Flu Forecasting System Tracks Geographic Spread of Disease
Researchers at Columbia's Mailman School developed a method to forecast the spatial transmission of influenza in the United States up to six weeks ahead of time
Scientists at Columbia University’s Mailman School of Public Health developed a system to accurately predict the geographic spread of seasonal influenza in the United States, as reported in a paper published in the journal PNAS.
For the public, the flu forecast may promote greater vaccination, the exercise of care around people sneezing and coughing, and a better awareness of personal health. For health officials, it could inform decisions on how to stockpile and distribute vaccines and antiviral drugs, and in the case of a virulent outbreak, whether other measures, like closing schools, are necessary.In a retrospective test for the 2008-2009 through 2012-2013 influenza seasons in 35 states, the Mailman School researchers found their forecasting system accurately predicted local onset of flu six weeks ahead of time. Compared to the previous version of the system, the new version improved forecasting accuracy with regard to onset, by 35 percent; peak timing, by 31 percent; and intensity, by 13 percent. Similar improvements were seen at the county level in a test using data from Virginia.
The researchers expect to use the system in their online forecasts for the 2018-19 flu season. Currently, it is being employed as one of the Mailman School entries in the 2017-18 Centers for Disease Control and Prevention flu forecast challenge, which the research team previously won outright in 2014 and tied for first in 2015 and 2017.
“The system could also be adapted for use with other respiratory viruses, and with some modification, for infectious diseases more broadly,” says lead author Sen Pei, PhD, a postdoctoral scientist in Environmental Health Sciences at Columbia’s Mailman School of Public Health.
The forecasting system employs techniques used in modern weather prediction to generate local forecasts. It starts with data from the Department of Defense on local incidence of influenza-like illness combined with laboratory-verified cases of influenza and adds a spatial element by incorporating information from Census data on commuting patterns. The system accounts for differences in population location between day and night and irregular travel such as for business trips and vacations.
“Influenza, like many infectious diseases, is spread from person-to-person and as people move from place to place,” says Jeffrey Shaman, the study’s senior author and associate professor of Environmental Health Sciences at the Mailman School. “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 study, titled “Forecasting the spatial transmission of influenza in the United States” was authored by Sen Pei, Sasikiran Kandula, Wan Yan, and Jeffrey Shaman, all in the Department of Environmental Health Sciences at Columbia’s Mailman School of Public Health with support from the National Institutes of Health (grants GM110748, GM100467, ES009089) and the Defense Threat Reduction Agency (contract HDTRA1-14-C-0018). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
UPDATE: On October 15, the International Society for Disease Surveillance named the paper one of its 2018 Outstanding Research Articles in Biosurveillance, awarding it first prize in the category of Scientific Achievement.
About the Columbia Flu Forecasting System
Each year since 2013, the Mailman School has published weekly forecasts of the flu season. Specific to more than 80 cities across the country and all 50 states, the online projections predict whether cases are expected to rise or fall and by how much. The researchers have also developed modified versions of the system that could predict the flu in a subtropical environment and on the neighborhood level. They have also forecast the spread of Ebola and West Nile Virus.