Infectious diseases are among the leading causes of death and disability globally, disproportionately
affecting people in low- and middle-income countries (LMIC). Vaccines have made
a significant contribution to reducing the global burden of infectious diseases, preventing an
estimated 5.1 million deaths annually. However, with changing infectious disease epidemiology,
other competing health challenges, and new vaccine products and technologies, the decisionmaking
environment for vaccine introductions is becoming more complex. To progress towards
elimination goals, programmes that promote immunisation access and health equity need to be
implemented. To this end, evidence, in its many forms, can inform policies and programmes.
To date, how specific forms of evidence are used at specific moments in vaccination programme
introductions in LMIC remains opaque. Understanding how the perspectives of stakeholders
in science and policy development can be integrated into evidence development and how this
evidence can be translated to support immunisation programmes, is vital to improving health
outcomes.
The aim of this thesis is to contribute to the global evidence base by exploring knowledge
translation in the context of vaccination programmes at the global and national levels, with a
particular focus on evidence generated with mathematical models. Through integrating the perspectives
of decision-makers in modelling the human papillomavirus vaccine impact in Mozambique,
this thesis reflects on how evidence can be shaped for health policy and programmes.
This work reflects on how mathematical models of infectious diseases can contribute to reducing
health disparities in settings with limited resources. It is envisioned that the findings of
this research will support operationalizing evidence-informed decision-making in immunisation
programmes, and will thereby contribute to more evidence-informed actions to advance global
health.