Researchers at Columbia University’s Mailman School of Public Health recently developed an improved system for predicting the geographic spread of seasonal influenza in the United States.
According to a paper published in the journal PNAS, the forecasting system can accurately predict local onset of flu six weeks ahead of time. The new version improved forecasting accuracy as compared to the previous version of the system, by 35 percent for onset, 31 percent for peak timing and 13 percent for intensity.
The Mailman School scientists tested their system with a retrospective test for the 2008-2009 through 2012-2013 influenza seasons in 35 states as well as a county-level test using data from Virginia.
The researchers plan to use the method in their online forecasts for the 2018-19 flu season.
“The system could also be adapted for use with other respiratory viruses, and with some modification, for infectious diseases more broadly,” lead author Sen Pei, a postdoctoral scientist in Environmental Health Sciences at Columbia’s Mailman School of Public Health, said.
The new system uses data from the Department of Defense on local incidence of influenza-like illness and laboratory-verified cases of influenza along with information from Census data on commuting patterns. It employs techniques used in weather prediction to create local forecasts and accounts for differences in population location between day and night and irregular travel for things like business trips and vacations.
“Influenza, like many infectious diseases, is spread from person-to-person and as people move from place to place,” Jeffrey Shaman, the study’s senior author and associate professor of Environmental Health Sciences at the Mailman School, said. “By assimilating information on commuting patterns, we’ve taken a big step forward and improved our ability to accurately forecast where the flu might crop up next.”
The researchers’ forecasting system is one of the Mailman School entries in the 2017-18 Center for Disease Control and Prevention flu forecast challenge. The research team won the challenge in 2014 and tied for first in 2015 and 2017.