ARI associated with non-influenza infections is independent of the transmission dynamics of and infection with influenza virus and vice versa. For each study design variant, we used a Monte Carlo approach to randomly simulate a set of 2500 datasets. For each dataset we used chi-squared tests of the difference between arms in the proportion of laboratory confirmed infections. The proportion of datasets in which the null-hypothesis of no difference was rejected at the 0.05 significance level was defined as the statistical power. Further technical details are provided in Text S1. For each study budget, we calculated the number of participants per arm that can be recruited given the chosen diagnostic method and consequent costs of follow-up, as well as the anticipated ��base case’level of ARI and FARI incidence. We investigated the effect on study power to variability in the activity of influenza and other respiratory viruses during the study as a key sensitivity analysis. Due to uncertainties in model parameters, we performed several sensitivity analyses to examine how sensitive power estimates were to variations in model parameters. Specifically, we examined the sensitivity of power estimates to differing influenza cumulative incidences, the effect of the NPI intervention on the rate of non-influenza ARI and FARI, the cost of RT-PCR testing, the cost of serological testing, the sensitivity of RT-PCR testing and the sensitivity and specificity of serology. In another sensitivity analysis, we assumed a longer, six-month influenza season with lower incidence rates but the same cumulative incidence of infection across the study as the base case. We found that study design variants based on collection of respiratory specimens for RT-PCR testing almost always performed better than study design variants based on serology. Unless the duration of influenza activity was greater than two months, biweekly RT-PCR plus RT-PCR upon ARI trigger was not dominated by any other method. It should be noted that study design variants relying on serological testing had lower statistical power than RT-PCR despite being able to identify a greater proportion of influenza infections. This is consistent with several studies which report higher cumulative incidence of influenza across a season based on serology than RT-PCR and is due to a number of factors: high specificity of RT-PCR, comparatively lower specificity of serology, the greater ability of serology to identify asymptomatic and subclinical infections, and underreporting of symptoms by participants. It is possible for the number of triggered RT-PCR tests required to exceed the number budgeted if more ARI or FARI cases are reported than expected. When this occurred in our simulations, we simulated the cessation of collection or analysis of specimens after the allotted field Etidronate budget was exhausted. However, if an investigator were able to procure additional funds to collect and 4-Chloropropiophenone analyze the additional specimens required by circumstance this would further increase the power of a design relying on RT-PCR.