Pertussis is a highly contagious respiratory tract disease that affects people of all ages, however young unimmunised or partially immunised infants are the most vulnerable group with the highest rates of complications and death (>90%). There are an estimated 50 million cases of pertussis and 300 000 pertussis-related deaths per annum globally. Despite well-established vaccination programmes, pertussis persists as a major public health concern. A growing body of evidence recommends supplementing childhood vaccination with strategies such as targeted booster doses and maternal immunization in order to tackle this burden. However, minimal research has been conducted in low- and middle-income countries. Mathematical modelling provides a tool for policy makers and public health planners to predict the impact and cost-effectiveness of possible intervention strategies. While such models are often considered difficult to interpret for non-experts, there are novel approaches for communicating modelling processes and results which may improve the applicability and acceptability of modelling to inform real-world public health policy decisions.
This project forms part of an ongoing collaboration between several national policy and academic groups in South Africa.
The aim of this project is to develop dynamic epidemiological-economic model for pertussis vaccination strategies in South Africa and explore different approaches for knowledge translation of the model and results to key policy stakeholders.
The objectives are:
This project would be most suited to a candidate with a strong background in health economics/economics and infectious disease modelling. Experience of programming in a high-level language as well as an understanding of health systems, public health policy, and effective stakeholder engagement is also desirable.
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 Prof. White and Dr. Silal. Throughout the DPhil, the student will visit MASHA for periods where they will become familiar with the context and engage with relevant stakeholders.
Project reference number: 1059
|Sheetal Silal||Oxford University,||Sheetal.Silal@uct.ac.za|
|Lisa White||Big Data Institute||Oxford University, Bangkok||THAfirstname.lastname@example.org|
There are no publications listed for this DPhil project.