Meta-analysis of predictive symptoms for ebola virus disease

Vageesh Jain*, Andre Charlett, Colin Brown

*Corresponding author for this work

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Introduction: One of the leading challenges in the 2013–2016 West African Ebola virus disease (EVD) outbreak was how best to quickly identify patients with EVD, separating them from those without the disease, in order to maximise limited isolation bed capacity and keep health systems functioning. 

Methodology: We performed a systematic literature review to identify all published data on EVD clinical symptoms in adult patients. Data was dual extracted, and random effects meta-analysis performed for each symptom to identify symptoms with the greatest risk for EVD infection. 

Results: Symptoms usually presenting late in illness that were more than twice as likely to predict a diagnosis of Ebola, were confusion (pOR 3.04, 95% CI 2.18–4.23), conjunctivitis (2.90, 1.92–4.38), dysphagia (1.95, 1.13–3.35) and jaundice (1.86, 1.20–2.88). Early non-specific symptoms of diarrhoea (2.99, 2.00–4.48), fatigue (2.77, 1.59–4.81), vomiting (2.69, 1.76– 4.10), fever (1.97, 1.10–4.52), muscle pain (1.65, 1.04–2.61), and cough (1.63, 1.24–2.14), were also strongly associated with EVD diagnosis. 

Conclusions: The existing literature fails to provide a unified position on the symptoms most predictive of EVD, but highlights some early and late stage symptoms that in combination will be useful for future risk stratification. Confirmation of these findings across datasets (or ideally an aggregation of all individual patient data) will aid effective future clinical assessment, risk stratification tools and emergency epidemic response planning.

Original languageEnglish
Article numbere0008799
JournalPLoS Neglected Tropical Diseases
Issue number10
Publication statusPublished - 23 Oct 2020

Bibliographical note

Funding Information: The author(s) received no specific funding for this work.

Open Access: This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Publisher Copyright: © 2020 Jain et al.

Citation: Jain V, Charlett A, Brown CS (2020) Meta-analysis of predictive symptoms for Ebola virus disease. PLoS Negl Trop Dis 14(10): e0008799.



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