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 cross-protection 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.
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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Laboratory models are often used to understand the interaction of related pathogens via host immunity. For example, recent
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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
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
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can be recovered from the data
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using Markov chain Monte Carlo methods that within a simulation estimation framework, a model fitted to sequential infection data accurately captures
that virological data from sequential infection experiments can be used to accurately extract
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, and attributes such cross-protection to the correct
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responsible for such cross-protection can be determined. Such data can also be used to infer
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strength of some immune components
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. A major limitation of previous studies using
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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
cannot be used to reliably recover this information. Hence, sequential infection data