Multiple Streams Of Evidence Research Articles

Assessing Multiple Evidence Streams to Decide on Confidence for Identification of Post-Translational Modifications, within and Across Data Sets.

Phosphorylation is a post-translational modification of great interest to researchers due to its relevance in many biological processes. LC-MS/MS techniques have enabled high-throughput data acquisition, with studies claiming identification and localization of thousands of phosphosites. The identification and localization of phosphosites emerge from different analytical pipelines and scoring algorithms, with uncertainty embedded throughout the pipeline. For many pipelines and algorithms, arbitrary thresholding is used, but little is known about the actual global false localization rate in these studies. Recently, it has been suggested to use decoy amino acids to estimate global false localization rates of phosphosites, among the peptide-spectrum matches reported. Here, we describe a simple pipeline aiming to maximize the information extracted from these studies by objectively collapsing from peptide-spectrum match to the peptidoform-site level, as well as combining findings from multiple studies while maintaining track of false localization rates. We show that the approach is more effective than current processes that use a simpler mechanism for handling phosphosite identification redundancy within and across studies. In our case study using eight rice phosphoproteomics data sets, 6368 unique sites were confidently identified using our decoy approach compared to 4687 using traditional thresholding in which false localization rates are unknown.

Linking repeat photography and remote sensing to assess treeline rise with climate warming: Mount of the Holy Cross, Colorado

ABSTRACT Most ecological studies are by necessity cast on rather short timescales, such that documenting change in phenomena that occur slowly (e.g., over decades to centuries) is quite difficult. Here, we explore variation in and covariation of two sources of information to address rates of treeline change on one mountain face in the central Rocky Mountains: repeat photography and remote sensing. The repeat photography work involved assembling and quantitatively comparing four photographs that span 148 years. The remote sensing analyses covered a shorter period (35 years) but provided quantitative measurements and fuller landscape coverage. The repeat photography results demonstrated pronounced elevational advance of erect forest, at a rate <0.3 m/year during 1873–1979 but accelerating to >1.8 m/year during 2004–2020. The remote sensing analyses reflected similar processes but also reflected considerable infilling of tree cover near treeline; consequent increases in Normalized Difference Vegetation Index (NDVI) values in the upper forested areas may thus indicate treeline advance or changes in forest characteristics below treeline. Overall, these results document (1) acceleration in rise of treeline at this site in recent decades and (2) elevational compression of vegetation zones above treeline, with important implications for conservation of nonforested alpine ecosystems. Integrating multiple streams of evidence offers complementary views and insights in analyzing this phenomenon.

Natural language processing in toxicology: Delineating adverse outcome pathways and guiding the application of new approach methodologies

Adverse Outcome Pathways (AOPs) are conceptual frameworks that tie an initial perturbation (molecular initiating event) to a phenotypic toxicological manifestation (adverse outcome), through a series of steps (key events). They provide therefore a standardized way to map and organize toxicological mechanistic information. As such, AOPs inform on key events underlying toxicity, thus supporting the development of New Approach Methodologies (NAMs), which aim to reduce the use of animal testing for toxicology purposes.However, the establishment of a novel AOP relies on the gathering of multiple streams of evidence and information, from available literature to knowledge databases. Often, this information is in the form of free text, also called unstructured text, which is not immediately digestible by a computer. This information is thus both tedious and increasingly time-consuming to process manually with the growing volume of data available. The advancement of machine learning provides alternative solutions to this challenge. To extract and organize information from relevant sources, it seems valuable to employ deep learning Natural Language Processing techniques.We review here some of the recent progress in the NLP field, and show how these techniques have already demonstrated value in the biomedical and toxicology areas. We also propose an approach to efficiently and reliably extract and combine relevant toxicological information from text. This data can be used to map underlying mechanisms that lead to toxicological effects and start building quantitative models, in particular AOPs, ultimately allowing animal-free human-based hazard and risk assessment.

Development of an Evidence-Based Risk Assessment Framework.

Assessment of potential human health risks associated with environmental and other agents requires careful evaluation of all available and relevant evidence for the agent of interest, including both data-rich and data-poor agents. With the advent of new approach methodologies in toxicological risk assessment, guidance on integrating evidence from mul-tiple evidence streams is needed to ensure that all available data is given due consideration in both qualitative and quantitative risk assessment. The present report summarizes the discussions among academic, government, and private sector participants from North America and Europe in an international workshop convened to explore the development of an evidence-based risk assessment framework, taking into account all available evidence in an appropriate manner in order to arrive at the best possible characterization of potential human health risks and associated uncertainty. Although consensus among workshop participants was not a specific goal, there was general agreement on the key consider-ations involved in evidence-based risk assessment incorporating 21st century science into human health risk assessment. These considerations have been embodied into an overarching prototype framework for evidence integration that will be explored in more depth in a follow-up meeting.

