Developing a general research framework for long COVID using causal modelling

Long 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.

Using 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 (t₁ to t₄). We used qualitative parametrisation for design and validation, and tested the framework against four scenarios: A) mild COVID-19 at t₁ (start of acute infection); B) severe acute COVID-19 at t₁; C) symptoms reported at t₁ (acute COVID-19 disease); and D) symptoms reported at t₁ and t₃ (e.g., 3-to-6 months post-acute infection), indicating long COVID.

Here 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 t₁ and t₃ have the highest probability of developing persistent organ dysfunction beyond the acute infection period.

Our 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.