biosurrveillance tools used to investigate infectious disease threats.
59 BASIC BIOLOGICAL SCIENCES↗
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Centers for Disease Control and Prevention (CDC) Radiation Hazard Scale Data Product Review Feedback Report
In support of the Department of Energy (DOE) National nuclear Security Administration (NNSA) and the Centers for Disease Control and Prevention (CDC), Lawrence Livermore National Laboratory (LLNL) assisted in the development of new data templates for disseminating and communicating FRMAC1 data products using the CDC Radiation Hazard Scale communication tool. To ensure these data products will be useful to stakeholders during a radiological emergency, LLNL facilitated opportunities for product socialization and review.
61 RADIATION PROTECTION AND DOSIMETRY↗
Game Theoretic Approach for Understanding and Modeling Clinical Pathways (Stable Ischemic Heart Disease)
This research develops a game-theoretic agent model for stable ischemic heart disease (SIHD) clinical decisions, incorporating aspects of Bayesian game theory to model hidden information such as the patient’s true underlying medical condition(s). Preexisting observational medical data contained in the United States Department of Veterans Affairs (VA) Corporate Data Warehouse (CDW) is analyzed to obtain the required game-theoretic parameter values. Various statistical adjustments are performed to correct for bias in the preexisting treatment protocol selections and to obtain game-theoretic parameter values for contrafactual treatment protocol choices. Once the game-theoretic parameter values are determined, clinical pathways can be viewed as being mathematical strategies for playing the game and thus can be studied and evaluated using the techniques of game theory.
59 BASIC BIOLOGICAL SCIENCES↗
Lab Home Testing of Residential Isolation Space Control to Minimize Infectious Disease Transmission in Existing Single-Family Homes
Existing evidence strongly suggests that viral infectious diseases can be transmitted via an airborne route across distances in indoor environments. Accordingly, the risk of airborne transmission within homes should be managed. The public health emergency associated with SARS-CoV-2 makes controlling airborne transmission of respired viruses in indoor environments critical, especially in poorly ventilated indoor environments. The effectiveness of engineering interventions requiring minor efforts that create a negative-pressure isolation zone (IZ) for a contagious person has yet to be tested for existing residential homes. To mitigate the risk of airborne virus transmission and maximize health protection for the population in existing single-family homes, the relative effectiveness of several control strategies are investigated in this report. While very high-efficiency MERV filtration, high ventilation rates, and other controls can help be effective, most occupants are not likely to have the time or means for advanced measures found in hospitals. This project focused on testing relatively simple efforts that utilize existing or easy to acquire materials and simple processes.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Spatio-temporal Estimates of Disease Transmission Parameters for COVID-19 with a Fully-Coupled, County-Level Model of the United States
Sandia National Laboratories has developed a capability to estimate parameters of epidemiological models from case reporting data to support responses to the COVID-19 pandemic. A differentiating feature of this work is the ability to simultaneously estimate county-specific disease transmission parameters in a nation-wide model that considers mobility between counties. The approach is focused on estimating parameters in a stochastic SEIR model that considers mobility between model patches (i.e., counties) as well as additional infectious compartments. The inference engine developed by Sandia includes (1) reconstruction and (2) transmission parameter inference. Reconstruction involves estimating current population counts within each of the compartments in a modified SEIR model from reported case data. Reconstruction produces input for the inference formulations, and it provides initial conditions that can be used in other modeling and planning efforts. Inference involves the solution of a large-scale optimization problem to estimate the time profiles for the transmission parameters in each county. These provide quantification of changes in the transmission parameter over time (e.g., due to impact of intervention strategies). This capability has been implemented in a Python-based software package, epi_inference, that makes extensive use of Pyomo [5] and IPOPT [10] to formulate and solve the inference formulations.
