Using GLEAM, we were able to study and forecast the 2009 H1N1 pandemic in real time, and investigate the effect of various mitigation policies that were implemented around the world.
We profiled the H1N1 infection dynamics and transmissibility using the early data on the H1N1 spreading. During the spring of 2009, we continuously calibrated our H1N1 models based on actual disease monitoring data. By using elaborate statistical techniques such as a Monte Carlo likelihood-based approach informed by the chronological data of the pandemic invasion up to June 18, 2009, GLEAM was able to produce forecast of the activity peak of the fall/winter wave of the H1N1 pandemic in the Northern Hemisphere along with other quantities of interest.
By July that year our H1N1 calibrated model predicted that the disease activity would peak in the northern hemisphere during the autumn of 2009, in contrast to most other years in which seasonal influenza activity peaks in January or February. This early timing of the peak was significant: it coincided with the start of planned large-scale vaccination campaigns, making those largely ineffective at the population level for mitigation purposes. Vaccination campaigns should take place well before the activity peak to have any meaningful impact in the control of the epidemic and reduction of the number of cases. Our findings were published in September 2009.
The GLEAM H1N1 pandemic forecasts have now been validated using actual data from surveillance and virological sources collected in 46 countries of the Northern Hemisphere during the course of the pandemic. The close match between our simulated results and actual monitoring data demonstrates the success of the GLEAM system and its relevance for predicting epidemic spread in a modern, connected world.