I shall discuss the utility of mechanistic mathematical models as aids in the design and development of experiments. The impact of model parameters on model outputs can be assessed using techniques from uncertainty quantification. Thus one can determine those parameters for which additional knowledge would best improve the predictive ability of a model. Furthermore, one can gain understanding of what data is needed, and how much and when it should be collected in order to best achieve this aim. I shall illustrate these ideas using some examples from infectious disease projects on which I have worked, including some in the area of mosquito-borne diseases.