Development of an Evidence-Based Risk Assessment Framework

Assessment of potential human health risks associated with environmental and other agents requires careful evaluation of all available and relevant evidence for the agent of interest, including both data-rich and data-poor agents. With the advent of new approach methodologies in toxicological risk assessment, guidance on integrating evidence from multiple evidence streams is needed to ensure that all available data is given due consideration in both qualitative and quantitative risk assessment. The present report summarizes the discussions among academic, government, and private sector participants from North America and Europe in an international workshop convened to explore the development of an evidence-based risk assessment framework, taking into account all available evidence in an appropriate manner in order to arrive at the best possible characterization of potential human health risks, and associated uncertainty. Although consensus among workshop participants was not a specific goal, there was general agreement on the key considerations involved in evidence-based risk assessment incorporating 21st century science into human health risk assessment. These considerations have been embodied into an overarching prototype framework for evidence integration that will be explored in more depth in a follow-up meeting.

The use of categorical regression in the assessment of the risks of nutrient deficiency and excess

Exposure-response assessment methods have shifted towards more quantitative approaches, with health risk assessors exploring more statistically driven techniques. These assessments, however, usually rely on one critical health effect from a single key study. Categorical regression addresses this limitation by incorporating data from all relevant studies – including human, animal, and mechanistic studies – thereby including a broad spectrum of health endpoints and exposure levels for exposure-response analysis in an objective manner. Categorical regression requires the establishment of ordered response categories corresponding to increasingly severe adverse health outcomes and the availability of a comprehensive database that summarizes all data on different outcomes from different studies, including the exposure or dose at which these out-comes are observed and their severity. It has found application in the risk assessment of essential nutrients and trace metals. Since adverse effects may arise from either deficient or excess exposure, the exposure-response curve is U-shaped, which provides a basis for determining optimal intake levels that minimize the joint risks of deficiency and excess. This article provides an overview of the use of categorical regression fit exposure-response models incorporating data from multiple evidence streams. An extension of categorical regression that permits the simultaneous analysis of excess and deficiency toxicity data is presented and applied to comprehensive databases on copper and manganese. Future applications of cat-egorical regression will be able to make greater use of diverse data sets developed using new approach methodologies, which can be expected to provide valuable information on toxic responses of varying severity.

Fifty years of comprehensive cancer hazard identification: the IARC {Monographs} Programme’s systematic review and synthesis across multiple evidence streams

BACKGROUND AND AIM: For the past 50 years, the {Monographs} Programme of the International Agency for Research on Cancer (IARC) has provided the world’s most comprehensive compendium of cancer hazard identification. More than 1020 agents have been evaluated by the {Monographs}, selected based on evidence of human exposure and of carcinogenicity. These agents comprise chemicals, physical and biological agents, complex mixtures, occupational exposure circumstances, dietary factors, personal habits, and other exposures of everyday life. The aim of this presentation is to describe the comprehensive evidence synthesis used by the IARC {Monographs}. METHODS: Expert working groups are assembled by the {Monographs} scientific secretariat, based on expertise with the agent or relevant methodology, freedom from conflicts of interest, and diversity of scientific viewpoints and approaches. These working groups review and synthesize all publicly available evidence in three streams: human cancer, cancer in experimental animals, and carcinogen mechanisms. Evidence from these three streams is synthesized first within and then across streams, to reach an overall conclusion about whether the agent is {carcinogenic}, {probably carcinogenic}, {possibly carcinogenic}, or {not classifiable as to its carcinogenity} to humans. Exposure information about the agent is also characterized in each {Monograph}. RESULTS:Exposure settings for agents classified as {carcinogenic to humans} include the environment (e.g., air pollution), workplace (e.g., welding fumes), diet (e.g., processed meat), infections (e.g., human papilloma virus), medicines (e.g., estrogen-progestogen oral contraceptives), and personal habits (e.g, opium consumption). Recent advances made within this unique and influential program include comprehensive and transparent incorporation of systematic review principles, explicit consideration of the impact of exposure assessment quality in human cancer and mechanistic studies, and novel methods for mechanistic evidence evaluation. CONCLUSIONS:The systematic review process undertaken by IARC {Monographs} working groups is robust and well-accepted. Recent advances to increase transparency of expert decision-making provide a foundation for sound evidence synthesis into the future. KEYWORDS: Evidence synthesis, carcinogenicity, hazard identification, risk assessment, mechanisms, cancer

Ventilation Techniques and Risk for Transmission of Coronavirus Disease, Including COVID-19: A Living Systematic Review of Multiple Streams of Evidence.

