Modeling COVID-19 disease processes by remote elicitation of causal Bayesian networks from medical experts.

ABSTRACT:

Background

COVID-19 is a new multi-organ disease causing considerable worldwide morbidity and mortality. While many recognized pathophysiological mechanisms are involved, their exact causal relationships remain opaque. Better understanding is needed for predicting their progression, targeting therapeutic approaches, and improving patient outcomes. While many mathematical causal models describe COVID-19 epidemiology, none have described its pathophysiology.

Methods

In early 2020, we began developing such causal models. 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

SUBMITTER: Mascaro S 

PROVIDER: S-EPMC10050813 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature