There are several strengths and limitations in our study. First, one shortcoming of sentinel surveillance is the potential for sampling and selection bias, which preclude us from calculating reliable incidence rates in our study. Most of the 50 sentinel sites comprising our surveillance network covered populations across all age groups except for six sentinel sites that mostly captured adult populations. These sentinel sites were set up in two hospitals in Lima (Hospital 2 de Mayo and Hospital Edgardo Rebagliati Martins), three military hospitals in Lima, and one military health center in Trujillo (northwestern Peru). On the other hand, sentinel surveillance data can allow the identification of spatial-temporal variations in disease trends and of the viruses associated with those trends using fewer resources than required by a population-based study [13, 19] . A second caveat is related to the assumption that the initial growth rate estimated from Sentinel data closely tracks the ''true'' growth rate of the pandemic in the community, which we use to estimate the reproduction number. In our study .60% of all ILI cases were consistently tested for influenza in all regions throughout the main pandemic period. It is also reassuring that our R estimate for the initial wave was in close agreement with estimates obtained during the early pandemic phase in other countries [29, 37] . Third, the practice of testing changed on July 7 when the Peru Ministry of Health prioritized hospitalized cases for influenza testing in all health centers, except for the 50 original sentinel units. Nevertheless, the resulting decline in testing rates took place after the pandemic reached the peak across all regions of Peru, which eliminates the possibility that our estimates of the reproduction number and correlation analysis could be biased by changes in surveillance methodology.