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.
validationinternalsubjective Bayes modelfatalityEbolaFunding information This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.Introduction
The Ebola virus (EBOV) causes acute and severe disease. This virus was first identified in 1976 during two simultaneous outbreaks in South Sudan and the Democratic Republic of the Congo (DRC).1 Ebola virus is very often fatal in humans, with high case fatality rates ranging from 25% to 90%.1 However, as of July 2015, 27 741 cases of Ebola virus disease (EVD) and 11 284 deaths were reported in West Africa, particularly in Guinea, Liberia and Sierra Leone.2 In 2014, a total of 162 (7.3%) individuals with confirmed or probable EVD among 2210 health workers with more or less than 15 years of professional experience died during the epidemic in Guinea.3
The tenth EVD epidemic in the DRC (in the provinces of North Kivu and Ituri) was the second most deadly epidemic (2280 deaths [case fatality rate of 66%]) after the epidemic in West Africa in 2014 (2196 deaths and 1075 cured patients reported by December 2019).4,5
Since the emergence of EVD, its case fatality rate has remained alarmingly high. Therefore, identifying signs or symptoms that worsen a patient’s condition and may lead to a fatal outcome is essential for improving clinical management. In this study, we aim to determine which clinical characteristics observable in Ebola Treatment Centres (ETCs) (signs, symptoms and biological variables) can effectively predict fatal outcomes in patients with EVD. Early detection of aggravating factors is crucial for optimising patient care in these settings. To address this research question, we will employ a Bayesian approach to identify predictors of mortality.
Mark J. Siedner and Lawrence highlighted that large-scale threats posed by EVD mainly occur in countries with weak public health systems6; the DRC is no exception; the country consistently records high case fatality rates during every Ebola outbreak (see Table 1)7.
Chronology of Ebola virus disease epidemics and the number of deaths in the Democratic Republic of the Congo from 1976 to 2022.
Order
Year
Dates
Main outbreak areas
Strain
Deaths
Mortality rate (%)
Duration (days)
1st
1976
August–November
Yambuku, Equateur
EBOV
280
88
-
2nd
1977
May–June
Tandala, Equateur
EBOV
1
100
-
3rd
1995
May–June
Kikwit, Bandundu, Kwuilu
EBOV
250
79
-
4th
2007
August–November
Mweka, Kasai-Occidental/Central
EBOV
187
71
-
5th
2009
December 2008 – February
Mweka, Kaluamba, Kasai-Occidental
EBOV
15
47
-
6th
2012
June–November
Isiro, Oriental Province
EBOV
36
47
-
7th
2014
August–November
Djera, Boende, Tshuapa
EBOV
49
74
-
8th
2015
May–July
Likati, Orientale, Bas Uélé
EBOV
4
50
-
9th
2018
08 May – 24 July
Wangata, Bikoro, Equateur
EBOV
33
61
78
10th
2020
01 August and 18–25 June
North‒South, South Kivu and Ituri
EBOV
2280
66
695
11th
2020
01 June – 18 November
Mbandaka, Equateur
EBOV
55
42
172
12th
2021
07 February – 03 May
Biena, Butembo, North Kivu
EBOV
6
50
86
13th
2021
08 October – 16 October
Beni, North Kivu
EBOV
9
82
100
14th
2022
23 April – 04 July
Mbandaka, Equateur
EBOV
5
100
73
15th
2022
21 August – 27 October
Beni, North Kivu
EBOV
1
100
53
Source: Judson SD, Munster VJ. The multiple origins of Ebola disease outbreaks. J Infect Dis. 2023;228(Suppl 7):S465–S473. https://doi.org/10.1093/infdis/jiad352
EBVO, Ebola virus.
Numerous researchers have conducted investigations to identify risk factors that may worsen the health status of patients affected by Ebola.8,9,10,11,12,13,14,15,16,17,18 These studies uniformly employed frequentist statistical methods and were limited to data derived from a single source, namely, one ETC, thus preventing the integration of new data from other treatment sites or from subsequent outbreaks. In contrast, a Bayesian approach offers notable advantages: it is more flexible, allows the incorporation of prior knowledge and facilitates better control of errors, particularly in the context of clinical research. By enabling the continual updating of predictive models as new data become available, Bayesian methods are particularly suited for responding to emerging threats like Ebola epidemics.
At present, Bayesian methods hold a prominent position in specialised biostatistics publications, and their adoption is gradually expanding within the epidemiological literature.19
The outcomes of patients with EVD are uncertain; therefore, the Bayesian approach is ideal in this context because it integrates the subjective and objective aspects of the phenomena to be studied. Table 1 shows the mortality of EVD from the first case to date in the DRC.
Research methods and designStudy design
This was a retrospective cross-sectional study in which the opinions of Ebola experts on Ebola management in the ETC were collected during the tenth epidemic.
The experts in EVD management listed 50 factors that affect the severity of EVD. Through consensus, they grouped the various factors cited into eight key independent factors influencing EVD mortality. The elements, grouped into exclusively independent factors, are shown in Table 2.
Factors and symptoms and/or clinical signs of the severity of Ebola virus disease according to Ebola management experts in the Democratic Republic of the Congo.
Number
Factors (signs/symptoms) and/or patient dying of Ebola
Grouping of factors (into independent and mutually exclusive elements)
Operational definition
1
Persistent fever
Fever over 38 °C
Uncontrolled fever
Any person with a body temperature > 37 °C
2
Alteration of general condition and comorbidity
Physical asthenia
General fatigue
State of prostration
Altered general condition
Malnutrition
Late arrival
Hypertension
Dyspnoea
Not vaccinated against Ebola
Medical history
Age under 5 years and/or over 45 years
Rash
Any person with an altered general condition
Any person with a major morbidity in their medical history
3
Bleeding syndrome
Bleeding and altered consciousness
Redness of the eyes
External haemorrhage
Haematemesis
Melena
Haemorrhage
Permanent bleeding
Anaemia
Decompensated anaemia
Any person presenting with a set of signs and symptoms and biological manifestations related to bleeding at a particular site
4
Pain syndrome
Joint pain
Chest pain
Muscle pain
Sore throat
Headache
Myalgia
Arthritis
Any patient reporting localised or generalised pain
5
Digestive signs
Diarrhoea
Vomiting
Anorexia
Abdominal problems
Abdominal pain
Hiccups
Any patient with at least one sign of digestive system damage
6
Neurological disorders
Coma
Unconsciousness
Psychosis
Convulsions
Neurological disorders
Aggression
Loss of vision
All signs and symptoms indicating damage to the nervous system
7
Biological alteration and dehydration
Jaundice
Impaired liver balance
Biological state disturbed between Dx and follow-up (haematological factors, biochemical factors, protein factors, etc.)
Hyperglycaemia
Impaired renal function
Dehydration
Any biological alteration noted in the laboratory and reported
8
High viral load and/or detection threshold at the time of diagnosis
High viral load and/or detection threshold at diagnosis
Any sample with a positive test result in which the viral specimen has been quantified by a quantitative method
In this study, the inclusion criteria were applied to all Ebola management experts who agreed to be available throughout the study period and who had worked at an ETC in West Africa between 2014 and 2015 and/or in the DRC between 2018 and 2020.
Data collection
We used a paper questionnaire to collect data on the opinions of the Ebola management experts.
Data entry
The experts’ opinions on the 42 hypothetical patient cases in the ETC were entered into Microsoft® Excel. Consensus data were also entered using the same software.
