Abstract
Vectors are responsible for the transmission of many important endemic and emerging diseases. The functional traits of these animals have important consequences for pathogen transmission, but also for fitness and population dynamics of the vectors themselves. Increasing empirical evidence suggests that vector traits vary significantly at time scales relevant to transmission dynamics. Currently, an understanding of how this variation in key traits impacts transmission is hindered by a lack of empirical data as well theoretical methods as for mechanistically incorporating traits into transmission models. Here, we present a framework for incorporating both intrinsic and environment-driven variation in vector traits into empirical and theoretical vector-borne disease research. This framework mechanistically captures the effect of trait variation on vector fitness, the correlation between vector traits, and how these together determine transmission dynamics. We illustrate how trait-based vector-borne disease modelling can make novel predictions, and identify key steps and challenges in the construction, empirical parameterization and validation of such models. Perhaps most importantly, this framework can also be used to prioritize data collection efforts.
Author Contributions
LJC, SP, LRJ, EM and PJH conceived the study. LJC and SP wrote the manuscript with inputs from MBT, AGP, MB, LRJ, EM, and PJH. SP, LRJ, TS, and FEM developed the mathematical models and worked examples.