The importance of understanding, predicting, and controlling
infectious disease became increasingly evident during the
COVID-19 pandemic. In particular, the pandemic highlighted the
need for interpretable, quantitative models that link mechanism with
data while accounting for variability. Despite significant effort and
advances, infectious disease dynamics remain incompletely
understood, in part due to the lack of heterogeneity considered in
immunological, ecological, and epidemiological aspects, which
produce complicated, non-linear feedbacks. In this talk, we will focus
on an age-structured PDE model of malaria, one of the deadliest
infectious diseases globally. Our novel model of malaria specifically
tracks acquisition and loss of immunity across a population. We
study the role of vaccination and immunity feedback on severe
disease and malaria incidence, through a combination of our
analytical calculation of the basic reproduction number (R_0) and
numerical simulations. Using demographic and immunological data,
we parameterize our model to simulate realistic scenarios in Kenya.
Our work sheds new light on the role of natural- and vaccine-
acquired immunity in malaria dynamics in the presence of
demographic effects.