Data analysis
Microsoft Excel was used to programme all the Bayes mathematical modelling formulae, calculate kappa statistics to measure the concordance of opinions between experts and evaluate the model according to the following parameters: sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), overall effectiveness (EVG) and the likelihood ratio (LR).
Bayes model for predicting mortality
The Bayesian prediction model is grounded in a probabilistic framework that integrates prior knowledge with observed data to estimate the likelihood of future events. In the context of predicting mortality associated with EVD, this model facilitates the assessment of risk factors by synthesising available information.
In this study, available data will be collected from experts involved in the clinical management of patients with EVD.
The selected experts were chosen based on their years of experience and involvement in Ebola response efforts. They were drawn from the Ministry of Public Health, Hygiene and Prevention, specifically the Epidemiological Surveillance Division, as well as from the National Biomedical Research Institute, Africa Centres for Disease Control and Prevention (CDC) and the World Health Organization (WHO).
Seven experts participated in this study. With respect to the experts’ opinions, between 3 and 6 experts for a simple model and between 7 and 12 experts for a complex or interdisciplinary model are recommended for Bayesian statistics to ensure diverse opinions. The following steps were taken to develop this mortality model:
Expert panel on Ebola case management
To ensure the validity and relevance of our Bayesian prediction model for Ebola mortality, experts were selected based on the following criteria:
Professional Qualifications: Medical Doctor, Nurse.
Professional Experience: Minimum of 5 years in managing cases of EVD.
Research Participation: Contributed to at least two relevant or similar scientific studies.
Availability: Commitment to active participation throughout all phases of the study, including model development and validation.
Additionally, an extra criterion was introduced:
Epidemic Response Experience: Active involvement in the Ebola response in Guinea (2014–2016) and in the provinces of North Kivu, South Kivu and Ituri in the DRC (2018–2020), specifically in clinical management activities.
These criteria were established in accordance with recommendations from the WHO and the CDC, emphasising the importance of technical expertise, familiarity with study objectives and effective communication skills in selecting experts for epidemic response missions.
Expert interviews and capacity building in Bayesian statistics
The experts selected for the development of the model were interviewed individually. Each presented their curriculum vitae highlighting their experience in the management of the Ebola epidemic. Following this step, the objectives, scientific relevance and methodology of the study were explained, including a simplified overview of the Bayesian approach.
An individual questionnaire was then administered to identify a provisional list of factors associated with EBOV-related mortality. This questionnaire pursued two main objectives, namely to:
Confirm and retain, among the preselected candidates, those who met the participation criteria.
Collect preliminary elements necessary for the development of the model.
In practice, each interview was structured around three key components:
Presentation of the expert’s identity, qualifications, current role and professional experience.
Briefing on the objectives and methodology of the study.
Clarification of the operational definitions of the factors associated with EBOV-related mortality in North Kivu, South Kivu and Ituri provinces.
Finally, an official invitation letter, signed by the Dean of the Faculty of Medicine of the University of Kinshasa, was sent to the experts to formalise their participation in the study.
Development of a comprehensive list of factors associated with Ebola virus disease severity
The provisional list of factors generated from the expert interviews was used to establish the final list of variables. Experts then proceeded to define the operational parameters for each independent factor. Subsequently, the factors were organised into groups of independent and mutually exclusive variables.
Calculation of the prior probability quotient
To determine the prior LR, the following question was posed to obtain the a priori probability (QAPRI) of mortality:
Among 10 individuals who died from laboratory-confirmed Ebola virus disease, how many exhibited the clinical factors/signs/symptoms included in the grouping?
To obtain the complementary probability, a second question was asked:
Among 10 individuals who died from laboratory-confirmed Ebola virus disease, how many did not present the clinical factors/signs/symptoms included in the grouping?
Based on these questions, the probability of actual death because of EVD (P(D)) for a patient admitted to the ETC with the specified signs or symptoms, as well as the probability of survival (P(S)) given the presence of these signs or symptoms, was determined.
After calculating the arithmetic mean of the different expert estimates, the a priori Ebola mortality prediction ratio was computed using the following formula (Equation 1):
PriorLikelihoodRatio=P(D)P(S)
This approach allowed the integration of expert knowledge with observed clinical patterns to inform a Bayesian predictive model of Ebola mortality.
Estimating the likelihood ratio for each factor with respect to fatal outcomes in Ebola virus disease (likelihood ratio)
To estimate the LR for predicting EVD mortality, experts were asked the following question regarding deceased patients:
Among 10 individuals who died from laboratory-confirmed Ebola virus disease, how many exhibited each of the consensus clinical factors/signs/symptoms: F1, F2, F3, …, Fn?
From the experts’ responses, the probability of each factor occurring given death (P(Fi|D)) was calculated (Equation 2):
The arithmetic means of the expert estimates were then used to compute the LR for each factor, summarised in a table (see Table 4). The interpretation of the LR values is as follows:
LR > 1: The factor increases the probability of EVD mortality and has a significant positive impact on predicting death.
LR < 1: The factor decreases the probability of EVD mortality and has a non-significant or protective effect.
LR = 0: The factor is neutral; no conclusion can be drawn regarding its effect.
Calculation of the posterior probability quotient
Using the QAPRI of mortality and the LRs for each factor, the QAPO for predicting EVD mortality was calculated.
For the prediction of EVD mortality, the QAPO is computed as (Equation 6):
Equivalently, this can be expressed as the product of individual factor LRs multiplied by the a priori probability ratio (Equation 7):
QAPO=P(F1ouF1¯/D)P(F1ouF1¯/S)xP(F2ouF2¯/D)P(F2ouF2¯/S) x ..…P(FnouFn¯/D)P(FnouFn¯/S)xP(D)P(S)
Once the QAPO is obtained, the posterior probability for a patient to die from EVD is calculated using the following formula (Equation 8):
P=QAPO1+QAPO
This approach allows integration of expert knowledge and observed clinical factors to provide a quantitative prediction of mortality for patients with EVD.
Internal validation of the mortality prediction model
Each expert constructed six hypothetical cases: three in which mortality occurred in the presence of severity factors and three in which mortality occurred in their absence.
We validated the Bayesian predictive model against expert consensus using 42 hypothetical Ebola cases carefully constructed by the experts. Their responses served as the primary reference standard against which model performance was assessed.
Internal validation of the Bayesian model was carried out by comparing the experts’ a priori probability estimates across hypothetical Ebola mortality cases. We then calculated the LR, posterior probability quotients (PPQs) and standard diagnostic performance metrics including Se, Sp, PPV, NPV, overall accuracy and assessed inter-rater agreement using Cohen’s kappa statistic.
Data availability statement
The data available for the development of this predictive model were generated by a group of experts in the clinical management of Ebola during the development of the subjective prognostic model for Ebola patients.
Ethical considerations
Ethical clearance to conduct this study was obtained from the University of Kinshasa School of Public Health Ethics Committee (No. ESP/CE/114B/2025), dated 12 September 2022. Confidentiality was ensured, as the patients’ names were removed from the database, and analyses were conducted anonymously. Informed consent was obtained from each expert to participate in the development of the subjective Ebola mortality model. They were free to agree or refuse to provide their opinion on the EVD case.
ResultsVarious severity factors listed by the experts in the management of Ebola virus disease
The list of 50 factors (signs and symptoms) indicating the severity of EVD, as cited by the Ebola management experts, is provided in Table 2. These 50 factors are grouped into eight independent factors in Table 2.
