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BackgroundIn the absence of proper guidelines and algorithms, available rapid diagnostic tests (RDTs) for common acute undifferentiated febrile illnesses are often used inappropriately.MethodsUsing prevalence data of 5 common febrile illnesses from India and Cambodia, and performance characteristics (sensitivity and specificity) of relevant pathogen-specific RDTs, we used a mathematical model to predict the probability of correct identification of each disease when diagnostic testing occurs either simultaneously or sequentially in various algorithms. We developed a web-based application of the model so as to visualize and compare output diagnostic algorithms when different disease prevalence and test performance characteristics are introduced.ResultsDiagnostic algorithms with appropriate sequential testing predicted correct identification of etiology in 74% and 89% of patients in India and Cambodia, respectively, compared with 46% and 49% with simultaneous testing. The optimally performing sequential diagnostic algorithms differed in India and Cambodia due to varying disease prevalence.ConclusionsSimultaneous testing is not appropriate for the diagnosis of acute undifferentiated febrile illnesses with presently available tests, which should deter the unsupervised use of multiplex diagnostic tests. The implementation of adaptive algorithms can predict better diagnosis and add value to the available RDTs. The web application of the model can serve as a tool to identify the optimal diagnostic algorithm in different epidemiological settings, while taking into account the local epidemiological variables and accuracy of available tests.

Original publication




Journal article


Clinical infectious diseases : an official publication of the Infectious Diseases Society of America

Publication Date





2262 - 2269


Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, United Kingdom.


Humans, Diagnostic Tests, Routine, Sensitivity and Specificity, Algorithms, Cambodia, India