6/5/2023 0 Comments Enlisted pc download sizeOur method was iterative and incremental: systematically refining and validating the group output using one-on-one follow-up meetings with original and new experts. We encouraged inclusion of theoretically salient latent (unobservable) variables, likely mechanisms by extrapolation from other diseases, and documented supporting literature while noting controversies. Groups of clinical and other specialists were enlisted to filter, interpret and discuss the literature and develop a current consensus. To obtain the DAGs, we used extensive expert elicitation (exploiting Australia’s exceptionally low COVID-19 burden) in structured online sessions. Hence, they can incorporate both expert opinion and numerical data, and produce explainable, updatable results. We used Bayesian network (BN) models, which provide powerful calculation tools and directed acyclic graphs (DAGs) as comprehensible causal maps. 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. In early 2020, we began developing such causal models. While many mathematical causal models describe COVID-19 epidemiology, none have described its pathophysiology. Better understanding is needed for predicting their progression, targeting therapeutic approaches, and improving patient outcomes. While many recognized pathophysiological mechanisms are involved, their exact causal relationships remain opaque. COVID-19 is a new multi-organ disease causing considerable worldwide morbidity and mortality.
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