Determination of the a priori probability quotient of Ebola mortality
The experts answered the following question: ‘Out of 10 patients with Ebola, what is the probability of dying for a patient who has various symptoms and/or clinical signs?’ The various answers were used to calculate the QAPRI, which is shown in Table 320 (see Equation 9):
QAPRI=P(D)P(S)=0.700.30=2.33
A priori probability quotient of mortality.
Expert
1
2
3
4
5
6
7
Sum
N
Average
P(D)
0.7
0.7
0.7
0.5
0.7
0.8
0.8
4.9
7
0.70
P(S)
0.3
0.3
0.3
0.5
0.3
0.2
0.2
2.1
7
0.30
The QAPRI value was greater than 1, so the experts’ predictions were that a patient with the EVD factors listed above is more likely to die than to survive.
Determination of the likelihood ratio according to each factor related to Ebola mortality
Each expert predicted the probability of death in a patient with biologically confirmed EVD according to each individual factor. This prediction was used to determine the LR of each factor for predicting mortality. The results in Table 4 indicate that all eight factors in the LR table have a significantly positive effect on EVD mortality (LR > 1).
Likelihood ratios based on expert responses in terms of the Ebola mortality prediction model.
Probability of dying given factor present or not
Exp1
Exp2
Exp3
Exp4
Exp5
Exp6
Exp7
S
N
M
Probability of surviving given factor present or not
Exp1
Exp2
Exp3
Exp4
Exp5
Exp6
Exp7
S
N
M
LR
Impact
P (F+/D)
P (F +/S)
P (F1 +/D)
0.8
0.7
0.8
0.5
1
0.6
0.5
4.9
7
0.70
P (F1 +/S)
0.8
0.1
0.2
0.5
1
0.4
0.3
3.3
7
0.47
1.48
+
P (F2 +/D)
0.9
0.9
0.5
0.7
1
0.6
0.8
5.4
7
0.77
P (F2 +/S)
0.1
0.2
0.5
0.3
0.8
0.4
0.6
2.9
7
0.41
1.86
+
P (F3 +/D)
0.3
0.5
0.8
0.9
0.6
0.8
0.3
4.2
7
0.60
P (F3 +/S)
0.3
0.1
0.2
0.1
0.1
0.2
0.2
1.2
7
0.17
3.50
+
P (F4 +/D)
1
0.8
0.2
0.5
0.9
0.3
0.8
4.5
7
0.64
P (F4 +/S)
0.6
0.2
0.7
0.5
0.8
0.7
0.8
4.3
7
0.61
1.05
+
P (F5 +/D)
0.8
0.7
0.7
0.7
0.8
0.3
0.8
4.8
7
0.69
P (F5 +/S)
0.7
0.2
0.3
0.3
0.6
0.7
0.7
3.5
7
0.50
1.37
+
P (F6 +/D)
0.7
0.5
0.8
0.8
0.2
0.8
0.2
4.0
7
0.57
P (F6 +/S)
0.2
0.1
0.2
0.2
0.0
0.2
0.2
1.1
7
0.16
3.64
+
P (F7 +/D)
0.8
0.8
0.9
0.9
1
0.7
1
6.1
7
0.87
P (F7 +/S)
0.7
0.2
0.1
0.1
0.7
0.3
0.9
3.0
7
0.43
2.03
+
P (F8 +/D)
1
0.7
0.9
0.7
0.7
0.5
1
5.5
7
0.79
P (F8 +/S)
0.4
0.3
0.1
0.3
0.3
0.5
1
2.9
7
0.41
1.90
+
P (F −/D)
P (F −/S)
P (F1 −/D)
0.2
0.3
0.2
0.5
0.0
0.4
0.5
2.1
7
0.30
P (F1 −/S)
0.2
0.9
0.8
0.5
0.0
0.6
0.7
3.7
7
0.53
0.57
−
P (F2 −/D)
0.1
0.1
0.5
0.3
0.0
0.4
0.2
1.6
7
0.23
P (F2 −/S)
0.9
0.8
0.5
0.7
0.2
0.6
0.4
4.1
7
0.59
0.39
−
P (F3 −/D)
0.7
0.5
0.2
0.1
0.4
0.2
0.7
2.8
7
0.40
P (F3 −/S)
0.7
0.9
0.8
0.9
0.9
0.8
0.8
5.8
7
0.83
0.48
−
P (F4 −/D)
0.0
0.2
0.8
0.5
0.1
0.7
0.2
2.5
7
0.36
P (F4 −/S)
0.4
0.8
0.3
0.5
0.2
0.3
0.2
2.7
7
0.39
0.93
−
P (F5 −/D)
0.2
0.3
0.3
0.3
0.2
0.7
0.2
2.2
7
0.31
P (F5 −/S)
0.3
0.8
0.7
0.7
0.4
0.3
0.3
3.5
7
0.50
0.63
−
P (F6 −/D)
0.3
0.5
0.2
0.2
0.8
0.2
0.8
3.0
7
0.43
P (F6 −/S)
0.8
0.9
0.8
0.8
1
0.8
0.8
5.9
7
0.84
0.51
−
P (F7 −/D)
0.2
0.2
0.1
0.1
0.0
0.3
0.0
0.9
7
0.13
P (F7 −/S)
0.3
0.8
0.9
0.9
0.3
0.7
0.1
4.0
7
0.57
0.23
−
P (F8 −/D)
0.0
0.3
0.1
0.3
0.3
0.5
0.0
1.5
7
0.21
P (F8 −/S)
0.6
0.7
0.9
0.7
0.7
0.5
0.0
4.1
7
0.59
0.37
−
Note: LR > 1, the factor increases the probability of dying with EVD; LR < 1, the factor decreases the probability of dying with EVD; LR = 0, the factor is indifferent, so no conclusion can be drawn in this case.
LR, likelihood ratio.
On the basis of the data shown in Table 4, we used the different LRs and a priori probabilities from Table 2 to calculate the corresponding a posteriori probabilities, as shown in Table 5.
Probabilities of mortality as a function of specific combinations of Ebola virus disease severity factors.
Case
EVD risk factors
LR
QAPRI
QAPO
Probability of death from EVD
As % of
1
F1,F2,F3,F4,F5,F6,F7,F8
194.76
2.33
453.8
0.998
99.8
2
F1,F2,F3,F4,F5,F6,F7
37.57
2.33
87.5
0.989
98.9
3
F1,F2,F3,F4,F5,F76,F8
13.55
2.33
31.6
0.969
96.9
4
F1,F2,F3,F4,F5,F7,F8
27.23
2.33
63.5
0.984
98.4
5
F1,F2,F3,F6,F7,F8
78.98
2.33
184.0
0.995
99.5
6
F1,F2,F5,F6,F7,F8
23.77
2.33
55.4
0.982
98.2
7
F1,F4,F5,F6,F7,F8
5.63
2.33
13.1
0.929
92.9
8
F2,F3,F4,F5,F6,F7
14.36
2.33
33.5
0.971
97.1
9
F3,F6,F7
1.22
2.33
2.24
0.740
74.0
10
F1,F2,F4,F5,F8
0.42
2.33
1.0
0.492
49.2
11
F1,F2,F3,F6
1.69
2.33
3.9
0.797
79.7
12
F4,F5,F7
0.06
2.33
0.1
0.119
11.9
13
F1,F2,F6
0.23
2.33
0.5
0.351
35.1
14
F1,F2,F3
0.24
2.33
0.5
0.355
35.5
15
All factors absent
0.00
2.33
0.0
0.006
0.6
EVD, Ebola virus disease; LR, likelihood ratio; QAPRI, priori probability ratio; QAPO, posterior probability ratio.