Background:Mechanical ventilation is used to treat respiratory failure in coronavirus disease 2019 (COVID-19).Purpose:To review multiple streams of evidence regarding the benefits and harms of ventilation techniques for coronavirus infections, including that causing COVID-19. (PROSPERO registration: CRD42020178187)Data Sources:21 standard, World Health Organization–specific and COVID-19–specific databases, without language restrictions, until 1 May 2020.Study Selection:Studies of any design and language comparing different oxygenation approaches in patients with coronavirus infections, including severe acute respiratory syndrome (SARS) or Middle East respiratory syndrome (MERS), or with hypoxemic respiratory failure. Animal, mechanistic, laboratory, and preclinical evidence was gathered regarding aerosol dispersion of coronavirus. Studies evaluating risk for virus transmission to health care workers from aerosol-generating procedures (AGPs) were included.Data Extraction:Independent and duplicate screening, data abstraction, and risk of bias assessment (GRADE for certainty of evidence and AMSTAR 2 for included systematic reviews).Data Synthesis:123 studies were eligible (45 on COVID-19, 70 on SARS, 8 on MERS), but only 5 studies (1 on COVID-19, 3 on SARS, 1 on MERS) adjusted for important confounders. A study in hospitalized patients with COVID-19 reported slightly higher mortality with noninvasive ventilation (NIV) than with invasive mechanical ventilation (IMV), but 2 opposing studies, 1 in patients with MERS and 1 in patients with SARS, suggest a reduction in mortality with NIV (very low-certainty evidence). Two studies in patients with SARS report a reduction in mortality with NIV compared with no mechanical ventilation (low-certainty evidence). Two systematic reviews suggest a large reduction in mortality with NIV compared with conventional oxygen therapy. Other included studies suggest increased odds of transmission from AGPs.Limitation:Direct studies in COVID-19 are limited and poorly reported.Conclusion:Indirect and low-certainty evidence suggests that use of NIV, similar to IMV, probably reduces mortality but may increase the risk for transmission of COVID-19 to health care workers.Primary Funding Source:World Health Organization.

Bayesian network-based framework for exposure-response study design and interpretation

Conventional environmental-health risk-assessment methods are often limited in their ability to account for uncertainty in contaminant exposure, chemical toxicity and resulting human health risk. Exposure levels and toxicity are both subject to significant measurement errors, and many predicted risks are well below those distinguishable from background incident rates in target populations. To address these issues methods are needed to characterize uncertainties in observations and inferences, including the ability to interpret the influence of improved measurements and larger datasets. Here we develop a Bayesian network (BN) model to quantify the joint effects of measurement errors and different sample sizes on an illustrative exposure-response system. Categorical variables are included in the network to describe measurement accuracies, actual and measured exposures, actual and measured response, and the true strength of the exposure-response relationship. Network scenarios are developed by fixing combinations of the exposure-response strength of relationship (none, medium or strong) and the accuracy of exposure and response measurements (low, high, perfect). Multiple cases are simulated for each scenario, corresponding to a synthetic exposure response study sampled from the known scenario population. A learn-from-cases algorithm is then used to assimilate the synthetic observations into an uninformed prior network, yielding updated probabilities for the strength of relationship. Ten replicate studies are simulated for each scenario and sample size, and results are presented for individual trials and their mean prediction. The model as parameterized yields little-to-no convergence when low accuracy measurements are used, though progressively faster convergence when employing high accuracy or perfect measurements. The inferences from the model are particularly efficient when the true strength of relationship is none or strong with smaller sample sizes. The tool developed in this study can help in the screening and design of exposure-response studies to better anticipate where such outcomes can occur under different levels of measurement error. It may also serve to inform methods of analysis for other network models that consider multiple streams of evidence from multiple studies of cumulative exposure and effects.

