Predicting Ebola infection: A malaria-sensitive triage score for Ebola virus disease.

The non-specific symptoms of Ebola Virus Disease (EVD) pose a major problem to triage and isolation efforts at Ebola Treatment Centres (ETCs). Under the current triage protocol, half the patients allocated to high-risk "probable" wards were EVD(-): a misclassification speculated to predispose nosocomial EVD infection. A better understanding of the statistical relevance of individual triage symptoms is essential in resource-poor settings where rapid, laboratory-confirmed diagnostics are often unavailable. This retrospective cohort study analyses the clinical characteristics of 566 patients admitted to the GOAL-Mathaska ETC in Sierra Leone. The diagnostic potential of each characteristic was assessed by multivariate analysis and incorporated into a statistically weighted predictive score, designed to detect EVD as well as discriminate malaria. Of the 566 patients, 28% were EVD(+) and 35% were malaria(+). Malaria was 2-fold more common in EVD(-) patients (p<0.05), and thus an important differential diagnosis. Univariate analyses comparing EVD(+) vs. EVD(-) and EVD(+)/malaria(-) vs. EVD(-)/malaria(+) cohorts revealed 7 characteristics with the highest odds for EVD infection, namely: reported sick-contact, conjunctivitis, diarrhoea, referral-time of 4-9 days, pyrexia, dysphagia and haemorrhage. Oppositely, myalgia was more predictive of EVD(-) or EVD(-)/malaria(+). Including these 8 characteristics in a triage score, we obtained an 89% ability to discriminate EVD(+) from either EVD(-) or EVD(-)/malaria(+). This study proposes a highly predictive and easy-to-use triage tool, which stratifies the risk of EVD infection with 89% discriminative power for both EVD(-) and EVD(-)/malaria(+) differential diagnoses. Improved triage could preserve resources by identifying those in need of more specific differential diagnostics as well as bolster infection prevention/control measures by better compartmentalizing the risk of nosocomial infection

Predicting Ebola Severity: A Clinical Prioritization Score for Ebola Virus Disease.

Despite the notoriety of Ebola virus disease (EVD) as one of the world's most deadly infections, EVD has a wide range of outcomes, where asymptomatic infection may be almost as common as fatality. With increasingly sensitive EVD diagnosis, there is a need for more accurate prognostic tools that objectively stratify clinical severity to better allocate limited resources and identify those most in need of intensive treatment. This retrospective cohort study analyses the clinical characteristics of 158 EVD(+) patients at the GOAL-Mathaska Ebola Treatment Centre, Sierra Leone. The prognostic potential of each characteristic was assessed and incorporated into a statistically weighted disease score. The mortality rate among EVD(+) patients was 60.8% and highest in those aged <5 or >25 years (p<0.05). Death was significantly associated with malaria co-infection (OR = 2.5, p = 0.01). However, this observation was abrogated after adjustment to Ebola viral load (p = 0.1), potentially indicating a pathologic synergy between the infections. Similarly, referral-time interacted with viral load, and adjustment revealed referral-time as a significant determinant of mortality, thus quantifying the benefits of early reporting as a 12% mortality risk reduction per day (p = 0.012). Disorientation was the strongest unadjusted predictor of death (OR = 13.1, p = 0.014) followed by hiccups, diarrhoea, conjunctivitis, dyspnoea and myalgia. Including these characteristics in multivariate prognostic scores, we obtained a 91% and 97% ability to discriminate death at or after triage respectively (area under ROC curve). This study proposes highly predictive and easy-to-use prognostic tools, which stratify the risk of EVD mortality at or after EVD triage

The bii4africa dataset of faunal and floral population intactness estimates across Africa's major land uses

This is the final version. Available on open access from Nature Research via the DOI in this recordCode availability: R code for calculating aggregated intactness scores for a focal region (e.g., ecoregion or country) and/or taxonomic group can be downloaded with the bii4africa dataset on Figshare; see Data Records section.Sub-Saharan Africa is under-represented in global biodiversity datasets, particularly regarding the impact of land use on species' population abundances. Drawing on recent advances in expert elicitation to ensure data consistency, 200 experts were convened using a modified-Delphi process to estimate 'intactness scores': the remaining proportion of an 'intact' reference population of a species group in a particular land use, on a scale from 0 (no remaining individuals) to 1 (same abundance as the reference) and, in rare cases, to 2 (populations that thrive in human-modified landscapes). The resulting bii4africa dataset contains intactness scores representing terrestrial vertebrates (tetrapods: ±5,400 amphibians, reptiles, birds, mammals) and vascular plants (±45,000 forbs, graminoids, trees, shrubs) in sub-Saharan Africa across the region's major land uses (urban, cropland, rangeland, plantation, protected, etc.) and intensities (e.g., large-scale vs smallholder cropland). This dataset was co-produced as part of the Biodiversity Intactness Index for Africa Project. Additional uses include assessing ecosystem condition; rectifying geographic/taxonomic biases in global biodiversity indicators and maps; and informing the Red List of Ecosystems.Jennifer Ward Oppenheimer Research Gran

The bii4africa dataset of faunal and floral population intactness estimates across Africa’s major land uses