The table presenting all combinations of Ebola severity factors is provided in Table 1-A1. A total of 248 combinations were identified.
We used the following formulae to determine the PPQ with all eight mortality-related severity factors present in a patient (Equation 10):
The same formula was used to determine the PPQ with all eight mortality-related severity factors absent in a patient (Equation 11):
QAPO=P(F1+/D)P(F1+/S)*P(F2+/D)P(F2+/S)*P(F3+/D)P(F3+/S)*P(F4+/D)P(F4+/S)*P(F5+/D)P(F5+/S)*P(F6+/D)P(F6+/S)*P(F7+/D)P(F7+/S)*P(F8+/D)P(F8+/S)*P(D)P(S)QAPO=RV*QAPRIQAPO=0.57*0.39*0.48*0.93*0.63*0.51*0.23*0.37*2.33=0.006Determining the probability of mortality with signs or symptoms related to Ebola virus disease severity
Given the QAPO findings, we calculated the different probabilities of mortality with the different factors (signs and symptoms) related to the severity of EVD using the following formula (Equation 12):
P=QAPO1+QAPO
Generally, as shown in this Table 5, when the number of factors increases, the probability of mortality also increases. However, certain factors, even in small numbers, increase the probability of mortality. This is the case for one combination of factors: F3, haemorrhagic syndrome; F6, neurological disorders and F7, biological alteration and dehydration. Moreover, when a patient presents with all eight factors, the chance of survival is close to zero. A total of 248 combinations of predictive factors for Ebola severity were identified. Among these, one combination of eight factors, four combinations of seven factors and two combinations of six factors each yielded mortality predictions ≥ 99.0%. Notably, any combination that included factor F6 was consistently associated with a markedly high probability of mortality (Table 1-A1). The complete set of factor combinations is presented in Table 1-A1.
Evaluation of the Ebola virus disease mortality prediction model
This model was evaluated by determining the degree of agreement between and among experts in two rounds, depending on the answer to the following question: ‘Out of 6 patients that you know of with different EVD-related factors, how many died?’ The ‘cut-off point’ discrimination criterion was determined to evaluate the effectiveness of the subjective Bayes model.
Determination of the discrimination criterion for mortality (cut-off point)
For our study, this criterion was defined as the value at which the model simultaneously exhibited both very high Se and Sp. The criterion is given by a value between 0.1 and 0.9. Table 6 was constructed for each value between 0.1 and 0.9.
Mortality discrimination criterion – Using hypothetical cases and expert consensus for mortality prediction.
The highest values for both Se and Sp were obtained at the 0.4 cut-off point, which was chosen for the internal validation of our model, enabling us to compare subjective Bayesian model (SBM) with the expert consensus.
Determining the degree of agreement within and between experts
On the basis of the construction of six hypothetical cases by each expert in the first and second rounds and the determination of a consensus on 42 hypothetical cases generated by all the experts, we used the kappa statistic to analyse these results (cut-off point of 0.4).
The results obtained in relation to intraexpert approval showed that six out of seven experts had an acceptable degree of approval for themselves, as their K values were between 0.4 ≤ K ≤ 75. These values are shown diagonally in Table 7. Regarding interexpert approval, 15 out of 21 approvals, which are shown below the diagonal values, were acceptable.
Degree of agreement within and between experts regarding the mortality of Ebola.
Kappa
Intra- and interexpert agreement for EVD mortality
E1
E2
E3
E4
E5
E6
E7
E1
0.39
-
-
-
-
-
-
E2
0.49
0.61
-
-
-
-
-
E3
0.65
0.32
0.49
-
-
-
-
E4
0.39
0.65
0.28
0.76
-
-
-
E5
0.33
0.51
0.22
0.67
0.85
-
-
E6
0.39
0.60
0.28
0.62
0.57
0.76
-
E7
0.42
0.61
0.34
0.55
0.54
0.50
0.90
Note: The kappa coefficient varies between 0 and 1: < 0.20 slight; 0.21–0.40 medium; 0.41–0.60 moderate; 0.61–0.80 substantial; > 0.80 almost perfect.
EVB, Ebola virus disease.
Given that the majority of experts had an acceptable degree of agreement, we considered the group to be a single person, and we used the arithmetic mean of their estimates to represent agreement to evaluate the Bayes model.
Internal validation of the mortality prediction model
The internal validation of the mortality prediction model was performed by calculating the following validation parameters: Se, Sp, PPV, NPV, overall efficacy value and the kappa statistic. The data used for these different calculations came from the comparison of the subjective Bayes model and the expert consensus on hypothetical cases (Table 8).
Comparison of the subjective Bayes model and expert consensus.
Model performance parameter calculations (Equation 13):
Se=aa+c*100=3838+1*100=97.4%Sp=db+d*100=30+3*100=100.0%PPV=aa+b*100=3838+0*100=100.%NPV=dc+d*100=31+3*100=75.0%Overallefficiencyvalue=a+da+b+c+d*100=97.6%*100=38+338+0+1+3a10.90a20.10Po0.98MSB (0.8) and Expert Consensus0.84b10.93Pe0.85b20.07P0=a+da+b+c+d=38+338+0+1+3=0.98Pe=(a1*b1)+(a2*b2)=0.85K=P0−Pe1−Pe=0.98−0.851−0.85=0.84
The internal validity of the subjective Bayes model for predicting Ebola mortality was established, with a K value of 0.84. The experts’ agreement among themselves on the Ebola severity factors (EVM signs/symptoms) was almost perfect because it was between 0.81 and 1.00.
Discussion
Knowledge of the factors that worsen the health status of patients with EVD is essential for good medical management.
Our mortality prediction model highlights eight key factors that are considered to worsen the state of health of a patient with EVD. Among these eight factors, the Bayesian model identified three factors (signs and symptoms) as signs of severe EVD. F3 is among these eight factors: haemorrhagic syndrome (unexplained bleeding, bleeding gums, bleeding from the injection, bloody nose, bloody stools, haematemesis, bloody vomit, bloody cough, bleeding from the vagina, bleeding skin, bloody urine and other bleeding). This result aligns with the findings reported by the WHO Ebola response team in West Africa, as well as those of Aylward et al.,10 Barry et al.17 and Hunt et al.16
F6 is also included among the factors indicating severe EVD: neurological disorders (coma, unconsciousness, psychosis, convulsions, aggression and loss of vision). Some researchers have also identified this factor, such as Qin et al.14 and Zhang et al.15 In addition to F3 and F6, F7 is also included: biological alteration and dehydration (jaundice, altered liver function, altered biological state between diagnosis and follow-up (haematological factors, biochemical factors, protein factors), hyperglycaemia, altered renal function and dehydration). Some researchers have also identified this factor as a sign of severe EVD. This was the case in the study by Schieffelin et al.,11 Hunt et al.16 and de La Vega et al.18
In her article entitled ‘Predicting Ebola Severity: A Clinical Prioritization Score for Ebola virus disease’, Mary-Anne Hartley prioritised clinical signs and symptoms in patients with fatal outcomes because of EVD.13 The eight factors identified through the Bayesian approach were consistently validated by the Mary-Anne scoring method, which relies on clinical data. This concordance highlights the robustness of the Bayesian model and underscores the complementarity of the two approaches in predicting EBV severity.
With respect to the Bayesian approach, the results indicate that if a patient exhibits all eight of the factors identified by management experts, the patient has a 99.8% chance of dying; conversely, if all eight factors are absent, the patient has a 0.6% chance of dying.
