The application of epidemiological-economic models for policy guidance: the case of pertussis vaccination in South Africa

Project Overview

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:

  1. Develop and apply a mathematical model to predict the impact and cost-effectiveness of vaccination strategies on the burden of pertussis in South Africa, considering the influence of HIV and incorporating spatial heterogeneity
  2. Create an interactive, visual web-based application of model scenarios and outputs
  3. Explore the approaches to and limitations of using modelling to support evidence-based policy making

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.

Training Opportunities

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.


Tropical Medicine & Global Health


Project reference number: 1059

Funding and admissions information


Name Department Institution Country Email
Sheetal Silal Oxford University,
Lisa White Big Data Institute Oxford University, NDM Research Building GBR

There are no publications listed for this DPhil project.