Tracking and predicting U.S. influenza activity with a real-time surveillance network
Fig 2
The estimated influenza tests that are positive developed from the ITS Model 1 track well the actual influenza tests that are positive as reported by the CDC.
ITS Model 1 estimates the CDC proportion of influenza tests that are positive (ILIppt(ta)) by using the proportion of influenza tests that are positive as recorded by ITS (Vppt(ta)), the CDC proportion of influenza tests that are positive with a 1-week lag (ILIppt(t(a−1))), and the absolute value of the difference between the proportion of tests that are positive as recorded by ITS with a 1-week lag and the proportion of influenza tests that are positive as reported by the CDC with a 1-week lag (|Vppt(t(a−1)) − ILIppt(t(a−1))|). Each graph shows two peaks with each peak relating to one flu season which occurs in the winter. The proportion of influenza tests that are positive is along the y-axis. The CDC proportion of influenza tests that are positive is in black, the ITS model estimates are in red, and the 95% prediction intervals are outlined by dark red dotted lines. The epidemiological week (epi week) is along the x-axis and spans from epi week 36 in 2015 to epi week 19 in 2017, except ITS data collection (and thus analysis) began later for Region 2 (epi week 13 in 2016 to epi week 19 in 2017) and Region 10 (epi week 2 in 2016 to epi week 19 in 2017). See Figure C in S1 Text for a visualization of raw data and estimates.