Among the eight factors listed by the experts, the Bayesian model identified three factors with a very high impact on calculating the probability of death. These factors include F3 (haemorrhagic syndrome), F6 (neurological disorders) and F7 (biological alterations and dehydration). The elements of F3, F6 and F7 are shown in Table 5. Apart from these three factors, with an LR well above 1, F8 (High viral load) and F2 (alteration of general state and comorbidity) are included, which have an RV close to 2.
When we calculate the probability of a patient dying of Ebola with F3, F6 and F7, the probability of death is 74.0%. However, if we remove F7 and add F1 and F2, that is, combining F1, F2, F3 and F6, the patient will have a 79.7% chance of dying.
According to the scale proposed by Landis and Koch,20 given that our K values (0.84) ranged from 0.81 to 1, there was almost perfect agreement between our experts regarding the prediction of mortality as a function of the eight factors listed above.
This study has the limitation of lacking external validation of this Bayesian model using real Ebola death data.
Conclusion
This study highlights the factors influencing the fatal outcome (mortality) of Ebola patients at ETCs. These factors are of vital importance to those providing medical care to patients in ETCs. When providers know the risk factors for death in advance, management is improved by focusing interventions on the harmful factors that can very easily lead to death. Our results, obtained using Bayes’ mathematical model, are similar to those reported by other researchers using different methodological approaches. Our results are more telling because we considered the opinions of experts and internally validated the model using the kappa statistic.
The results of this research will enable ETCs to save more lives during future Ebola epidemics by effectively triaging patients who require intensive care. This model not only predicts mortality but also supports preparedness by guiding upgrades of ETCs and training experts in advanced clinical care. Such measures could improve patient survival and strengthen outbreak response.
Other contributing factors, for example, economic conditions (availability of medical supplies, therapies, …) at ETCs should be discussed as additional factors that could contribute to a patient’s death despite applying the knowledge from the Bayesian model.
Acknowledgements
Serge Kyembo Mukabe, David Kwibe Bitakwira, Willy Beya Tshibitshabu, Roger Makwa Makunzi, Reagan Amani Bikali, Emmanuel Kukangidila Ndenga and Cedrick Bope Miongolo: The authors would like to thank the experts in the clinical management of Ebola who agreed to participate freely in the session aimed at developing this model for predicting Ebola lethality despite their busy schedules.
Competing interests
The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.
Authors’ contributions
J.K.K. and S.M.M. designed the study, performed the experiments, analysed the data and wrote the article. P.K., J.N.K., R.M., O.M. and B.B. performed the experiments. G.M., R.B., J.K., S.N. and E.B.M. wrote the article.
Data availability
The data available for the development of this predictive model were generated by a group of experts in the clinical management of Ebola during the development of the subjective prognostic model for Ebola patients. To obtain the data generated by the experts, please contact the corresponding author, J.K.K.
Disclaimer
The views and opinions expressed in this article are those of the authors and are the product of professional research. It does not necessarily reflect the official policy or position of any affiliated institution, funder, agency or publisher. The authors are responsible for this article’s results, findings and content.
ReferencesMartinezMJ, SalimAM, HurtadoJC, KilgorePE. Ebola virus infection: Overview and update on prevention and treatment. Infect Dis Ther. 2015;4(4):365–390. https://doi.org/10.1007/s40121-015-0079-5Centers for Disease Control and Prevention. Ebola Outbreak History. Atlanta (GA): National Center for Emerging and Zoonotic Infectious Diseases; 2024.GrinnellM, DixonMG, PattonM, et al. Ebola virus disease in health care workers – Guinea, 2014. MMWR Morb Mortal Wkly Rep. 2014;64(38):1083–1087. https://doi.org/10.15585/mmwr.mm6438a6World Health Organization. Ebola virus disease – Democratic Republic of the Congo: Health emergency update [homepage on the Internet]. Geneva: World Health Organization; 2019[updated 2019 Nov 21]. Available from: https://www.who.int/emergencies/disease-outbreak-news/item/2019-DON209Ministry of Health, Democratic Republic of the Congo. Strategic response plan for the Ebola virus disease outbreak in North Kivu and Ituri provinces, Democratic Republic of the Congo (July–December 2019). Kinshasa: Ministry of Health; 2019.SiednerMJ, GostinLO, CranmerHH, KraemerJD. Strengthening the detection of and early response to public health emergencies: Lessons from the West African Ebola epidemic. PLoS Med. 2015;12(3):e1001804. https://doi.org/10.1371/journal.pmed.1001804JudsonSD, MunsterVJ. The multiple origins of Ebola disease outbreaks. J Infect Dis. 2023;228(Suppl 7):S465–S473. https://doi.org/10.1093/infdis/jiad352LevineAC, ShettyPP, BurbachR, et al. Derivation and internal validation of the Ebola prediction score for risk stratification of patients with suspected Ebola virus disease. Ann Emerg Med. 2015;66(3):285–293.e1. https://doi.org/10.1016/j.annemergmed.2015.03.011MooleH, ChittaS, VictorD, et al. Association of clinical signs and symptoms of Ebola viral disease with case fatality: A systematic review and meta-analysis. J Community Hosp Intern Med Perspect. 2015;5(4):28406. https://doi.org/10.3402/jchimp.v5.28406AylwardB, BarbozaP, BawoL, et al. Ebola virus disease in West Africa – The first 9 months of the epidemic and forward projections. N Engl J Med. 2014;371(16):1481–1495. https://doi.org/10.1056/NEJMoa1411100SchieffelinJS, ShafferJG, GobaA, et al. Clinical illness and outcomes in patients with Ebola in Sierra Leone. N Engl J Med. 2014;371(22):2092–2100. https://doi.org/10.1056/NEJMoa1411680BahEI, LamahMC, FletcherT, et al. Clinical presentation of patients with Ebola virus disease in Conakry, Guinea. N Engl J Med. 2015;372(1):40–47. https://doi.org/10.1056/NEJMoa1411249HartleyMA, YoungA, TranAM, et al. Predicting Ebola severity: A clinical prioritization score for Ebola virus disease. PLoS Negl Trop Dis. 2017;11(2):e0005265. https://doi.org/10.1371/journal.pntd.0005265QinE, BiJ, ZhaoM, et al. Clinical features of patients with Ebola virus disease in Sierra Leone. Clin Infect Dis. 2015;61(4):491–495. https://doi.org/10.1093/cid/civ319ZhangX, RongY, SunL, et al. Prognostic analysis of patients with Ebola virus disease. PLoS Negl Trop Dis. 2015;9(9):e0004113. https://doi.org/10.1371/journal.pntd.0004113HuntL, Gupta-WrightA, SimmsV, et al. Clinical presentation, biochemical, and haematological parameters and their association with outcome in patients with Ebola virus disease: An observational cohort study. Lancet Infect Dis. 2015;15(11):1292–1299. https://doi.org/10.1016/S1473-3099(15)00144-9BarryM, TraoréFA, SakoFB, et al. Ebola outbreak in Conakry, Guinea: Epidemiological, clinical, and outcome features. Med Mal Infect. 2014;44(11–12):491–494. https://doi.org/10.1016/j.medmal.2014.09.009De la VegaMA, CaleoG, AudetJ, et al. Ebola viral load at diagnosis associates with patient outcome and outbreak evolution. J Clin Invest. 2015;125(124):4421–4428. https://doi.org/10.1172/JCI83162MeyerN, VinzioS, GoichotB. Bayesian statistics: A statistical approach adapted to clinical practice. Rev Med Interne. 2009;30(3):242–249. https://doi.org/10.1016/j.revmed.2008.07.004LandisJR, KochGG. An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers. Biometrics. 1977;33(2):363–374. https://doi.org/10.2307/2529786Appendix 1
Estimated probabilities of mortality based on various combinations of factors associated with the severity of Ebola virus disease.
