Ebola-related datasets

This study investigated the causes of fever of unknown origin (FUO) in 550 Guinean patients who initially tested negative for Ebola virus during the 2014 outbreak. This repository contains data and analysis files from our study investigating infectious causes of fever of unknown origin (FUO) in 550 Guinean patients during the 2014 Ebola outbreak: -Appendix_I_ONT_Pipeline_Results.xlsx Complete 16S sequencing data including quality metrics, BLAST alignment results, and taxonomic assignments for bacterial identification. -Appendix_II_FUO_Guinea_Clinical_Data.xlsx: Complete dataset including patient demographics, symptoms, pathogen detection results, and clinical parameters. -Appendix_III_Statistical_Analysis_Report.zip: Jamovi project file containing all statistical analyses, enabling reproducibility and further exploration of the data and statistical report detailing associations between cohort variables, parasitemia, coinfections, and symptoms. Includes all statistical tests, significance values, and visualizations. Key findings include: Major Results: - Identified infectious causes in 52.3% of patients through molecular and serological testing - Main pathogens found: - Plasmodium (35.6%, predominantly P. falciparum) - Pathogenic bacteria (18.4%), including Salmonella and Klebsiella - Hemorrhagic fever viruses (5.8%), including previously missed Ebola cases Notable Findings: 1. Missed Diagnoses: - 28 previously undetected Ebola cases (5.1%) - Demonstrates compromise in diagnostic capabilities during outbreaks 2. Antimicrobial Resistance: - 60% of samples showed resistance to first-line antibiotics - High prevalence of resistance genes against aminoglycosides, beta-lactams, tetracyclines 3. Coinfections: - 20% of infected patients had multiple pathogens - Most common: Malaria with sepsis-causing bacteria - Similar rates in adults (12.1%) and children (12.5%) Data Collection/Methods: - Blood samples from March-December 2014 - Analysis through PCR, serological tests, and high-throughput sequencing - Patient demographics: median age 29 years, 63% male - Most patients from Western Guinea regions Interpretation: 1. Clinical Impact: - Demonstrates need for comprehensive testing beyond single pathogens - Supports empirical antimalarial and antibiotic therapy in resource-limited settings - Highlights importance of monitoring antibiotic resistance 2. Public Health Implications: - Shows necessity of strengthening laboratory capacity in sub-Saharan Africa - Emphasizes need for syndromic testing during outbreaks - Suggests value of point-of-care diagnostics for multiple pathogens The data provides actionable insights for improving diagnostic approaches and treatment guidelines in resource-limited settings during disease outbreaks.

Open data was recognized as essential to preventing and treating pandemic infection through sharing, disseminating, and using relevant information. We explored how and to what extent open data influenced the response of science to such emergencies from a quantitative perspective. Based on the genetic datasets for viruses associated with EBOLA, SARS, MERS, and COVID-19, we analyze the efficiency of data sharing and citation from a knowledge flow perspective: "datasets→papers", "datasets→patents", and "datasets→papers→patents". These datasets were created to investigate and analyze the efficiency of open data in the perspective of knowledge flown across the four pandemics, including Ebola, Sars, Mers, and Covid-19. The details of collection and processions were descripted in our study titled: Does Open Data Have the Potential to Improve the Response of Science to Public Health Emergencies?

This dataset represents the analysis of antibody-mediated innate immune effector functions from Ebola virus disease survivors and a panel of engineered Fc-mutants for the Ebola-specific antibody VIC16.

Code and datasets associated with manuscript CELL-REPORTS-D-19-05125R1

Data set made available contains data on the reporting delays for the submitted manuscript "Assessing reporting delays and the effective reproduction number: The 2018-19 Ebola epidemic in DRC, May 2018-January 2019". Data set contains 7 variables and 661 observations in total. Data has been extracted from the epidemic curves of the WHO situation reports and disease outbreak news using an online software, WebPlotDigitizer.

Two files are being shared: - data file (txt) - dictionnary (word) The data file comprises de-identified individual data on the exposures, symptoms after exposure, serological responses against three viral antigens.

Dataset of the immune effector function induced by 168 Ebola virus glycoprotein-specific monoclonal antibodies.

Hydrogen deuterium exchange mass spectrometry data for paper title "Electron cryo-microscopy structure of Ebola nucleoprotein reveals a mechanism for nucleocapsid-like assembly" CELL-D-17-02921

Datapoints, uncropped blots and images, raw ITC data and sequences of oligonucleotides used.