- HIV prevention policy and programme planning: What can mathematical modelling contribute?
- Award date
- 31 January 2014
- Number of pages
- Document type
- PhD thesis
- Faculty of Medicine (AMC-UvA)
This thesis explores the potential contribution of mathematical modelling to informed decision-making on policy and programme planning for novel HIV prevention tools. Its hypothesis is that, under certain conditions, modelling results can be a useful addition to the evidence and other factors that influence the HIV prevention policy and programme development process. The emerging HIV prevention tools that serve to illustrate this are voluntary medical male circumcision, systemic pre-exposure prophylaxis with antiretroviral drugs, HIV vaccines, and structural interventions for people who inject drugs.
Comparing model structures and findings, including the results of sensitivity analyses, through consensus processes, systematic reviews, and other methodologies serves to strengthen the potential contribution that modelling can make to HIV prevention policy. Modelling will increasingly be useful to biomedical HIV prevention trial design, given the evolving standard of prevention being offered to all trial participants. Populating mathematical models with up-to-date, context-relevant, and accurate information will remain a key challenge, along with effective knowledge translation strategies. Engaging policy makers from the start can help ensure that modelling addresses relevant policy questions, is informed by the best available locally available information, and finds a receptive audience when results are presented.
Modellers can play an important role by generating modelling results on questions of key importance that provide insights into the potential impact of competing HIV prevention scenarios in the context of constrained resources. In effect, they can paint pictures for policy makers of the paths that can lead to a future in which HIV transmission is increasingly rare.
- Title on cover: HIV prevention policy and programming planning.
Research conducted at: Universiteit van Amsterdam
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