Number
Different combinations
EVD risk factors
LR
QAPRI
QAPO
Probability of death from EVD
As % of
1
8 factors
F1,F2,F3,F4,F5,F6,F7,F8
194.76
2.33
453.79
0.998
99.8
2
7 factors
F1,F2,F3,F4,F5,F6,F7
37.57
2.33
87.54
0.989
98.9
3
F1,F2,F3,F4,F5,F6,F8
21.55
2.33
50.22
0.980
98.0
4
F1,F2,F3,F4,F5,F7,F8
27.23
2.33
63.45
0.984
98.4
5
F1,F2,F3,F4,F6,F7,F8
89.27
2.33
207.99
0.995
99.5
6
F1,F2,F3,F5,F6,F7,F8
172.32
2.33
401.51
0.998
99.8
7
F1,F2,F4,F5,F6,F7,F8
26.86
2.33
62.59
0.984
98.4
8
F1,F3,F4,F5,F6,F7,F8
40.82
2.33
95.10
0.990
99.0
9
F2,F3,F4,F5,F6,F7,F8
74.45
2.33
173.46
0.994
99.4
10
6 factors
F1,F2,F3,F4,F5,F6
4.16
2.33
9.69
0.906
90.6
11
F1,F2,F3,F4,F5,F7
3.01
2.33
7.02
0.875
87.5
12
F1,F2,F3,F4,F5,F8
3.01
2.33
7.02
0.875
87.5
13
F1,F2,F3,F4,F6,F7
17.22
2.33
40.12
0.976
97.6
14
F1,F2,F3,F4,F6,F8
9.88
2.33
23.02
0.958
95.8
15
F1,F2,F3,F4,F7,F8
12.48
2.33
29.08
0.967
96.7
16
F1,F2,F3,F5,F6,F7
33.24
2.33
77.45
0.987
98.7
17
F1,F2,F3,F5,F6,F8
19.07
2.33
44.43
0.978
97.8
18
F1,F2,F3,F5,F7,F8
24.10
2.33
56.14
0.982
98.2
19
F1,F2,F3,F6,F7,F8
78.98
2.33
184.02
0.995
99.5
20
F1,F2,F4,F5,F6,F7
5.18
2.33
12.07
0.924
92.4
21
F1,F2,F4,F5,F6,F8
2.97
2.33
6.93
0.874
87.4
22
F1,F2,F4,F5,F7,F8
3.76
2.33
8.75
0.897
89.7
23
F1,F2,F4,F6,F7,F8
12.31
2.33
28.69
0.966
96.6
24
F1,F2,F5,F6,F7,F8
23.77
2.33
55.38
0.982
98.2
25
F1,F3,F4,F5,F6,F7
7.87
2.33
18.35
0.948
94.8
26
F1,F3,F4,F5,F6,F8
4.52
2.33
10.52
0.913
91.3
27
F1,F3,F4,F5,F7,F8
5.71
2.33
13.30
0.930
93.0
28
F1,F3,F4,F6,F7,F8
18.71
2.33
43.59
0.978
97.8
29
F1,F3,F5,F6,F7,F8
36.11
2.33
84.15
0.988
98.8
30
F1,F4,F5,F6,F7,F8
5.63
2.33
13.12
0.929
92.9
31
F2,F3,F4,F5,F6,F7
14.36
2.33
33.46
0.971
97.1
32
F2,F3,F4,F5,F6,F8
8.24
2.33
19.19
0.950
95.0
33
F2,F3,F4,F5,F7,F8
10.41
2.33
24.25
0.960
96.0
34
F2,F3,F4,F6,F7,F8
34.12
2.33
79.50
0.988
98.8
35
F2,F3,F5,F6,F7,F8
65.87
2.33
153.47
0.994
99.4
36
F2,F4,F5,F6,F7,F8
10.27
2.33
23.93
0.960
96.0
37
F3,F4,F5,F6,F7,F8
15.60
2.33
36.35
0.973
97.3
38
5 factors
F1,F2,F3,F4,F5
0.58
2.33
1.35
0.575
57.5
39
F1,F2,F3,F4,F6
1.91
2.33
4.44
0.816
81.6
40
F1,F2,F3,F4,F7
2.41
2.33
5.61
0.849
84.9
41
F1,F2,F3,F4,F8
1.38
2.33
3.22
0.763
76.3
42
F1,F2,F3,F5,F6
3.68
2.33
8.57
0.896
89.6
43
F1,F2,F3,F5,F7
4.65
2.33
10.83
0.915
91.5
44
F1,F2,F3,F5,F8
2.67
2.33
6.21
0.861
86.1
45
F1,F2,F3,F6,F7
15.24
2.33
35.50
0.973
97.3
46
F1,F2,F3,F6,F8
8.74
2.33
20.36
0.953
95.3
47
F1,F2,F3,F7,F8
11.04
2.33
25.73
0.963
96.3
48
F1,F2,F4,F5,F6
0.57
2.33
1.34
0.572
57.2
49
F1,F2,F4,F5,F7
0.72
2.33
1.69
0.628
62.8
50
F1,F2,F4,F5,F8
0.42
2.33
0.97
0.492
49.2
51
F1,F2,F4,F6,F7
2.38
2.33
5.53
0.847
84.7
52
F1,F2,F4,F6,F8
1.36
2.33
3.17
0.760
76.0
53
F1,F2,F4,F7,F8
1.72
2.33
4.01
0.800
80.0
54
F1,F2,F5,F6,F7
4.59
2.33
10.68
0.914
91.4
55
F1,F2,F5,F6,F8
2.63
2.33
6.13
0.860
86.0
56
F1,F2,F5,F7,F8
3.32
2.33
7.74
0.886
88.6
57
F1,F2,F6,F7,F8
10.89
2.33
25.38
0.962
96.2
58
F1,F3,F4,F5,F6
0.87
2.33
2.03
0.670
67.0
59
F1,F3,F4,F5,F7
1.10
2.33
2.57
0.720
72.0
60
F1,F3,F4,F5,F8
0.63
2.33
1.47
0.595
59.5
61
F1,F3,F4,F6,F7
3.61
2.33
8.41
0.894
89.4
62
F1,F3,F4,F6,F8
2.07
2.33
4.82
0.828
82.8
63
F1,F3,F4,F7,F8
2.62
2.33
6.10
0.859
85.9
64
F1,F3,F5,F6,F7
6.97
2.33
16.23
0.942
94.2
65
F1,F3,F5,F6,F8
4.00
2.33
9.31
0.903
90.3
66
F1,F3,F5,F7,F8
5.05
2.33
11.77
0.922
92.2
67
F1,F3,F6,F7,F8
16.55
2.33
38.57
0.975
97.5
68
F1,F4,F5,F6,F7
1.09
2.33
2.53
0.717
71.7
69
F1,F4,F5,F6,F8
0.62
2.33
1.45
0.592
59.2
70
F1,F4,F5,F7,F8
0.79
2.33
1.83
0.647
64.7
71
F1,F4,F6,F7,F8
2.58
2.33
6.01
0.857
85.7
72
F1,F5,F6,F7,F8
4.98
2.33
11.61
0.921
92.1
73
F2,F3,F4,F5,F6
1.59
2.33
3.70
0.787
78.7
74
F2,F3,F4,F5,F7
2.01
2.33
4.68
0.824
82.4
75
F2,F3,F4,F5,F8
1.15
2.33
2.68
0.729
72.9
76
F2,F3,F4,F6,F7
6.58
2.33
15.34
0.939
93.9
77
F2,F3,F4,F6,F8
3.78
2.33
8.80
0.898
89.8
78
F2,F3,F4,F7,F8
4.77
2.33
11.12
0.917
91.7
79
F2,F3,F5,F6,F7
12.71
2.33
29.61
0.967
96.7
80
F2,F3,F5,F6,F8
7.29
2.33
