Sequential infection experiments for quantifying innate and adaptive immunity during influenza infection
AuthorYan, AWC; Zaloumis, SG; Simpson, JA; McCaw, JM
Source TitlePLoS Computational Biology
PublisherPUBLIC LIBRARY SCIENCE
AffiliationMelbourne School of Population and Global Health
School of Mathematics and Statistics
Document TypeJournal Article
CitationsYan, A. W. C., Zaloumis, S. G., Simpson, J. A. & McCaw, J. M. (2019). Sequential infection experiments for quantifying innate and adaptive immunity during influenza infection. PLOS COMPUTATIONAL BIOLOGY, 15 (1), https://doi.org/10.1371/journal.pcbi.1006568.
Access StatusOpen Access
Laboratory models are often used to understand the interaction of related pathogens via host immunity. For example, recent experiments where ferrets were exposed to two influenza strains within a short period of time have shown how the effects of cross-immunity vary with the time between exposures and the specific strains used. On the other hand, studies of the workings of different arms of the immune response, and their relative importance, typically use experiments involving a single infection. However, inferring the relative importance of different immune components from this type of data is challenging. Using simulations and mathematical modelling, here we investigate whether the sequential infection experiment design can be used not only to determine immune components contributing to cross-protection, but also to gain insight into the immune response during a single infection. We show that virological data from sequential infection experiments can be used to accurately extract the timing and extent of cross-protection. Moreover, the broad immune components responsible for such cross-protection can be determined. Such data can also be used to infer the timing and strength of some immune components in controlling a primary infection, even in the absence of serological data. By contrast, single infection data cannot be used to reliably recover this information. Hence, sequential infection data enhances our understanding of the mechanisms underlying the control and resolution of infection, and generates new insight into how previous exposure influences the time course of a subsequent infection.
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