Low incidence of vaccine-preventable diseases and rising concerns about adverse events increasingly lead to the delay or refusal of vaccinations. This threatens the high immunization coverage, established through decades of prevention, which is important at the community level to protect risk groups who cannot be vaccinated due to age or medical reasons (e.g., very young children or immunocompromised individuals). The availability of options to control and prevent the emergence of pathogens warrants continuous evaluation. Mathematical models provide a powerful set of tools in this process, as timely, budgetary or ethically feasible alternatives are often lacking.
Measles is highly transmissible so vaccination coverage of 90-95% is required in order to achieve herd immunity. According to the WHO, measles immunisation in South Africa is below 80% and every year, outbreaks occur. In addition, the country faces the most severe HIV epidemic in the world, and measles in children with HIV infection is more often severe and results in higher mortality.
In this project, we will survey and quantify preferences through a discrete choice experiment by forcing individuals to choose between competing profiles. We aim to discover the drivers of the decision-making process regarding immunization and to assess prevention, control – and eventually eradication – programs. We will calibrate our open-source individual-based model "Stride" to reproduce measles outbreaks in South Africa and perform scenario analyses for measles outbreaks.