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This paper examines the atmosphere of mistrust that permeated the response to the tenth Ebola epidemic in Eastern DRC (2018–2020). The concept of an 'atmosphere of mistrust' that we develop in this article directs attention to the elusive-yet-pervasive presence of mistrust in interactions between responders and communities during the Ebola epidemic. This analysis focuses on the popular notion that 'Ebola is a business'. Our interviewees frequently used this saying during our research on the Ebola response to explain why mistrust had emerged, how it materialized, and against whom it was directed. Based on these interviews, we examine 'Ebola is a business' as a slogan that enabled people to voice mistrust. This slogan, as we aim to show, resonated with a wider atmosphere of mistrust that governed the emergency situation in Eastern DRC. In using it, people responded to their perceptions of mistrust whilst simultaneously perpetuating and extending this atmosphere of mistrust. Our analysis of the atmosphere of mistrust highlights the power of atmospheres in governing situations, mobilizing people, and disrupting structures of discrimination. It aims to contribute to a better understanding of the barriers inhibiting the collaborations between affected communities and responders, which are required to deliver effective epidemic responses. Moreover, we argue that voice and the atmosphere are important analytics for exploring the histories of mistrust that Ebola epidemics ask for. [ABSTRACT FROM AUTHOR]

The low specificity of Ebola virus disease clinical signs increases the risk for nosocomial transmission to patients and healthcare workers during outbreaks. Reducing this risk requires identifying patients with a high likelihood of Ebola virus infection. Analyses of retrospective data from patients suspected of having Ebola virus infection identified 13 strong predictors and time from disease onset as constituents of a prediction score for Ebola virus disease. We also noted 4 highly predictive variables that could distinguish patients at high risk for infection, independent of their scores. External validation of this algorithm on retrospective data revealed the probability of infection continuously increased with the score.