Introduction: rethinking protohistories: texts, material culture and new methodologies

ABSTRACT This introduction problematizes the use of the term protohistory across a wide range of cultures and periods. The methodological issues surrounding periodization and the integrated use of different, multiple evidence streams is discussed, especially the difficulties of combining material and textual data, and how we as archaeologists can best construct narratives.

Keeping science’s seat at the decision-making table: Mechanisms to motivate policy-makers to keep using scientific information in the age of disinformation

Policy-makers are confronted with complex problems that require evaluating multiple streams of evidence and weighing competing interests to develop and implement solutions. However, the policy interventions available to resolve these problems have different levels of supporting scientific evidence. Decision-makers, who are not necessarily scientifically trained, may favour policies with limited scientific backing to obtain public support. We illustrate these tensions with two case studies where the scientific consensus went up against the governing parties’ chosen policy. What mechanisms exist to keep the weight of scientific evidence at the forefront of decision-making at the highest levels of government? In this paper, we propose that Canada create “Departmental Chief Science Advisors” (DCSAs), based on a program in the UK, to help complement and extend the reach of the newly created Chief Science Advisor position. DCSAs would provide advice to ministers and senior civil servants, critically evaluate scientific work in their host department, and provide public outreach for the department’s science. We show how the DCSAs could be integrated into their departments and illustrate their potential benefits to the policy making process and the scientific community.

Democratic peace versus contractualism

If contractualism causes both democracy and peace, the correlation between democracy and peace is spurious. But the definition of contractualism is sufficiently unclear as to create doubts that it has such an impact. In addition, if democracy has an impact on contractualism, then, even with an otherwise perfectly specified model, controlling for contractualism will bias estimates of the effect of democracy on peace. International trade involves contracting behavior between strangers in different states. In addition, unlike contracting behavior within states, trade involves interactions in the same arena where interstate conflict needs to be deterred. Contractualist states have existed in significant numbers only recently. This limits contractualism’s ability to compete with democracy as a predictor of peace. Mousseau’s previous findings have been extraordinarily fragile. Finally, observational data should be complemented by multiple streams of evidence. Some experimental data support democratic peace theory; analogous experiments would almost certainly not provide evidence to support contractualism.

A proposed framework for the systematic review and integrated assessment (SYRINA) of endocrine disrupting chemicals.

BackgroundThe issue of endocrine disrupting chemicals (EDCs) is receiving wide attention from both the scientific and regulatory communities. Recent analyses of the EDC literature have been criticized for failing to use transparent and objective approaches to draw conclusions about the strength of evidence linking EDC exposures to adverse health or environmental outcomes. Systematic review methodologies are ideal for addressing this issue as they provide transparent and consistent approaches to study selection and evaluation. Objective methods are needed for integrating the multiple streams of evidence (epidemiology, wildlife, laboratory animal, in vitro, and in silico data) that are relevant in assessing EDCs.MethodsWe have developed a framework for the systematic review and integrated assessment (SYRINA) of EDC studies. The framework was designed for use with the International Program on Chemical Safety (IPCS) and World Health Organization (WHO) definition of an EDC, which requires appraisal of evidence regarding 1) association between exposure and an adverse effect, 2) association between exposure and endocrine disrupting activity, and 3) a plausible link between the adverse effect and the endocrine disrupting activity.ResultsBuilding from existing methodologies for evaluating and synthesizing evidence, the SYRINA framework includes seven steps: 1) Formulate the problem; 2) Develop the review protocol; 3) Identify relevant evidence; 4) Evaluate evidence from individual studies; 5) Summarize and evaluate each stream of evidence; 6) Integrate evidence across all streams; 7) Draw conclusions, make recommendations, and evaluate uncertainties. The proposed method is tailored to the IPCS/WHO definition of an EDC but offers flexibility for use in the context of other definitions of EDCs.ConclusionsWhen using the SYRINA framework, the overall objective is to provide the evidence base needed to support decision making, including any action to avoid/minimise potential adverse effects of exposures. This framework allows for the evaluation and synthesis of evidence from multiple evidence streams. Finally, a decision regarding regulatory action is not only dependent on the strength of evidence, but also the consequences of action/inaction, e.g. limited or weak evidence may be sufficient to justify action if consequences are serious or irreversible.Electronic supplementary materialThe online version of this article (doi:10.1186/s12940-016-0156-6) contains supplementary material, which is available to authorized users.

Multiple stages of learning in perceptual categorization: evidence and neurocomputational theory.

