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Background: Stringent infection control requirements at Ebola treatment centers (ETCs), which are specialized facilities for isolating and treating Ebola patients, create substantial challenges for recording and reviewing patient information. During the 2014-2016 West African Ebola epidemic, paper-based data collection systems at ETCs compromised the quality, quantity, and confidentiality of patient data. Electronic health record (EHR) systems have the potential to address such problems, with benefits for patient care, surveillance, and research. However, no suitable software was available for deployment when large-scale ETCs opened as the epidemic escalated in 2014.Objective: We present our work on rapidly developing and deploying OpenMRS-Ebola, an EHR system for the Kerry Town ETC in Sierra Leone. We describe our experience, lessons learned, and recommendations for future health emergencies.Methods: We used the OpenMRS platform and Agile software development approaches to build OpenMRS-Ebola. Key features of our work included daily communications between the development team and ground-based operations team, iterative processes, and phased development and implementation. We made design decisions based on the restrictions of the ETC environment and regular user feedback. To evaluate the system, we conducted predeployment user questionnaires and compared the EHR records with duplicate paper records.Results: We successfully built OpenMRS-Ebola, a modular stand-alone EHR system with a tablet-based application for infectious patient wards and a desktop-based application for noninfectious areas. OpenMRS-Ebola supports patient tracking (registration, bed allocation, and discharge); recording of vital signs and symptoms; medication and intravenous fluid ordering and monitoring; laboratory results; clinician notes; and data export. It displays relevant patient information to clinicians in infectious and noninfectious zones. We implemented phase 1 (patient tracking; drug ordering and monitoring) after 2.5 months of full-time development. OpenMRS-Ebola was used for 112 patient registrations, 569 prescription orders, and 971 medication administration recordings. We were unable to fully implement phases 2 and 3 as the ETC closed because of a decrease in new Ebola cases. The phase 1 evaluation suggested that OpenMRS-Ebola worked well in the context of the rollout, and the user feedback was positive.Conclusions: To our knowledge, OpenMRS-Ebola is the most comprehensive adaptable clinical EHR built for a low-resource setting health emergency. It is designed to address the main challenges of data collection in highly infectious environments that require robust infection prevention and control measures and it is interoperable with other electronic health systems. Although we built and deployed OpenMRS-Ebola more rapidly than typical software, our work highlights the challenges of having to develop an appropriate system during an emergency rather than being able to rapidly adapt an existing one. Lessons learned from this and previous emergencies should be used to ensure that a set of well-designed, easy-to-use, pretested health software is ready for quick deployment in future. [ABSTRACT FROM AUTHOR]

Human and animal infections with bacteria of the genus Sarcina (family Clostridiaceae) are associated with gastric dilation and emphysematous gastritis. However, the potential roles of sarcinae as commensals or pathogens remain unclear. Here, we investigate a lethal disease of unknown etiology that affects sanctuary chimpanzees (Pan troglodytes verus) in Sierra Leone. The disease, which we have named "epizootic neurologic and gastroenteric syndrome" (ENGS), is characterized by neurologic and gastrointestinal signs and results in death of the animals, even after medical treatment. Using a case-control study design, we show that ENGS is strongly associated with Sarcina infection. The microorganism is distinct from Sarcina ventriculi and other known members of its genus, based on bacterial morphology and growth characteristics. Whole-genome sequencing confirms this distinction and reveals the presence of genetic features that may account for the unusual virulence of the bacterium. Therefore, we propose that this organism be considered the representative of a new species, named "Candidatus Sarcina troglodytae". Our results suggest that a heretofore unrecognized complex of related sarcinae likely exists, some of which may be highly virulent. However, the potential role of "Ca. S. troglodytae" in the etiology of ENGS, alone or in combination with other factors, remains a topic for future research. Infections with bacteria of the genus Sarcina are associated with gastric diseases of unclear etiology. Here, Owens et al. show that infection with a distinct Sarcina species is strongly associated with a lethal disease that affects sanctuary chimpanzees in Sierra Leone. [ABSTRACT FROM AUTHOR]

Background: Assessment of the response to the 2014–15 Ebola outbreak indicates the need for innovations in data collection, sharing, and use to improve case detection and treatment. Here we introduce a Machine Learning pipeline for Ebola Virus Disease (EVD) prognosis prediction, which packages the best models into a mobile app to be available in clinical care settings. The pipeline was trained on a public EVD clinical dataset, from 106 patients in Sierra Leone. Methods/Principal Findings: We used a new tool for exploratory analysis, Mirador, to identify the most informative clinical factors that correlate with EVD outcome. The small sample size and high prevalence of missing records were significant challenges. We applied multiple imputation and bootstrap sampling to address missing data and quantify overfitting. We trained several predictors over all combinations of covariates, which resulted in an ensemble of predictors, with and without viral load information, with an area under the receiver operator characteristic curve of 0.8 or more, after correcting for optimistic bias. We ranked the predictors by their F1-score, and those above a set threshold were compiled into a mobile app, Ebola CARE (Computational Assignment of Risk Estimates). Conclusions/Significance: This method demonstrates how to address small sample sizes and missing data, while creating predictive models that can be readily deployed to assist treatment in future outbreaks of EVD and other infectious diseases. By generating an ensemble of predictors instead of relying on a single model, we are able to handle situations where patient data is partially available. The prognosis app can be updated as new data become available, and we made all the computational protocols fully documented and open-sourced to encourage timely data sharing, independent validation, and development of better prediction models in outbreak response. [ABSTRACT FROM AUTHOR]