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Laboratory models are often used to understand the interaction of relatedpathogens via host immunity. For example, recent experiments where ferrets wereexposed to two influenza strains within a short period of time have shown howthe effects of cross-immunity vary with the time between exposures and thespecific strains used. On the other hand, studies of the workings of differentarms of the immune response, and their relative importance, typically useexperiments involving a single infection. However, inferring the relativeimportance of different immune components from this type of data ischallenging. Using simulations and mathematical modelling, here we investigatewhether the sequential infection experiment design can be used not only todetermine immune components contributing to cross-protection, but also to gaininsight into the immune response during a single infection.  We show that virological data from sequential infection experiments can beused to accurately extract the timing and extent of cross-protection. Moreover,the broad immune components responsible for such cross-protection can bedetermined. Such data can also be used to infer the timing and strength of someimmune components in controlling a primary infection, even in the absence ofserological data. By contrast, single infection data cannot be used to reliablyrecover this information. Hence, sequential infection data enhances ourunderstanding of the mechanisms underlying the control and resolution ofinfection, and generates new insight into how previous exposure influences thetime course of a subsequent infection.

Sequential infection experiments for quantifying innate and adaptive immunity during influenza infection