42 ENGINEERING↗
Centers for Disease Control and Prevention (CDC) Radiation Hazard Scale (Expanded Data Product Feedback Report)
During radiation emergencies, it is important to communicate clearly as radiation risks are often unfamiliar to many members of the general public and emergency responders, including public health workers. To partially address this issue, the Centers for Disease Control and Prevention (CDC) developed the Radiation Hazard Scale to communicate radiation risk to the general public, community leaders, and decision makers. The simple numeric scale does not require a background in radiation physics or health effects and can be used to assist people in understanding their risk and motivate protective action compliance. Lawrence Livermore National Laboratory has assisted the US Department of Energy National Nuclear Security Administration and the CDC in the development of Radiation Hazard Scale data products. This report documents the feedback received from Federal response officials on an expanded data product set. This expanded set accounts for the effects of building protection on radiation exposure and allows for the comparison between different response actions, including sheltering in place, based on the LLNL developed Regional Shelter Analysis methodology.
54 ENVIRONMENTAL SCIENCES↗
Building a Computational and Experimental Rapid Response Pipeline to Counter the Coronavirus Disease 2019 Outbreak and Emerging Biothreats
The LDRD ER “Building a Computational and Experimental Rapid Response Pipeline to Counter the Coronavirus Disease 2019 Outbreak and Emerging Biothreats” was conceived to address a need for rapid, scalable, evaluation of computationally designed therapeutic or prophylactic antibodies and vaccine antigens, two important classes of protein medical countermeasure (MCM). This was done in complement to a computationally driven LDRD 20ERD032 “Active Learning for Rapid Design of Vaccines and Antibodies.” Natural antibodies and antigens are often insufficiently broad or robust across different pathogens and their variants. Leveraging a collaboration of simulation driven machine learning, structural expertise, and high-throughput characterization of candidate antibodies, we successfully re-targeted three different anti-SARS-CoV-1 antibodies to neutralize SARS-CoV-2 in vitro. Our antibody design work reached its most important stage in rapid response to the emergence of the Omicron variant of concern (VOC) in late 2021. In a matter of weeks, we computationally designed derivative antibodies of COV2-2130, one of two antibodies from Vanderbilt that form the basis of the AstraZeneca Evusheld prophylactic drug product. This drug product suffers a serious loss of efficacy against Omicron BA.1 and BA.1.1, the first Omicron strains. Our designs were successful, including a pair of designs which provide potent neutralization of not only Omicron BA.1 and BA.1.1, but also the earlier Delta variant, and subsequent Omicron strains including BA.2, BA.4, BA.5, and BA.2.75, demonstrating that our multi-target design process can, by its nature, produce robust antibody designs that strictly improve over the parental antibody. These results, recognized by a 2022 Director’s Science and Technology award, have enabled the follow-on GUIDE program, to commence in FY23.
59 BASIC BIOLOGICAL SCIENCES↗
Model-form Error Correction using Universal Differential Equations for an Agent-Based Model of Infectious Disease
This report demonstrates universal differential equations (UDEs) as an approach to bridge the gap between ordinary differential equations (ODE) models and agent-based models (ABMs). Using UDE models as surrogates for ABMs allows us to preserve the foundational ODE that represents global disease dynamics while coupling it with a neural network model to approximate functions for the local behaviors of the ABM.
59 BASIC BIOLOGICAL SCIENCES↗
A spatially regularized detector for emergent/re-emergent disease outbreaks
Early detection of outbreaks caused by emergent pathogens, using epidemiological surveillance data i.e., daily case counts, is difficult. This is because the data tend to be noisy during the early epoch of the outbreak. In contrast, the spread-rate of the disease tends to be well-behaved, as it depends only on the mixing patterns of the population and the characteristics of the pathogen, neither of which behave erratically in space-time. In this report, we explore whether the spread-rate can be used for epidemiological surveillance, conditional on case count data. Estimating the spread-rate from case count data allows us to exploit exogenous information, e.g., incubation period distributions etc., which can considerably smooth out any erratic temporal behavior. Further, epidemiological dynamics are spatially correlated, and if case counts are available for multiple areal units e.g., counties, these correlations could potentially be used to suppress noise in the early epoch data. These exogenous information and structure are not exploited by conventional syndromic surveillance detectors to extract a well-behaved latent variable for monitoring purposes. The technical challenge lies in the estimation of the spread-rate field jointly over a collection of areal units; further, the spread-rate varies over time. We develop a method based on mean-field variational inference to approximately estimate the spread-rate field, using a Gaussian Random Field Model for spatial regularization. The method is tested on the estimation of spread-rate in the thirty-three counties of New Mexico and detect the arrival of the Fall 2020 COVID-19 wave in September 2020. We find that the method is scalable, but underestimates the uncertainty in the estimated spread-rate field. We detect the arrival of the Fall 2020 wave a week ahead of conventional syndromic surveillance algorithms, but our simplistic detection algorithm, based on simple anomaly detection, suffers from a high false positive rate, similar to conventional detectors.
