Dataset Information

Delayed recognition of Ebola virus disease is associated with longer and larger outbreaks.

ABSTRACT: The average time required to detect an Ebola virus disease (EVD) outbreak following spillover of Ebola virus (EBOV) to a primary human case has remained essentially unchanged for over 40 years, with some of the longest delays in detection occurring in recent decades. In this review, our aim was to examine the relationship between delays in detection of EVD and the duration and size of outbreaks, and we report that longer delays are associated with longer and larger EVD outbreaks. Historically, EVD outbreaks have typically been comprised of less than 100 cases (median?=?60) and have lasted less than 4 months (median?=?118 days). The ongoing outbreak in Democratic Republic of the Congo, together with the 2013-2016 west Africa outbreak, are stark outliers amidst these trends and had two of the longest delays in detection on record. While significant progress has been made in the development of EVD countermeasures, implementation during EVD outbreaks is problematic. Thus, EVD surveillance must be improved by the broad deployment of modern diagnostic tools, as prompt recognition of EVD has the potential to stem early transmission and ultimately limit the duration and size of outbreaks.

SUBMITTER: Matson MJ

PROVIDER: S-EPMC7034085 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Delayed recognition of Ebola virus disease is associated with longer and larger outbreaks.

Matson M Jeremiah MJ Chertow Daniel S DS Munster Vincent J VJ

Emerging microbes & infections 20200204 1

The average time required to detect an Ebola virus disease (EVD) outbreak following spillover of Ebola virus (EBOV) to a primary human case has remained essentially unchanged for over 40 years, with some of the longest delays in detection occurring in recent decades. In this review, our aim was to examine the relationship between delays in detection of EVD and the duration and size of outbreaks, and we report that longer delays are associated with longer and larger EVD outbreaks. Historically, E ...[more]

PMID: 32013784

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Project description:ImportanceSurvivors of Ebola virus disease (EVD) may experience ocular sequelae. Comparison with antibody-negative individuals from the local population is required to characterize the disease.ObjectiveTo assess features of ophthalmic disease specific to EVD.Design, setting, and participantsThis baseline cross-sectional analysis of survivors of EVD and their close contacts was conducted within PREVAIL III, a 5-year, longitudinal cohort study. Participants who enrolled at John F. Kennedy Medical Center in Liberia, West Africa from June 2015 to March 2016 were included in this analysis. Close contacts were defined as household members or sex partners of survivors of EVD. Data were analyzed from July 2016 to July 2020.ExposuresAll participants, both survivors and close contacts, underwent testing of IgG antibody levels against Ebola virus surface glycoprotein.Main outcomes and measuresOcular symptoms, anterior and posterior ophthalmologic examination findings, and optical coherence tomography images were compared between antibody-positive survivors and antibody-negative close contacts.ResultsA total of 564 antibody-positive survivors (320 [56.7%] female; mean [SD] age, 30.3 [14.0] years) and 635 antibody-negative close contacts (347 [54.6%] female; mean [SD] age, 25.8 [15.5] years) were enrolled in this study. Survivors were more likely to demonstrate color vision deficit (28.9% vs 19.0%, odds ratio [OR], 1.6; 95% CI, 1.2-2.1) and lower intraocular pressure (12.4 vs 13.5 mm Hg; mean difference, -1.2 mm Hg; 95% CI, -1.6 to -0.8 mm Hg) compared with close contacts. Dilated fundus examination revealed a higher percentage of vitreous cells (7.8% vs 0.5%; OR, 16.6; 95% CI, 5.0-55.2) and macular scars (4.6% vs 1.6%; OR, 2.8; 95% CI, 1.4-5.5) in survivors than in close contacts. Uveitis was present in 26.4% of survivors and 12.1% of close contacts (OR, 2.4; 95% CI, 1.8-3.2). Among all participants with uveitis, survivors were more likely than close contacts to have intermediate uveitis (34.2% vs 6.5% of all cases; OR, 7.8; 95% CI, 3.1-19.7) and had thicker mean central subfield thickness on optical coherence tomography (222 vs 212 μm; mean difference, 14.4 μm; 95% CI, 1.9-26.9 μm).Conclusions and relevanceIn this cross-sectional study, survivors of EVD had a distinct spectrum of ocular and neuro-ophthalmologic findings compared with close contacts that potentially require medical and surgical treatment.

Project description:Uganda has reported eight outbreaks caused by filoviruses between 2000 to 2016, more than any other country in the world. We used species distribution modeling to predict where filovirus outbreaks are likely to occur in Uganda to help in epidemic preparedness and surveillance. The MaxEnt software, a machine learning modeling approach that uses presence-only data was used to establish filovirus - environmental relationships. Presence-only data for filovirus outbreaks were collected from the field and online sources. Environmental covariates from Africlim that have been downscaled to a nominal resolution of 1km x 1km were used. The final model gave the relative probability of the presence of filoviruses in the study area obtained from an average of 100 bootstrap runs. Model evaluation was carried out using Receiver Operating Characteristic (ROC) plots. Maps were created using ArcGIS 10.3 mapping software. We showed that bats as potential reservoirs of filoviruses are distributed all over Uganda. Potential outbreak areas for Ebola and Marburg virus disease were predicted in West, Southwest and Central parts of Uganda, which corresponds to bat distribution and previous filovirus outbreaks areas. Additionally, the models predicted the Eastern Uganda region and other areas that have not reported outbreaks before to be potential outbreak hotspots. Rainfall variables were the most important in influencing model prediction compared to temperature variables. Despite the limitations in the prediction model due to lack of adequate sample records for outbreaks, especially for the Marburg cases, the models provided risk maps to the Uganda surveillance system on filovirus outbreaks. The risk maps will aid in identifying areas to focus the filovirus surveillance for early detection and responses hence curtailing a pandemic. The results from this study also confirm previous findings that suggest that filoviruses are mainly limited by the amount of rainfall received in an area.

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Delayed recognition of Ebola virus disease is associated with longer and larger outbreaks.

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Delayed recognition of Ebola virus disease is associated with longer and larger outbreaks.

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