Mathematical models are being used to evaluate the potential impact of control programs and identify the best implementation scenarios for countries from vaccination to vector control strategies. Further mathematical modelling has recently been used to estimate the impact of high sensitive diagnostic tools on the malaria elimination in South East Asia and beyond, including which use cases are most suitable to the use of the test. In the context of malaria elimination and reducing malaria cases it is important to start to consider other causes of febrile illnesses to support patient management. To enable improved fever management modelling can help to optimise trials for new and old diagnostic tools, roll-out interventions and the establishments of testing algorithms, particularly when multiple testing pathways are possible. The latter is especially relevant in the context of integrated fever management to enable the most effective way of using resources while striving for universal health care.
The aim of this project is to use a mixed methods approach to understand and develop strategies to improve integrated fever management with a focus on diagnostic or algorithm innovations in the context of universal health care. The objectives are:
This project would be most suited to a candidate with experience in clinical fever management, economic or mathematical modelling and diagnostic uses and implementation policies.
The successful candidate will be provided with a bespoke training package with online and residential courses to support their research project and fill in knowledge gaps designed in partnership with Professor Lisa White. The candidate will be working closely with the Head of the Malaria and Fever program at FIND the only global product development partnership (PDP) based in Geneva (Switzerland) dedicated to integrated fever diagnostic development and implementation.
Project reference number: 998
|Dr Sabine Dittrich||Tropical Medicine||Oxford University, Vientiane||LAO||Sabine@tropmedres.ac|
|Lisa White||Big Data Institute||Oxford University, NDM Research Building||GBRemail@example.com|
|Wirichada Pan-ngum||Tropical Medicine||Oxford University, Bangkok||THAfirstname.lastname@example.org|
BACKGROUND: In the absence of proper guidelines and algorithms, available rapid diagnostic tests (RDTs) for common acute undifferentiated febrile illnesses (AUFI) are often used inappropriately. METHODS: Using prevalence data of five 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 visualise and compare output diagnostic algorithms when different disease prevalence and test performance characteristics are introduced. RESULTS: Diagnostic algorithms with appropriate sequential testing predicted correct identification of aetiology in 74% and 89% of patients in India and Cambodia respectively compared to 46% and 49% with simultaneous testing. The optimally performing sequential diagnostic algorithms differed in India and Cambodia due to varying disease prevalence. CONCLUSION: Simultaneous testing is not appropriate for the diagnosis of AUFIs 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, whilst taking into account the local epidemiological variables and accuracy of available tests. Hide abstract