59 BASIC BIOLOGICAL SCIENCES↗
Enhancing Biopreparedness through a Model System to Understand the Molecular Mechanisms that Lead to Pathogenesis and Disease Transmission: NW-BRaVE
The science of biopreparedness to counter biological threats hinges on understanding the fundamental principles and molecular mechanisms that lead to pathogenesis and disease transmission. Our vision to address this challenge is to create a powerful and user-friendly platform to elucidate the fundamental principles of how molecular interactions drive pathogen-host relationships and host shifts. We will enable groundbreaking discoveries by integrating a wide range of structural, genomics, proteomics, and other advanced omics measurements, along with evolutionary and artificial intelligence predictions. To make sure the system is applicable to real-world problems, we will develop it in the context of a tractable model system, the small, abundant, and accessible photosynthetic cyanobacteria and their constantly co-adapting viral pathogens, cyanophages. This model will maintain the system’s applicability to real-world problems and techniques, but the overall focus will be on elucidating general principles of detecting, assessing, and surveilling molecular interaction, adaptation, and coevolution that are system agnostic and therefore extensible to other viral-host interactions. Our overall objectives are to (1) identify the molecular complexes that comprise the cyanobacteria redox macromolecular subsystem and how they dynamically change with bacteriophage infection in situ, using cryo-electron tomography; (2) profile regulatory changes during infection using proteomics, multiomics, and experimental validation, and integrate the data with in situ structures; (3) use genomics and metagenomics to determine environmental and population factors across time scales that impact the interactions between marine cyanobacteria and their cyanophage parasites, predicting the evolutionary origins of in situ structural and functional interactions, convergence and coevolution; and (4) develop a data integration and transformation platform that facilitates the integration of in situ, proteomic, and evolutionary measurements of molecular interactions to surveil diverse hosts and parasites in various environmental contexts. These objectives address Focus Area 2 Reveal Molecular Interactions Across Biological Scales for Design of Targeted Interventions. Our powerful and user-friendly platform will enhance connections between the often-siloed fields of structure, molecular phenotype, and evolutionary genomics that are key to biopreparedness, but in need of integration (Figure 1). We will build an integrated navigation tool to facilitate the effective use of globally distributed experimental data for integrated analysis and predictive modeling. The project will develop, implement, and test a platform to assess host-pathogen molecular interactions, adaptation to hosts and host shifts, and coevolution between hosts and pathogens, successfully impacting the research community by revolutionizing abilities to study any host-pathogen interaction, encourage diverse community contributions, and gain fundamental insights into how proteins adapt to new contexts. This ability will be critical for designing early interventions to address future threats. We will build surveillance training capability, aiming for a fair and equitable response to future pandemics and biothreats.
59 BASIC BIOLOGICAL SCIENCES↗
Relating Oxidative Protein Damage to Antioxidant Status in Health and Disease (Abbreviated Final Report for 24-LW-026)
Antioxidant supplements are widely used to protect health, yet some studies suggest they can sometimes worsen disease, including certain cancers. We set out to clarify how antioxidants affect the body’s chemistry, especially markers of oxidative damage, so that patients, clinicians, and public-health agencies can make better, evidence-based decisions.
