Viral evolutionary dynamics predict Influenza-Like-Illnesses in patients

Viral infections such as those caused by the influenza virus can put a strain on healthcare systems. However, such a burden is typically difficult to predict. In order to improve such predictions, we hypothesize that the severity of epidemics can be linked to viral evolutionary dynamics. More specifically, we posit the existence of a negative association between patients’ health and the stability of coevolutionary networks at key viral proteins. To test this, we performed a thorough evolutionary analysis of influenza viruses circulating in continental US between 2010 and 2019, assessing how measures of the stability of these coevolutionary networks correlate with clinical data based on outpatient healthcare visits showing Influenza-Like Illness (ILI) symptoms. 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 on genetic data collected between week 20 and week 40/52, that is one to fifteen weeks prior to peak ILI. Viral evolutionary dynamics may hence be used by health authorities to further guide non-pharmaceutical interventions.