Sub-Saharan Africa is under-represented in global biodiversity datasets, particularly regarding the impact of land use on species’ population abundances. Drawing on recent advances in expert elicitation to ensure data consistency, 200 experts were convened using a modified-Delphi process to estimate ‘intactness scores’: the remaining proportion of an ‘intact’ reference population of a species group in a particular land use, on a scale from 0 (no remaining individuals) to 1 (same abundance as the reference) and, in rare cases, to 2 (populations that thrive in human-modified landscapes). The resulting bii4africa dataset contains intactness scores representing terrestrial vertebrates (tetrapods: ±5,400 amphibians, reptiles, birds, mammals) and vascular plants (±45,000 forbs, graminoids, trees, shrubs) in sub-Saharan Africa across the region’s major land uses (urban, cropland, rangeland, plantation, protected, etc.) and intensities (e.g., large-scale vs smallholder cropland). This dataset was co-produced as part of the Biodiversity Intactness Index for Africa Project. Additional uses include assessing ecosystem condition; rectifying geographic/ taxonomic biases in global biodiversity indicators and maps; and informing the Red List of Ecosystems

The bii4africa dataset of faunal and floral population intactness estimates across Africa’s major land uses

Sub-Saharan Africa is under-represented in global biodiversity datasets, particularly regarding the impact of land use on species’ population abundances. Drawing on recent advances in expert elicitation to ensure data consistency, 200 experts were convened using a modified-Delphi process to estimate ‘intactness scores’: the remaining proportion of an ‘intact’ reference population of a species group in a particular land use, on a scale from 0 (no remaining individuals) to 1 (same abundance as the reference) and, in rare cases, to 2 (populations that thrive in human-modified landscapes). The resulting bii4africa dataset contains intactness scores representing terrestrial vertebrates (tetrapods: ±5,400 amphibians, reptiles, birds, mammals) and vascular plants (±45,000 forbs, graminoids, trees, shrubs) in sub-Saharan Africa across the region’s major land uses (urban, cropland, rangeland, plantation, protected, etc.) and intensities (e.g., large-scale vs smallholder cropland). This dataset was co-produced as part of the Biodiversity Intactness Index for Africa Project. Additional uses include assessing ecosystem condition; rectifying geographic/taxonomic biases in global biodiversity indicators and maps; and informing the Red List of Ecosystems

Local perceptions of the socio-demographic changes triggered by large-scale plantation forests: Evidence from rural communities in Northern Province of Sierra Leone

Global concerns about forest sustainability have shifted the attention to plantation forests as a potential candidate to fill the wood and ecosystem service demand. In this regard, their contribution to lessening the pressure on natural forests has been recognized, and this is becoming increasingly important in the context of a changing climate. This study was designed to conceptualize the influence of large-scale plantation forests on changes in the socio-demographic characteristics in local communities. A mixed-method approach combining qualitative data from two key informant interviews with a household survey of 125 respondents was deployed to explore the local perceptions of the influence of plantation forestry on socio-demographic changes. Our results revealed mixed perceptions of the socio-demographic changes, reflecting both increasing and decreasing trends. All of the socio-demographic factors were positively influenced in a societal desired manner by plantation forestry, except household income and construction materials. The socio-demographic factors were identified as the principal determinants shaping the plantation forestry's contribution to the socio-economic development of respondents’ households. The direction of socio-demographic changes was reported to be positive across all the communities, with the magnitude of influence on the respondent's households varying between low and high. Our results suggest the need for understanding the dynamics associated with land use conversion to forest plantations in rural areas to inspire the search for options to implement an integrated landscape approach for tree plantation development with minimal social impacts on local populations

Epidemiological characteristics of EVD outcome.

<p><b>(A)</b> Kaplan-Meier survival analysis of patients in the ETC according to their EVD status. <b>(B)</b> Mortality among EVD(-) and EVD(+) admissions according to gender. <b>(C)</b> Average age of death among EVD(-) and EVD(+) patients. <b>(D)</b> Mortality rate across age groups in EVD(-) and EVD(+) cohorts. Dotted lines represent the average mortality rate across all ages in the cohort. Statistics in <b>(C)</b> calculated by unpaired t test *: p<0.05, **: p<0.005, ***: p<0.001, ns: not significant.</p

Accuracy of current triage methods.

<p><b>(A)</b> Number of EVD(+) and EVD(-) patients triaged into the low-risk “suspect” and high-risk “probable” wards using the WHO triage protocol [<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0005356#pntd.0005356.ref007" target="_blank">7</a>]. <b>(B)</b> Number of days spent in the ETC according to the probability of being diagnosed as either EVD(+) (red) or EVD(-) with malaria (green) or with neither EVD nor malaria (blue). <b>(C)</b> The sensitivity and specificity of predicting EVD(+) patients in our cohort using the scoring system of Levine et al. [<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0005356#pntd.0005356.ref011" target="_blank">11</a>]. The area under the receiver-operator characteristic (ROC) curve represents the discriminative power of the score. <b>(D)</b> Percentage of EVD(+) and EVD(-) patients in our cohort classified in the various risk categories as proposed by the scoring system of Levine et al. [<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0005356#pntd.0005356.ref011" target="_blank">11</a>].</p

Prognostic value of Ebola virus load (Ct value).

<p><b>(A)</b> Distribution of Ct values¹ for EVD(+) patients considered to have a high viral load (Ct ≤ 20) and low viral load (Ct > 20). <b>(B)</b> Ct value distribution across age in the EVD(+) cohort. The red line plots the fractional polynomial prediction of the Ct value. <b>(C)</b> Ct values amongst survivors and fatalities in the EVD(+) cohort. <b>(D)</b> Kaplan-Meier survival analysis of EVD(+) patients according their Ebola virus loads, either considered as high viral (Ct ≤ 20) or low viral load (Ct > 20). ¹ Ct values represent Ebola-specific qRT-PCR results (inversely proportional to the viral load). Statistics in <b>(C)</b> calculated by unpaired t test *: p<0.05, **: p<0.005, ***: p<0.001, ns: not significant.</p