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

Development of a Bayesian Subjective Model for Predicting the Clinical Diagnosis of Ebola in the Democratic Republic of the Congo | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Development of a Bayesian Subjective Model for Predicting the Clinical Diagnosis of Ebola in the Democratic Republic of the Congo John Kamwina Kebela, Prince Kimpanga, Jean Nyandwe, Jack Kokolomami, and 19 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5314558/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract 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 Internal Validation Subjective Bayes Model Clinical Diagnosis Ebola Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5314558","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":382164600,"identity":"b68b37ed-23fd-4f27-9c44-5165fd88ea25","order_by":0,"name":"John Kamwina 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