16.98
0.944
94.4
81
F2,F3,F5,F7,F8
9.21
2.33
21.46
0.955
95.5
82
F2,F3,F6,F7,F8
30.19
2.33
70.34
0.986
98.6
83
F2,F4,F5,F6,F7
1.98
2.33
4.62
0.822
82.2
84
F2,F4,F5,F6,F8
1.14
2.33
2.65
0.726
72.6
85
F2,F4,F5,F7,F8
1.44
2.33
3.35
0.770
77.0
86
F2,F4,F6,F7,F8
4.71
2.33
10.97
0.916
91.6
87
F2,F5,F6,F7,F8
9.09
2.33
21.17
0.955
95.5
88
F3,F4,F5,F6,F7
3.01
2.33
7.01
0.875
87.5
89
F3,F4,F5,F6,F8
1.73
2.33
4.02
0.801
80.1
90
F3,F4,F5,F7,F8
2.18
2.33
5.08
0.836
83.6
91
F3,F4,F6,F7,F8
4.71
2.33
10.97
0.916
91.6
92
F3,F5,F6,F7,F8
13.80
2.33
32.16
0.970
97.0
93
F4,F5,F6,F7,F8
2.15
2.33
5.01
0.834
83.4
94
4 factors
F1, F2, F3, F4
0.27
2.33
0.62
0.383
38.3
95
F1, F2, F3, F5
0.51
2.33
1.20
0.545
54.5
96
F1, F2, F3, F6
1.69
2.33
3.93
0.797
79.7
97
F1, F2, F3, F7
2.13
2.33
4.96
0.832
83.2
98
F1, F2, F3, F8
1.22
2.33
2.85
0.740
74.0
99
F1, F2, F4, F5
0.08
2.33
0.19
0.157
15.7
100
F1, F2, F4, F6
0.26
2.33
0.61
0.380
38.0
101
F1, F2, F4, F7
0.33
2.33
0.77
0.436
43.6
102
F1, F2, F4, F8
0.19
2.33
0.44
0.307
30.7
103
F1, F2, F5, F6
0.51
2.33
1.18
0.542
54.2
104
F1, F2, F5, F7
0.64
2.33
1.49
0.599
59.9
105
F1, F2, F5, F8
0.37
2.33
0.86
0.461
46.1
106
F1, F2, F6, F7
2.10
2.33
4.90
0.830
83.0
107
F1, F2, F6, F8
1.21
2.33
2.81
0.737
73.7
108
F1, F2, F7, F8
1.52
2.33
3.55
0.780
78.0
109
F1, F3, F4, F5
0.12
2.33
0.28
0.221
22.1
110
F1, F3, F4, F6
0.40
2.33
0.93
0.482
48.2
111
F1, F3, F4, F7
0.50
2.33
1.18
0.540
54.0
112
F1, F3, F4, F8
0.29
2.33
0.67
0.403
40.3
113
F1, F3, F5, F6
0.77
2.33
1.80
0.642
64.2
114
F1, F3, F5, F7
0.97
2.33
2.27
0.694
69.4
115
F1, F3, F5, F8
0.56
2.33
1.30
0.566
56.6
116
F1, F3, F6, F7
3.19
2.33
7.44
0.882
88.2
117
F1, F3, F6, F8
1.83
2.33
4.27
0.810
81.0
118
F1, F3, F7, F8
2.31
2.33
5.39
0.844
84.4
119
F1, F4, F5, F6
0.12
2.33
0.28
0.219
21.9
120
F1, F4, F5, F7
0.15
2.33
0.35
0.261
26.1
121
F1, F4, F5, F8
0.09
2.33
0.20
0.169
16.9
122
F1, F4, F6, F7
0.50
2.33
1.16
0.537
53.7
123
F1, F4, F6, F8
0.29
2.33
0.67
0.400
40.0
124
F1, F4, F7, F8
0.36
2.33
0.84
0.457
45.7
125
F1, F5, F6, F7
0.96
2.33
2.24
0.691
69.1
126
F1, F5, F6, F8
0.55
2.33
1.28
0.562
56.2
127
F1, F5, F7, F8
0.70
2.33
1.62
0.619
61.9
128
F1, F6, F7, F8
2.28
2.33
5.32
0.842
84.2
129
F2, F3, F4, F5
0.22
2.33
0.52
0.341
34.1
130
F2, F3, F4, F6
0.73
2.33
1.70
0.629
62.9
131
F2, F3, F4, F7
0.92
2.33
2.14
0.682
68.2
132
F2, F3, F4, F8
0.53
2.33
1.23
0.552
55.2
133
F2, F3, F5, F6
1.41
2.33
3.28
0.766
76.6
134
F2, F3, F5, F7
1.78
2.33
4.14
0.805
80.5
135
F2, F3, F5, F8
1.02
2.33
2.37
0.704
70.4
136
F2, F3, F6, F7
5.82
2.33
13.57
0.931
93.1
137
F2, F3, F6, F8
3.34
2.33
7.78
0.886
88.6
138
F2, F3, F7, F8
4.22
2.33
9.84
0.908
90.8
139
F2, F4, F5, F6
0.22
2.33
0.51
0.338
33.8
140
F2, F4, F5, F7
0.28
2.33
0.65
0.392
39.2
141
F2, F4, F5, F8
0.16
2.33
0.37
0.270
27.0
142
F2, F4, F6, F7
0.91
2.33
2.12
0.679
67.9
143
F2, F4, F6, F8
0.52
2.33
1.21
0.548
54.8
144
F2, F4, F7, F8
0.66
2.33
1.53
0.605
60.5
145
F2, F5, F6, F7
1.75
2.33
4.08
0.803
80.3
146
F2, F5, F6, F8
1.01
2.33
2.34
0.701
70.1
147
F2, F5, F7, F8
1.27
2.33
2.96
0.747
74.7
148
F2, F6, F7, F8
4.16
2.33
9.70
0.907
90.7
149
F3, F4, F5, F6
0.33
2.33
0.78
0.437
43.7
150
F3, F4, F5, F7
0.42
2.33
0.98
0.495
49.5
151
F3, F4, F5, F8
0.24
2.33
0.56
0.360
36.0
152
F3, F4, F6, F7
1.38
2.33
3.21
0.763
76.3
153
F3, F4, F6, F8
0.79
2.33
1.84
0.648
64.8
154
F3, F4, F7, F8
1.00
2.33
2.33
0.700
70.0
155
F3, F5, F6, F7
2.66
2.33
6.20
0.861
86.1
156
F3, F5, F6, F8
1.53
2.33
3.56
0.781
78.1
157
F3, F5, F7, F8
1.93
2.33
4.50
0.818
81.8
158
F3, F6, F7, F8
6.33
2.33
14.74
0.936
93.6
159
F4, F5, F6, F7
0.42
2.33
0.97
0.492
49.2
160
F4, F5, F6, F8
0.24
2.33
0.55
0.357
35.7
161
F4, F5, F7, F8
0.30
2.33
0.70
0.412
41.2
162
F4, F6, F7, F8
0.99
2.33
2.30
0.697
69.7
163
F5, F6, F7, F8
0.27
2.33
0.62
0.383
38.3
164
3 factors
F1, F2, F3
0.24
2.33
0.55
0.355
35.5
165
F1, F2, F4
0.04
2.33
0.09
0.079
7.9
166
F1, F2, F5
