ArticleCommunications medicine2026
Developing a general research framework for long COVID using causal modelling.
Article in Communications medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLong COVID is an infection-associated chronic condition with uncertain evolution, leading to ambiguity in case definitions and various hypotheses about its pathophysiology. Despite this diversity, causal models may offer a unified understanding of post-acute COVID-19 mechanisms. This study aimed to examine whether dynamic Bayesian networks could facilitate inferences on long COVID.
methodsUsing a causal engineering approach, we developed directed acyclic graphs and qualitatively parametrised them as Bayesian networks to depict the hypothesised mechanisms of long COVID in a theory-agnostic manner. Based on the literature and expert knowledge, we created a general modelling framework summarising biological pathways from mild or severe COVID-19 to the development of respiratory symptoms and fatigue over four key periods (t
resultsHere we show that, in scenario A, the probability of progressing to severe disease and developing persistent organ dysfunction 1-to-2 years post-acute COVID-19 was lower than in scenario C. Those reporting symptoms at t
conclusionsOur findings lay the foundations for a better understanding of the progression of long COVID syndromes. Illustrative simulations support the use of causal models to help address both diagnostic and prognostic questions in long COVID research.
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Registered trials
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