|Scientists are harnessing the power of big data to fight the flu /Photo Credit: Pop Tika (via Shutterstock)|
Between 5% to 20% of Americans catch the flu every year, as found by Fariss Samarrai of the University of Virginia’s news platform UVA Today. Hundreds of thousands of individuals are hospitalized, while some die. Nearly 80,000 Americans died of influenza infection when a severe flu plagued the US from 2017 to 2018. Alarmingly, only around 40% of individuals aged 18 and older are vaccinated every year.
This year, people residing in the Northern Hemisphere may experience a “particularly bad season.” However, it’s difficult to pinpoint when and where the flu will strike. In the words of epidemiologists, “The only thing predictable about the flu is that it’s unpredictable.”
The US Centers for Disease Control and Prevention are working to address that by utilizing supercomputing and big data analysis to formulate solutions on how to mitigate the flu season with targeted intervention programs.
The University of Virginia’s Biocomplexity Institute was awarded a one-year contract by the agency to find out if computer modeling and simulation could help develop “coordinated, multilayered interventions.” Epidemiologist and research professor at the UVA Biocomplexity Institute Bryan Lewis and research scientist at the said institute Srinivasan Venkatramanan are co-leading the project by gathering a team of machine-learning experts, epidemiologists, and data scientists from UVA and other institutions.
Lewis and his colleagues will use computer modeling and simulation studies to better understand and predict how various mitigation practices could improve responses to flu outbreaks and reduce the severity and duration of the flu season. “This allows us to run a complex range of possible scenarios to help uncover the possible outcomes of various mitigation efforts, he added.”
They want to find out what kind of policies and actions are required to reduce infection rates. Lewis’ team will construct a “nuanced and actionable national-scale model” to gauge the possible effects of “multi-layered interventions” on seasonal and pandemic epidemics alike. “Our goal is to make the country more resilient to both seasonal and potential influenza pandemics and to bolster the public health infrastructure,” Lewis explained.