Virtually all current theories of category learning assume that humans learn new categories by gradually forming associations directly between stimuli and responses. In information-integration category-learning tasks, this purported process is thought to depend on procedural learning implemented via dopamine-dependent cortical-striatal synaptic plasticity. This article proposes a new, neurobiologically detailed model of procedural category learning that, unlike previous models, does not assume associations are made directly from stimulus to response. Rather, the traditional stimulus-response (S-R) models are replaced with a two-stage learning process. Multiple streams of evidence (behavioral, as well as anatomical and fMRI) are used as inspiration for the new model, which synthesizes evidence of multiple distinct cortical-striatal loops into a neurocomputational theory. An experiment is reported to test a priori predictions of the new model that: (1) recovery from a full reversal should be easier than learning new categories equated for difficulty, and (2) reversal learning in procedural tasks is mediated within the striatum via dopamine-dependent synaptic plasticity. The results confirm the predictions of the new two-stage model and are incompatible with existing S-R models.

The Navigation Guide - evidence-based medicine meets environmental health: systematic review of nonhuman evidence for PFOA effects on fetal growth.

Background: In contrast to current methods of expert-based narrative review, the Navigation Guide is a systematic and transparent method for synthesizing environmental health research from multiple evidence streams. The Navigation Guide was developed to effectively and efficiently translate the available scientific evidence into timely prevention-oriented action.Objectives: We applied the Navigation Guide systematic review method to answer the question “Does fetal developmental exposure to perfluorooctanoic acid (PFOA) or its salts affect fetal growth in animals ?” and to rate the strength of the experimental animal evidence.Methods: We conducted a comprehensive search of the literature, applied prespecified criteria to the search results to identify relevant studies, extracted data from studies, obtained additional information from study authors, conducted meta-analyses, and rated the overall quality and strength of the evidence.Results: Twenty-one studies met the inclusion criteria. From the meta-analysis of eight mouse gavage data sets, we estimated that exposure of pregnant mice to increasing concentrations of PFOA was associated with a change in mean pup birth weight of –0.023 g (95% CI: –0.029, –0.016) per 1-unit increase in dose (milligrams per kilogram body weight per day). The evidence, consisting of 15 mammalian and 6 nonmammalian studies, was rated as “moderate” and “low” quality, respectively.Conclusion: Based on this first application of the Navigation Guide methodology, we found sufficient evidence that fetal developmental exposure to PFOA reduces fetal growth in animals.Citation: Koustas E, Lam J, Sutton P, Johnson PI, Atchley DS, Sen S, Robinson KA, Axelrad DA, Woodruff TJ. 2014. The Navigation Guide—evidence-based medicine meets environmental health: systematic review of nonhuman evidence for PFOA effects on fetal growth. Environ Health Perspect 122:1015–1027; http://dx.doi.org/10.1289/ehp.1307177

Implementing Systematic Review at the National Toxicology Program: Status and Next Steps

