Background: This study aimed to identify the clinical signs and symptoms most associated with fatal outcomes in Ebola virus disease (EVD) using a Bayesian framework. Aim: The goal was to develop a prognostic model capable of predicting mortality in EVD patients treated in Ebola Treatment Centres (ETCs) based on observed clinical indicators. Setting: A retrospective expert-based study of the 10th Ebola outbreak was conducted to identify key mortality factors using hypothetical cases in the Democratic Republic of the Congo. Methods: Clinical experts assessed mortality predictors in Ebola cases using Bayesian methods to estimate likelihood ratios and post-test probabilities, with analyses conducted in Excel and SPSS. Results: Eight clinical factors were identified as potential predictors of poor outcomes in Ebola virus disease. Five showed strong associations with mortality: deterioration in general condition and comorbidity, hemorrhagic syndrome, neurological disorders, biological deterioration with dehydration, and high viral load at diagnosis. Internal validation using 42 hypothetical cases demonstrated excellent performance (sensitivity [Se] = 97.4%, specificity [Sp] = 100.0%, positive predictive value [PPV] = 100.0%, negative predictive value [NPV] = 75.0%, accuracy = 97.6%) and strong expert agreement (κ = 0.84). Conclusion: The model demonstrated strong internal validity in predicting mortality from Ebola virus disease. Among five key predictors, bleeding syndrome, neurological disorders, and biological alteration with dehydration were the most accurate, each correctly predicting fatal outcomes in 83% of cases. Contribution: This Bayesian model offers a useful decision-support tool for managing Ebola outbreaks.

Background/Objectives: Diagnosing Ebola virus disease (EVD) based solely on clinical signs remains challenging in settings where its manifestations overlap with other endemic infections. This study aimed to validate a Bayesian clinical diagnostic model to support decision-making for suspected EVD cases in resource-limited settings without access to laboratory confirmation. Methods: We conducted a retrospective analysis using data from the 10th Ebola outbreak in the Democratic Republic of the Congo (2018–2020). Clinical information from 450 suspected and laboratory-confirmed EVD cases was evaluated by seven epidemiological surveillance experts. Data were analyzed using descriptive statistics, binary logistic regression, discriminant analysis, and receiver operating characteristic (ROC) curves to assess diagnostic performance. Results: At the predefined 0.80 threshold, the SBM showed a sensitivity of 82.4%, specificity of 33.7%, PPV of 80.5%, and NPV of 36.5%. Balanced accuracy was 58.0%, with LR+ of 1.24 and LR− of approximately 0.52. Thus, although the model identified a relatively high proportion of RT-PCR confirmed cases, its ability to discriminate non-EVD cases was limited. At the symptom level, vomiting (p = 0.003), anorexia (p = 0.041), abdominal pain (p = 0.024), and muscle pain (p = 0.006) were independently linked to confirmed cases. Conclusions: This Bayesian model shows potential as a decision support tool for early identification of EVD in settings lacking laboratory capacity. Its use could improve case detection and support outbreak response in resource-limited contexts. The expert-derived SBM showed relatively high sensitivity but limited specificity in this selected outbreak cohort. These findings support further recalibration and independent validation rather than use of the model as a stand-alone diagnostic tool.

The symptoms and clinical signs of Ebola virus disease are similar to those of malaria, thus leading to difficulties in terms of making differential diagnoses. Therefore, we developed a subjective model for the clinical diagnosis of Ebola. Excel and SPSS software were used to analyse data. The likelihood ratio, the kappa statistic and various internal evaluation parameters of the model were calculated. These analyses revealed that 4 factors strongly influence the clinical diagnosis of Ebola: haemorrhagic signs, neurological signs, digestive signs and epidemiological links. Among these 4 factors, the combination of haemorrhagic signs and epidemiological links in a patient yields a 60.5% chance of the case being confirmed as Ebola. Therefore, all health care providers in areas with the potential for Ebola must prioritise classifying any patient with these 2 factors as a genuine case of Ebola

Early clinical diagnosis of Ebola virus disease (EVD) is challenging in outbreak settings due to symptom overlap with malaria. We assessed whether a Bayesi