Identifiability in a Multiscale Vector-Borne Model of Usutu Virus Infection

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 dynamics through a viral load–dependent transmission function. Using identifiability analysis, we examine how parameter estimates behave under different levels of measurement noise. The results show that fitting data from multiple biological scales simultaneously leads to improved identifiability and more robust parameter estimates. This demonstrates the importance of combining microscale and macroscale data and highlights the value of multiscale modeling for understanding and predicting the transmission dynamics of vector-borne diseases.