59 BASIC BIOLOGICAL SCIENCES↗
Clinical Features of and Risk Factors for Fatal Ebola Virus Disease, Moyamba District, Sierra Leone, December 2014–February 2015
The current outbreak of Ebola virus disease (EVD) in West Africa has infected more than 28,000 people, killed more than 11,000 and disrupted social life. We studied retrospectively the clinical presentation and risk factors for fatal outcome among the 31 Ebola virus (EBV) positive patients admitted to the Ebola Treatment Center (ETC) in Moyamba District, Sierra Leone. We found a higher rate of bleeding manifestations than reported elsewhere during the current outbreak. Significant predictors for fatal outcome were shorter time from onset to admission, male sex, high viral load on initial lab test, severe pain, diarrhea, bloody stools, and development of other bleeding manifestations during hospital admission. Awareness of risk factors for fatal outcome could be used to identify patients in need of more intensive medical support. The lack of fever in as much as a third of EVD cases may have implications for temperature screening practices and case definitions.
60 APPLIED LIFE SCIENCES↗
Assessing the Dynamics and Complexity of Disease Pathogenicity Using 4-Dimensional Immunological Data
Investigating disease pathogenesis and personalized prognostics are major biomedical needs. Because patients sharing the same diagnosis can experience different outcomes, such as survival or death, physicians need new personalized tools, including those that rapidly differentiate several inflammatory phases. To address these topics, a pattern recognition-based method (PRM) that follows an inverse problem approach was designed to assess, in <10 min, eight concepts: synergy , pleiotropy , complexity , dynamics , ambiguity , circularity , personalized outcomes , and explanatory prognostics (pathogenesis). By creating thousands of secondary combinations derived from blood leukocyte data, the PRM measures synergic, pleiotropic, complex and dynamic data interactions, which provide personalized prognostics while some undesirable features—such as false results and the ambiguity associated with data circularity-are prevented. Here, this method is compared to Principal Component Analysis (PCA) and evaluated with data collected from hantavirus-infected humans and birds that appeared to be healthy. When human data were examined, the PRM predicted 96.9 % of all surviving patients while PCA did not distinguish outcomes. Demonstrating applications in personalized prognosis, eight PRM data structures sufficed to identify all but one of the survivors. Dynamic data patterns also distinguished survivors from non-survivors, as well as one subset of non-survivors, which exhibited chronic inflammation. When the PRM explored avian data, it differentiated immune profiles consistent with no, early, or late inflammation. Yet, PCA did not recognize patterns in avian data. Findings support the notion that immune responses, while variable, are rather deterministic: a low number of complex and dynamic data combinations may be enough to, rapidly, unmask conditions that are neither directly observable nor reliably forecasted.
60 APPLIED LIFE SCIENCES↗
Personalized, disease-stage specific, rapid identification of immunosuppression in sepsis
Introduction Data overlapping of different biological conditions prevents personalized medical decision-making. For example, when the neutrophil percentages of surviving septic patients overlap with those of non-survivors, no individualized assessment is possible. To ameliorate this problem, an immunological method was explored in the context of sepsis. Methods Blood leukocyte counts and relative percentages as well as the serum concentration of several proteins were investigated with 4072 longitudinal samples collected from 331 hospitalized patients classified as septic (n=286), non-septic (n=43), or not assigned (n=2). Two methodological approaches were evaluated: (i) a reductionist alternative, which analyzed variables in isolation; and (ii) a non-reductionist version, which examined interactions among six (leukocyte-, bacterial-, temporal-, personalized-, population-, and outcome-related) dimensions. Results The reductionist approach did not distinguish outcomes: the leukocyte and serum protein data of survivors and non-survivors overlapped. In contrast, the non-reductionist alternative differentiated several data groups, of which at least one was only composed of survivors (a finding observable since hospitalization day 1). Hence, the non-reductionist approach promoted personalized medical practices: every patient classified within a subset associated with 100% survival subset was likely to survive. The non-reductionist method also revealed five inflammatory or disease-related stages (provisionally named ‘early inflammation, early immunocompetence, intermediary immuno-suppression, late immuno-suppression, or other’). Mortality data validated these labels: both ‘suppression’ subsets revealed 100% mortality, the ‘immunocompetence’ group exhibited 100% survival, while the remaining sets reported two-digit mortality percentages. While the ‘intermediary’ suppression expressed an impaired monocyte-related function, the ‘late’ suppression displayed renal-related dysfunctions, as indicated by high concentrations of urea and creatinine. Discussion The data-driven differentiation of five data groups may foster early and non-overlapping biomedical decision-making, both upon admission and throughout their hospitalization. This approach could evaluate therapies, at personalized level, earlier. To ascertain repeatability and investigate the dynamics of the ‘other’ group, additional studies are recommended.
