Developing a general research framework for long COVID using causal modelling

• Gladymar Pérez Chacón, Steven Mascaro, Marie J. Estcourt, et. al, Communications Medicine 

Abstract

Background

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.

Methods

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.

Results

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.

Conclusions

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.

Plain Language Summary

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.

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Data availability

All data supporting the findings of this study are available within the article, its Supplementary Information, and through the Open Science Framework (OSF; Files tab). The source data for Fig. 3 are provided in the Supplementary Information (Supplementary Table 4) under the column “Qualitative parameterisation (illustrative model assumptions)” and are also accessible via the OSF (Files tab, Supplement folder, Supplementary tables [Supplementary Table 4]). Source data for the statistical simulations (Supplementary Data and Supplementary Data 2) are provided in the OSF (Files tab, Supplement folder).

Code availability

Simulations were conducted in R version 4.4.1 ²⁹. The code used for statistical simulations is available in the Open Science Framework (Files Tab, Supplement folder).

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