Background In response to the coronavirus disease (COVID-19) pandemic, Project HOPE®, an international humanitarian organization, partnered with Brown University to develop and deploy a virtual training-of-trainers (TOT) program to provide practical knowledge to healthcare stakeholders. This study is designed to evaluate this TOT program. Objective The goal of this study is to assess the effectiveness of this educational intervention in enhancing knowledge on COVID-19 concepts and to present relative change in score of each competency domains of the training. Methods The training was created by interdisciplinary faculty from Brown University and delivered virtually. Training included eight COVID-19 specific modules on infection prevention and control, screening and triage, diagnosis and management, stabilization and resuscitation, surge capacity, surveillance, and risk communication and community education. The assessment of knowledge attainment in each of the course competency domain was conducted using 10 question pre-and post-test evaluations. Paired t-test were used to compare interval knowledge scores in the overall cohort and stratified by WHO regions. TOT dissemination data was collected from in-country partners by Project Hope. Results Over the period of 7 months, 4,291 personnel completed the TOT training in 55 countries, including all WHO regions. Pre-test and post-test were completed by 1,198 and 706 primary training participants, respectively. The mean scores on the pre-test and post-test were 68.45% and 81.4%, respectively. The mean change in score was 11.72%, with P value <0.0005. All WHO regions had a statistically significant improvement in their score in post-test. The training was disseminated to 97,809 health workers through local secondary training. Conclusion Innovative educational tools resulted in improvement in knowledge related to the COVID-19 pandemic, significantly increasing the average score on knowledge assessment testing. Academic – humanitarian partnerships can serve to implement and disseminate effective education rapidly across the globe.

Background Ebola Virus Disease (EVD) causes high case fatality rates (CFRs) in young children, yet there are limited data focusing on predicting mortality in pediatric patients. Here we present machine learning-derived prognostic models to predict clinical outcomes in children infected with Ebola virus. Methods Using retrospective data from the Ebola Data Platform, we investigated children with EVD from the West African EVD outbreak in 2014–2016. Elastic net regularization was used to create a prognostic model for EVD mortality. In addition to external validation with data from the 2018–2020 EVD epidemic in the Democratic Republic of the Congo (DRC), we updated the model using selected serum biomarkers. Findings Pediatric EVD mortality was significantly associated with younger age, lower PCR cycle threshold (Ct) values, unexplained bleeding, respiratory distress, bone/muscle pain, anorexia, dysphagia, and diarrhea. These variables were combined to develop the newly described EVD Prognosis in Children (EPiC) predictive model. The area under the receiver operating characteristic curve (AUC) for EPiC was 0.77 (95% CI: 0.74–0.81) in the West Africa derivation dataset and 0.76 (95% CI: 0.64–0.88) in the DRC validation dataset. Updating the model with peak aspartate aminotransferase (AST) or creatinine kinase (CK) measured within the first 48 hours after admission increased the AUC to 0.90 (0.77–1.00) and 0.87 (0.74–1.00), respectively. Conclusion The novel EPiC prognostic model that incorporates clinical information and commonly used biochemical tests, such as AST and CK, can be used to predict mortality in children with EVD.

E bola virus disease (EVD) is a potentially fatal infectious disease, easily transmitted through direct contact with infected body fluids. Children exhibit a range of nonspecific clinical signs that mirror common endemic febrile diseases, such as malaria and gastroenteritis. Few children experience hemorrhage, and some are afebrile (1).