Immunology↗
Genome-Wide Analysis of Corynespora cassiicola Leaf Fall Disease Putative Effectors
Corynespora cassiicola is an Ascomycetes fungus with a broad host range and diverse life styles. Mostly known as a necrotrophic plant pathogen, it has also been associated with rare cases of human infection. In the rubber tree, this fungus causes the Corynespora leaf fall (CLF) disease, which increasingly affects natural rubber production in Asia and Africa. It has also been found as an endophyte in South American rubber plantations where no CLF outbreak has yet occurred. The C. cassiicola species is genetically highly diverse, but no clear relationship has been evidenced between phylogenetic lineage and pathogenicity. Cassiicolin, a small glycosylated secreted protein effector, is thought to be involved in the necrotrophic interaction with the rubber tree but some virulent C. cassiicola isolates do not have a cassiicolin gene. This study set out to identify other putative effectors involved in CLF. The genome of a highly virulent C. cassiicola isolate from the rubber tree (CCP) was sequenced and assembled. In silico prediction revealed 2870 putative effectors, comprising CAZymes, lipases, peptidases, secreted proteins and enzymes associated with secondary metabolism. Comparison with the genomes of 44 other fungal species, focusing on effector content, revealed a striking proximity with phylogenetically unrelated species ( Colletotrichum acutatum , Colletotrichum gloesporioides , Fusarium oxysporum , nectria hematococca , and Botrosphaeria dothidea ) sharing life style plasticity and broad host range. Candidate effectors involved in the compatible interaction with the rubber tree were identified by transcriptomic analysis. Differentially expressed genes included 92 putative effectors, among which cassiicolin and two other secreted singleton proteins. Finally, the genomes of 35 C. cassiicola isolates representing the genetic diversity of the species were sequenced and assembled, and putative effectors identified. At the intraspecific level, effector-based classification was found to be highly consistent with the phylogenomic trees. Identification of lineage-specific effectors is a key step toward understanding C. cassiicola virulence and host specialization mechanisms.
59 BASIC BIOLOGICAL SCIENCES↗
Molecular Epidemiology of Foot-and-Mouth Disease Virus in the Context of Transboundary Animal Movement in the Far North Region of Cameroon
Transboundary movement of animals is an important mechanism for foot-and-mouth disease virus (FMDV) spread in endemic regions, such as Cameroon. Several transboundary animal trade routes cross the Far North Region of Cameroon, and cattle moved on foot along these routes often come in contact with native (sedentary and transhumant) herds. The purpose of this study was to investigate the role of transboundary trade cattle in the epidemiology of FMDV in the Far North Region of Cameroon. A total of 582 oropharyngeal fluid (OPF) samples were collected from asymptomatic transboundary trade cattle at official border check points and 57 vesicle epithelial tissues were collected from clinically affected native cattle in the Far North Region of Cameroon during 2010–2014. Viral protein 1 (VP1) coding sequences were obtained from 6 OPF samples from transboundary cattle (4 serotype O, 2 serotype SAT2) and 19 epithelial tissue samples from native cattle (7 serotype O, 3 serotype SAT2, 9 serotype A). FMDV serotype O viruses belonged to two topotypes (East Africa-3 and West Africa), and phylogenetic analyses suggested a pattern of continuous transmission in the region. Serotype SAT2 viruses belonged to a single topotype (VII), and phylogenetic analysis suggested a pattern of repeated introductions of different SAT2 lineages in the region. Serotype A viruses belonged to topotype AFRICA/G-IV, and the pattern of transmission was unclear. Spearman rank correlation analysis of VP1 coding sequences obtained in this study from transboundary and native cattle showed a positive correlation between genetic distance and time for serotype O ( ρ = 0.71, p = 0.003) and between genetic distance and geographic distance for serotype SAT2 ( ρ = 0.54, p = 0.1). These data suggest that transboundary trade cattle participate in the transmission of FMDV in the Far North Region of Cameroon, however the dynamics and direction of transmission could not be determined in this study. Results of this study contribute to the understanding of transboundary FMDV epidemiology in Central Africa and will help to inform control programs in Cameroon and in the region.
