Modeling COVID-19 disease processes by remote elicitation of causal Bayesian networks from medical experts.

BACKGROUND: COVID-19 is a new multi-organ disease causing considerable worldwide morbidity and mortality. While many recognized pathophysiological mechanisms are involved, their exact causal relationships remain opaque. Better understanding is needed for predicting their progression, targeting therapeutic approaches, and improving patient outcomes. While many mathematical causal models describe COVID-19 epidemiology, none have described its pathophysiology. METHODS: In early 2020, we began developing such causal models. The SARS-CoV-2 virus's rapid and extensive spread made this particularly difficult: no large patient datasets were publicly available; the medical literature was flooded with sometimes conflicting pre-review reports; and clinicians in many countries had little time for academic consultations. We used Bayesian network (BN) models, which provide powerful calculation tools and directed acyclic graphs (DAGs) as comprehensible causal maps. Hence, they can incorporate both expert opinion and numerical data, and produce explainable, updatable results. To obtain the DAGs, we used extensive expert elicitation (exploiting Australia's exceptionally low COVID-19 burden) in structured online sessions. Groups of clinical and other specialists were enlisted to filter, interpret and discuss the literature and develop a current consensus. We encouraged inclusion of theoretically salient latent (unobservable) variables, likely mechanisms by extrapolation from other diseases, and documented supporting literature while noting controversies. Our method was iterative and incremental: systematically refining and validating the group output using one-on-one follow-up meetings with original and new experts. 35 experts contributed 126 hours face-to-face, and could review our products. RESULTS: We present two key models, for the initial infection of the respiratory tract and the possible progression to complications, as causal DAGs and BNs with corresponding verbal descriptions, dictionaries and sources. These are the first published causal models of COVID-19 pathophysiology. CONCLUSIONS: Our method demonstrates an improved procedure for developing BNs via expert elicitation, which other teams can implement to model emergent complex phenomena. Our results have three anticipated applications: (i) freely disseminating updatable expert knowledge; (ii) guiding design and analysis of observational and clinical studies; (iii) developing and validating automated tools for causal reasoning and decision support. We are developing such tools for the initial diagnosis, resource management, and prognosis of COVID-19, parameterized using the ISARIC and LEOSS databases.

A flexible method for parameterizing ranked nodes in Bayesian networks using Beta distributions.

When novice modelers first attempt to build a Bayesian network, they are often impressed with the intuitive graphical structures that capture their causal understanding. This favorable impression evaporates on proceeding to parameterization. Conditional probability tables (CPT) require parameters for often hundreds of very similar scenarios and specifying them in the absence of data can be overwhelming. The problem is even more severe when eliciting parameters from experts with limited time. Often, there is local structure with fewer parameters that better describes the relationship. Such structures include the Noisy OR, decision trees, and equations. These work well for modelers, but can be an issue for experts and particularly groups of experts. An alternative approach is to elicit only a few CPT rows and interpolate the remainder. This is a promising approach, as it can handle unknown structures and multiple experts, but existing techniques can be limited. Here, we present a flexible approach called InterBeta for performing CPT interpolation with ordered nodes. In the simplest case, just two CPT rows are needed, but this can be easily augmented with further information. The basic approach assumes input independence, but allows dependencies to be reintroduced as required, and can also be combined with other local structures such as decision trees or equations, leaving the interpolator to fill in the gaps. We explain the InterBeta method, describe its capabilities and limitations and how it compares to similar approaches and show how it can trade-off elicitation effort against faithfully representing expert understanding.

Risk Analysis Frameworks Used in Biological Control and Introduction of a Novel Bayesian Network Tool.

Classical biological control, the introduction of natural enemies to new environments to control unwanted pests or weeds, is, despite numerous successful examples, associated with rising concerns about unwanted environmental impacts such as population decline of nontarget species. Recognition of these biosafety risks is globally increasing, and prerelease assessments of biological control agents (BCAs) have become more rigorous in many countries. We review the current approaches to risk assessment for BCAs as used in Australasia, Europe, and North America. Traditionally, these assessments focus on providing assurance about the specificity of a proposed BCA, generally via a list of suitable versus nonsuitable hosts determined through laboratory specificity tests (i.e., by determining the BCA's physiological host range). The outcome of interactions of proposed agents in the natural environment can differ from laboratory-based predictions. Potential nontarget host testing may be incomplete, additional ecological barriers under field conditions may limit encounters between BCA and nontargets or reduce attack levels, and BCAs could disperse to habitats beyond those used by the target species and adversely affect nontarget species. We advocate for the adoption of more comprehensive, ecologically-based, probabilistic risk assessment approaches to BCA introductions. An example is provided using a Bayesian network that can integrate information on probabilities and uncertainties of a BCA to spread and establish in new habitats, interact with nontarget species in these habitats, and eventually negatively impact the populations of these nontarget species. Our new model, Biocontrol Adverse Impact Probability Assessment, aims to be incorporated into a structured decision-making framework to support national regulatory authorities.

An Integrated Biosecurity Risk Assessment Model (IBRAM) For Evaluating the Risk of Import Pathways for the Establishment of Invasive Species.

