Integral projection models and infectious disease

Population evolutionary ecologists are increasingly turning to integral projection models to understand how changes performance (e.g., growth) influence population dynamics, but this type of modeling is rarely applied to understand host-parasite feed-backs. After being introduced to this modeling approach by Tim Coulson and Shelly Lachish, I’ve been thinking about how they could be applied to disease ecology. I’m not the first one to do so and there is a great review by Metcalf et al (see link below) on the topic. The technique appeals to me it’s quantitative data-driven approach to understanding host-pathogen dynamics that can account for variation at a within-host, individual and population scale. Recently, Bayesian IPMs have been developed and these offer further advantages (see, but maybe more time consuming to construct.

It’s not surprising however, that these models haven’t really taken off in the field yet. One obvious reason for this could be due to high number of parameters necessary to run the model – although you can use this IPMs in a theoretical context also.  For most wildlife systems detailed individual longitudinal data on things such as parasite load over the term of infection is near impossible to get. I wonder if new molecular tools (e.g., measuring viral load using RT PCR from feces) may help fill this data gap? Currently, it looks like you need extensive lab experiments before you can really use this approach (see Wilber et al below). Anyway,  I’m looking forward to learning more the next time we meet with Tim and Shelly.

Mecalf et al (2015):

Wilber et al:


From Metcalf et al (2015).