Viral evolutionary dynamics predict Influenza-Like-Illnesses in patients

While viral infections such as those cause by the influenza virus can put a strain on any healthcare system. However, such a burden is typically difficult to predict. In order to improve such predictions, we here leverage on recent work that showed the existence of an association between viral evolutionary dynamics and severe outbreaks in the case of select viruses such as Ebola in 2014-16, Zika in 2015-16, and of the 2009 pandemic influenza. As this previous work examined case studies, it remains unclear whether such associations can be generalized, and how they translate in the clinic. To address these issues, we performed a systematic survey of influenza viruses circulating in continental US between 2010 and 2019, hypothesizing that measures of the evolutionary dynamics of these viruses correlate with the severity of the epidemic with respect to clinical data based on outpatient healthcare visits showing Influenza-like illness symptoms. By reconstructing the networks of coevolving amino acids in the hemagglutinin and neuraminidase genes, we first show evidence of a significant correlation between viral evolutionary dynamics and increased influenza activity during seasonal epidemics, and then show that these dynamics closely follow the progression of epidemics through each season, providing us with predictive power based off genetic data collected between week 20 and week 40/52, i.e. one to fifteen weeks prior to peak ILI burden. This may leave enough time for health authorities to adjust their logistics.