Development of a Bayesian Subjective Model for Predicting the Clinical Diagnosis of Ebola in the Democratic Republic of the Congo

Author(s): John Kamwina Kebela, Prince Kimpanga, Jack Kokolomami, Odrague Chabikuli, Steve Bwira, Steve Ahuka, Rostin Mabela, Dorothée Bulenfu, Tresor Sundika, Willy Beya, Bibiche Matadi, Gisele Malu, Fidèle Dyamba, Annie Mutombo, Jean- Paul Buhalagarha, Jean Nyandwe, Benoit Kebela, Dieudonné Mwamba, Godfroid Musema, Cedrick Bope, Bienvenu Kabasele , Emmanuel Kukangindila , Sylvain Munyanga

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 an-alyse 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