Quantifying innate and adaptive immunity during influenza infection using sequential infection experiments and mathematical models

Recent experiments where ferrets were exposed to two influenza strains within a short time have provided insight into the timing and contribution of each immune component to cross-protection. While mathematical models which capture the novel observations have been developed and analysed, they have many parameters and it is unclear whether the contributions of each immune component to crossprotection can be recovered from the data. We show using Markov chain Monte Carlo methods that within a simulation estimation framework, a model fitted to sequential infection data accurately captures the timing and extent of cross-protection, and attributes such cross-protection to the correct broad immune components. In addition, sequential infection data enables recovery of the timing and role of each immune component in controlling a primary infection. A major limitation of previous studies using single infection data is that they have produced discrepant estimates of these quantities. This evaluation demonstrates that sequential infection experiments provide richer information for quantifying the contribution of each immune component to cross-protection. This information 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.