Work

Developing a general research framework for long COVID using causal modelling

Year: 2026

Type: article

Abstract: 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. Long COVID can affect some people after having COVID-19. It usually starts within three months of infection and can last for at least two months. People with long COVID may struggle with work, daily tasks, and social activities. Researchers find it hard to study because its causes aren’t directly visible. This study uses diagrams to show expert ideas about what might cause long COVID and how symptoms relate to those causes. By looking at four examples, it shows how explanations can change when new information appears. The approach helps researchers think in terms of “cause and effect”, which can improve understanding and communication about long COVID. It could also guide clinical trials, public health studies, and experiments. Pérez Chacón et al. develop a causal modelling framework to guide diagnosis and prognosis of long COVID. The framework based on modelling scenarios involving expert knowledge, qualitative parameters, and statistical simulations can predict progression to severe and persistent organ dysfunction associated with long COVID beyond the acute infection.

Source: Communications Medicine

DOI: 10.1038/s43856-026-01488-8