Predictive Modeling of Influenza Shows the Promise of Applied Evolutionary Biology. Issue 2 (February 2018) - Morris, Dylan H.; Gostic, Katelyn M.; Pompei, Simone; Bedford, Trevor; Łuksza, Marta; Neher, Richard A.; Grenfell, Bryan T.; Lässig, Michael; McCauley, John W.
Abstract : Seasonal influenza is controlled through vaccination campaigns. Evolution of influenza virus antigens means that vaccines must be updated to match novel strains, and vaccine effectiveness depends on the ability of scientists to predict nearly a year in advance which influenza variants will dominate in upcoming seasons. In this review, we highlight a promising new surveillance tool: predictive models. Based on data-sharing and close collaboration between the World Health Organization and academic scientists, these models use surveillance data to make quantitative predictions regarding influenza evolution. Predictive models demonstrate the potential of applied evolutionary biology to improve public health and disease control. We review the state of influenza predictive modeling and discuss next steps and recommendations to ensure that these models deliver upon their considerable biomedical promise. Trends: Seasonal influenza evolves to evade immune recognition, necessitating regular vaccine updates. The World Health Organizationhas collaborated with academic institutions and national public health organizations to build a global surveillance program for monitoring influenza evolution. Scientists have built predictive models grounded in evolutionary theory that use surveillance data to forecast which viral strains or clades will predominate in the coming months. Output from these models is already being used to inform influenza vaccine strain selection. This modeling sheds light on basic science questions: the degree to which evolution is directed and the phylogenetic and genomic signatures of fitness. This is a success story for large-scale collaborative science.