COVID-19 is not the flu. The disease caused by the novel SARS-CoV-2 virus is more transmissible, and deadlier, than most influenza epidemics we’ve encountered in our lifetimes, and scientists and physicians are still learning new things about the disease and its long-term effects. But COVID-19 and the flu do have a few things in common — they are both caused by viruses that primarily infect the upper respiratory system, and both are spread by droplets, fomites and contact.
For Ishanu Chattopadhyay, PhD, it therefore made sense to consider whether or not these similarities could be used to help predict the spread of COVID-19. Chattopadhyay, assistant professor of medicine ( Section of Hospital Medicine ), and his postdoctoral scholar Yi Huang, PhD, drew on their previous experience modeling epidemics and expertise in machine learning to analyze years of past influenza epidemics. The new risk measure they developed — denoted as the Universal Influenza-like Transmission (UnIT) score — has proven to be better at predicting weekly case count forecasts than the best models currently described. The work was published October 14 in PLoS Computational Biology.
Originally reported in The Forefront, 10/14/2021