An important aspect of analyzing the risk of unwanted organisms establishing in an area is understanding the pathways by which they arrive. Evaluating the risks of these pathways requires use of data from multiple sources, which frequently are uncertain. To address the needs of agencies responsible for biosecurity operations, we present an Integrated Biosecurity Risk Assessment Model (IBRAM) for evaluating the risk of establishment and dispersal of invasive species along trade pathways. The IBRAM framework consists of multiple linked models which describe pest entry into the country, escape along trade pathways, initial dispersal into the environment, habitat suitability, probabilities of establishment and spread, and the consequences of these invasions. Bayesian networks (BN) are used extensively to model these processes. The model includes dynamic BN components and geographic data, resulting in distributions of output parameters over spatial and temporal axes. IBRAM is supported by a web-based tool that allows users to run the model on real-world pest examples and investigate the impact of alternative risk management scenarios, to explore the effect of various interventions and resource allocations. Two case studies are provided as examples of how IBRAM may be used: Queensland fruit fly (Bactrocera tryoni) (Diptera: Tephritidae) and brown marmorated stink bug (Halyomorpha halys) (Hemiptera: Pentatomidae) are unwanted organisms with the potential to invade Aotearoa New Zealand, and IBRAM has been influential in evaluating the efficacy of pathway management to mitigate the risk of their establishment in the country.

Novel method to select meaningful outcomes for evaluation in clinical trials.

BACKGROUND: A standardised framework for selecting outcomes for evaluation in trials has been proposed by the Core Outcome Measures in Effectiveness Trials working group. However, this method does not specify how to ensure that the outcomes that are selected are causally related to the disease and the health intervention being studied. Causal network diagrams may help researchers identify outcomes that are both clinically meaningful and likely to be causally dependent on the intervention, and endpoints that are, in turn, causally dependent on those outcomes. We aimed to (1) develop a generalisable method for selecting outcomes and endpoints in trials and (2) apply this method to select outcomes for evaluation in a trial investigating treatment strategies for pulmonary exacerbations of cystic fibrosis (CF). METHODS: We conducted a series of online surveys and workshops among people affected by CF. We used a modified Delphi approach to develop a consensus list of important outcomes. A workshop involving domain experts elicited how these outcomes were causally related to the underlying pathophysiological processes. Meaningful outcomes were prioritised based on the extent to which each outcome captured separate rather than common aspects of the underlying pathophysiological process. RESULTS: The 10 prioritised outcomes were: breathing difficulty/pain, sputum production/clearance, fatigue, appetite, pain (not related to breathing), motivation/demoralisation, fevers/night sweats, treatment burden, inability to meet personal goals and avoidance of gastrointestinal symptoms. CONCLUSIONS: This proposed method for selecting meaningful outcomes for evaluation in clinical trials may improve the value of research as a basis for clinical decisions.

Predicting forest insect flight activity: A Bayesian network approach.

Daily flight activity patterns of forest insects are influenced by temporal and meteorological conditions. Temperature and time of day are frequently cited as key drivers of activity; however, complex interactions between multiple contributing factors have also been proposed. Here, we report individual Bayesian network models to assess the probability of flight activity of three exotic insects, Hylurgus ligniperda, Hylastes ater, and Arhopalus ferus in a managed plantation forest context. Models were built from 7,144 individual hours of insect sampling, temperature, wind speed, relative humidity, photon flux density, and temporal data. Discretized meteorological and temporal variables were used to build naïve Bayes tree augmented networks. Calibration results suggested that the H. ater and A. ferus Bayesian network models had the best fit for low Type I and overall errors, and H. ligniperda had the best fit for low Type II errors. Maximum hourly temperature and time since sunrise had the largest influence on H. ligniperda flight activity predictions, whereas time of day and year had the greatest influence on H. ater and A. ferus activity. Type II model errors for the prediction of no flight activity is improved by increasing the model's predictive threshold. Improvements in model performance can be made by further sampling, increasing the sensitivity of the flight intercept traps, and replicating sampling in other regions. Predicting insect flight informs an assessment of the potential phytosanitary risks of wood exports. Quantifying this risk allows mitigation treatments to be targeted to prevent the spread of invasive species via international trade pathways.

Predicting island biosecurity risk from introduced fauna using Bayesian Belief Networks.

Around the globe, islands are the last refuge for many threatened and endemic species. Islands are frequently also important sites for recreation, cultural activities, and industrial development, all of which facilitate the establishment of invasive species. Surveillance is employed on islands to detect the establishment of invasive species after their arrival, leading to decisions about follow-up actions. Unless surveillance is prioritised according to risk of establishment of invasives, it may be infeasible to implement efficiently over large tracts of publicly accessible land, especially in data-deficient areas. The key biosecurity problem for many regions is one of prioritizing sites for surveillance activities and identifying invasive species most likely to disperse to, and establish, and proliferate on those sites. We created a series of Bayesian Belief Networks (BBNs), linked by Java computing code and the freely available GeNIe application to automate the creation and computation of species- and site-specific biosecurity BBNs. The BBNs require data on island attributes, recreational or industrial visitor load, infrastructure, habitat availability, and animal behaviour and dispersal via swimming, flying, human movement, land bridges, or flood plumes. We used this biosecurity BBN to estimate the risk of 11 invasive faunal species arriving and establishing on 600 islands along the Pilbara coastline, Western Australia. Sensitivity analyses were conducted to identify nodes within the BBNs that required refined data inputs. Propagule pressure was the node with the greatest influence over the number of arrivals. Other nodes such as the number of visitors to islands and swimming capabilities of invasive animals greatly influenced the model results. Across the 11 species studied, our models predicted one arrival per 300 visitors. The biosecurity BBN can be used to identify the islands at highest risk from establishment of invasive species within any archipelago/s, and the invasive species most likely to establish on each island.