Febrile illness is a major contributor to morbidity and mortality in South East Asia. In resource poor settings, individuals seek treatment and report cases through a complex mix of public and private health care delivery infrastructures. Often treatment is delivered by volunteers or private providers with little or no clinical training and with no guarantee of quality or appropriateness. The number of pathogens associated with febrile illness in South East Asia is so high that it is more intuitive to consider groups of pathogens rather than individual species. The multiple species can be grouped in different ways depending on perspective (patient, healthcare provider, pharma, control program). Intervening for a specific species could have a positive or negative effect on other species. In resource poor settings, the added value of a positive interaction for a particular intervention could influence the decision to adopt it. On the other hand, disregarding potential negative interactions, like the promotion of antimicrobial resistance, incurs a high public health risk. Another layer of complexity is the issue of the aetiology of the causes of febrile illness being both spatially and temporally heterogeneous. This heterogeneity will become more pronounced as public health interventions are deployed. Information about the aetiology is intrinsically linked to the public health infrastructure (community health workers, health centres, clinics, hospitals delivered by governmental, non-governmental and private institutions). Policy decisions regarding empirical treatment can be driven by this system and its relationship to clinical research. With new diagnostics combining with reporting tools for health care workers (smartphones etc) and strengthened surveillance systems being trialled and implemented, there is a need to predict the potential impact of this new technology in preparation for its integration into public health infrastructure in resource-poor settings.
Aims and Objectives: The aim of this project is to support the deployment cost-effective integrated strategy designs for febrile illness detection and control which minimise the impact on the spread of antibiotic resistance. The objectives are:
 Newton PN, Green MD, Fernandez FM, Day NP, White NJ (2006) Counterfeit anti-infective drugs. Lancet Infect Dis 6: 602-613.
 White LJ, Newton PN, Maude RJ, Pan-ngum W, Fried JR, et al. (2012) Defining disease heterogeneity to guide the empirical treatment of febrile illness in resource poor settings. PLoS One 7: e44545.
 Wellington EM, Boxall AB, Cross P, Feil EJ, Gaze WH, et al. (2013) The role of the natural environment in the emergence of antibiotic resistance in gram-negative bacteria. Lancet Infect Dis 13: 155-165.
The successful candidate will be based in the Mathematical and Economic Modelling (MAEMOD) group at the Mahidol-Oxford Tropical Medicine Research Unit (MORU) in Bangkok, Thailand. MAEMOD provide an extensive programme of in house training in mathematical and economic modelling. The supervisory team will also work with the student to put together a bespoke training package including online and residential courses to support their research project and fill in knowledge gaps. They will also benefit from the MORU generic research skills training portfolio.
Project reference number: 998
|Professor Lisa J White||Tropical Medicine||Oxford University, Bangkok||THAemail@example.com|
|Assistant Professor Wirichada Pan-ngum||Tropical Medicine||Oxford University, Bangkok||THAfirstname.lastname@example.org|
There are no publications listed for this DPhil project.