The National Toxicology Program (NTP), an interagency program headquartered at the National Institute of Environmental Health Sciences (NIEHS), carries out a broad range of toxicology research and testing and serves as a resource for identification of substances in our environment that are hazards for human health. One of the ways that the NTP identifies hazards is through carrying out literature-based health assessments. Approximately 2 years ago we began exploring systematic-review methodology as a means to enhance transparency and increase efficiency in summarizing and synthesizing findings from studies in our literature-based health assessments. A systematic review uses an explicit, prespecified approach to identify, select, assess, and appraise the data from studies that focus on addressing a specific scientific question (Institute of Medicine 2011). Although traditionally used to grade the quality of evidence and strength of scientific support for recommendations for clinical practice guidelines and healthcare interventions [Agency for Healthcare Research and Quality (AHRQ) 2012; Guyatt et al. 2011; Higgins and Green 2011], we—and others—were interested in how systematic review methodology might be applied to environmental health questions (Agency for Toxic Substances and Disease Registry 2012; National Research Council 2011; Silbergeld and Scherer 2013; U.S. Environmental Protection Agency 2013; Woodruff and Sutton 2011). With the establishment of the Office of Health Assessment and Translation (OHAT) in 2011, the NIEHS launched a new problem-solving resource for the NTP, particularly with respect to identification of noncancer hazards in our environment (Bucher et al. 2011). OHAT took the lead in investigating how systematic review methodology might be used by the NTP. We embraced systematic review methodology as a useful approach for providing thorough documentation of the steps, inputs, and decisions in a literature-based evaluation. However, we also recognized the necessity to extend existing systematic review methods to accommodate our need in environmental health to integrate data from multiple evidence streams (human, animal, in vitro) and focus on observational human studies rather than on the randomized clinical trials more commonly encountered in the field of health-care intervention (NTP 2012a, 2012b). In late February 2013, the NTP released the Draft OHAT Approach for Systematic Review and Evidence Integration for Literature-based Health Assessments – February 2013 [Draft OHAT Approach; Department of Health and Human Services (DHHS) 2013] for public comment; the deadline for receipt of comments is 11 June 2013. The Draft OHAT Approach adopts or adapts guidance from authoritative systematic review groups (AHRQ 2012; Guyatt et al. 2011; Higgins and Green 2011) to handle the breadth of data from human, animal, in vitro, and mechanistic studies relevant for addressing environmental health questions. In developing a draft approach, OHAT sought advice on systematic review through educational webinars and consultation with technical experts, the NTP Executive Committee, a working group of the NTP Board of Scientific Counselors, the NTP Board of Scientific Counselors, and the public. The draft approach involves a seven-step framework for incorporating systematic review methodology into OHAT literature-based health assessments. In early April of 2013, OHAT will release protocols for two case studies to illustrate application of this framework in specific evaluations. We will test our approach in these case studies to help determine whether additional refinement or revision to the Draft OHAT Approach might be needed. To help the public understand the draft approach and protocols, the NTP will hold a web-based informational meeting on 23 April 2013 to provide an overview of the framework, describe the contents of the case-study protocols, and respond to questions (DHHS 2013). Our intent is to carefully consider all public comments received on the draft approach and to present the Draft OHAT Approach to the NTP Board of Scientific Counselors at its meeting on 25–26 June 2013, with discussion by the NTP of any plans to update the document on the basis of the public’s input. Moving forward, our goal is to increase efficiency and provide greater transparency to the rigorous and objective approach that has been the hallmark of OHAT literature-based health assessments.

Viral hemorrhagic septicemia virus (VHSV IVb) risk factors and association measures derived by expert panel

Viral hemorrhagic septicemia virus (VHSV) is an OIE-listed pathogen of fish, recently expanding in known host and geographic range in North America. Through a group process designed for subjective probability assessment, an international panel of fish health experts identified and weighted risk factors perceived important to the emergence and spread of the viral genotype, VHSV IVb, within and from the Great Lakes region of the US and Canada. Identified factors included the presence of known VHSV-susceptible species, water temperatures conducive for disease, hydrologic connectivity and proximity to known VHSV-positive areas, untested shipments of live or frozen fish from known positive regions, insufficient regulatory infrastructure for fish health oversight, and uncontrolled exposure to fomites associated with boat and equipment or fish wastes from known VHSV-positive areas. Results provide qualitative insights for use in VHSV surveillance and risk-management planning, and quantitative estimates of contextual risk for use in a Bayesian model combining multiple evidence streams for joint probability assessment of disease freedom status. Consistency checks suggest that the compiled factors positively reflect expert judgment of watershed risk for acquiring VHSV IVb. External validation is recommended as the availability of empirical data permits.

Combining surveillance and expert evidence of viral hemorrhagic septicemia freedom: A decision science approach

The ability to combine evidence streams to establish disease freedom or prioritize surveillance is important for the evaluation of emerging diseases, such as viral hemorrhagic septicemia virus (VHSV) IVb in freshwater systems of the United States and Canada. Waterways provide a relatively unconstrained pathway for the spread of VHSV; and structured surveillance for emerging disease in open systems has many challenges. We introduce a decision framework for estimating VHSV infection probability that draws from multiple evidence streams and addresses challenges associated with the assessment of emerging disease. Using this approach, historical and risk-based evidence, whether empirical or expert-derived, supplement surveillance data to estimate disease probability. Surveillance-based estimates of VHSV prevalence were described using beta distributions. Subjective likelihood ratios (LRs), representing contextual risk, were elicited by asking experts to estimate the predicted occurrence of risk factors among VHSV-affected vs. VHSV-unaffected watersheds. We used the odds form of Bayes’ theorem to aggregate expert and surveillance evidence to predict the risk-adjusted posterior probability of VHSV-infection for given watersheds. We also used LRs representing contextual risk to quantify the time value of past surveillance data. This evidence aggregation model predicts disease probability from the combined assessment of multiple sources of information. The method also provides a flexible framework for iterative revision of disease freedom status as knowledge and data evolve.