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Purpose: The 2014 outbreak of Ebola virus disease (EVD) in West Africa was the largest in history. Starting in September 2014, International Medical Corps (IMC) operated five Ebola treatment units (ETUs) in Sierra Leone and Liberia. This paper explores how future infectious disease outbreak facilities in resource-limited settings can be planned, organized, and managed by analyzing data collected on water, sanitation, and hygiene (WASH) and infection prevention control (IPC) protocols. Design/Methodology/Approach: We conducted a retrospective cohort study by analyzing WASH/IPC activity data routinely recorded on paper forms or white boards at ETUs during the outbreak and later merged into a database from two IMC-run ETUs in Sierra Leone between December 2014 and December 2015. Findings: The IMC WASH/IPC database contains data from over 369 days. Our results highlight parameters key to designing and maintaining an ETU. High concentration chlorine solution usage was highly correlated with both daily patient occupancy and high-risk zone staff entries; low concentration chlorine usage was less well explained by these measures. There is high demand for laundering and disinfecting of personal protective equipment (PPE) on a daily basis and approximately 1 (0–4) piece of PPE is damaged each day. Research limitations/Implications: Lack of standardization in the type and format of data collected at ETUs made constructing the WASH/IPC database difficult. However, the data presented here may help inform humanitarian response operations in future epidemics. [ABSTRACT FROM AUTHOR]

This paper investigates suitability of supervised machine learning classification methods for classification of biomes using pollen datasets. We assign modern pollen samples from Africa and Arabia to five biome classes using a previously published African pollen dataset and a global ecosystem classification scheme. To test the applicability of traditional and machine-learning based classification models for the task of biome prediction from high dimensional modern pollen data, we train a total of eight classification models, including Linear Discriminant Analysis, Logistic Regression, Naïve Bayes, K-Nearest Neighbors, Classification Decision Tree, Random Forest, Neural Network, and Support Vector Machine. The ability of each model to predict biomes from pollen data is statistically tested on an independent test set. The Random Forest classifier outperforms other models in its ability correctly classify biomes given pollen data. Out of the eight models, the Random Forest classifier scores highest on all of the metrics used for model evaluations and is able to predict four out of five biome classes to high degree of accuracy, including arid, montane, tropical and subtropical closed and open systems, e.g. forests and savanna/grassland. The model has the potential for accurate reconstructions of past biomes and awaits application to fossil pollen sequences. The Random Forest model may be used to investigate vegetation changes on both long and short time scales, e.g. during glacial and interglacial cycles, or more recent and abrupt climatic anomalies like the African Humid Period. Such applications may contribute to a better understanding of past shifts in vegetation cover and ultimately provide valuable information on drivers of climate change. [ABSTRACT FROM AUTHOR]

Background: Crime and violence causes massive disruptions to the health, survival and development of populations. In South Africa, incredibly high rates of crime and violence are noted. The country also has a very large youth population whose health, survival and development are key to economic growth. Among other efforts to encourage healthy youth development and the promotion of social activities such as sports, youth groups, choirs and so forth. This study examines the relationship between perceived community safety and the uptake of social activities among youth in South Africa. Data and methods: This paper uses data from the National Youth Lifestyle Survey (2008) with an unweighted sample of 4,391 youth (age12-22 years old). Using chi-square and logistic regression analysis the association between perceived community safety and social activity participation are tested. Findings: The results indicate that youth participation in social activities in South Africa is high (55% of males and 45% of females). Among males, the most prominent activity is sports (51.8%), while for females there is high participation in choir and singing groups (55.68%). More than 50% of males perceive their communities as risky while less than half of females feel the same. Male youth are more likely to participate in social activities if they perceive their communities as risky (OR = 1.04). Females (OR = 0.83), youth have a negative view of their future (OR = 0.43) are less likely to participate in social activities. Conclusions: There exists an association between youth’s perception of community safety and their participation in social activities. Whether sports and groups are protective or enabling environments for youth from unsafe communities is moot. More in-depth research is needed on why youth participate in these clubs and groups to truly understand the role of social activities in South African societies. [ABSTRACT FROM AUTHOR]