0.07
2.33
0.17
0.142
14.2
167
F1, F2, F6
0.23
2.33
0.54
0.351
35.1
168
F1, F2, F7
0.29
2.33
0.68
0.406
40.6
169
F1, F2, F8
0.17
2.33
0.39
0.282
28.2
170
F1, F3, F4
0.06
2.33
0.13
0.115
11.5
171
F1, F3, F5
0.11
2.33
0.25
0.201
20.1
172
F1, F3, F6
0.26
2.33
0.60
0.374
37.4
173
F1, F3, F7
0.45
2.33
1.04
0.510
51.0
174
F1, F3, F8
0.26
2.33
0.60
0.374
37.4
175
F1, F4, F5
0.02
2.33
0.04
0.038
3.8
176
F1, F4, F6
0.06
2.33
0.13
0.114
11.4
177
F1, F4, F7
0.07
2.33
0.16
0.140
14.0
178
F1, F4, F8
0.04
2.33
0.09
0.085
8.5
179
F1, F5, F6
0.11
2.33
0.25
0.199
19.9
180
F1, F5, F7
0.13
2.33
0.31
0.238
23.8
181
F1, F5, F8
0.08
2.33
0.18
0.152
15.2
182
F1, F6, F7
0.44
2.33
1.03
0.506
50.6
183
F1, F6, F8
0.25
2.33
0.59
0.371
37.1
184
F1, F7, F8
0.32
2.33
0.74
0.427
42.7
185
F2, F3, F4
0.10
2.33
0.24
0.192
19.2
186
F2, F3, F5
0.20
2.33
0.46
0.314
31.4
187
F2, F3, F6
0.64
2.33
1.50
0.600
60.0
188
F2, F3, F7
0.81
2.33
1.90
0.655
65.5
189
F2, F3, F8
0.47
2.33
1.09
0.521
52.1
190
F2, F4, F5
0.03
2.33
0.07
0.067
6.7
191
F2, F4, F6
0.10
2.33
0.23
0.190
19.0
192
F2, F4, F7
0.13
2.33
0.30
0.228
22.8
193
F2, F4, F8
0.07
2.33
0.17
0.145
14.5
194
F2, F5, F6
0.19
2.33
0.45
0.311
31.1
195
F2, F5, F7
0.25
2.33
0.57
0.363
36.3
196
F2, F5, F8
0.14
2.33
0.33
0.247
24.7
197
F2, F6, F7
0.80
2.33
1.87
0.652
65.2
198
F2, F6, F8
0.46
2.33
1.07
0.518
51.8
199
F2, F7, F8
0.58
2.33
1.36
0.576
57.6
200
F3, F4, F5
0.05
2.33
0.11
0.098
9.8
201
F3, F4, F6
0.15
2.33
0.36
0.262
26.2
202
F3, F4, F7
0.19
2.33
0.45
0.310
31.0
203
F3, F4, F8
0.11
2.33
0.26
0.205
20.5
204
F3, F5, F6
0.29
2.33
0.69
0.407
40.7
205
F3, F5, F7
0.37
2.33
0.87
0.465
46.5
206
F3, F5, F8
0.21
2.33
0.50
0.332
33.2
207
F3, F6, F7
1.22
2.33
2.84
0.740
74.0
208
F3, F6, F8
0.70
2.33
1.63
0.620
62.0
209
F3, F7, F8
0.88
2.33
2.06
0.673
67.3
210
F4, F5, F6
0.05
2.33
0.11
0.097
9.7
211
F4, F5, F7
0.06
2.33
0.14
0.119
11.9
212
F4, F5, F8
0.03
2.33
0.08
0.072
7.2
213
F4, F6, F7
0.19
2.33
0.44
0.307
30.7
214
F4, F6, F8
0.11
2.33
0.25
0.203
20.3
215
F4, F7, F8
0.14
2.33
0.32
0.243
24.3
216
F5, F6, F7
0.37
2.33
0.86
0.461
46.1
217
F5, F6, F8
0.21
2.33
0.49
0.329
32.9
218
F5, F7, F8
0.05
2.33
0.12
0.107
10.7
219
F6, F7, F8
0.87
2.33
2.03
0.670
67.0
220
2 factors
F1, F2
0.03
2.33
0.08
0.070
7.0
221
F1, F3
0.05
2.33
0.12
0.103
10.3
222
F1, F4
0.01
2.33
0.02
0.018
1.8
223
F1, F5
0.01
2.33
0.03
0.033
3.3
224
F1, F6
0.05
2.33
0.11
0.102
10.2
225
F1, F7
0.06
2.33
0.14
0.125
12.5
226
F1, F8
0.04
2.33
0.08
0.076
7.6
227
F2, F3
0.09
2.33
0.21
0.174
17.4
228
F2, F4
0.01
2.33
0.03
0.032
3.2
229
F2, F5
0.03
2.33
0.06
0.059
5.9
230
F2, F6
0.09
2.33
0.21
0.172
17.2
231
F2, F7
0.11
2.33
0.26
0.207
20.7
232
F2, F8
0.06
2.33
0.15
0.131
13.1
233
F3, F4
0.02
2.33
0.05
0.047
4.7
234
F3, F5
0.04
2.33
0.10
0.088
8.8
235
F3, F6
0.14
2.33
0.31
0.239
23.9
236
F3, F7
0.17
2.33
0.40
0.285
28.5
237
F3, F8
0.10
2.33
0.23
0.186
18.6
238
F4, F5
0.01
2.33
0.01
0.015
1.5
239
F4, F6
0.02
2.33
0.05
0.047
4.7
240
F4, F7
0.03
2.33
0.06
0.058
5.8
241
F4, F8
0.02
2.33
0.04
0.034
3.4
242
F5, F6
0.04
2.33
0.09
0.087
8.7
243
F5, F7
0.05
2.33
0.12
0.107
10.7
244
F5, F8
0.03
2.33
0.07
0.064
6.4
245
F6, F7
0.17
2.33
0.39
0.282
28.2
246
F6, F8
0.10
2.33
0.23
0.184
18.4
247
F7, F8
0.12
2.33
0.28
0.221
22.1
248
Absence of factors
0 factors
0.003
2.33
0.01
0.006
0.6
EVD, Ebola virus disease; LR, likelihood radio; QAPRI, priori probability ratio; QAPO, posterior probability ratio.
How to cite this article: Kebela JK, Kimpanga P, Kyloka JN, et al. Bayesian predictive model of Ebola fatality: Tenth Ebola epidemic in the Democratic Republic of the Congo. J Public Health Africa. 2025;16(4), a1533. https://doi.org/10.4102/jphia.v16i4.1533
Note: The article is a contribution to the themed collection titled ‘Strengthening Scientific Publication Capacity of African Researchers’, under the expert guidance of guest editor Prof. Peter Nyasulu.