Identifiability in Multiscale Models of Vector-Borne Disease Transmission

Vector-borne disease transmission depends on biological processes acting across multiple scales, from viral replication within hosts to population-level spread. In this talk, I present a multiscale model of Usutu virus transmission that links within-host viral dynamics in birds to bird–mosquito population-level transmission through a viral load dependent transmission function.

Identifiability analysis is used to examine the effects of measurement noise on parameter estimates. The results indicate that fitting data from multiple biological scales improves identifiability and leads to more robust parameter estimates, highlighting the importance of combining microscale and macroscale data in multiscale disease models.