@article{07125908970149f284c41468214d3b43,
title = "Predictive Modeling of Influenza Shows the Promise of Applied Evolutionary Biology",
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.",
keywords = "Influenza, Predictive evolution, predictive modeling, vaccine strain selection",
author = "Morris, {Dylan H.} and Gostic, {Katelyn M.} and Simone Pompei and Trevor Bedford and Marta {\L}uksza and Neher, {Richard A.} and Grenfell, {Bryan T.} and Michael L{\"a}ssig and McCauley, {John W.}",
note = "Funding Information: This paper originated at the Exploring Predictive Models for Improving Influenza Vaccine Virus Selection workshop, held in July 2016 at Princeton University. The workshop brought together WHO Collaborating Centers on Influenza and academic research groups. The organizers were Nancy Cox (CDC Atlanta), Bryan Grenfell (Princeton University), Jaqueline Katz (CDC Atlanta), Michael L{\"a}ssig (Cologne University), John McCauley (Crick Worldwide Influenza Centre London), and Wenqing Zhang (WHO). We thank the WHO, Princeton University, and Deutsche Forschungsgemeinschaft for their support of the meeting. We thank Wenqing Zhang for productive discussion of these ideas and two anonymous reviewers for helpful comments on the manuscript. KMG is supported by the National Institute of Allergy and Infectious Disease of the National Institutes of Health (F31AI134017). SP and ML acknowledge support by Deutsche Forschungsgemeinschaft grant SFB 680. TB is a Pew Biomedical Scholar and is supported by NIH R35 GM119774-01 and NIH R01 AI127893-01. M{\L} acknowledges support from NCI-NIH grant P01CA087497. BTG acknowledges support from the Bill & Melinda Gates Foundation (OPP1091919), the RAPIDD program of the Science and Technology Directorate, the Department of Homeland Security, and the Fogarty International Center, National Institutes of Health (NIH). JWM was supported by the Francis Crick Institute, which receives its core funding from Cancer Research UK (FC001030), the Medical Research Council (FC001030), and the Wellcome Trust (FC001030). Funding Information: This paper originated at the Exploring Predictive Models for Improving Influenza Vaccine Virus Selection workshop, held in July 2016 at Princeton University. The workshop brought together WHO Collaborating Centers on Influenza and academic research groups. The organizers were Nancy Cox (CDC Atlanta), Bryan Grenfell (Princeton University), Jaqueline Katz (CDC Atlanta), Michael L?ssig (Cologne University), John McCauley (Crick Worldwide Influenza Centre London), and Wenqing Zhang (WHO). We thank the WHO, Princeton University, and Deutsche Forschungsgemeinschaft for their support of the meeting. We thank Wenqing Zhang for productive discussion of these ideas and two anonymous reviewers for helpful comments on the manuscript. KMG is supported by the National Institute of Allergy and Infectious Disease of the National Institutes of Health (F31AI134017). SP and ML acknowledge support by Deutsche Forschungsgemeinschaft grant SFB 680. TB is a Pew Biomedical Scholar and is supported by NIH R35 GM119774-01 and NIH R01 AI127893-01. BTG acknowledges support from the Bill & Melinda Gates Foundation (OPP1091919), the RAPIDD program of the Science and Technology Directorate, the Department of Homeland Security, and the Fogarty International Center, National Institutes of Health (NIH). JWM was supported by the Francis Crick Institute, which receives its core funding from Cancer Research UK (FC001030), the Medical Research Council (FC001030), and the Wellcome Trust (FC001030). Publisher Copyright: {\textcopyright} 2017 Elsevier Ltd",
year = "2018",
month = feb,
doi = "10.1016/j.tim.2017.09.004",
language = "English (US)",
volume = "26",
pages = "102--118",
journal = "Trends in Microbiology",
issn = "0966-842X",
publisher = "Elsevier Limited",
number = "2",
}