# Bayesian predictive model of Ebola fatality: Tenth Ebola epidemic in the Democratic Republic of the Congo

## TL;DR

This study developed a Bayesian model to predict Ebola mortality based on clinical signs, showing high accuracy in hypothetical cases from the 10th Ebola outbreak in the Democratic Republic of the Congo.

## Contribution

A novel Bayesian prognostic model for predicting Ebola mortality using clinical indicators and expert assessments.

## Key findings

- Five clinical factors strongly associated with mortality: deterioration in general condition, hemorrhagic syndrome, neurological disorders, dehydration, and high viral load.
- The model achieved high performance metrics (97.4% sensitivity, 100% specificity) in predicting fatal outcomes.
- Bleeding syndrome, neurological disorders, and dehydration were the most accurate predictors, correctly identifying 83% of fatal cases.

## Abstract

This study aimed to identify the clinical signs and symptoms most associated with fatal outcomes in Ebola virus disease (EVD) using a Bayesian framework.

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.

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.

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.

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).

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.

This Bayesian model offers a useful decision-support tool for managing Ebola outbreaks.