Veterinary Sciences↗
Geo-temporal patterns to design cost-effective interventions for zoonotic diseases -the case of brucellosis in the country of Georgia
Control of zoonosis can benefit from geo-referenced procedures. Focusing on brucellosis, here the ability of two methods to distinguish disease dissemination patterns and promote cost-effective interventions was compared. Geographical data on bovine, ovine and human brucellosis reported in the country of Georgia between 2014 and 2019 were investigated with (i) the Hot Spot (HS) analysis and (ii) a bio-geographical (BG) alternative. More than one fourth of all sites reported cases affecting two or more species. While ruminant cases displayed different patterns over time, most human cases described similar geo-temporal features, which were associated with the route used by migrant shepherds. Other human cases showed heterogeneous patterns. The BG approach identified small areas with a case density twice as high as the HS method. The BG method also identified, in 2018, a 2.6–2.99 higher case density in zoonotic (human and non-human) sites than in non-zoonotic sites (which only reported cases affecting a single species) –a finding that, if corroborated, could support cost-effective policy-making. Three dissemination hypotheses were supported by the data: (i) human cases induced by sheep-related contacts; (ii) human cases probably mediated by contaminated milk or meat; and (iii) cattle and sheep that infected one another. This proof-of-concept provided a preliminary validation for a method that may support cost-effective interventions oriented to control zoonoses. To expand these findings, additional studies on zoonosis-related decision-making are recommended.
60 APPLIED LIFE SCIENCES↗
Adenosine A2A Receptor Up-Regulation Pre-Dates Deficits of Synaptic Plasticity and of Memory in Mice Exposed to Aβ1–42 to Model Early Alzheimer’s Disease
The intracerebroventricular (icv) injection of amyloid peptides (Aβ) models Alzheimer’s disease (AD) in mice, as typified by the onset within 15 days of deficits of memory and of hippocampal long-term potentiation (LTP) that are prevented by the blockade of adenosine A2A receptors (A2AR). Since A2AR overfunction is sufficient to trigger memory deficits, we tested if A2AR were upregulated in hippocampal synapses before the onset of memory deficits to support the hypothesis that A2AR overfunction could be a trigger of AD. Six to eight days after Aβ-icv injection, mice displayed no alterations of hippocampal dependent memory; however, they presented an increased excitability of hippocampal synapses, a slight increase in LTP magnitude in Schaffer fiber-CA1 pyramid synapses and an increased density of A2AR in hippocampal synapses. A2AR blockade with SCH58261 (50 nM) normalized excitability and LTP in hippocampal slices from mice sacrificed 7–8 days after Aβ-icv injection. Fifteen days after Aβ-icv injection, mice displayed evident deficits of hippocampal-dependent memory deterioration, with reduced hippocampal CA1 LTP but no hyperexcitability and a sustained increase in synaptic A2AR, which blockade restored LTP magnitude. This shows that the upregulation of synaptic A2AR precedes the onset of deterioration of memory and of hippocampal synaptic plasticity, supporting the hypothesis that the overfunction of synaptic A2AR could be a trigger of memory deterioration in AD.
Lopes, Cátia R.↗