Understanding the dynamics of rapidly evolving pathogens through modeling the tempo of antigenic change: Influenza as a case study

• Type of Material: Article
• Authors: Katharina V. Koelle, Emory University; Meredith Kamradt, Duke University; Mercedes Pascual, University of Michigan
• Language: English
• Date: 2009-06-01
• Publisher: Elsevier
• Publication Version: Final Published Version
• Copyright Statement: 2009 Elsevier Inc. All rights reserved.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): https://doi.org/10.1016/j.epidem.2009.05.003
• Title of Journal or Parent Work: Epidemics
• Volume: 1
• Issue: 2
• Start Page: 129
• End Page: 137
• Grant/Funding Information: Support for K.K. and M.P. was provided by a Centennial fellowship of the James S. McDonnell Foundation to M.P.; further support for K.K. and support for M.K. was provided by grant NSF-EF-08-27416 and by the RAPIDD program of the Science and Technology Directorate, Department of Homeland Security, and the Fogarty International Center, National Institutes of Health. MP is an investigator of the Howard Hughes Medical Institute.
• Keywords: multi-strain model; RNA virus; rapid evolution
• Research Categories: Biology, Virology; Biology, Genetics

Abstract

Rapidly evolving pathogens present a major conceptual and mathematical challenge to our understanding of disease dynamics. For these pathogens, the simulation of disease dynamics requires the use of computational models that incorporate pathogen evolution. Currently, these models are limited by two factors. First, their computational complexity hinders their numerical analysis and the ease with which parameters can be statistically estimated. Second, their formulations are frequently not sufficiently general to allow for alternative immunological hypotheses to be considered. Here, we introduce a new modeling framework for rapidly evolving pathogens that lessens both of these limitations. At its core, the proposed framework differs from previous multi-strain models by modeling the tempo of antigenic change instead of the pathogen's genetic change. This shift in focus results in a new model of reduced computational complexity that can accommodate different immunological hypotheses. We demonstrate the utility of this antigenic tempo model in an application to influenza. We show that, under different parameterizations, the model can reproduce the qualitative findings of a diverse set of previously published flu models, despite being less computationally intensive. These advantages of the antigenic tempo model make it a useful alternative to address several outstanding questions for rapidly evolving pathogens.