Speaker
Description
Many compartmental infectious disease models assume that sojourn times (the time spent in each state) are exponentially distributed; that is, the probability of exiting a particular state at an instant in time is independent of time already spent in that state. While this simplifying assumption may be sufficient in certain contexts, for complex diseases like malaria where control and elimination efforts are hindered by drug resistance, relaxing this assumption to enhance biological realism and better facilitate multi-scale modeling is particularly advantageous. In this talk, we present a flexible PDE model framework of within-host malaria parasite dynamics that allows for non-exponential distributions for the sojourn times for each red blood cell and parasite life cycle stage. We discuss how our framework accommodates empirically driven distributions and demonstrate that different distribution assumptions can substantially impact malaria transmission probability estimates.
